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• Review Article
• Published: 10 August 2021

Causes, impacts and patterns of disastrous river floods

• Bruno Merz   ORCID: orcid.org/0000-0002-5992-1440 1 , 2 ,
• Günter Blöschl 3 ,
• Sergiy Vorogushyn   ORCID: orcid.org/0000-0003-4639-7982 1 ,
• Francesco Dottori   ORCID: orcid.org/0000-0002-1388-3303 4 ,
• Jeroen C. J. H. Aerts   ORCID: orcid.org/0000-0002-2162-5814 5 , 6 ,
• Paul Bates 7 ,
• Miriam Bertola   ORCID: orcid.org/0000-0002-5283-0386 3 ,
• Matthias Kemter 1 , 2 , 8 ,
• Heidi Kreibich 1 ,
• Upmanu Lall   ORCID: orcid.org/0000-0003-0529-8128 9 &
• Elena Macdonald   ORCID: orcid.org/0000-0003-0198-6556 1  

Nature Reviews Earth & Environment volume  2 ,  pages 592–609 ( 2021 ) Cite this article

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• Natural hazards

Disastrous floods have caused millions of fatalities in the twentieth century, tens of billions of dollars of direct economic loss each year and serious disruption to global trade. In this Review, we provide a synthesis of the atmospheric, land surface and socio-economic processes that produce river floods with disastrous consequences. Disastrous floods have often been caused by processes fundamentally different from those of non-disastrous floods, such as unusual but recurring atmospheric circulation patterns or failures of flood defences, which lead to high levels of damage because they are unexpected both by citizens and by flood managers. Past trends in economic flood impacts show widespread increases, mostly driven by economic and population growth. However, the number of fatalities and people affected has decreased since the mid-1990s because of risk reduction measures, such as improved risk awareness and structural flood defences. Disastrous flooding is projected to increase in many regions, particularly in Asia and Africa, owing to climate and socio-economic changes, although substantial uncertainties remain. Assessing the risk of disastrous river floods requires a deeper understanding of their distinct causes. Transdisciplinary research is needed to understand the potential for surprise in flood risk systems better and to operationalize risk management concepts that account for limited knowledge and unexpected developments.

The causative mechanisms of floods with disastrous consequences tend to be different from those of non-disastrous floods, and show anomalies in one or several flood- and loss-generating processes.

Past trends in flood hazard show both upward and downward changes. In some regions, anthropogenic warming is already strong enough to override other drivers of change.

Flood hazards and impacts are projected to increase for many regions around the globe. Future flooding hotspots are expected in Asia and Africa, owing to climate and socio-economic changes.

Reducing vulnerability is a particularly effective way of reducing flood impacts. Global decreases in flood-affected people and fatalities since the mid-1990s (despite a growing population) are signs of effective risk reduction.

Disastrous floods often come as a surprise. Effective risk reduction requires an understanding of the causative processes that make these events distinct and to address the sources of surprise, including cognitive biases.

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Acknowledgements

This work was supported by the DFG projects ‘SPATE’ (FOR 2416) and ‘NatRiskChange’ (GRK 2043/1), the FWF ‘SPATE’ project (I 3174), the ERC Advanced Grant ‘FloodChange’ project (number 291152), the Horizon 2020 ETN ‘System Risk’ project (number 676027) and the Helmholtz Climate Initiative. P.B. was supported by a Royal Society Wolfson Research Merit award. J.C.J.H.A. was supported by an ERC Advanced Grant COASTMOVE (number 884442) and a NWO-VICI grant (number 453-13-006).

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(AAL). A widespread indicator for risk, it is the estimated average loss per year considering the full range of scenarios from frequent events (zero or small loss) to extreme events (large loss or worst-case scenario).

Fall of rain onto existing snow, leading to flood runoff composed of snowmelt and rainfall.

Long, narrow and transient corridors of strong horizontal water vapour, transporting on average more than double the flow of the Amazon river and delivering moisture as heavy precipitation.

The fraction of the event water input (precipitation or snowmelt within the catchment) that is not retained in the catchment and that directly contributes to discharge during the event.

Consequences occurring in the inundated region during a flooding event.

Consequences occurring far away from the flooded region and/or after a flooding event.

Consequences of a flooding event that are difficult or impossible to monetarize, such as loss of life or loss of memorabilia.

The ratio of the number of people who lose their lives in a flood to the number of people affected by the flooding event.

The highest streamflow peak in each year.

The dates of the year when floods occur.

The distance over which flooding occurs simultaneously.

Level at which a flood causes extensive inundation, significant evacuations, or property transfer to higher ground.

Level at which a flood does not cause damage but requires mitigation action in preparation for more substantial flooding.

According to the DFO, either the total number of people left homeless after the incident, or the number of people evacuated during the flood.

Coupled Model Intercomparison Project Phase 5; for coordinated climate change experiments for the Fifth Assessment Report AR5 of the Intergovernmental Panel on Climate Change and beyond.

An indicator expressing the exceedance probability or rarity of an event. For instance, a 100-year flood discharge has a probability of 1/100 of being exceeded in a given year.

Relation between flood discharge and the associated return period.

Optimizing risk reduction measures based on the best available knowledge.

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Merz, B., Blöschl, G., Vorogushyn, S. et al. Causes, impacts and patterns of disastrous river floods. Nat Rev Earth Environ 2 , 592–609 (2021). https://doi.org/10.1038/s43017-021-00195-3

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Risk Analysis and Uncertainty in Flood Damage Reduction Studies (2000)

Chapter: case studies, case studies.

This chapter illustrates the Corps of Engineers's application of risk analysis by reviewing two Corps flood damage reduction projects: Beargrass Creek in Louisville, Kentucky, and the Red River of the North in East Grand Forks, Minnesota, and Grand Forks, North Dakota. The Beargrass Creek case study describes the entire procedure of risk-based engineering and economic analysis applied to a typical Corps flood damage reduction project. The Red River of the North case study focuses on the reliability of the levee system in Grand Forks, which suffered a devastating failure in April 1997 that resulted in more than $1 billion in flood damages and related emergency services.

The Corps of Engineers has used risk analysis methods in several flood damage reduction studies across the nation, any of which could have been chosen for detailed investigation. Given the limits of the committee's time and resources, the committee chose to focus upon the Beargrass Creek and Red River case studies for the following reasons: committee member proximity to Corps offices, a high level of interest in these two studies, and the availability of documentation from the Corps that adequately described their risk analysis applications.

Differences in approaches taken at Beargrass Creek and along the Red River of the North to reducing flood damages are reflected in these studies. At Beargrass Creek, the primary flood damage reduction measures were detention basins; at the Red River of the North, the primary measures were levees. The Corps uses rainfall-runoff models in nearly all of its flood damage reduction studies to simulate streamflows needed for flood-frequency analysis, and a rainfall-runoff model was employed in the Beargrass Creek study. In the Red River study, however, the goal

was to design a system that would, with a reasonable degree of reliability, contain a flood of the magnitude of 1997's devastating flood. The Corps focused on traditional flood–frequency analysis and manipulated the frequency curve at a gage location to derive frequency curves at other locations (vs. using a rainfall-runoff model to derive those curves).

BEARGRASS CREEK

In 1997 the Corps held a workshop (USACE, 1997b) at which experience accumulated since 1991 in risk analysis for flood damage reduction studies was reviewed. O'Leary (1997) described how the new procedures had been applied in the Corps's Louisville, Kentucky, district office. In particular, O'Leary described an application to a flood damage reduction project for Beargrass Creek, economic analyses for which were done both under the old procedures without risk and uncertainty analysis and under the new procedures that include those factors. Conclusions of the Beargrass Creek study are summarized in two volumes of project reports (USACE, 1997c,d). These documents, plus a site visit to the Louisville district by a member of this committee, form the basis of this discussion of the Beargrass Creek study. The Beargrass Creek data are distributed with the Corps's Hydrologic Engineering Center Flood Damage Assessment (HEC-FDA) computer program for risk analysis as an example data set. The Beargrass Creek study is also used for illustration in the HEC-FDA program manual and in the Corps 's Risk Training course manual. Although there are variations from study to study in the application of risk analysis, Beargrass Creek is a reasonably representative case with which to examine the methodology.

As shown Figure 5.1 , Beargrass Creek flows through the city of Louisville, Kentucky, and into the Ohio River on its south bank. The Beargrass Creek basin has a drainage area of 61 square miles, which encompasses about half of Louisville. The basin currently (year 2000) has a population of about 200,000. This flood damage reduction study's focal point is the lower portion of the basin shown in Figure 5.1 —the South Fork of Beargrass Creek and Buechel Branch, a tributary of the South Fork.

Locally intense rainstorms (rather than regional storms) cause flooding in Beargrass Creek. A 2-year return period storm causes the creek to overflow its banks and produces some flood damage. Under existing conditions, the Corps estimates that a 10-year flood will impact

FIGURE 5.1 The Beargrass Creek basin in Louisville, Kentucky. SOURCE: USACE (1997a) (Figure II-1).

about 300 buildings and cause about $7 million in flood damages, while a 100-year flood will impact about 750 buildings and cause about $45 million in flood damages (USACE, 1997c). The expected annual flood damage under existing conditions is approximately $3 million per year.

Flood Damage Reduction Measures

Beargrass Creek has several flood damage reduction structures, the most notable of which is a very large levee at its outlet on the Ohio River ( Figure 5.2a ). This levee was built following a disastrous flood on the Ohio in January 1937, and the levee crest is an elevation of 3 feet above the 1937 flood level on the Ohio River. During the 1937 flood it was reported that “at the Public Library, the flood waters reached a height such that a Statue of Lincoln appeared to be walking on water!” (USACE, 1997b, p. III-2). Near the mouth of Beargrass Creek, a set of

gates can be closed to prevent water from the Ohio River from flowing back up into Louisville. In the event of such a flood, a massive pump station with a capacity of 7,800 cubic feet per second (cfs) is activated to discharge the flow of Beargrass Creek over the levee and into the Ohio River.

Between 1906 and 1943, a traditional channel improvement project was constructed on the lower reaches of the South Fork of Beargrass Creek. It consists of a concrete lined rectangular channel with vertical sides, with a small low-flow channel down the center ( Figure 5.2b ). The channel's flood conveyance capacity is perhaps twice that of the natural channel it replaced, but the concrete channel is a distinctive type of landscape feature that environmental concerns will no longer permit. Other structures have been added since then, including a dry bed reservoir completed in 1980, which functions as an in-stream detention basin during floods.

The proposed flood damage reduction measures for Beargrass Creek form an interesting contrast to traditional approaches. The emphasis of the proposed measures is on altering the natural channel as little as possible and detaining the floodwaters with detention basins. These basins are either located on the creek itself or more often in flood pool areas adjacent to the creek into which excessive waters can drain, be held for a few hours until the main flood has passed, and then gradually return to the creek. Figure 5.2c shows a grassed detention pond area with a concrete weir (in the center of the picture) adjacent to the creek. Figure 5.2d shows Beargrass Creek at this location (a discharge pipe from the pond is visible on the right side of the photograph). Water flows from the creek into the pond over the weir and discharges back into the creek through the pipe. The National Economic Development flood damage reduction alternative on Beargrass Creek called for a total of eight detention basins, one flood wall or levee, and one section of modified channel. Other alternatives such as flood-proofing, flood warning systems, and enlargement of bridge openings were considered but were not included in the final plan.

The evolution of flood damage reduction on Beargrass Creek represents an interesting mixture of the old and the new—massive levees and control structures on the Ohio River, traditional approaches (the concrete-lined channel) in the lower part of the basin, more modern instream and off-channel detention basins in the upstream areas, and local channel modifications and floodwalls. Maintenance and improvement of stormwater drainage facilities in Beargrass Creek are the responsibility of the Jefferson County Metropolitan Sewer District, which is the principal local partner working with the Corps to plan and develop flood damage reduction measures.

(a) Levee on the Ohio River

(b) Concrete-lined channel

(c) Detention pond

(d) Beargrass Creek at the detention pond

FIGURE 5.2 Images of Beargrass Creek at various locations: (a) the levee on the Ohio River, (b) a concrete-lined channel, (c) a detention pond, and (d) the Beargrass Creek at the detention pond.

In some locations, development has been prohibited in the floodway; but in other places, buildings are located adjacent to the creek. The Corps's feasibility report includes the following comments: “Urbanization continues to alter the character of the watershed as open land is converted to residential, commercial and industrial uses. The quest for open area residential settings in the late 1960s and early 1970s caused a tremendous increase in urbanization of the entire basin. Several developers have utilized the aesthetic beauty of the streambanks as sites for residential as well as commercial developments. This has resulted in increased runoff throughout the drainage area as development has occasionally encroached on the floodplain and, less frequently, the floodway” (USACE, 1997b, p. II-2).

Damage Reaches

To conduct the flood damage assessment, the two main creeks— South Fork of Beargrass Creek and Buechel Branch—are divided into damage reaches. Flood damage and risk assessment results are summarized for each damage reach, and the expected annual damage for the project as a whole is found by summing the expected annual damages for each reach. As shown in Figure 5.3 , the South Fork was divided into 15 damage reaches and the Buechel Branch into 5 reaches (a sixth damage reach on Buechel Branch is not shown in this figure). Approximately 12 miles of Beargrass Creek, and 2.2 miles of Buechel Branch are covered by the these damage reaches. The average length of a damage reach is thus 0.8 miles for the South Fork of the Beargrass Creek, and the average length for Buechel Branch is 0.4 miles. The shorter reaches on Buechel Branch are adjacent to similarly short, upstream reaches in Beargrass Creek where most flood damage occurs. Longer damage reaches are used downstream on Beargrass Creek where less damage occurs.

The highest expected annual flood damage is on Reach SF-9 on the upper portion of the South Fork of Beargrass Creek. Results from this damage reach are used for illustrative purposes at various points in this chapter.

FIGURE 5.3 Damage reaches on the South Fork of Beargrass Creek and Buechel Branch. SOURCE: USACE (1997a) (Figure III-3).

Flood Hydrology

Most of the flood damage reduction measures being considered are detention basins, which diminish flood discharge by temporarily storing floodwater. It follows that the study's flood hydrology component has to be conducted using a time-varying rainfall–runoff model because this allows for the routing of storage water through detention basins. In this case, the HEC-1 rainfall–runoff model from the Corps's Hydrologic Engineering Center (HEC) was used to quantify the flood discharges. The Hydrologic Engineering Center has subsequently released a successor rainfall-runoff model to HEC-1, called HEC-HMS (Hydrologic Modeling System), which can also be used for this type of study (HEC, 1998b).

In each damage reach, and for each alternative plan considered, the risk analysis procedure for flood damage assessment requires a flood – frequency curve defining the annual maximum flood discharge at that location which is equaled or exceeded in any given year with a given probability. In this study all these flood–frequency curves were produced through rainfall–runoff modeling. In other words, a storm of a given

return period was used as input to the HEC-1 model, the water was routed through the basin, and the magnitude of the discharge at the top end of each damage reach was determined (Corps hydrologists have assumed, based on experience in the basin, that storms of given return periods produce floods of the equivalent return period). By repeating this exercise for each of the annual storm frequencies to be considered, a flood–frequency curve was produced for each damage reach. There are eight standard annual exceedance probabilities normally used to define this frequency curve: p = 0.5, 0.2, 0.1, 0.04, 0.02, 0.01, 0.004, and 0.002, corresponding to return periods of 2, 5, 10, 25, 50, 100, 250, and 500 years, respectively. In this study, because even small floods cause damage, a 1-year return period event was included in the analysis and assigned an exceedance probability of 0.999.

Considering that there are 21 damage reaches in the study area and 8 annual frequencies to be considered, each alternative plan considered requires the development of 21 flood–frequency curves involving 168 discharge estimates. During project planning, as dozens of alternative components and plans were considered, the sheer magnitude of the tasks of hydrologic simulation and data assembly becomes apparent.

The hydrologic analysis is further complicated by the fact that the design of detention basins is not simply a cut-and-dried matter. A basin designed to capture a 100-year flood requires a high–capacity outlet structure. Such a basin will have little impact on smaller floods because the outlet structure is so large that smaller events pass through almost unimpeded. If smaller floods are to be captured, a more confined outlet structure is needed, which in turn increases the required storage volume for larger floods. This situation was resolved in the Beargrass Creek study by settling on a 10-year flood as the nominal design event for sizing flood ponds and outlet works. The structures designed in this manner were then subjected to the whole range of floods required for the economic analysis.

Rainfall–Runoff Model

The HEC-1 model was validated by using historical rainfall and runoff data for four floods (March 1964, April 1970, July 1973, February 1990). Modeling results were within 5 percent to 10 percent of observed flows at two U.S. Geological Survey (USGS) streamflow gaging stations: South Fork of Beargrass Creek at Trevallian Way and Middle Fork

of Beargrass Creek at Old Cannons Lane, which have flow records beginning in 1940 and 1944, respectively, and continuing to the present. A total of 42 subbasins were used in the HEC-1 model, and runoff was computed using the U.S. Soil Conservation Service (renamed the Natural Resources Conservation Service in 1994) curve number loss rates and unit hydrographs. The Soil Conservation Service curve numbers were adjusted to allow the matching of observed and modeled flows for the historical events. A 6-hour design storm was used, which is about twice the time of concentration of the basin. The design storm duration chosen is longer than the time of concentration of the basin so that the flood hydrograph has time to rise and reach its peak outflow at the basin outlet while the storm is still continuing. If the design storm is shorter than the time of concentration, rainfall could have ceased in part of the basin before the outflow peaks at the basin outlet. The storm rainfall hydrograph was based on National Weather Service 1961 Technical Paper 40 (NWS, 1961) and on a Soil Conservation Service storm hydrograph, and a 5-minute time interval of computation was used for determining the design discharges.

There is a long flood record of 56 years of data (1940–1996) available in the study area (USGS gage on the South Fork of Beargrass Creek at Trevallian Way). A comparison was made of observed flood frequencies at this site with those simulated by HEC-1, with some adjustment of the older flood data to allow for later development. Traditional flood frequency analysis of observed flow data had little impact in the study. This may have been the case because there was only one gage available within the study area, or because the basin has changed so much over time that the flood record there does not represent homogeneous conditions. Furthermore, the alternatives mostly involve flood storage, which requires computation of the entire flood hydrograph, not just the peak discharge.

Uncertainty in Flood Discharge

Uncertainty in flood hydrology is represented by a range in the estimated flood–frequency curve at each damage reach. In the HEC-FDA program, there are two options for specifying this uncertainty: an analytical method based on the log-Pearson distribution and a more approximate graphical method. The log-Pearson distribution is a mathematical function used for flood–frequency analysis, the parameters of which are determined from the mean, standard deviation, and coefficient

of skewness of the logarithms of the annual maximum discharge data. The graphical method is a flood frequency analysis performed directly on the annual maximum discharge data without fitting them with a mathematical function. In this case the graphical method was used with an equivalent record length of 56 years of data, the length of the flood record of the USGS gage station at Trevallian Way at the time of the study. Figure 5.4 shows the flood–frequency curve for damage reach SF-9 on the South Fork of Beargrass Creek, with corresponding confidence limits based on ± 2 standard deviations about the mean curve.

The confidence limits in this graph are symmetric about the mean when the logarithm to base 10 of the discharge is taken, rather than the discharge itself. This can be expressed mathematically as:

where Q is the discharge value at the confidence limit, log Q is the expected flood discharge, σ log Q is the standard deviation (shown in the rightmost column of Table 5.1 ), and K is the number of standard deviations above or below the mean that the confidence limit lies. Because these confidence limits are defined in the log space, it follows that they are not symmetric in the real flood discharge space. As Table 5.1 shows, the expected discharge for the 100-year flood ( p = 0.01) is 4,310 cfs, the upper confidence limit is 6,176 cfs, and the lower limit is 3,008 cfs. The difference between the mean and the upper confidence limit is thus about 40 percent larger than the difference between the mean and the lower confidence limit. The confidence limits for graphical frequency analysis are computed using a method based on order statistics, as described in USACE (1997d). In this method, a given flood discharge estimate is considered a sample from a binomial distribution, whose parameters p and n are the nonexceedance probability of the flood and the equivalent record length of flood observations in the area, respectively. In this case, n = 56 years, since this is the record length of the Trevallian Way gage.

River Hydraulics

Water surface profiles for all events were determined using the HEC-2 river hydraulics program from the Corps's Hydrologic Engineering Center in Davis, California. Field-surveyed cross sections were obtained

FIGURE 5.4 The flood–frequency curve and its uncertainty at damage reach SF-9 on the South Fork of Beargrass Creek.

at all bridges and at some stream sections near bridges. Maps with a scale of 1 inch = 100 feet with contour intervals of 2 feet were used to define cross sections elsewhere on the stream reaches and were used for measuring the distance between cross sections on the channel and in the left and right overbank areas. Manning's n values for roughness were based on field inspection, on reproduction of known high-water marks from the March 1964 flood on Beargrass Creek, and on reproduction of the rating curve of the USGS gage at Trevallian Way. Manning's equation relates the channel velocity to the channel's shape, slope, and roughness. Manning's n is a numerical value describing the channel roughness. Manning's n values in the concrete channel ranged from 0.015 at the channel invert to 0.027 near the top of the bank. In the natural channels, Manning 's n values ranged from 0.035 to 0.050. In the overbank areas, these values ranged from 0.045 to 0.065. Where buildings blocked the flow, the cross sections were cut off at the effective

TABLE 5.1 Uncertainties in Estimated Discharge Values at Reach SF-9

flow limits. A total of 201 cross sections were used for the South Fork of Beargrass Creek, and 61 cross sections were used for Buechel Branch. The average distance between cross sections was 330 feet on the South Fork of Beargrass Creek and 245 feet on Buechel Branch. Cross sections are spaced more closely than this near bridges and more sparsely in reaches where the cross section is relatively constant.

Figure 5.5 shows the water surface profiles along Beargrass Creek for the eight flood frequencies considered, under existing conditions without any planned control measures. The horizontal axis of this graph is the distance in miles upstream from Beargrass Creek's outlet on the Ohio River. The vertical axis is the elevation of the water surface in feet above mean sea level. The bottom profile in this graph is the channel invert or channel bottom elevation. The top profile is for p = 0.002—the 500-year flood. This particular profile shows a sharp drop near the bottom end of the channel, caused by a bridge at that location that constricts the flow. The flat water surface elevation upstream of the bridge is a backwater effect produced by the inadequate capacity of the bridge opening to convey the flow that comes to it.

For each flood profile computed, the number of structures flooded and the degree to which they are flooded must be assessed. Figure 5.6 shows the locations of the first-floor elevations of structures affected by flooding on the South Fork of Beargrass Creek in relation to several flood water surface profiles under existing conditions. Damage reach SF-9 is located between river miles (RM) 9.960 and 10.363, near the point where there is a sharp drop in the channel bed and water surface elevation on Beargrass Creek. It can be seen that the density of development varies along the channel. Flood damage reduction measures are most effective when they are located close to damage reaches with significant numbers of structures, and they are least effective when they are distant from such reaches.

FIGURE 5.5 Water surface profiles for design floods in Beargrass Creek under existing conditions.

Each damage reach has an index location, which is an equivalent point at which all of the damages along the reach are assumed to occur. On reach SF-9, this index location is at river mile 10.124. To assess damages to structures within each reach, an equivalent elevation is found for each structure at the index location such that its depth of flooding at that location is the same as it would have been at the correct location on the flood profile, as shown in Figure 5.7 .

The technique of assigning an elevation at the index location can be far more complex than Figure 5.7 implies, because allowance is made in the HEC-FDA program for the various flood profiles to be nonparallel and also to change in gradient upstream of the index location compared to downstream. In the Beargrass Creek study, a single flood profile for the p = 0.01 event was chosen, and all other profiles were assumed parallel to this one. One damage reach on Beargrass Creek was subdivided into three subreaches to make this assumption more nearly correct. A spatial distribution of buildings over the damage reach is thus converted

FIGURE 5.6 Locations of structures on floodwater surface profiles along the damage reaches of the South Fork of Beargrass Creek. SOURCE: USACE, 1997c.

FIGURE 5.7 Assignment of structures to an index location.

into a probability distribution of buildings at the index location, where the uncertainty in flood stage is quantified.

Uncertainty in Flood Stage

The uncertainty in the water surface elevation was quantified by assuming that the standard deviation of the elevation at the index location for the 100-year discharge is 0.5 feet. The 100-year discharge at reach SF-9 is 4,310 cfs, which is the next to last set of points in Fugure 5.8 . To the right of these points, between the 100-year and 500-year flood discharges, the uncertainties are assumed to be constant. For discharges lower than the 100-year return period, the uncertainties in stage height are reduced linearly in proportion to the depth of water in the channel. The various lines shown in Figure 5.8 are drawn as the expected water surface elevation ± 1 or 2 standard deviations determined in this manner.

Economic Analysis

The Corps's analysis of a flood damage reduction project's economic costs and benefits is guided by the Principles and Guidelines ( Box 1.1 provides details on the P&G's application to flood damage reduction

FIGURE 5.8 Uncertainty in the flood stage for existing conditions at reach SF-9 of the South Fork of Beargrass Creek.

studies). According to the P&G , the economic analysis of damages avoided to floodplain structures because of a flood damage reduction project is restricted to existing structures (i.e., federal policy does not allow damages avoided to prospective future structures to be counted as benefits). The P&G do, however, call for the benefits of increased net income generated by floodplain activities after a project has been constructed (so-called “intensification benefits”) to be included in the economic analysis.

Economic analysis of flood damages considers various sorts of flood damage, principal among them being the damage to flooded structures. Information about the structures is quantified using a “structure inventory,” an exhaustive tabulation of every building and other kind of structure subjected to flooding in the study region. A separate computer program called Structure Inventory for Damage Analysis (SID) was used

to evaluate the number of structures flooded as a function of water surface elevation. Structures are divided into four categories: single-family residential, multifamily residential, commercial, and public. A structure is considered to be flooded if the computed flood elevation is above its first-floor elevation. The amount of damage D is a function of the depth of flooding h and the type of structure, and is expressed by a factor, r ( h ), which is equal to a percentage of the value of the structure ( V ) and of its contents (C). This analysis can be expressed as

D = r 1 ( h ) V + r 2 ( h ) C . (5.2)

For residential structures, these damage factors were quantified in 1995 by the Federal Emergency Management Agency (FEMA) using data from flood damage claims. For example, for a one-story house without a basement flooded to a depth of 3 feet, the FEMA estimate is that the damage factors are r 1 = 27% of the value of the structure and r 2 = 35% of the value of the contents. For the same house flooded to a depth of 6 feet, the corresponding damage factors are r 1 = 40% for the structure, and r 2 = 45% for the contents, respectively. The Marshall and Swift Residential Cost Handbook (Marshall and Swift, 1999) was used to estimate the value of single- and multi-family structures (it bears mentioning that the use of standard references such as the Marshall and Swift handbook may potentially represent another source of “knowledge uncertainty ”). The values of their contents were assumed to be 40 percent to 44 percent of the value of the structure. For commercial and public buildings, the values of the structures and their contents were established through personal interviews by Corps personnel. About 85 percent of the structures subject to flood damage are residential buildings.

Types of flood damages beyond those to structures were also considered. For instance, there are several automobile sales lots in the floodplain, and prospective damages to cars parked there during a flood were estimated. Nonphysical damage costs include the costs of emergency services and traffic diversion during flooding. Damage to roads and utilities were also considered.

Uncertainty in Flood Damage

The economic analysis has three sources of uncertainty:

the elevation of the first floor of the building,

the degree of damage given the depth of flooding within the building, and

the economic value of the structure and its contents.

For most structures in Beargrass Creek, the first-floor elevation was estimated from the ground elevation on maps with a scale of 1 inch = 100 feet and with contour intervals of 2 feet. For a sample of 195 structures (16% of the total number), the first-floor elevations were surveyed. It was found that the average difference between estimated and surveyed first-floor elevations of these structures was 0.62 feet.

Corps Engineering Manual (EM) 1110-2-1619 (USACE, 1996b) was used to estimate values for the uncertainties in economic analysis. A standard deviation of 0.2 feet was used to define the uncertainty in first-floor elevations. The uncertainty in the degree of damage given a depth of inundation was estimated by varying the percent damage factor described previously. For residential structures the value of the structure was assigned a standard deviation of 10 percent of the building value, and the ratio of the value of the contents to the structure was allowed to vary with a standard deviation of 20 percent to 25 percent.

For commercial property a separate damage estimate, based on interviews with the owners, was made for each significant property and was expressed as a triangular distribution with a minimum, expected, and maximum damage value for the property. Because every individual structure potentially affected by flooding is inventoried in the damage estimate data, the amount of work required to collect all these damage data was extensive.

The end result of these estimates at each damage reach and damage category is a damage–stage curve (such as Figure 5.9 ) that accumulates the damage to all multifamily structures in this damage reach for various water surface elevations at the index location, denoted by stage on the horizontal axis. This curve is prepared by first dividing the range of the stage (476–486 feet) into increments —increments of 0.5 feet in this case. For each structure, a cycle of 100 Monte Carlo simulations is carried out in which the first-floor elevation and the values of the structure and contents are randomly varied. From these simulations estimates are formed for each 0.5-foot stage height increment of what the expected damage and standard deviation of the damage to that structure would be if the flood stage were to rise to that elevation. For each stage increment, these means and standard deviations are accumulated over all structures in the

reach to form the estimate of the mean and standard deviation of the reach damage ( Figure 5.9 ).

A similar function is prepared for each of the damage categories. At any flood stage, the sum of the damages across all categories is the total flood damage for that reach.

Project Planning

The discussion of the Beargrass Creek study reviewed the technical means by which a particular flood damage reduction plan is evaluated. A plan consists of a set of flood damage reduction measures, such as detention ponds, levees or floodwalls, and channel modifications, implemented at particular locations on the creek. The base plan against which all others are considered is the “without plan,” which means a plan that considers existing conditions in the floodplain and the development expected to occur even in the absence of a flood damage reduction plan. Such development must meet floodplain management policies and have structures elevated out of the 100-year floodplain. A base year of 1996 was chosen for the Beargrass Creek study.

In carrying out project planning, the spatial location of the principal damage reaches is important because flood damage reduction measures located just upstream of or within such reaches have greater economic impact than do flood damage reduction measures located in areas of low flood damage. Project planning also involves a great deal of interaction with local and state agencies, in this case principally the Jefferson County Metropolitan Sewer District.

The Beargrass Creek project planning team consisted primarily of three individuals in the Corps's Louisville district office: a project planner from the planning division, a hydraulic engineer from the hydrology and hydraulics design section, and an economic analyst from the economics branch. The HEC-FDA computer program with risk analysis was carried out by the economic analyst using flood–frequency curves and water surface profiles supplied by the hydrology and hydraulics section and using project alternatives defined by the project planner. The hydrology and hydraulics section was also responsible for the preliminary sizing of potential project structures being considered as plan components. The bulk of the work of implementing the risk analysis aspects of flood damage assessment thus fell within the domain of the Corps economic analyst.

The HEC-FDA program is applied during the feasibility phase of

FIGURE 5.9 The damage–stage curve with uncertainty for multifamily residential property in Reach SF-9 of the South Fork of Beargrass Creek.

flood damage reduction planning. This had been preceded by a reconnaissance phase, a preliminary assessment of whether reasonable flood damage reduction planning can be done in the area. As explained in Chapter 2 , the reconnaissance phase is fully funded by the federal government, but the feasibility phase must have half the costs met by a local sponsor. Assuming the feasibility phase yields an acceptable plan and additional funds are authorized, the project proceeds to a detailed design and construction phase, which also requires local cost sharing. The Beargrass Creek project is now (as of May 2000) in the detailed design phase.

Evaluation of Project Alternatives

Expected annual flood damages in Beargrass Creek under existing conditions are estimated to be $3 million. Project benefits are calculated as the difference between this figure and the lower expected annual damages that result with project components in place. Project costs are annualized values of construction costs discounted over a 50-year period using an interest rate of 7.625 percent. Project net benefits are the differ-

ence between project benefits and costs. For components to be included in the project, they must have positive net benefits.

The first step in evaluating project alternatives is to consider each component flood damage reduction measure by itself to see if it yields positive net benefits. A total of 22 components were examined individually, 11 on the South Fork of Beargrass Creek and 11 on Buechel Branch. All 11 of the South Fork components were economically justified on a stand-alone basis. Only 3 of the 11 components on Buechel Branch were justified individually: the other 8 components were thus deleted from further consideration.

The next step is to formulate the National Economic Development (NED) plan. In theory, this is supposed to proceed by selecting first the component with the largest net benefits, adding the component with the next largest net benefits, evaluating them together, and continuing to add more components until the combined set of components has the largest overall net benefits. It turned out that this idealized approach could not be used at the South Fork of Beargrass Creek because of economic and hydraulic interactions among the components. The study team commented: “Therefore, the formulation process was different and more complicated than originally anticipated. The study team could not follow the incremental analysis procedure to build up the NED plan because the process became a loop of H&H computer runs. Our component with the greatest net benefits is located near the midpoint of the stream; thus, each time we would add a component upstream it would affect all components downstream and vice versa. We could never truly optimize or identify the plan which produces the greatest net benefits” (USACE, 1997c, p. IV-62).

The problems were further complicated by the fact that there are three separate sections of the study region: the South Fork of Beargrass Creek and Buechel Branch upstream of their junction and the South Fork downstream of this junction ( Figure 5.3 ). In the downstream region, flood damage reduction measures on the upper South Fork and Buechel Branch compete for project benefits by reducing flood damages. The result of these complications is that the plan was built up incrementally by separately considering the three sections of the region. First, the most upstream control structure in each section was selected, then structures downstream were added. At the end—when the components from the three sections had been aggregated into a single overall plan—it was determined whether the plan could be improved by omitting individual marginal components. The end result of this iterative process was a recommended plan with 10 components: 8 detention basins, 1 floodwall,

and 1 channel improvement.

Each plan has to be evaluated using the Monte Carlo simulation process. The number of simulations varies by reach, with 10,000 required for Reach SF-9 and with a range of 10,000–100,000 required for the other reaches. On a 300 MHz Pentium computer, evaluation of a single plan takes about 25 minutes of computation time.

Risk of Flooding

The HEC-FDA program also produces a set of statistics that quantify the risk of being flooded in any reach for a given plan, as shown in Table 5.2 . For reach SF-9, the target elevation is 477.2 feet, which is the elevation of the overbank area in this reach. The probability estimates shown are annual exceedance probability and conditional nonexceedance probability. The annual exceedance probability refers to the risk that flooding will occur considering all possible floods in any year. The conditional nonexceedance probability describes the likelihood that flooding will not occur during a flood of defined severity, such as the 100-year (1 percent chance) flood.

There is a subtle but important distinction between these two types of risk measures. The annual exceedance probability accumulates all the uncertainties into a single estimate both from the natural variability of the unknown severity of floods and from the knowledge uncertainty in estimating methods and computational parameters. The conditional non-exceedance probability estimate divides these two uncertainties, because it is conditional on the severity of the natural event and thus represents only the knowledge uncertainty component. In this sense, the conditional nonexceedance probability corresponds most closely to the traditional idea of adding 1 foot or 3 feet on the 100-year base flood elevation, while the annual exceedance probability corresponds more closely to the goal of ensuring that the chance of being flooded is less than a given value, such as 1 percent, considering all sources of uncertainty.

The “target stage annual exceedance probability” values in Table 5.2 are the median and the expected value or mean of the chance that flooding will occur in any given year for the various reaches. Thus, for reach SF-9, there is approximately a 36 percent chance that flooding will occur beyond the target stage in any given year, while in reach SF-14 upstream, that chance is only about 9 percent. The “long term risk” values in the

TABLE 5.2 Risk of Flooding in Damage Reaches Calculated Uncertainty for 1996 at Beargrass Creek

figure refer to the chance (Rn) that there will be flooding above the target stage at least once in n years, determined by the formula

R n = 1− (1− p e ) n , (5.3)

where p e is the expected annual exceedance probability. For example, for reach SF-9, where p e = 0.3640, for n = 10 years, R 10 = 1− (1 − 0.3640) 10 = 0.9892, as shown in Table 5.2 .

The conditional nonexceedance probability values shown on the right-hand side of Table 5.2 are conditional risk values that correspond to the reliability that particular floods can be conveyed without causing damage in this reach. Thus, in reach SF-9, a 10 percent chance event (10-year flood) has about a 0.27 percent chance of being conveyed without exceeding the target stage, while for a 1 percent chance event (100-year flood), there is essentially no chance that it will pass without exceeding the target stage. By contrast, in Reach SF-14 at the upstream end of the study area, the conditional nonexceedance probability of the reach passing the 10-year flood is about 52 percent; that of the reach passing the 100-year flood is about 100 percent. As the flood severity increases, the chance of a reach being passed without flooding diminishes.

Effect on Project Economics of Including Risk and Uncertainty

The HEC-FDA program that includes risk and uncertainty factors in project analysis became available to the Beargrass creek project team late in the study period. Before then, the team used an earlier economic analysis program (Expected Annual Damage, or EAD) which computed expected annual damages without these uncertainties. O' Leary (1997) presented the data shown in Table 5.3 to compare the two approaches. It is evident that including risk and uncertainty increases the expected annual damage both with and without flood damage reduction plans. The net effect of their inclusion on the Beargrass Creek project is to increase the annual flood damage reduction benefits from $2.078 million to $2.314 million. The study team made a comparison between the components included in the National Economic Development plan in the two computer programs and found that there was no change. Hence, although the inclusion of risk and uncertainty increased project benefits, it did not result in changing the flood damage reduction components included in the National Economic Development plan.

O'Leary (1997) also presented statistics of the project benefits derived from the HEC-FDA program for the National Economic Development plan. The expected annual benefits of the National Economic Development plan—$2.314 million—are the same in Table 5.3 and Table 5.4 . The net benefits in the fourth column of Table 5.4 are found by subtracting the annual project costs from the expected annual benefits; the benefit-to-cost ratio is the ratio of the expected benefits to costs.

The 25 th percentile, median (50 th percentile), and 75 th percentile of the expected annual benefits are also shown. The project net benefits are positive at all levels of assessment, and all benefit-to-cost ratios are greater than 1.00. It is interesting to see that the median expected annual benefits ($2.071 million) are nearly the same as the expected value of these benefits without considering uncertainty ($2.078 million). Moreover, the expected value ($2.314 million) is greater than the median, and the difference between the 75 th percentile and the median is greater than the difference between the median and the 25 th percentile. All these characteristics point to the fact that the distributions of flood damages and of expected annual benefits are positively skewed when uncertainties in project hydrology, hydraulics, and economics are considered. This is why the project benefits increase when these uncertainties are considered. The project benefits for the 25 th percentile, 50 th percentile, and 75 th percentile in Table 5.4 should be read with caution because they are compiled for the project by adding together the corresponding values for all the damage reaches. The percentile value of a sum of random variables is not necessarily equal to the sum of the percentile values of each variable.

TABLE 5.3 Expected Annual Damages (EAD) With and Without Uncertainty in Damage Computations (millions of dollars per year)

TABLE 5.4 Statistics of project benefits under the NED plan using the HEC-FDA Program

RED RIVER OF THE NORTH AT EAST GRAND FORKS, MINNESOTA, AND GRAND FORKS, NORTH DAKOTA

A devastating flood occurred at East Grand Forks, Minnesota, and Grand Forks, North Dakota, in April 1997. After the flood, flood damage reduction studies previously done for the two cities were combined into a joint study, and risk analysis was performed to evaluate the reliability of the proposed alternatives and to evaluate their economic impacts. A risk analysis study performed before the flood was presented in a paper at the Corps's 1997 Pacific Grove, California, workshop (Lesher and Foley, 1997). This paper and subsequent analysis (USACE, 1998a, b, c), as well as a visit to the Corps's St. Paul district office by a member of this committee, form the basis of this discussion of the East Grand Forks–Grand Forks study.

East Grand Forks, Minnesota, and Grand Forks, North Dakota, are located on opposite banks of the Red River of the North and are approximately 300 miles above the river's mouth at Lake Winnipeg, Manitoba, Canada ( Figure 5.10 ). The East Grand Forks–Grand Forks metropolitan area has a population of approximately 60,000 and is located about 100 miles south of the U.S.–Canadian border. The total drainage area of the East Grand Forks–Grand Forks basin is 30,100 square miles. Included in this drainage area is the Red Lake River subbasin that effectively drains about 3,700 square miles in Minnesota and joins the mainstream of the Red River at East Grand Forks. The study area of East Grand Forks–Grand Forks lies in the middle of the Red

FIGURE 5.10 Schematic of the Red River of the North (RRN) and Red Lake River (RLR) at the East Grand Forks, Minnesota and Grand Forks, North Dakota study area. Numbers indicate USGS stream gages.

River Valley. The valley is exceptionally flat with a gradient that slopes 3–10 feet per mile toward the river with the north–south axis having a gradient of about three-quarters of a foot per mile. The valley extends approximately 23 miles west and 35 miles east of East Grand Forks– Grand Forks and is a former glacial lake bed.

Both cities have a long history of significant flooding from the Red River of the North and the Red Lake River. The most damaging flood of record occurred in April 1997 (see Table 5.5 ), when the temporary levee systems and flood-fighting efforts of both communities could not hold back the floodwaters of the Red River. The resulting damages were disastrous and affected both cities dramatically. Total damages to existing structures and contents during the 1997 flood were estimated to exceed $800 million. An additional $240 million was spent for emergency-related costs.

TABLE 5.5 Maximum Recorded Instantaneous Peak Flows; Red River of the North at Grant Forks, North Dakota

Risk Analysis

A risk analysis for the proposed flood damage reduction project for the Red River of the North at East Grand Forks, Minnesota, and Grand Forks, North Dakota, used a Latin Hypercube analysis to sample interactions among uncertain relationships associated with flood discharge and elevation estimation. Latin Hypercube is a stratified sampling technique used in simulation modeling. Stratified sampling techniques, as opposed to Monte Carlo-type techniques, tend to force convergence of a sampled distribution in fewer samples. Because the Hydrologic Engineering Center Flood Damage Analysis program (HEC-FDA) was new at the time, and in the interest of saving time, the analysis was performed using a spreadsheet template. The flood damage reduction alternatives analyzed included levees of various heights and a diversion channel in conjunction with levees. The project reliability option in the HEC risk spreadsheet was used to determine the reliability of the alternative levee heights and of the diversion channel in conjunction with levees. The following sections discuss the sensitivity in quantifying the uncertainties and the representation of risk for the alternatives.

Discharge–Frequency Relationships

The log-Pearson Type III distribution, recommended in the Water Resource Council's Bulletin 17B (IACWD, 1981) and incorporated

within the Corps's HEC Flood Frequency Analysis (HEC-FFA) computer program, was used for frequency analysis of maximum annual streamflows, and the noncentral t distribution was used for the development of confidence limits. Discharge–frequency relationships were needed for both the levees and the diversion channel in combination with levees. An analysis (coincidental frequency) was performed to develop the discharge– frequency curves for the Red River of the North downstream and upstream of the Red Lake River for the levees only condition. A graphical method was used to develop the discharge–frequency curves for the diversion channel in combination with levees. Details of these procedures can be found in a Corps instruction manual from the St. Paul district (USACE, 1998a). A brief discussion of these procedures is provided below.

The Grand Forks USGS stream gage (XS 44) is currently located 0.4 miles downstream from the Red Lake River in Grand Forks, North Dakota ( Figure 5.10 ). The discharge–frequency curve for this station along with the 95 percent and 5 percent confidence limits (90% confidence band) are plotted in Figure 5.11 . An illustration of the noncentral t probability density function for the 1 percent event is also shown in that figure. Selected quantities of that discharge–frequency relationship are shown in column 2 of Table 5.6 . The coincidental discharge–frequency relationship for the Red River just upstream of the mouth of the Red Lake River (column 3 of Table 5.6 ) was computed with the HEC-FFA computer program. The basic flow values were obtained by routing the 96 years of available data on Red Lake River flows from Crookston (55 miles upstream of the mouth) downstream to Grand Forks. The resulting flows were subtracted from the Red River at Grand Forks flows to obtain coincident discharges on the Red River upstream of Red Lake River. The two-station comparison method of Bulletin 17B was used to adjust the logarithmic mean and standard deviation of this short record (96 years) based on regression analysis with the long-term record at the Grand Forks station (172 years). Correlation of coincident flows for the short record with concurrent peak flows for the long record produced a correlation coefficient of 0.975.

Adjustment of the statistics yielded an equivalent record length of 165 years. The adopted coincidental discharge–frequency curve for the Red River upstream of the Red Lake River is shown in column 3 of Table 5.6 for selected annual exceedance probabilities. The coincidental discharge –frequency curve for the Red Lake River at the mouth was determined by computing the difference in Red River flows both upstream and downstream of Red Lake River (see column 4 in Table 5.6 ). Statistics for the adopted relationship were approximated by synthetic methods presented in Bulletin 17B (for more details, see USACE (1998a)).

FIGURE 5.11 Flood (discharge) frequency curve for the Red River at Grand Forks.

TABLE 5.6 Instantaneous Annual Peak Discharges (cfs) and their Annual Exceedance Probabilities (%) — Existing Conditions

and downstream of Red Lake River (see column 4 in Table 5.6). Statistics for the adopted relationship were approximated by synthetic methods presented in Bulletin 17B (for more details, see USACE (1998a)).

The Plan Comparison Letter Report developed in February 1998 for flood damage reduction studies for East Grand Forks, Minnesota, and Grand Forks, North Dakota, evaluated an alternative flood damage reduction plan that included a split-flow diversion channel along with permanent levees. The discharge–frequency relationships for the modified conditions, shown in Table 5.7 , were developed as follows. The modified-condition discharge–frequency curve for the Red River upstream of Red Lake River was graphically developed based upon the operation of the diversion channel inlet. Red River flows are not diverted until floods start to exceed those having return periods of 5 years (20% annual exceedance probability). The channel is designed to continue to divert Red River flows at a rate that allows the design flood (0.47%) discharge of 102,000 cfs (upstream of the diversion) to be split such that 50,500 cfs is diverted and 51,500 cfs is passed through the cities. This operation is reflected in the modified discharge–frequency relationship shown in Table 5.7 for the Red River upstream of Red Lake River (columns 2 and

TABLE 5.7 Instantaneous Annual Peak Discharges (cfs) and their Annual Exceedance Probabilities (%)—Condition with Diversion Channels

3).Synthetic statistics (mean, standard deviation, and skewness) in accordance with methodology presented in Bulletin 17B were computed for the discharge-frequency relationships of the below-diversion flows.

The modified-condition discharge–frequency curve for the Red River downstream of Red Lake River was graphically computed based upon the operation of the diversion channel. The modified-condition Red River discharges upstream of Red River were added to the coincident flows on Red Lake River (column 4). The resulting discharges were plotted for graphical development of the modified-condition discharge– frequency relationship for the Red River downstream of Red Lake River and are summarized in Table 5.7 (column 5). Synthetic statistics for this discharge–frequency relationship were computed for use in the risk analysis.

Elevation–Discharge Relationships

The water surface elevations computed using the HEC-2 computer program are shown in Table 5.8 for three cross sections (7790, 7800, and 7922) corresponding to the previous USGS gage locations and for cross

section 44, which corresponds to the current USGS gage location (see Figure 5.10 for the cross section locations). These computed water surface elevations (CWSE) were based on the expected discharge quantities from the coincidental frequency analysis performed in June 1994 for the Grand Forks Feasibility Study. These data were used to transfer observed elevations from previous USGS gage sites to the current site (cross section 44) at river mile 297.65, and they were used in determining the elevation –discharge uncertainty. The water surface profile analysis was performed using cross-sectional data obtained from field surveys. Data were also obtained from field surveys and from USGS topographic maps. The HEC-2 model was calibrated to the USGS stream gage data and to high-water marks for the 1969, 1975, 1978, 1979 and 1989 flood events throughout the study area. Note that these water surface elevations assume the existing East Grand Forks and Grand Forks emergency levees are effective. The levees were assumed effective because through extraordinary efforts, they have generally been effective for past floods with the exception of the 1997 flood.

Ratings at stream gage locations provide an opportunity to directly analyze elevation–discharge uncertainty. The measured data are used to derive the “best fit” elevation-discharge rating at the stream gage location, which generally represents the most reliable information available. In this study, the adopted rating curve for computing elevation uncertainty is based on the computed water surface elevations from the calibrated HEC-2 model shown in Table 5.8 .

This adopted rating curve for cross section 44 at the current USGS gage is shown in Figure 5.12 . Measurements at the gage location were used directly to assess the uncertainty of the elevation–discharge relationship. The normal distribution was used to describe the distribution of error from the “best-fit” elevation–discharge rating curve. The observed gage data (for the four cross sections presented in Table 5.8 ) were transferred to the current gage site at river mile 297.65 based on the gage location adjustments presented in Table 5.9 , which were computed from the water surface elevations in Table 5.8 . These adjustments were plotted against the corresponding discharge below the Red Lake River, and curves were developed to obtain adjustments for other discharges.

The deviations of the observed elevations from the fitted curve were used to estimate the uncertainty of the elevation–discharge rating curve shown in Figure 5.11 . The deviations reflect the uncertainty in data values as a result of changes in flow regime, bed form, roughness/resistance to flow, and other factors inherent to flow in natural streams. Errors also

TABLE 5.8 Computed Water Surface Elevations of the Red River of the North at Grand Forks, North Dakota (units in feet above sea level)

FIGURE 5.12 Rating curve (water elevation vs. discharge)for the Red River at Grand Forks.

TABLE 5.9 Adjustments Used in Transferring Observed Elevations from Previous USGS Gage Sites to Current Gage Site at RM 297.65 (XS 44)

result from field measurements or malfunctioning equipment. A minimum of 8–10 measurements is normally required for meaningful results. The measure used to define the elevation–discharge relationship uncertainty is the standard deviation:

Where X = observed elevation adjusted to current gage location (if 5.12 necessary), M = computed elevation from adopted rating curve, and N = number of measured discharge values (events).

The elevation uncertainty was computed for two different discharge ranges for this analysis. Based on the observed elevations plotted on the adopted rating curve, it appeared that there was greater uncertainty for discharges less than about 10% of annual exceedance probability event due to ice effects on flow. Therefore, the standard deviation was computed for discharges greater than between 22,000 cfs, which corresponds approximately to the zero damage elevation based on the adopted rating curve, and 44,000 cfs, which is slightly greater than the 10 percent annual exceedance probability. The standard deviation was also computed for discharges greater than 50,000 cfs. During the period of record, there were 25 events with a discharge between 22,000 and 44,000 cfs and 10 events with a discharge greater than 50,000 cfs. The standard deviation was 1.66 feet for discharges between 22,000 and 44,000 cfs and was 1.55

feet for discharges greater than 50,000 cfs. In the risk and uncertainty simulations, the standard deviation was linearly interpolated between 1.66 and 1.55 feet for discharges between 44,000 and 50,000 cfs. (See USACE (1998b) for more details.)

In an earlier risk analysis that was performed for the Grand Forks Feasibility Study, a much lower standard deviation of 0.50 feet was used for discharges greater than 50,000 cfs. However, adding the 1997 flood to the analysis resulted in a standard deviation of 1.55 feet, which is similar to that computed for discharges less than 44,000 cfs. It should be noted that the discharge and elevation used in this analysis for the 1997 flood was the peak discharge of 136,900 cfs occurring on April 18, 1997 (see Table 5.4 ), and an elevation of 831.21 feet (Stage 52.21). The peak elevation of 833.35 feet (Stage 54.35) occurred on April 22, 1997 at a discharge of 114,000 cfs. The elevation of 831.21 feet was almost 5 feet below the rating curve at a discharge of 136,900 cfs; however, the peak elevation of 833.35 feet at a discharge of 114,000 cfs was essentially on the adopted rating curve. Both of these points are plotted on the rating curve in Figure 5.12 . Lines representing ± 2 standard deviations for the normal distribution, which encompasses approximately 95 percent of all possible outcomes, are also shown on the rating curve. An illustration of the normal distribution at the 1 percent (100-year) event for the project levee condition is also shown in Figure 5.12 .

Risk and Uncertainty Analysis Results

Four index locations were selected to evaluate project performance and project sizing. These locations are cross sections 57, 44 (current USGS gage), 27, and 15 ( Figure 5.10 ). The four locations were selected based on economic requirements for project sizing (see USACE, 1998c). The elevation–discharge rating curves (based on HEC-2 analysis) for existing and project conditions at these locations can be found in the USACE (1998b). Each of these rating curves shows three conditions, where applicable: (1) existing conditions, (2) removal of the pedestrian bridge at cross sections 7920-7922 and with project levees (“levee only”); and (3) with removal of the pedestrian bridge, with project levees, and with the diversion channel (“diversion channel”). Existing conditions means that the existing emergency levees are assumed to be effective up to and including the 5 percent (20-year) event and are ineffective for larger floods. The 5 percent (20-year) event was selected based

on comparison of water surface profiles with effective and probable failure point (PFP) levee elevations provided by the Geotechnical Design Section analysis (see USACE, 1998b, paragraph A.2.11 and Appendix B of this report). The pedestrian bridge was removed based on input from the cities of East Grand Forks and Grand Forks. The rating curves for the diversion channel alternative were based on limited information. The Red River to the North would start to divert into the diversion channel at the 20 percent (5-year) flood; therefore, up to this point the rating curve for existing conditions with levees was used.

An additional location was also selected to evaluate the performance of the levee only and diversion channel with 1 percent (100-year) levee alternatives. This location is at cross section 7700 at the downstream end of the project levees (see Figure 5.10 ). Cross section 7700 was selected based on hydraulic analysis as the least critical location—the location where the levees in combination with the diversion channel would first overtop from downstream backwater (see USACE, 1998b).

Project Reliability

The project reliability results are summarized in Table 5.10 , Table 5.11 through Table 5.12 . Table 5.9 contains the results for the levees-only alternatives. Table 5.11 contains the results for the diversion channel in combination with 1 percent (100-year) levees. Note that in Table 5.10 , three different alternative top-of-levee heights are evaluated, whereas in Table 5.11 , it is always the same alternative—diversion channel with 1 percent levees— but for the three different events. The top-of-levee elevations were computed based on a water surface elevation profile to ensure initial overtopping would occur at the least-critical location (here, cross section 7700). The downstream top-of-levee elevations were selected with the intent of having 90 percent probability of containing the specified flood and were based on previous risk analysis for the Grand Forks Feasibility Study preliminarily updated to include the 1997 flood. The 2 percent (50-year), 1 percent (100-year), and 0.47 percent (210-year/1997 flood) top-of-levee profiles are 3.2, 3.4, and 2.7 feet above their respective water surface profiles at the downstream end ( Table 5.10 ).

As seen in Table 5.10 , the intent of having 90 percent probability of containing the specified flood is generally realized. The 2 percent levees have a 92 percent probability of containing the 2 percent flood. The 1 percent levees have a 90 percent probability of containing the 1 percent

flood. The 0.47 percent levees have an 87 percent probability of containing the 0.47 percent flood.

TABLE 5.10 Reliability at Top of Levee for Three Top-of-Levee Heights

TABLE 5.11 Project Reliability at Top of Levee for Diversion Channel with 1 Percent (100-Year) Levees for Three Different Events

Reliability results for the diversion channel with 1 percent levees are summarized in Table 5.11 . Note again that the levees constructed in combination with the diversion are the same as for the 1 percent flood without the diversion channel and are the same for all three floods analyzed. As seen in the table, there is a 99 percent or greater probability of containing the flood for all three floods considered when the project includes the diversion channel.

As previously noted, the most critical location for project performance is at cross section 7700 at the downstream end of the project. Table 5.12

summarizes the results for all the alternatives considered and for numerous floods. The probability of the diversion channel in combination with 1 percent levees for the 0.2 percent event is listed in the table as greater than 95%. A more specific reliability was not cited for the 0.2 percent event for two reasons: (1) the discharge–frequency curve based on the approximate statistics starts to diverge from the graphical curve for extreme events and, (2) there was limited information available to develop the Red River to the North rating curves for the diversion alternative. These reasons are also why more extreme events were not analyzed.

TABLE 5.12 Conditional Exceedance Probability of Alternative for Various Events (based on analysis at downstream end of project—XS 7700)

Table 5.13 presents the simulated conditional exceedance probabilities from the economic project sizing analysis. The without-project condition is also included in this table for comparison purposes. The without-project condition is based on a zero damage elevation of 824.5 feet, assumes credit is given to the existing levees, and assumes all properties that were substantially damaged (50% or more damage) in the 1997 flood have been removed.

Based on the above analysis of alternative plans and further economic and environmental considerations, the recommended National

TABLE 5.13 Residual Risk Comparison

Economic Development (NED) plan consists of a permanent levee and floodwall system designed to reliably contain the 210-year flood event. This equates to an 87.7 percent reliability of containing the 210-year flood event ( Table 5.12 ) and would reliably protect against a flood of the magnitude of the 1997 flood.

The recommended plan would remove protected areas from the regulatory floodplain, increase recreational opportunities, and enhance the biological diversity in the open space created. The recommended plan anticipates the need to acquire over 250 single-family residential structures, 95 apartment or condominium units, and 16 businesses along the current levee/floodwall alignment.

The total cost of the recommended multipurpose project is $350 million including recreation features and cultural resources mitigation costs. The federal share of the project would be $176 million and the nonfederal share would be $174 million. The benefit-to-cost ratio has been calculated as 1.07 for the basic flood reduction features of the project and as 1.90 for the separable recreation features (USACE, 1998b). The recommended project has an overall benefit-to-cost ratio of 1.10.

The cities of East Grand Forks, Minnesota, and Grand Forks, North Dakota, will serve as the project's nonfederal sponsors. Through legislation, the State of Minnesota has committed to provide financial support in the form of bonds and returned sales taxes to the city of East Grand Forks. In verbal and written comments from its governor, the State of North Dakota has committed to provide financial assistance to the city of Grand Forks.

Reducing flood damage is a complex task that requires multidisciplinary understanding of the earth sciences and civil engineering. In addressing this task the U.S. Army Corps of Engineers employs its expertise in hydrology, hydraulics, and geotechnical and structural engineering. Dams, levees, and other river-training works must be sized to local conditions; geotechnical theories and applications help ensure that structures will safely withstand potential hydraulic and seismic forces; and economic considerations must be balanced to ensure that reductions in flood damages are proportionate with project costs and associated impacts on social, economic, and environmental values.

A new National Research Council report, Risk Analysis and Uncertainty in Flood Damage Reduction Studies , reviews the Corps of Engineers' risk-based techniques in its flood damage reduction studies and makes recommendations for improving these techniques. Areas in which the Corps has made good progress are noted, and several steps that could improve the Corps' risk-based techniques in engineering and economics applications for flood damage reduction are identified. The report also includes recommendations for improving the federal levee certification program, for broadening the scope of flood damage reduction planning, and for improving communication of risk-based concepts.

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Open Access

Peer-reviewed

Research Article

A research on urban disaster resilience assessment system for rainstorm and flood disasters: A case study of Beijing

Roles Writing – original draft

* E-mail: [email protected]

Affiliation Beijing Academy of Emergency Management Science and Technology, Beijing, 101117, China

Roles Data curation

Affiliation China Academy of Urban Planning and Design, Beijing, 100044, China

Affiliation Beijing Institute of Astronautical System Engineering, Beijing, 100076, China

Roles Visualization

Affiliation Beijing Xiaoban Technology Co., Beijing, 100020, China

Affiliation Institute of Urban Safety and Environmental Science, Beijing Academy of Science and Technology, Beijing, 100054, China

Roles Resources

Affiliation Emergency Rescue Center (Fire Brigade) of Dongying City, Shandong Province, Dongying, 257000, China

• Shuangrui Yu, 
• Ruiqi Li, 
• Yuxi Zhang, 
• Mingfei Wang, 
• Peng Zhang, 
• Aizhi Wu, 
• Fucai Yu, 
• Xiaofeng Zhang, 
• Lin Yang, 
• Yong’an Cui

• Published: October 26, 2023
• https://doi.org/10.1371/journal.pone.0291674
• Reader Comments

Under the background of global climate change, rainstorm and flood disasters have become the most serious cataclysm. Under the circumstances of an increasingly severe risk situation, it is necessary to enhance urban disaster resilience. Based on the disaster resilience process of prevention, absorption, and enhancement, and considering the safety factors such as personnel, facility, environment and management, this paper forms a dual dimension of the urban disaster resilience assessment model covering the key elements of urban disaster response and the core capacity of urban disaster recovery. Furthermore, if taking into account the characteristics of rainstorm and flood disasters, the paper screens the key indicators to build up an assessment index system of an urban rainstorm and flood disaster. The practical application was implemented in Beijing to have an assessment of the ability to recover from rainstorm and flood disasters in all districts of Beijing. And then, some pertinent suggestions for enhancing the resilience of Beijing to rainstorm and flood disasters were proposed.

Citation: Yu S, Li R, Zhang Y, Wang M, Zhang P, Wu A, et al. (2023) A research on urban disaster resilience assessment system for rainstorm and flood disasters: A case study of Beijing. PLoS ONE 18(10): e0291674. https://doi.org/10.1371/journal.pone.0291674

Editor: Mohammed Sarfaraz Gani Adnan, University of Strathclyde, UNITED KINGDOM

Received: February 1, 2023; Accepted: September 1, 2023; Published: October 26, 2023

Copyright: © 2023 Yu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All data can be obtained from the official statistical yearbook and public documents of the goverment sectors. The specific information on where and how to access the statistical yearbook and government documentation is available in the Supporting information files. The URLs are provided.

Funding: This work is funded by the Young Elite Scientists Sponsorship Program by BAST (No.BYESS2022044). As the corresponding author of this paper, I’m also the principal investigator of this program, which is funded by China Association for Science and Technology. This program provided financial support to collect data and publish research paper for this work. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

1 Introduction

With the ceaseless proceeding of global warming, the accelerating regional water circulation leads to a high risk of extreme rainfall. Hence, flood disaster caused by rainstorm has been one of the dominant threats faced by modern cities. According to relevant research, there are currently 1.81 billion people in the world directly exposed to the threat of once-in-a-century flood disasters, including 1.24 billion people in East and South Asia, and China (395 million) accounts for the highest risk share [ 1 ]. On July 20, 2021, a flood disaster caused by extreme rainfall made major deaths and property losses in Zhengzhou City, Henan Province, China, which raised a great repercussions in the local society.

In an increasingly complex and severe disaster risk environment, improving urban disaster resilience has become a public topic of concern. The United Nations defines resilience as the ability of a system, community or society exposed to disasters to resist, absorb, accommodate and recover from the effects of a hazard in a timely and efficient manner, including through the preservation and restoration of its essential basic structures and functions [ 2 ]. The ISO/TC 292 Technical Committee on Safety and Resilience defines urban resilience to be the ability of any urban system, with its inhabitants, in a changing environment, to anticipate, prepare, respond to and absorb shocks, positively adapt and transform in the face of stresses and challenges, while facilitating inclusive and sustainable development [ 3 ]. It could be seen that disaster resilience emphasizes the city’s ability to resist and respond to disasters in the whole process of pre-disaster prevention, in-disaster absorption and post-disaster enhancement. Cities such as New York [ 4 ], London [ 5 ], and Rotterdam [ 6 ], have also carried out actions to improve urban disaster resilience against climate change.

Scientists and researchers have widely focused on the way to enhance urban resilience for rainstorm and flood disasters. Aerts et al. set up a management strategy assessment method for the coastal big cities’ resilience to flood disasters from the aspect of cost-benefit [ 7 ]. Bertilsson et al. proposed the multiple standard indexes plotting method for urban resistance for flood disasters [ 8 ]. One of the priorities to formulate the public policy to enhance metropolitan strength for rainstorm and flood disasters is to have a quantitative assessment of them. One of the main methods to assess this is setting up a scenario of rainstorm and flood disasters through the meteorological and hydrological models to analyze the exposure and vulnerability of disaster-bearing bodies. For example, Tayyab et al. set up an assessment model of municipalities’ recovery after downpour catastrophes based on the geographic information system and remote sensing data, which has a comprehensive thought of such influencing factors as the flood disaster risk, coping ability, and conduct the application in the city Bavasha, Pakistan [ 9 ]. Bisht et al. have studied the flooding process under extreme precipitation times in a region of West Bengal, India with the models of SWMM and MIKE [ 10 ]. However, the method of simulation assessment based on the meteorological and hydrological measures would need a large amount of data on the precipitation model, ground elevation, pipe network distribution and so on, and the analysis would need to be organized with drainage zoning of small areas as the basic unit. The large-scale application of the urban regional hierarchy would be restricted by insufficient basic data. The other method for the disaster risk and resilience assessment is to estimate based on the index system, whose advantage is to ponder on multiple factors and to have a flexible operation. This method has been applied in many resilience assessments on disasters related to climate change, such as drought disasters, sea ice disasters, and hurricane disasters [ 10 – 13 ]. Some researchers also carry out studies on the assessment system of resilience for rainstorm and flood disasters. For example, Ali et al. built up assessment principles from society, economy, politics, health, communication, education, infrastructures, and other dimensions to propose a community resilience index system in flood-prone areas of the city [ 14 ]. Balica et al. developed the flood vulnerability index system of coastal cities from the perspectives of exposure, vulnerability, and resistance, then selected 9 cities worldwide for evaluation and application [ 15 ]. It should be noted that researchers mainly proceed through two ways when constructing evaluation systems and presenting results in existing studies related to system resilience assessment. One focuses more on resilience, portraying the similarities and gaps between the system and an ideal resilient system through the description of the system’s resilience capabilities or characteristics [ 16 – 18 ]. The other focuses more on the system, sorting out the potential specific influences through the system’s component dimensions [ 19 – 21 ]. These two ways can be considered as two perspectives of considering system resilience and have been mentioned in the early literature about resilience studies [ 22 ]. However, considering the system resilience from only one way of the two above may have some impact on the study. Studying only from the perspective of system’s resilience capabilities or characteristics, the indicators are limited by and the comprehensiveness and precision of the capabilities or characteristics. And the descriptions of capabilities or characteristics are generally abstract, so it is not intuitive enough to assess how to enhance system resilience through practical work based on the assessment results. Studying only from the perspective of system’s components, it is easily lead to conceptual confusion between resilience research and traditional risk research for the lack of interpretation of what resilience is. Therefore, this paper proposes the assessment model with the dual dimensions of urban disaster resilience which integrates the two perspectives. In the empirical evaluation of disaster resilience in Beijing, in addition to the overall disaster resilience assessment, this paper further carries out separate assessments from the two perspectives in order to interpret system resilience more comprehensively.

Section 1 of this paper introduces the research background and analyzes the research status of urban resilience assessment for a rainstorm and flood disasters. Section 2 proposes an assessment model with the dual dimensions of urban disaster resilience with a comprehensive consideration of the MMEM (Man-Machine-Environment-Management) theory model and resilience process. Section 3 sets up an assessment index system of urban rainstorm and flood disasters with 3 level-2 indicators, 11 level-3 indicators and 31 level-4 indicators, to provide a tool for the examination of the urban resilience of rainstorm and flood disasters. Section 4 takes Beijing as the research object, determines the weights of indicators through the analytic hierarchy process, and carries out the application of rainstorms and the ability to estimate recovery from floods for each district of Beijing based on the government department data. Section 5 analyzes the general situation of the resilience of rainstorm and flood disasters in all districts of Beijing, as well as the various strength capabilities and safety factors, analyze the existing problems and proposes the improvement direction to have a comparative analysis of the resilience results in the central areas and the non-central areas. Section 6 is to make a summary of the study contents and the conclusion of this paper.

2 Theoretical model for urban disaster resilience assessment

For carrying out the urban disaster resilience assessment, it is necessary to establish the assessment dimensions and framework. Researchers mainly have two ideas when setting up the resilience assessment dimensions currently. The first idea is to begin with the principle to evaluate the several core abilities embodied in the cities during the disaster response process. The conduction of the assessment of urban disaster resilience based on this idea would be to understand the main tasks faced by cities in different disaster response stages, but there would be difficulty in the explanation of the internal logic between the sorted indicators. If it will fail to correspond to the specific responsibilities of the urban management departments, it would be harmful to the city to generate the actual strategy to enhance disaster resilience. The second idea is to conduct based on several key factors of the urban disaster response to have an assessment from the perspective of the system composition. Based on this idea, urban disaster resilience assessment is apt to integrate abundant kinds of factors involved and sort out the assessment indicators, but it is hard to explain the role of various elements in different stages of urban disaster response. Also, it would be difficult to highlight the resilience concept and the essence reflected in the traditional disaster vulnerability assessment.

To solve the problems mentioned above, better to build an assessment of the index system of urban disaster resilience with two dimensions, the core abilities of resilience and the key factors of urban disaster response. In addition, to provide support for the comprehensive and systemic assessment of metropolitan disaster resilience, there is a decoupling of the connotative relation on all indicators and the two assessment dimensions. Consequently, the paper proposes the following examination model with dual dimensions (as shown in Fig 1 ). In this model, there are two assessment vectors, including resilience abilities and safety factors.

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https://doi.org/10.1371/journal.pone.0291674.g001

There are different summarizations for the dimension of resilience abilities. It is generally believed that three main theories exist for the perception of the concept of resilience, namely, engineering resilience, ecological resilience, and socio-ecological resilience [ 23 ]. These three theories emphasize the characteristics that a resilient system should have. Engineering resilience is the earliest of the three perspectives. It refers to the ability of the system to minimize losses in order to maintain and recover as soon as possible the original [ 24 ]. It could be found that the theory of engineering resilience emphasizes the system should be as stable as possible. Thus prevention ability should be taken as a core ability of resilience. Ecological resilience emphasizes more on the magnitude of disturbance that a system can absorb before structural change, and emphasized the existence of multi-stability of the system [ 25 ]. From this theory, absorption ability should also be taken as a core ability of resilience. Then Walker et al. proposed the concept of socio-ecological resilience, which emphasis on the system’s ability to adapt and enhance in a constantly changing environment [ 26 ]. It could be found that enhancement ability is a core ability of resilience as well. Therefore, the paper concludes the resilience abilities into prevention, absorption and enhancement of these three aspects from a whole process of urban disaster response, including pre-disaster, in-disaster and post-disaster. The definitions are given with reference to other literature [ 18 ]. Prevention indicates the ability of a city to reduce the possibility of adverse consequences of disasters [ 27 ]. It takes place through various measures before the arrival of disaster events. Absorption means the ability of a city to minimize the damage to itself caused by a disaster event when it occurs [ 28 ]. Enhancement refers to the ability of the city to recover from the impact of disaster events as soon as possible and learn from them to reduce future disaster risks [ 29 ]. These three kinds of resilience abilities reflect various concerns and the requirements for enhancing urban cataclysm resilience.

Different researchers also proposed different perspectives on the analytical dimensions of safety factors. For example, Bruneau et al. conceptualized the factors as four interrelated dimensions: technical, organizational, social, and economic [ 22 ]. However, in order to present separate resilience evaluation results from each safety factor dimension, the division among factors needs clearer boundaries. The factors need to be less interrelated to ensure that the evaluation results could reflect the resilience characteristics of a certain aspect of the system elements independently. It will be more helpful for clarifying the direction of resilience enhancement. The MMEM is a theoretical model of risk management with guiding significance, which is widely applied to various kinds of risk management and safety accidents [ 30 ]. This model defines safety factors into four relatively independent fields, including man, machine, environment and management. The mutual impact and joint working of the safety factors are reflected by the chain angle of safety risk breedings, controls and disaster generating. The study object of urban disaster resilience assessment is the urban system. Compared with the specific safety incident risk management, the study object would be wider in the space scale, and the causes of risk are more complicated, so there should be much more consideration of the roles of various kinds of safety factors in urban disaster resilience. The paper applies the MMEM theory model to the study of urban disaster resilience and concludes the four influencing safety factors as personnel, facility, environment and management.

3 The assessment index system of urban resilience for rainstorm and flood disasters

According to the assessment model with the dual dimensions of urban disaster resilience mentioned above, the paper first analyzes the three aspects of the dimension of resilience abilities, prevention resilience, absorption resilience and enhancement resilience, and analyzes the leading factors in the process of urban rainstorms and flood disaster response.

In the aspect of prevention resilience, factors including land layout, monitoring and early warning, and information release are mainly considered [ 31 – 33 ]. The land layout affects the exposure and vulnerability of urban disaster-bearing bodies, which is the premise to reduce the physical loss caused by rainstorm and flood disasters. The enhancement of monitoring and early warning capability could precisely control the process of disaster breeding, development and extinction to achieve more effective disaster prevention and mitigation. The information release is a crucial link to guide the social mass to have disaster prevention and mitigation, which plays a key role in reducing the casualties due to disasters.

In terms of absorption resilience, factors including natural environment, engineering resistance, evacuation and risk avoidance, and social response are mainly considered [ 34 , 35 ]. The natural environment means the background of urban blue and green space [ 36 ]. Rich blue and green space could effectively absorb and weaken the strength of rainstorm and flood disasters. The engineering resistance is to protect the important disaster-bearing bodies through the way of engineering to reduce the vulnerability of disaster-bearing bodies [ 37 ]. Evacuation and risk avoidance refer to protecting the people threatened by disasters through transfer and temporary refuge in the case of disasters exceeding the fortification intensity [ 38 ]. Social response mainly shows the vulnerability of the urban population and the ability in medical aid, emergency rescue and other aspects [ 39 , 40 ].

In terms of enhancement resilience, factors including risk management, financial input, community preparation and social recovery are mainly considered [ 41 – 43 ]. Risk management primarily refers to the ability of cities to reduce casualties and economic losses caused by disasters, which is the embodiment of the comprehensive level of urban risk management [ 44 ]. The financial input is the capital invested by cities in risk prevention and post-disaster recovery, which is an essential guarantee to enhance urban healing from the cataclysm [ 45 ]. Community preparation emphasizes the disaster response ability of the grassroots community, which is the necessary fundamental job implemented to enhance urban disaster resilience [ 46 ]. Social recovery mainly indicates the ability of cities to recover quickly from the impact of disasters, which is the core requirement of metropolitan ability to resist and recover from cataclysm [ 47 ].

Furthermore, this paper selects 31 specific quantitative evaluation indicators based on the above factors, considering the availability and comparability of data and fully combining them with the government’s disaster prevention and emergency functions. These indicators are available in the public data statistics of government sectors. Ulteriorly, the paper has a classification of factors for the assessment dimension of safety factors to clarify the correspondent relation of all indicators with the four safety factors, personnel, facility, environment and management, as shown in Table 1 . And the the data source sectors are also presented.

https://doi.org/10.1371/journal.pone.0291674.t001

Table 1 also shows the vector of each specific indicator, which is divided into positive indicators and negative indicators. The direction of the indicator is the directional correlation between data value and urban resilience for rainstorm and flood disasters. The larger the data value of the positive indicator, the stronger the urban resilience for rainstorm and flood disasters; while the smaller the data value of the negative indicator, the stronger the urban resilience for rainstorm and flood disasters.

4 Data processing and quantitative evaluation methods

4.1 research object and data sources.

Beijing is the capital of the People’s Republic of China with a division into 16 districts, which is a pioneer in the enhancement of disaster resilience among China’s cities. The paper made Beijing a study object to conduct an assessment of the resilience of rainstorm and flood disasters at the level of the district. With a comparison of the assessment index system of urban resilience for rainstorm and flood disasters, there was data collected from 16 districts. The data sources were the official statistics of relevant government departments in Beijing, and the year of data was 2020.

4.2 Data standardization

The data of all indicators were processed with deviation standardization [ 48 ].

In this formula, the x i was the original data of the indicator and the X i was the data after the standardization process.

4.3 Weight determination

Since the hierarchical relationships among indicators are clear, and the indicators in the same hierarchical model have strong comparability, so this study adopts with analytic hierarchy process (AHP) as the method of determining indicator weights. The AHP approach is considered to be an efficient and flexible framework based on psychology and mathematics and is thus an ideal subjective weighting method [ 48 ].

The steps of AHP approach are as follows.

First, a hierarchical analogy based on the structure of the assessment index system needs to be established, as shown in Fig 2 . The indicators represented by serial numbers in each dashed box in the Fig 2 are analogous objects of the same hierarchy.

https://doi.org/10.1371/journal.pone.0291674.g002

Second, the importance of indicators in the same hierarchy are compared pair by pair and a judgment matrix is constructed. The relative importance scales are shown in Table 2 .

https://doi.org/10.1371/journal.pone.0291674.t002

The judgment matrix is a n × n matrix, in which n is the number of indicators in the same hierarchy. The element a ij in the judgment matrix is the scale quantification value of indicator i to indicator j , and the following conditions need to be met.

Third, the eigenvectors corresponding to the maximum eigenvalues of the judgment matrix are calculated as the relative weights of each indicator.

Fourth, a consistency test is needed to explore the discordances between the pairwise comparisons and the reliability of the obtained weights. The consistency test is conducted by calculating consistency ratio ( CR ), which is compared the consistency index ( CI ) with an given average random consistency index ( RI ). The calculation formulas are as follows.

λ max is the maximum eigenvalues of the judgment matrix, and n is the number of indicators in the hierarchy. RI is given by randomly generated matrices, which is determined by n .

Generally, 0.1 is adopted as the judgment value of CR , and a smaller result means better consistency among the subjective judgments.

Finally, the comprehensive weight of each indicator is calculated after the end of all the separate hierarchical analysis process.

Ten experts with different disciplinary backgrounds, such as emergency management, water conservancy, urban planning, remote sensing, and risk assessment were invited. And then it was adopted with an analytic hierarchy process to confirm the relative weight of all indicators at all levels, to gain the comprehensive weight of all specific assessment indicators. The weighted results of the indicators are shown in Table 3 . The relative weights should be the judge of the relative importance of the indicators at the same level, which were rated by the experts. Only the results that pass the consistency test were retained. The results of average consistency ratio ( CR ) are shown in Table 4 . The comprehensive weight was the judgement of the comprehensive importance of all specific assessment indicators, which was gained by the relative weight of the corresponding indicator and its superior indicator.

https://doi.org/10.1371/journal.pone.0291674.t003

https://doi.org/10.1371/journal.pone.0291674.t004

4.4 Resilience calculation

Resilience results are calculated based on the results of data normalization and indicator weights. The results are calculated by the following formula.

In this study, the overall urban resilience to rainstorm and flood disasters is calculated, and the resilience results of sub-items are calculated from two dimensions of resilience abilities and safety factors. Eight categories of resilience results are presented, including one overall resilience result (urban resilience to rainstorm and flood disasters), three categories for the resilience abilities dimension (prevention resilience, absorption resilience and enhancement resilience), and four categories for the safety factors dimension (personnel resilience, facility resilience, environment resilience and management resilience). When calculating the results for different categories of resilience, the indicators associated with that category are considered.

5 Results and discussion

5.1 study area.

In this study, Beijing was selected as the study area, which is the capital city and the first city proposed the concept of “resilient city” into urban planning in China. In applying the indicator system proposed in this study, the data standardization process requires the comparison of the same indicator data among different study subjects. 16 districts in Beijing become the research subjects. And the data of indicators of the 16 districts in Beijing were collected from the statistics of government sectors as listed in Table 1 .

5.2 Standard classification

The resilience results of the rainstorm and flood disasters in all districts of Beijing ware calculated based on the weight of all indicators and the standardization result of the indicator data in all districts. According to the assessment model with the dual dimensions of urban disaster resilience, there were respective resilience assessments of the indicators under the correspondent dimension from the perspective of the two dimensions, resilience abilities and safety factors, to conclude the resilience results for the respective dimension of rainstorm and flood disasters in all districts of Beijing.

There were statistics on the results of resilience for rainstorm and flood disasters in all districts of Beijing and the resilience results of respective dimensions. All the eight categories of resilience results of the 16 districts are counted. With the 25%, 50% and 75% quartiles of all the resilience results as reference values, the resilience results were divided into four grades, excellent, good, medium and bad. The value range of each grade was as shown in Table 5 .

https://doi.org/10.1371/journal.pone.0291674.t005

5.3 Assessment results of the disaster resilience of all districts

The assessment results of the resilience to rainstorm and flood disasters in all districts of Beijing are shown in Table 6 . There was a spatial display of the results with ArcGIS, as shown in Fig 3 .

https://doi.org/10.1371/journal.pone.0291674.g003

https://doi.org/10.1371/journal.pone.0291674.t006

It could be seen that the general resilience to rainstorm and flood disasters in all districts of Beijing was fine. The assessment results of Pinggu District and Miyun District were excellent. The results of Huairou District, Tongzhou District, Mentougou District and Shunyi District were good. The results of the rest of the districts were medium. There was no district with a bad result of resilience to rainstorm and flood disasters. The average value of the assessment result of resilience for rainstorm and flood disasters in 16 districts was 0.494, which was close to the grade of good.

5.4 Assessment result of the dimension of resilience abilities in all districts

There was an analysis of the dimension of resilience abilities, and the results of the prevention resilience, absorption resilience and enhancement resilience in all districts were as shown in Table 7 . The spatial display of the results was as shown in Fig 4 .

https://doi.org/10.1371/journal.pone.0291674.g004

https://doi.org/10.1371/journal.pone.0291674.t007

The average results of the prevention resilience, absorption resilience and enhancement resilience of all districts of Beijing were 0.522, 0.373 and 0.607. On the whole, the prevention resilience and enhancement resilience in all districts were better, but the absorption resilience was not that ideal. In the aspect of prevention resilience, the results of prevention resilience in 11 districts were in the grade of excellent or good; however, the results in Shijingshan District, Chaoyang District, Xicheng District, Dongcheng District were worse, and they should be further improved. In the aspect of absorption resilience, only the results of the 6 districts were in the grade of a medium, and the results of the rest of the districts were bad. There should be an improvement in the absorption resilience of all districts. For the enhancement resilience, only the result of Daxing District was medium and the one of Yanqing District was bad, while the results of the other 14 districts were excellent or good. It meant that Beijing showed a strongly enhanced resilience on the whole.

5.5 Assessment result of the dimension of safety factors of all districts

There was an analysis of the dimension of safety factors, and the results of the personnel resilience, facility resilience, ecological resilience and management resilience of all districts were shown in Table 8 . The spatial display of the results was as shown in Fig 5 .

https://doi.org/10.1371/journal.pone.0291674.g005

https://doi.org/10.1371/journal.pone.0291674.t008

The average results of the personnel resilience, facility resilience, cological resilience and management resilience of all districts were 0.556, 0.473, 0.475 and 0.509. The assessment results of the dimension of safety factors showed different features in the spatial distribution. For personnel resilience, there were excellent results in the districts in the northeast and central areas of Beijing. The reason for the strong personnel resilience in the northeast area was the lower population density and small density of disaster bearing bodies, and the reason why there was strong personnel resilience in the central area was the strong self-organization ability of residents and relatively complete supporting services. Given the facility’s resilience, there was a better result in the districts in the east and central areas of Beijing while there was a worse result in the districts in the western areas, which should be owed to the distribution of overall water conservancy, meteorology and disaster prevention facilities in Beijing. As for environmental resilience, there was a better overall situation in the districts in the north and west areas of Beijing; while there was a worse overall situation in the districts in the central and southeast areas of Beijing. It was caused by the natural environment in the city. The north and west areas had a better natural foundation with a higher proportion of blue and green space. In the management resilience, the results of all districts were medium or higher except for Fengtai District and Changping District whose results were mainly medium. There were relatively smaller differences among the results of all districts, which should be thanked for the common experience in the safety management systems and mechanisms in the governments of all districts. The difference was mainly related to the risk background and financial input of each district.

5.6 Comparison between the results of central districts and the ones of non-central districts

Dongcheng District, Xicheng District, Chaoyang District, Haidian District, Fengtai District and Shijingshan District, these 6 districts were the central ones in Beijing. And the rest of them were the non-central districts. Thus, there was a calculation of all assessment results of the resilience for rainstorm and flood disasters in the central districts and the non-central districts to gain the average results, and the results were as shown in Table 9 .

https://doi.org/10.1371/journal.pone.0291674.t009

It could be seen from the table that the overall situation of the resilience for rainstorm and flood disasters in central districts was worse than the one of the non-central districts, and the reasons could be compared and analyzed through the assessment results of all resilience abilities and safety factors. In the resilience ability, the prevention resilience in the central districts was far lower than that of the non-central districts since the central districts had a higher difficulty in the prevention of rainstorm and flood disasters due to the high density of disaster-bearing bodies, more complex urban systems and higher protection requirements. At the same time, the enhancement resilience in the central districts was better than that of the non-central districts since there was a more developed economy and a better risk transfer and post-disaster recovery system in the central districts. In the aspect of safety factors, the personnel resilience in the central districts was higher than in the non-central districts since there was a better disaster rescue and relief system in the central districts. However, the environmental resilience in the central districts was far lower than that of the non-central districts, which was related to the foundation of the natural environment and showed the negative influence of the high urbanization on disaster resilience.

5.7 Comparison with similar studies

The relationship between risk and resilience is vague. Different researchers have discussed the relationship between resilience and risk from a doctrinal perspective and have presented different opinions. Resilience could be seen as the opposite of risk, or a management approach to risk, or an interconnected but different concept with risk, or a completely separate concept to risk [ 28 , 49 – 52 ]. Instead of exploring the relationship between risk and resilience conceptually, this study verified the relationship between the two concepts from the perspective of empirical research. In 2022, Beijing published an urban waterlogging risk map to identify urban high-risk points under rainstorm and flood disasters [ 53 ]. The density of risk points in each district is compared with the resilience results of storm flooding hazards obtained from this study and ranked from highest to lowest resilience, as shown in Table 10 .

https://doi.org/10.1371/journal.pone.0291674.t010

As can be seen from the table, districts without high risk points also generally received higher resilience measurement results in this study, which also indicates that risk is an important driver of resilience. At the same time, the resilience results of each zone are not exactly opposite to the density of high-risk points, which also reflects the difference between resilience studies and risk studies, that is, resilience studies also consider the influence of the system’s own abilities and component factors.

5.8 Discussion

The following assessment conclusions can be drawn from above assessment process. According to the assessment results, the overall situation of the resilience to rainstorm and flood disasters in all districts of Beijing was fine, which showed the achievements gained by Beijing in urban disaster risk prevention and control and resilient city construction. Among the prevention, absorption and enhancement of the reliance abilities, the overall situation of the prevention resilience and enhancement resilience in all districts of Beijing was better, while there was a huge space for improvement in the absorption resilience. A comprehensive improvement of disaster emergency response capability by improving the regulation and storage of natural space, upgrading the engineering fortification standard, strengthening the ability of emergency rescue and refuge, and increasing social disaster relief preparation would be the key link for all districts of Beijing to enhance the resilience for rainstorm and flood disasters. Due to the natural location, and social and economic development, all districts had different situations in the resilience of personnel, facility, environment and management. In General, the highly developed central districts impose a negative impact on the prevention and control of rainstorm and flood disasters. The average resilience for rainstorm and flood disasters in central districts was worse than that in the non-central districts, so there should be further attention on the improvement of disaster adaptability of the natural environment and the built environment.

In addition to the general resilience assessment result (urban resilience for rainstorm and flood disasters) of each research subject like the traditional practice, this paper also give multi-dimensional resilience assessment results according to the categorization of resilience abilities and safety factors. This approach allows for a more intuitive and clearer understanding of the causes of the general resilience assessment result of each subject. It will be more helpful for clarifying the strengths and weaknesses of each research subject, and thus giving directions and suggestions for improving resilience. It is worth noting that the assessment model with the dual dimensions of urban disaster resilience proposed in this paper is a generalized model, which can be used not only for rainstorm and flood disasters, but also other types of disasters. The assessment index system of urban resilience for rainstorm and flood disasters proposed in this study could be seen as a specific application based on this model. When this model is used in the research on other types of disasters, the two-dimensional framework can be used to further construct the assessment system combining the characteristics of the disaster under study and the research subjects.

6 Conclusion

The paper suggests an assessment model with the dual dimensions of urban disaster resilience with the dimensions of resilience abilities and safety factors. And it starts from the three resilience abilities, including prevention resilience, absorption resilience and enhancement resilience to screen 31 specific assessment indicators according to the four safety factors, including personnel, facility, environment and management, with the combination of the demand for urban disaster prevention and emergency management in China, to build up an assessment index system of urban resilience for rainstorm and flood disasters. Ten experts are invited to confirm the indicator weight with the analytic hierarchy process. Furthermore, with Beijing as a study object, the paper assesses the resilience for rainstorm and flood disasters of 16 districts of Beijing and the respective resilience results for the resilience abilities and the safety factors. The result shows that the overall situation of resilience for rainstorm and flood disasters in all districts of Beijing is fine, while all districts have their situation in various resilience abilities and safety factors. Thus, in the next stage, there should be further improvement of the urban resilience for rainstorm and flood disasters with the key point of improving the absorption resilience. And there should be an improvement in the resilience to rainstorm and flood disasters in the central districts from disaster prevention and environmental improvement. It is worth mentioning that the assessment model with the dual dimensions proposed in this paper is of general significance and can be applied to resilience assessments of other disasters, not limited to rainstorm and flood disasters. The proposed asssessment index system of urban resilience for rainstorm and flood disasters also has strong generalizability for China’s cities due to the channel of government management statistics. It should be noted that the results obtained in this study are relative results for the comparison within the 16 districts of Beijing, and the method can also be applied to different cities to obtain comparative results of resilience between different cities. In addition, due to the limitation of the statistical channel and update time of the data, the data year taken in this study is a single year data, and the trajectory of city resilience improvement can be obtained by comparing the results between different years in further study.

Supporting information

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Flooding Case studies

Cockermouth, UK – Rich Country (MEDC) Picture Causes: Rain A massive downpour of rain (31.4cm), over a 24-hour period triggered the floods that hit Cockermouth and Workington in Cumbria in November 2009

What caused all the rain? The long downpour was caused by a lengthy flow of warm, moist air that came down from the Azores in the mid-Atlantic. This kind of airflow is common in the UK during autumn and winter, and is known as a ‘warm conveyor’. The warmer the air is, the more moisture it can hold.

What else helped to cause the Cumbrian Floods? · The ground was already saturated, so the additional rain flowed as surface run-off straight into the rivers · The steep slopes of the Cumbrian Mountains helped the water to run very rapidly into the rivers · The rivers Derwent and Cocker were already swollen with previous rainfall · Cockermouth is at the confluence of the Derwent and Cocker (i.e. they meet there)

The effects of the flood · Over 1300 homes were flooded and contaminated with sewage · A number of people had to be evacuated, including 50 by helicopter, when the flooding cut off Cockermouth town centre · Many businesses were flooded causing long-term difficulties for the local economy · People were told that they were unlikely to be able to move back into flood-damaged homes for at least a year. The cost of putting right the damage was an average of £28,000 per house · Insurance companies estimated that the final cost of the flood could reach £100 million · Four bridges collapsed and 12 were closed because of flood damage. In Workington, all the bridges were destroyed or so badly damaged that they were declared unsafe – cutting the town in two. People faced a huge round trip to get from one side of the town to the other, using safe bridges · One man died– PC Bill Barker

Responses to the flood · The government provided £1 million to help with the clean-up and repairs and agreed to pay for road and bridge repairs in Cumbria · The Cumbria Flood Recovery Fund was set up to help victims of the flood. It reached £1 million after just 10 days · Network Rail opened a temporary railway station in Workington The ‘Visit Cumbria’ website provided lists of recovery services and trades, and people who could provide emergency accommodation

Management of future floods at Cockermouth £4.4 million pound management scheme New flood defence walls will halt the spread of the river Funding from Government and local contributors River dredged more regularly to deepen the channel New embankments raise the channel height to reduce the likelihood of extra floods New floodgates at the back of houses in Waterloo street

Pakistan, Asia – Poor Country Picture At the end of July 2010 usually heavy monsoon rains in northwest Pakistan caused rivers to flood and burst their banks. The map below shows the huge area of Pakistan affected by flooding. The floodwater slowly moved down the Indus River towards the sea.

Continuing heavy rain hampered the rescue efforts. After visiting Pakistan, the UN Secretary General, Ban Ki-moon, said that this disaster was worse than anything he’d ever seen. He described the floods as a slow-moving tsunami.

The effect of the floods · At least 1600 people died · 20 million Pakistanis were affected (over 10% of the population), 6 million needed food aid · Whole villages were swept away, and over 700,000 homes were damaged or destroyed · Hundreds of thousands of Pakistanis were displaced, and many suffered from malnutrition and a lack of clean water · 5000 miles of roads and railways were washed away, along with 1000 bridges · 160,000km2 of land were affected. That’s at least 20% of the country · About 6.5 million acres of crops were washed away in Punjab and Sindh provinces

The responses to the floods · Appeals were immediately launched by international organisation, like the UK’s Disasters Emergency Committee – and the UN – to help Pakistanis hit by the floods · Many charities and aid agencies provided help, including the Red Crescent and Medecins Sans Frontieres · Pakistan’s government also tried to raise money to help the huge number of people affected · But there were complaints that the Pakistan government was slow to respond to the crisis, and that it struggled to cope · Foreign Governments donated millions of dollars, and Saudi Arabia and the USA promised $600 million in flood aid. But many people felt that the richer foreign governments didn’t do enough to help · The UN’s World Food Programme provided crucial food aid. But, by November 2010, they were warning that they might have cut the amount of food handed out, because of a lack of donations from richer countries

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Flood Response System—A Case Study

Related Papers

CURRENT SCIENCE

Panchagnula Manjusree

Satellite images provide information on the flood disaster footprints, which is essential for assessing the disaster impact and taking up flood mitigation activities. The Brahmaputra floods that occurred during June-July 2012 devastated a large part of Assam. This article discusses the maximum spatial extent affected due to the flood event, villages marooned and population affected, with the aid of multi-temporal satellite images coupled with the hydrological observations and freely available gridded population data. The study shows that about 4.65 lakh ha area was submerged, 23 of the 27 districts in Assam had more than 5% of the total geographical area submerged, about 3829 villages marooned and 23.08 lakh people were affected. Identification of the spatial extent of areas most vulnerable to flooding, captured from the satellite images acquired during the peak flood period will be helpful for prioritizing appropriate flood control measures in the flood-affected regions.

JASH MATHEW

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Lunamoni Das

Jiadhol River is one of the most frequently flooded north bank tributaries of Brahmaputra. There are many factors responsible for the intensification of floods in this basin. Flood management is very important in this river. Flood hazard zonation of the basin helps in proper management and adaptation of the foods in the basin. This paper aims to develop a simple method for flood hazard zonation of the basin by using GIS. The method used here is multi-criteria weighed overlay analysis of GIS. This method includes many factors such as geomorphology, elevation, slope, drainage density, drainage network, flow accumulation, soil, ground water table and land cover/land cover, lithology and also proximity to drainage, confluence and embankment breaching point for flood hazard zonation. All factors are weighted according to their role in the occurrence of floods. Using the mentioned method, four hazard zones are derived, they are the severely flood prone zone, moderately flood prone zone, less flood-prone zone and flood free zones. Most of the upper basin comes under flood free zone except the river valleys. There are two distinct severely flood-prone zones in the lower part of the basin. Introduction Flood is the most common hazard witness by the world today. The area effect by floods every year covers a large portion of the human inhabited area. These areas are also the most densely populated ones, and that is why flood hazards throughout the world are responsible for causing heavy damage to the human kind. These are the natural phenomenon associated with the flood plains of the world. Floods are unavoidable, so we have to adapt to it and proper flood forecasting helps in better adaptation and management of floods. The scholars from different fields are continuously contributing lots of knowledge for improving the methods of forecasting flood as exactly as possible. India is a country in which large part of the country experiences the problem of flood every year. The concentration of 70% of the total annual rainfall within a period of just four months of monsoon makes the situation worst. The mighty Ganga-Brahmaputra flood plain along with other major floodplains experiences seasonal floods in the monsoon season in almost every year. Along with that the occurrence of flash floods in the Himalayas Rivers due to cloud burst. There are also tidal floods associated with cyclonal activities in the coastal area of the country. It is difficult to predict different floods with one method so different studies are necessary to understand the nature of different floods. Flood plain zonation is very significant in management and development of river basins (Jana, 1997). Its effectiveness increases by many folds with the use of more recent high resolution multi sensor satellite data (Prasad, et al., 2006), but due to lack of accessibility imageries with lower resolution are used. Singh, et al., (2013) has used Microwave Passive Remote Sensing (AMSR-R) in parts of Brahmaputra basin for monitoring floods. Jana (1997) used Survey of India toposheets of 1:50000 scale and IRS LISS-II for preparing flood hazard zones in North Bengal. Besides using the data from the satellite imageries, the basin morphometry, such as Digital Elevation Model, flow direction, flow

Dr. Shukla Acharjee

Panchagnula Manjusree , Goru Srinivasa Rao

During the last few decades satellite images have become an integral part of disaster management and have extensively been used globally for mapping, monitoring and damage assessment of extreme hydrological disaster events. Satellite images because of their synoptic and multi temporal coverage of large, remote and inaccessible areas are the only means of obtaining quick information on the severity, persistence, spatial extent of inundated areas and dynamics of the flood water to have an overall view of the phenomenon and provide quick response. Today, India is one of the very few countries to have its indigenous, operational space and ground segment as well as necessary expertise and a dedicated Disaster Management Support (DMS) Programme to proactively provide space based information on disasters in the country. The present chapter discusses about the remote sensing of inundation and its successful operational use in India for flood disaster management.

Partha Jyoti Das

Sustainability

Nishikant Gupta, PhD

AJIT K U M A R SINGHA

Dr. Sunny Agarwal

Natural Hazards

Roxana Hoque

Remote sensing is the most practical method available to managers of flood-prone areas for quantifying and mapping flood impacts. This study explored large inundation areas in the Maghna River Basin, around the northeastern Bangladesh, as determined from passive sensor LANDSAT data and the cloud-penetrating capabilities of the active sensors of the remote imaging microwave RADARSAT. This study also used passive sensor LANDSAT wet and dry images for the year 2000. Spatial resolution was 30 m by 30 m for comparisons of the inundation area with RADARSAT images. RADARSAT images with spatial resolution of 50 m by 50 m were used for frequency analysis of floods from 2000 to 2004. Time series images for 2004 were also used. RADARSAT remote sensing data, GIS data, and ground data were used for the purpose of flood monitoring, mapping and assessing. A supervised classification technique was used for this processing. They were processed for creating a maximum water extent map and for estimati...

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Flood risk in Europe: a case study with World Bank and EU Commission

Natural hazards such as flooding and earthquakes have a significant economic and social impact on EU Member States, which is expected to increase in some areas in the future due to climate change, population change and economic growth. And, while the argument for investing in measures to strengthen disaster resilience is strong, a lack of data and analytics about the scale, location, and costs of these risks, amongst other factors, means it’s not always clear which measures are most effective or best value for money in each context.

Analysing EU risk and resilience

The World Bank undertook a technical assistance project with the European Commission’s European Civil Protection agency (DG ECHO) to overcome these barriers and analyse the costs and benefits of investing in disaster risk resilience measures. To do so, the project needed to model natural hazard risk across all 27 EU Member States, including country level risk analysis. Both current levels of risk and future risk due to climate change and increased economic growth had to be calculated for the hazards studied: floods, earthquake, wildfires, extreme heat and droughts.

European scale flood modelling: using JBA flood science

Modelling on such a scale, within the tight timelines of the project, required consistent, high quality coverage from models that were already readily available, with minimal development. JBA’s probabilistic model met the requirements for academic rigour, resolution, cost and availability and so was selected to underpin flood modelling for the study. Part of our global flood model coverage, first released in 2019 and providing probabilistic modelling worldwide for the first time, our Europe flood modelling combines high resolution flood maps, an extensive set of simulated events, and vulnerability and exposure components to provide high quality flood analysis. Although we offer our Europe modelling at 5m resolution with bespoke components, the project used our 30m maps and broad-scale components to provide the consistency required, while still ensuring a robust, high resolution analysis. All of our flood modelling is underpinned by JBA’s modelling engine, FLY Technology, which brings models into being at run-time rather than using pre-built components.

This enables users to change almost all data, analysis settings and methods for unprecedented customisation and model flexibility, which was key for this project.

Flexibility for improved flood analysis

Because JBA’s model for Europe was already available, the project could be focused on generating results for the specific types of exposure that the World Bank required:

• Residential
• Effects on the population

Vulnerability functions for residential, commercial and industrial exposure were already available in the modelling.

The flexibility of FLY meant that it was easy to introduce new vulnerability functions into the model for the education and healthcare facilities based on multiple studies in research literature. It also meant that it was simple to generate an additional set of risk outputs for a 2050 climate scenario. By updating the Europe region of JBA’s Global Flood Event Set, the modelling could represent the change in frequency of flood events caused by future rainfall changes.

Cost-benefit of flood mitigation measures

To demonstrate the benefits of investing in flood resilience, different flood mitigation mechanisms were modelled to provide an indication of the impact that could be expected for widespread implementation.

FLY Technology again showed its flexibility as a tool for adjusting components of the risk calculation to represent the effects of four different mechanisms:

• Additional flood defences (modelled as defended areas with an associated level of protection)
• Early warning systems (modelled by adjusting the level of damage to account for actions such as temporary defences and removal of household valuables from lower stories)
• Property level protection (modelling reduced losses for two urban areas by selecting properties most likely to benefit from protection and modifying their vulnerability)
• Nature-based solutions (assessed based on the change in peak flow as a result of environmental changes made)

Results from the probabilistic flood and earthquake modelling show the countries at highest risk of impacts. In absolute terms, the countries with the largest economies and highest exposure – Germany, France, Italy – display the highest economic impacts from flood. When looking at economic impacts as a proportion of exposure, Romania, Slovenia, Latvia and Bulgaria are ranked highest.

Supporting increased flood resilience

The JBA flood risk analysis has contributed to the full economic assessment of the project, which was released in the report Financial Risk and Opportunities to Build Resilience in Europe. Overall conclusions of the economic assessment include:

• More investment is required in mitigating disasters in Europe
• Disaster risk financing, such as insurance, can help to limit impacts of disaster on public finances
• Mitigation measures such as defences, early-warning systems, nature-based solutions, and infrastructure changes can help to reduce the risk

This report will play a vital part in not only highlighting the large-scale financial impact of natural disaster in Europe, but also supporting proposals for increasing mitigation and finance mechanisms throughout the region at government level. This need is underlined by the July 2021 floods in Central Europe , in which over 200 people lost their lives and thousands more people were affected.

For more information on JBA's global flood modelling capabilities and how it can help you manage your risk, get in touch with the team .

Read the full  Economics for Disaster Prevention and Preparedness in Europe report and summary highlights.

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Kerala flood case study

Kerala flood case study.

Kerala is a state on the southwestern Malabar Coast of India. The state has the 13th largest population in India. Kerala, which lies in the tropical region, is mainly subject to the humid tropical wet climate experienced by most of Earth’s rainforests.

A map to show the location of Kerala

Eastern Kerala consists of land infringed upon by the Western Ghats (western mountain range); the region includes high mountains, gorges, and deep-cut valleys. The wildest lands are covered with dense forests, while other areas lie under tea and coffee plantations or other forms of cultivation.

The Indian state of Kerala receives some of India’s highest rainfall during the monsoon season. However, in 2018 the state experienced its highest level of monsoon rainfall in decades. According to the India Meteorological Department (IMD), there was 2346.3 mm of precipitation, instead of the average 1649.55 mm.

Kerala received over two and a half times more rainfall than August’s average. Between August 1 and 19, the state received 758.6 mm of precipitation, compared to the average of 287.6 mm, or 164% more. This was 42% more than during the entire monsoon season.

The unprecedented rainfall was caused by a spell of low pressure over the region. As a result, there was a perfect confluence of the south-west monsoon wind system and the two low-pressure systems formed over the Bay of Bengal and Odisha. The low-pressure regions pull in the moist south-west monsoon winds, increasing their speed, as they then hit the Western Ghats, travel skywards, and form rain-bearing clouds.

Further downpours on already saturated land led to more surface run-off causing landslides and widespread flooding.

Kerala has 41 rivers flowing into the Arabian Sea, and 80 of its dams were opened after being overwhelmed. As a result, water treatment plants were submerged, and motors were damaged.

In some areas, floodwater was between 3-4.5m deep. Floods in the southern Indian state of Kerala have killed more than 410 people since June 2018 in what local officials said was the worst flooding in 100 years. Many of those who died had been crushed under debris caused by landslides. More than 1 million people were left homeless in the 3,200 emergency relief camps set up in the area.

Parts of Kerala’s commercial capital, Cochin, were underwater, snarling up roads and leaving railways across the state impassable. In addition, the state’s airport, which domestic and overseas tourists use, was closed, causing significant disruption.

Local plantations were inundated by water, endangering the local rubber, tea, coffee and spice industries.

Schools in all 14 districts of Kerala were closed, and some districts have banned tourists because of safety concerns.

Maintaining sanitation and preventing disease in relief camps housing more than 800,000 people was a significant challenge. Authorities also had to restore regular clean drinking water and electricity supplies to the state’s 33 million residents.

Officials have estimated more than 83,000km of roads will need to be repaired and that the total recovery cost will be between £2.2bn and $2.7bn.

Indians from different parts of the country used social media to help people stranded in the flood-hit southern state of Kerala. Hundreds took to social media platforms to coordinate search, rescue and food distribution efforts and reach out to people who needed help. Social media was also used to support fundraising for those affected by the flooding. Several Bollywood stars supported this.

Some Indians have opened up their homes for people from Kerala who were stranded in other cities because of the floods.

Thousands of troops were deployed to rescue those caught up in the flooding. Army, navy and air force personnel were deployed to help those stranded in remote and hilly areas. Dozens of helicopters dropped tonnes of food, medicine and water over areas cut off by damaged roads and bridges. Helicopters were also involved in airlifting people marooned by the flooding to safety.

More than 300 boats were involved in rescue attempts. The state government said each boat would get 3,000 rupees (£34) for each day of their work and that authorities would pay for any damage to the vessels.

As the monsoon rains began to ease, efforts increased to get relief supplies to isolated areas along with clean up operations where water levels were falling.

Millions of dollars in donations have poured into Kerala from the rest of India and abroad in recent days. Other state governments have promised more than $50m, while ministers and company chiefs have publicly vowed to give a month’s salary.

Even supreme court judges have donated $360 each, while the British-based Sikh group Khalsa Aid International has set up its own relief camp in Kochi, Kerala’s main city, to provide meals for 3,000 people a day.

International Response

In the wake of the disaster, the UAE, Qatar and the Maldives came forward with offers of financial aid amounting to nearly £82m. The United Arab Emirates promised $100m (£77m) of this aid. This is because of the close relationship between Kerala and the UAE. There are a large number of migrants from Kerala working in the UAE. The amount was more than the $97m promised by India’s central government. However, as it has done since 2004, India declined to accept aid donations. The main reason for this is to protect its image as a newly industrialised country; it does not need to rely on other countries for financial help.

Google provided a donation platform to allow donors to make donations securely. Google partners with the Center for Disaster Philanthropy (CDP), an intermediary organisation that specialises in distributing your donations to local nonprofits that work in the affected region to ensure funds reach those who need them the most.

Google Kerala Donate

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Review article, floods and flood management and its socio-economic impact on pakistan: a review of the empirical literature.

• 1 Department of Govt and Public Policy, Faculty of Contemporary Studies, National Defence University, Islamabad, Pakistan
• 2 Department of Earth and Environmental Sciences, Bahria University, Islamabad, Pakistan
• 3 School of Economics, Sapienza University of Rome, Rome, Italy
• 4 Department of Environmental Sciences, Faculty of Life Sciences and Informatics, Balochistan University of Information Technology, Engineering and Management Sciences, Quetta, Pakistan
• 5 School of Public Administration, Xiangtan University, Hunan, China
• 6 School of Public Administration, China University of Geosciences, Wuhan, China
• 7 The Faculty of Biosciences, Fisheries and Economics, UiT The Arctic University of Norway, Tromsø, Norway
• 8 Department of Earth Sciences, Faculty of Sciences, Ibn Zohr University, Agadir, Morocco
• 9 MARE-Marine and Environmental Sciences Centre—Sedimentary Geology Group, Department of Earth Sciences, Faculty of Sciences and Technology, University of Coimbra, Coimbra, Portugal

Flood is one of the most damaging natural disasters as the recent floods have shown their serious impact on Pakistan. Flood control and regulation policies are essential to reduce the risks of economic downturn, a threat to human existence, and to sustain the ecology. The severity of flood catastrophe activities represents a constant and severe issue in the world. Floods are rising year by year in severity and duration, causing negative impacts on the social and economic conditions of the nation concerned. While the frequency of floods cannot be avoided, their adverse impacts can be considerably reduced by adopting careful planning and efficient training. This paper reviews the socioeconomic impact of floods, and the existing condition of flood control policies outlines the flood protection problems and discusses opportunities for successful and efficient flood control in Pakistan. The paper also intends to propose several suggestions for efficient and sustainable flood control in Pakistan.

1 Introduction

Floods are one of the major sources of anthropological and ecological destruction. It affects the socio-economic conditions, worsens public health, generates unemployment, damages the ecosystem, etc. ( Allaire, 2018 ; Parida, 2019 ). Currently, public and private institutions are struggling to formulate and evaluate risk management and adjustment strategies, involving systems for flood prevention and advance alerts with urbanization patterns and land use planning under consideration in the wake of urban flooding. One major reason is the land adjudication and administration system of Pakistan is colonial in nature and lacks judicial augmentation, providing a chance for flawed urbanization ( Shafi et al., 2022 ). However, policymakers face substantial hiccups in the mitigation of natural disasters’ aftermath globally. In this wake, countries with stable economic structures and administration have reported fewer mortalities and lower socioeconomic damages as compared to developing countries ( Anbarci et al., 2005 ; Kahn, 2005 ).

Certain approaches are utilized to manage floods and mitigate their aftermath. Here, Effective Risk Avoidance involves a detailed understanding of the effects of floods on the public, the economy, and the efficiency of disaster management strategies. The realistic approaches focus on the potential risks and benefits analysis. Currently, prevention approaches are often decided based on active expert analysis of floods. However, cost-benefit analysis is seldom used in mitigation planning, possibly because of insufficient empirical evidence on a broad range of types of losses. Government and non-governmental sectors also transfer their resources from production to restoration and rehabilitation practices which slow down the growth of Gross Domestic Product (GDP) and overall Human Development Index (HDI) ( Sadia et al., 2013 ; Isik et al., 2021 ).

The South-Asian Subcontinent, with The Great Himalaya Glaciers, remains at primary risk. Though currently, the rainy season remains the major reason for the flooding in the region the presence of glaciers and the rise in average temperature remain a permanent threat to the region. Floods are the most common natural disaster in the region with 40% occurrence rate ( Chaudhry, 2017 ). Furthermore, urban flooding reasoned by solid waste management and decreases in urban vegetation has affected 9.6 million people in Bangladesh, India, and Nepal, with 6.8 million from India only. South Asia floods: 9.6 million people swamped as humanitarian crisis deepens ( IFRC, 2020 ).

It has been observed that the average global temperature has been rising at a higher rate since 1980. Due to this glaciers are melting rapidly, generating glacial lakes and associated hazards. Glacial Lake Outburst Floods (GLOFs) are extremely destructive because of large volumes of water flowing in narrow river channels. The greatest number of GLOFs, out of all the natural disasters that have occurred worldwide, have been reported in Central Asia ( Carrivick and Tweed, 2016 ; Nie et al., 2017 ; Mohanty and Maiti, 2021 ). Global warming as the sole reason GLOFS, the Hindukush-Karakoram-Himalaya regions of Pakistan, which contribute more than 50% of the entire flow of the Indus River System (a major water system), has observed a higher melting rate of glaciers. This condition has led to an increase in the frequency of glacial-related hazards in this Himalayan region. GLOFs which are either caused by the sudden failure of the “dams,” or in the absence of the dams release huge volumes of water and debris wreak havoc downstream. GLOFs have the potential to massively harm people living in the Himalayan region, especially Indus River Basin ( Ashraf et al., 2012 ; Ashraf et al., 2021 ; Ahmad et al., 2022 ).

Achieving sustainability in the country in terms of economic, social, political, and environmental issues is discussed more thoroughly nowadays because the phenomena of globalization as well as global warming are based to meet today’s requirements without compromising the ability of future generations ( Işık et al., 2021 ). The rising CO2 emission worldwide will be rising government spending as the result increases real GDP per capita. So, state policymakers are trying to limit or minimize CO2 emissions ( Işık et al., 2022 ). Pakistan is among the most vulnerable countries to floods and water-related disasters as Pakistan has the most glaciers outside the arctic circle. The climate changes and monsoon season have significant impacts on socio-economic degradation, specifically on agricultural production and livestock. The regular occurrence of flood catastrophes affects different regions of Pakistan (See Table 1 ). Floods are expensive environmental disasters, leading to property and agricultural land destruction. Floods are typically short-lived occurrences that can occur with a tiny alert ( Commission, 2007 ). Pakistan has experienced an unprecedented increase in floods in the last 20 years. Fifty-four (54) floods of differing intensity hit Pakistan, placing it 10th on Global Environment Risk Index ( Kreft et al., 2015 ; Rehman et al., 2015 ; Sardar et al., 2016 ). Pakistan is positioned within a hazard-prone region and is bare to a variety of natural catastrophes like floods ( Rafiq and Blaschke, 2012 ). The past of the region’s flooding is relatively lengthy. Many significantly disastrous floods caused considerable harm to economic development. Table 2 provides the history of floods in terms of losses incurred. Each flood is caused by heavy rainfall in the Indus River catchments and its main tributaries ( GoP Annual Flood Report, 2017 ). According to the World Health Organization (WHO) twenty (20) million residents in seventy-eight (78) areas were affected by 2010 floods, taking one thousand eight hundred (1800) life’s, causing damage, or destruction of nearly two million houses with a cumulative cost of $9.7 billion. Public infrastructure i.e., roads, bridges, hospitals, schools etc. , were immensely affected, deteriorating accessibility to health, education and mobility resulting in poverty, deprivation, and psychological and social trauma necessitating rehabilitation and restoration ( Sardar et al., 2016 ).

TABLE 1 . Most vulnerable districts of Pakistan for flood and flash flood ( NDMA, 2019 ).

TABLE 2 . Historical flood damages in Pakistan (period 1950–2017) ( GoP Annual Flood Report, 2017 ).

There are typically five forms of flooding frequently occurring in the country, such as flash floods, river floods, tidal floods, marine floods, and pluvial floods. The heavy rainfall in canals’ catchment areas triggers pluvial floods of canals or dams, and water goes out towards the dry land and affects the area ( Yaqub et al., 2015 ). Main floods in Pakistan are linked with the low depression monsoon rainfalls that form in the Bay of Bengal and spread west/northwest through India to Pakistan ( Kronstadt, 2010 ). Apart from Monsoon rains, meltdown of glaciers causes flashfloods in hilly areas rendering massive destruction. However, urban flooding is solely related to monsoon rains and unplanned urbanization restricting drainage passages and encroachments. Furthermore, coastal floods are mostly caused by tropical storms in South-East Sindh and Makran regions ( Yaqub et al., 2015 ). Pakistan faces severe floods from July to September period owing to heavy monsoon rains in all the regions of Pakistan inundating the Indus River Basin. Hydrologically, the region can be categorized into three main divisions: Indus Basin, Kharan Basin, and Makran Coastal Drainage Zone. Such basin’s flooding features differ significantly and require a detailed understanding ( Tariq and van de Giesen, 2012 ). The flood hazard map indicates the most vulnerable districts of Pakistan with major river systems (developed after ( NDMA, 2019 ) as shown in Figure 1 . A topographic map of Pakistan with recent flood events is shown in Figure 2 . The list of the most vulnerable districts of Pakistan concerning Flood and Flash Flood is shown in Table 1 . The geographical distribution of Pakistan’s 2010 floods’ direct and indirect damages is shown in Figure 3 and Figure 4 respectively.

FIGURE 1 . Flood hazard map indicates the most vulnerable districts of Pakistan with a major river system map developed after ( NDMA, 2019 ).

FIGURE 2 . Topographic map of Pakistan with recent flood events (Source: ESRI topographic layer used).

FIGURE 3 . Geographical distribution of Pakistan’s 2010 floods direct damages; Source: Pakistan floods 2010 preliminary damage and needs assessment ( Asian Development Bank, 2010 ).

FIGURE 4 . Geographical distribution of Pakistan’s 2010 floods indirect damages; Source: Pakistan floods 2010 preliminary damage and needs assessment ( Asian Development Bank, 2010 ).

Global Warming and climate change are serious issues, concerning Pakistan. Our article represents the impact of global warming and climate change. As evident the cloud bursting phenomenon is quite new for Pakistan, the same is the case with Glacier Bursts. Keeping floods as the focal point of the study we maintain this proposition that climate change is one of the primary reasons for wreaking havoc in Pakistan. With this background, this article examines the recent and emerging developments in catastrophe situations to evaluate the effects of floods on the socio-economic conditions of the higher flood-risk areas. The purpose of this study is to assess the socioeconomic cost of floods and to strengthen flood control policies in Pakistan, which is a resource-limited and vastly populated developing nation, and highly dependent on the agricultural sector. The paper utilizes a literature synthesis approach, extracting data from various resources ranging from published articles to government documents. Moreover, ESRI topographic layer was used to develop a topographic map of Pakistan. The study emphasizes that exploration of flood mitigation criteria is also needed as the floods in the country are becoming more regular and severe.

2 Material and methodology

This study reviews the existing literature on the flood, flood management, and socioeconomic cost in Pakistan focusing on riverine and extreme floods (2010). The theoretical framework of the study is shown in Figure 5 .

FIGURE 5 . Theoretical framework of the study.

The present study compiled and comprehensively analyzed numerous scientific and analytical reports, scholarly articles relevant to flood management studies, economic surveys of Pakistan, and other relevant reports released by different scholarly, consulting, and consultant institutions to extract and synthesize main studies on floods and flood management in Pakistan. A comprehensive empirical literature review was performed by using Google Scholar and a related search engine. The literature that has the greatest relevance to Pakistan was cited. The study also reported crucial findings and suggestions.

The past research suggests various review methods, empirical analysis, and meta-analysis ( Toya and Skidmore, 2007 ; Jonkman et al., 2008 ; McMullen and Lytle, 2012 ). The studies aim to create an empirical link by collecting data such as the impact of floods on GDP ( Kirigia et al., 2004 ; Rufat et al., 2015 ). But in the case of Pakistan, the data available is insufficient to either adopt or develop any complicated method, however empirical analysis of the facts and figures provided through reports, government documents, and policy papers, and correlate these with studies reviewed. The focus of this study remains to provide insight into the flood problem by reviewing it through data synthesis and textual analysis approach, which will access the impacts of floods. This study will establish an understanding of the importance of the topic, where impact analysis will ensure the need for extended studies conducted in the Netherlands and Africa ( Kirigia et al., 2004 ; Jonkman et al., 2008 ).

3 Floods and their impacts

Our study will steer through past research which includes a broad range of topics concerning measuring the impact of floods i.e., agriculture and livestock, impact on human health, the economic cost of floods, flood forecasting, flood warning mechanisms, and flood management.

As, the recent floods have devastated the country, and have inundated more than 40% of Pakistan, we can only speculate the losses in wholistic terms. It has been recorded that more than 30 million people have been displaced, with most parts of the Sindh province remaining under imminent threat. So currently, the estimates can only be compared in terms of Macro losses, like no. of people migrated, an area inundated, infrastructure destruction, etc. but the exact figures will remain ambiguous. As far as the scale and scope of the current flood are concerned, we can only conclude this fact that it has immensely damaged the country as compared to the flood of 2010.

3.1 Impact on agriculture and livestock

The impact of the flood on the agriculture sector can be divided into the following six categories: 1) Livestock evacuation in an urgent situation; 2) Avoidance of spring field exposure, allowing livestock to be sheltered or moved to certain other flood-free places; 3) Harm to crop and grass productivity in worst-affected areas with massive loss of pasture 4) Driven production loss and affected the performance of cultivation and agricultural land; 5) Destroyed irrigation structures and facilities at the farm; 6) Loss of advantageous soil invertebrates, in particular earthworms, elevated risk of animal disease, including infection of liver fluke ( Morris and Brewin, 2014 ). As agriculture remains the largest sector of Pakistan’s economy, employing 43 percent of the population, its yearly contribution to the GDP period 1969–2011 is shown in Figure 6 .

FIGURE 6 . Agriculture sector’s contribution to gross domestic product (GDP) period 2011–2019 (Economic Survey of Pakistan 2018–2019) ( Gop Pakistan Economic Survey, 2019 ).

Climate change typically has minor effects on the world food supply, but the consequences of climate change are widely distributed unevenly. Low-income economies, such as those in South Asia and Africa, experienced most of the casualties. By taking the data from 1989 to 2015 and by using the Feasible General Least Square (FGLS) model ( Ali et al., 2017 ) studied the climate changes impact on Pakistan’s major crop yields. The results showed that except for wheat, the impact of rainfall on the production of any other selected crop is negative ( Iqbal et al., 2018 ). Evaluated the impact of Pakistan’s 2010 flood on Khyber Pakhtunkhwa’s agriculture sector and found that after the flood, the family income of agricultural labor dropped, resultantly, in the usage of chemical fertilizers and other agricultural inputs also declined. The floods have adversely affected agricultural output where there was a considerable fall in wheat, maize, and sugar cane output. In Pakistan, the floods and heavy rains have not only affected agriculture crops, livestock, and forests but also devastated necessary facilities such as tube wells, domestic water storage facilities, animal shelters, private seeds inventories/pesticides, and agricultural equipment ( Iqbal et al., 2018 ). Baluchistan and KPK experienced mostly heavy rains, while Punjab and Sindh experienced mostly slow-rising floods on the canal. 2011, 2012, 2013, and 2014s floods damaged residential and agricultural properties, livestock, and crops. ( Ashraf et al., 2013 ). proposed that floods made food shortages and food insecurity for the citizens as they had to utilize polluted resources, particularly water.

Due to the 2010s flood, the agriculture industry has suffered a total loss of about Rs. 429 billion. For example, the profitability of cotton production decreased to 11.76 million bales as compared to the expected production of 14 million bales. Rates of the inputs of agricultural products such as urea, and chemicals, diesel had increased dramatically ( Bukhari and Rizvi, 2017 ). According to ( Rehman et al., 2015 ), twenty percent of the country’s overall landmass was inundated, affected by the 2010s flood, with cumulative damages of above USD 10 billion. The agriculture sector’s contribution to GDP is shown in Table 3 showing a significant decrease in the fiscal year 2010–11.

TABLE 3 . Agriculture sector’s contribution to gross domestic product (GDP) period 1969–2011 (Economic survey of Pakistan, 2010–2011) ( GoP Economic Survey, 2011 ).

The flood caused the loss of millions of livestock including around 200,000 dead in the 2010 flood in Pakistan. The final number of losses was higher. The Food and Agriculture Organization (FAO) announced that after the disaster, millions of surviving animals were confronting a food shortage situation that was alarming. The call for 5.7 million dollars has been made by UNO for emergency assistance for livestock. However, funds of 1.4 million dollars have been mobilized by FAO to secure feedstuff and the healthcare of livestock. Currently, the full scale and scope of the catastrophe are not clear, which will require more resource allocations once the situation becomes clearer ( Deen, 2015 ).

3.2 Impact on human health

Floods pose an enormous challenge to the healthcare system and its efficacy. For example, it can damage access to drinkable water by infiltrating the aquifers, thus increasing the transmission of waterborne diseases. The health concerns are classified as direct and indirect. Where direct effects arise from deep water and flooding penetration, including death, debris injury, environmental pollution, and hypothermia. Indirect effects include threats related to the water disruption to the natural and physical environment, including communicable diseases, obesity, famine-related diseases, and displaced population-related diseases. The number of people affected by the different diseases by the 2010’s flood is shown in Figure 7 ( Ahern et al., 2005 ; Du et al., 2010 ).

FIGURE 7 . Number of people affected by the different diseases by the 2010’s flood ( GoP, 2011 ).

The floods’ health effects could also be categorized as instantaneous medium and long-term. Flooding can also develop a high number of breeding grounds for insect and infection-borne diseases such as malaria. There have been various reports of increased risk throughout Asia, Africa, and Latin America in historically tropical countries. Public medical professionals and relief workers also warn after natural disasters that the dead bodies of affected people can trigger disease outbreaks such as cholera. The anxiety induced by such statements enables societies, local governments, and institutions to dispose of affected people quickly without identification. This leads to psychological stress for family members alive and causes legal issues including land, insurance claims, and inherited wealth ( Kondo et al., 2002 ; Morgan et al., 2005 ).

Reacher et al. (2004) evaluated the impact of floods on public health by taking the data of massive floods for the period of 12 October 2000 in the region of Lewes in the south of England. They found that floods are linked with an earache, psychological distress, and gastroenteritis. Psychological damage may illustrate some of the additional physical illnesses recorded by affected people, and likely even by children. Strategies to encourage the adaptation of the community to disasters, where flood management has deteriorated will provide logistical assistance for flood victims and adequate therapeutic help. However, in Bangladesh, studies conducted on the causes and spread of diarrhea at the expense of flooding. It highlighted the important demographic, economic, and social aspects from the preview of amenities provided by the system and how the provision of food and clean drinking water becomes essential during and after flooding ( Kunii et al., 2002 ).

The prevalence of diarrheal disease and its associated epidemiological factors were examined by ( Mondal et al., 2001 ), by applying a systematic random sampling analysis on the data of two identified flood-prone regions in West Bengal’s Midnapur area. The research found the diarrheal disease to be the most severe morbidity in flood-prone communities. Some habits, such as using pond water for utensil washing and cooking purposes, hand washing after soap-less defecation, inadequate washing hands before feeding, open area defecation, storing of drinkable water in large mouth containers, etc. , were found to be correlated with increased diarrhea attack levels, both in research and control community during flooding to the pre-flooded period ( Mondal et al., 2001 ).

Access to medical care and drugs is of central concern in the flood-affected areas of Pakistan, as is the reconstruction of community health facilities in the region. According to the WHO, 2010s flood affected more than 20 million people, many of whom were homeless. At that time, at least 8 million people required urgent humanitarian aid. More than 400 of the approximately 3000 clinics and hospitals in flood-affected areas had been affected or closed, thereby restricting the availability of urgent and daily healthcare. The availability to clean water for drinking and standards of health and safety were severely affected, though the risks of occurrences of waterborne diseases. Specific health issues, such as tuberculosis , skin diseases, severe respiratory diseases, and starvation, were of utmost concern ( WHO, 2010 ).

3.3 Economic cost of floods

The future of the world economy is more uncertain than it has ever been, and this uncertainty is sensitive to uncertainties relating to a range of economic policy decisions made by all parties involved, including governments ( Işık et al., 2019 ). Natural climatic risks have direct interaction with core macroeconomic factors and can affect economic development and market performance rapidly. Jonkman et al. (2008) described a framework established for the assessment of flood damage in the Netherlands and suggested that the economic loss resulting from the floods relies on the country’s vulnerable location, coastal zone, and economy. According to ( Toya and Skidmore, 2007 ), high-income economies, high schooling, more transparency, more robust financial structures, and reduced government suffer fewer casualties from natural disasters. They argue that private preference for safety nets rises because of increasing people’s wages because higher salaries encourage individuals to mitigate the danger by investing extra in precautionary measures.

With improved GDP growth, citizens could have improved facilities, alarming networks, and flood-prone precautionary and protective steps that can mitigate the effect of floods. Sadia et al. (2013) examined the impact of natural disaster deaths on Pakistan’s GDP per capita from the disaster by taking the data for the period 1975 to 2009, using the ordinary least square model, and found a strong positive impact on per capita GDP from disaster-related deaths, human capital, and life expectancy.

Ahmad et al. (2011) proposed that two levels of flood damage could be evaluated. The harm to facilities (infrastructure) and mortality can be called the first tragedy accompanied by second disasters like the families facing poverty due to the death of earning hands. The risk of the second catastrophe may be higher than that of the first. The risk of natural hazards along with people’s socio-economic insecurity presents a major threat to the system of Pakistan’s government.

Sardar et al. (2016) discuss three threats linked to floods: death, property destruction, and non-fatal community consequences, and calculated the effect of these disasters on Pakistan’s GDP growth for the duration 1972–2013. Their results showed that the per capita GDP development and emergency prevention reduce the severity of flood risks associated with it. Most notably, and contrary to evidence from several nations, flood intensity accentuates flood-related hazards in Pakistan which indicate a lack of understanding of previous flood experience. Concerning the flood-to-economic growth connection, their analysis showed that flood-related threats have a substantial negative effect on the economy’s GDP performance. Property loss causes economic development with the greatest effects.

3.4 Housing

According to the economic survey of Pakistan in 2011, approximately 392,786 houses were affected, and 728,192 were lost. In the districts of Muzaffargarh and Rajanpur in Punjab, Nowshera, and D. I. Khan in KPK and Jaffarabad, Jacobabad, Shikarpur, and Thatta in Sindh, the harm was more noticeable also shown in Figure 1 and Table 1 . The geographical distribution of Pakistan’s 2010 floods direct and indirect showed in Figure 3 and Figure 4 , respectively. The flood damages and reconstruction costs by sector as shown in Table 4 .

TABLE 4 . Flood damages and reconstruction cost by sectors (Rs. in Billion) ( Pakistan: Flood Impact Assessment, 2011 ).

4 Flood management in Pakistan

The flood prevention strategy is a relatively complicated problem in Pakistan. In each of the four provinces, the complexity of the issue differs because of their specific physiographic, climatic, geographical, and socioeconomic circumstances ( Chaudhri, 1981 ). Early severe flooding happened in 1950, 1956, and 1957 after independence. However, no systematic flood control program was implemented at the national level, owing to scarce funding and administrative structures. Protection and control of flooding remained the exclusive responsibility of regional governments until 1976. That improved after 1973 destroying floods that took 474 lives and caused damage of 160 billion Pakistani Rupees ( Tariq and van de Giesen, 2012 ).

4.1 Evolution of flood management system in Pakistan

In 1973 Pakistan faced severe floods which led to the establishment of the Federal Flood Commission (FFC) in 1977. The commission worked under the Ministry of Water and Power and was established to implement nationwide flood management, particularly concerning the Indus River Basin. The main functions of the FFC include developing national flood management measures; approving flood management plans drawn up by local governments and federal entities; examining flood damage to facilities in the public sector, and analysis of repair and rehabilitation plans; flood forecasting, and alert program enhancement measures; providing guidelines on standards for the management of flood protection reservoirs ( GoP Annual Flood Report, 2009 ).

The first National Flood Protection Plan (NFPP-I) was developed after the creation of the FFC, with a spending timeline to be introduced throughout the 1978–1988 decade. A Federal Coordination Cell (now reshaped as FID Cell) was formed in 1982 to organize the Provincial Irrigation Departments ' operations, especially in the drainage region. In 1987 Dam Protection Council was set up to examine established dams via DSO WAPDA and proposals for new dams etc. ( GoP Annual Flood Report, 2009 ).

In Pakistan, flood control initiatives consist primarily of flood-protection embankments, spurs, studs, and sophisticated flood-prediction strategies. The provincial governments have developed numerous flood-protection systems to address local flood challenges ( Baig, 2008 ). According to Ahmed et al. (2014) flood risk reduction in Pakistan was handled primarily by federal and provincial authorities, which typically need to be reconsidered to find innovative methods and strategies to counter the threat. Flood management institutions and their responsibilities are presented in Figure 8 .

FIGURE 8 . Evolution of flood management and relief system in Pakistan.

4.2 Flood warning system in Pakistan

According to Jain et al. (2018) , Flood Forecasting and Warning System (FFWS) main aim is to inform the public and other stakeholders of an imminent flood as early and effectively as possible. In Pakistan, the mechanism of flood warning and control undertakes three phases: 1) The PMD tracks the monsoon weather pattern, which produces either from the west in the Arabian Sea or from the East in the Bay of Bengal, in the first stage. Their movements are monitored for the upper catchments lying in Pakistan or over the border, and estimates are generated one to 2 days in advance for expected rains and the severity of such rains. In the case of rains, the volume of rainfall is estimated and evaluated above the rim stations for their possible run-off relationship) after that is the stage of flood creation that starts with the generation of runoff from the rim stations and flows down into the Indus River and its tributary. Projected rainfall and flow data and real upstream flows are the hydro-meteorological portions of the flood prediction method 3) ultimately, the hydrological part of the forecasting network is to track and control the route of the flood wave below the rim station of the rivers at the downstream locations. This is handled by WAPDA and controlled in respective provinces by the irrigation departments ( Hussain, 2015 ).

The early warning system of Pakistan is efficient to some extent; however, the flood warning system only aims to provide information, rather protective measures. In our opinion, as we have also mentioned in the study, such acts can only help lessen the losses to be incurred. However, the implementation in the form of evacuation, protection, and migration is based upon the behavioral choices of the people of the area. We can mention the example of Mardan, where legal force was used to vacate the city during the 2022 floods. Thus, flood warning system calculates and provides relevant information. However, the impact must reach the grassroots level, furthermore, there is a strong need of creating a civic sense among the people of Pakistan. As can be seen in the case of Sawat River flash floods, it is speculated that most of the socio-economic loss incurred was caused by illegitimate activities, land grabbing on the riverbed, and around the torrent flow, which was against the River Protection Ordinance, 2002.

4.2.1 Flood forecasting division’s flood forecasting models

The FFD employs the Flood Early Warning System (FEWS) and the Indus Integrated Flood Analysis System (IFAS) for flood forecasts. In terms of urban areas, flood prediction and warning systems were developed in the Nallah Lai Basin ( Afsa et al., 2013 ; Sugiura et al., 2014 ). Since 2007 the FFD has used the flood forecasting framework FEWS built by Delft Hydraulic in the Netherlands. The FEWS contains (1) the rainfall-runoff interface Sacramento Soil Moisture Accounting (SAC-SMA) and (2) the SOBEK, an interface for hydraulic fluid routing ( Shrestha et al., 2019 ). The Layout contains data from forty-four (44) WAPDA telemetry stations. The rainfall-runoff process performance determines the entrance into the routing mechanism ( Awan, 2003 ). Early warning mechanism components are shown in Figure 9 .

FIGURE 9 . Early warning mechanism components ( Hussain, 2015 ).

From the perspective of South Asia, the hierarchical institutional structure Flash Flood Guidance System with Global Coverage (FFGS) can provide warnings about 6–24 h in advance for South Asian countries, including India, Nepal, Bhutan, Bangladesh, and Sri Lanka. However, the respective NDMA’s of these countries perform the same functions as Pakistan, Since Bangladesh and India are more densely populated than Pakistan, the riverbank protection in India and Bangladesh is much more rooted at the state level, and powers are transferred to the grassroots level for implementation. But, as the cultural roots of the subcontinent remain the same along with legal roots, the implementation and behavioral intention part remain at certain risk.

4.2.1.1 Criticism

The present flood forecasting method of the FEWS model, however, has minimal regional scope, and rainfall-runoff knowledge and downstream routing of the Tarbela and Kabul rivers are not integrated with the model. The tributaries included in the FEWS are not determined by the amount and rate of discharge necessary for reliable flood forecasting ( Shrestha et al., 2019 ). The FFD also introduces the IFAS established by the International Center for Water Hazard and Risk Management (ICHARM) under the Critical Reinforcing of Pakistan’s Flood Warning and Management Capacity Program and the Japan Aerospace Exploration Agency (JAXA), with UNESCO funding. More than thirty-nine (39) districts in Pakistan are protected by flood prediction and early warning services from the IFAS ( Ahmad, 2015 ; Mustafa et al., 2015 ).

Disaster management institutions in Pakistan are relatively new and need time to strengthen their foothold. National disaster management authority (NDMA) claims a preparedness-oriented approach, but at the core, the country’s disaster management system is still operating on a top-down approach. Practically, it is more relief-focused, and season-based planning is being implemented. Under the 18th amendment of the constitution of Pakistan, PDMAs are not legally bound to follow NDMA. As a result, NDMA just acts as a general policy-defining institution. Mutual coordination needs to be encouraged to cover potential oversights. District disaster management authorities are composed of officials from different local institutions and are virtually non-existent in the field. Thus, a dedicated DDMA is needed, which could act as a catalyst in disaster risk reduction ( Rana et al., 2021 ).

There is also a need for regular technical and institutional capacity assessments for effective disaster preparedness and response. Consequently, ad-hoc disaster risk management is happening in the study areas. More research could be conducted to identify potential weaknesses in these urban institutions. There is a need to improve local institutions’ image in front of communities as strong distrust further complicates flood risk reduction initiatives. Local institutions and communities need to work together to realize the full potential of the disaster management cycle and disaster risk reduction approach ( Rana et al., 2021 ). Flood management institutions and their responsibilities are shown in Figure 10 .

FIGURE 10 . Flood management institutions and their responsibilities developed after ( Ali, 2013 ; Rana et al., 2021 ).

To cope with the situation, there must be a multi-pronged approach, as suggested there is a strong need for water reservoirs, which can be fulfilled through water policy. Secondly, the early warning system and relevant institutions must reach the grassroots level to cope with the emergency in a better way. Thirdly, the whole management machinery of the area remains dependent upon a certain institution, Pakistan Administrative Services. There is a strong to segregate the departmental responsibilities and develop new departments ensuring implementation. Lastly, the citizens must have a civic sense or understanding of the nature of the information provided to them in the wake of floods.

In April 2018, Pakistan’s four provincial chief ministers endorsed the country’s first regional water policy. The National Water Policy’s goal is to address the growing water problem and include an overarching policy structure and recommendations for a robust action plan. The National Water Policy provides a specific policy structure and collection of water protection standards based on which the Provincial Governments will devise their development plans and water conservation, water improvement, and water management projects ( GoP National Water Policy, 2018 ).

5 Conclusion

Floods are a frequently occurring phenomenon due to heavy monsoon rains in Pakistan. The size and magnitude of the floods that Pakistan has confronted in the last few years would have been a problem for any nation. Recent severe floods have shown that there is a lack of adequate cooperation between flood control agencies, due in part to shortcomings in current technological capacities, such as warning signals, preparedness initiatives, disaster response, and systemic flood prevention measures. This is important to further develop flood monitoring and alert systems to reduce the damages of potential floods. Although Pakistan’s flood warning and detection systems have shown their effectiveness, the forecasting ability of the network is still weak. At the same time the institutions, NDMA and PDMA, operate at national and provincial levels, dispersing the rehabilitation responsibilities to local bureaucracy rather than establishing a grassroots-level structure with reference to small cities and villages. It also makes rehabilitation procedures weak and ineffective in most areas. Nonetheless, a cohesive response by the Pakistani community and the combined efforts of all the international and domestic agencies concerned remained crucial. Only providing damage reimbursements to flood victims is not the remedy; we need to get rid of this problem. Pakistan’s water management also needs to develop more reservoirs—lakes, and dams as a way to combat floods. The government should generate and incorporate robust public awareness initiatives to educate the public on flood risks and flood preparedness. In conclusion, to achieve effective flood control, a risk-based proactive strategy is needed.

Author contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

Open access funding provided by UiT The Arctic University of Norway.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords: major floods, flood management, social and economic cost, flood control, flood impacts

Citation: Manzoor Z, Ehsan M, Khan MB, Manzoor A, Akhter MM, Sohail MT, Hussain A, Shafi A, Abu-Alam T and Abioui M (2022) Floods and flood management and its socio-economic impact on Pakistan: A review of the empirical literature. Front. Environ. Sci. 10:1021862. doi: 10.3389/fenvs.2022.1021862

Received: 17 August 2022; Accepted: 22 November 2022; Published: 01 December 2022.

Reviewed by:

Copyright © 2022 Manzoor, Ehsan, Khan, Manzoor, Akhter, Sohail, Hussain, Shafi, Abu-Alam and Abioui. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Muhsan Ehsan, m[email protected] ; Tamer Abu-Alam, [email protected]

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Eco-Innovation and Green Productivity for Sustainable Production and Consumption

• Original Article
• Published: 12 October 2023

Wheel cleaning jet (WCJ) strategy for green grinding: mitigating greenhouse impact in VP50IM steel machining with green silicon carbide wheel

• Guilherme Bressan Moretti 1 ,
• Jorge Luiz Cuesta 1 ,
• Bianca Marcusso Perili Noronha 1 ,
• Fernando Sabino Fonteque Ribeiro 2 ,
• Anthony Gaspar Talon 1 ,
• Luiz Eduardo de Angelo Sanchez 1 ,
• Eduardo Carlos Bianchi 1 &
• José Claudio Lopes 1  

The International Journal of Advanced Manufacturing Technology volume  129 ,  pages 2125–2137 ( 2023 ) Cite this article

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Grinding is a machining process used to achieve high geometric accuracy and excellent surface finish for parts in various industries. In this process, a tool called a grinding wheel interacts with the workpiece’s surface, facilitating the removal of material. Due to the lack of defined geometry, multiple cutting edges engage with the material during machining, leading to a significant rise in temperature within the cutting zone. This temperature increase can jeopardize the process, potentially resulting in damage or even the loss of the workpiece. To address these issues, a technique has been developed involving the use of an emulsion comprising oil and water. This fluid is generously applied to the workpiece to provide lubrication and cooling during the machining process. However, the use of cutting oil raises environmental concerns due to its high pollution potential and its adverse effects on the health of machine operators. In this context, the minimum quantity lubrication (MQL) method was introduced, employing a spray nozzle and a compressed air system to directly apply oil to the cutting zone, facilitating lubrication. Nonetheless, this approach revealed limitations in effectively dissipating heat, leading to a phenomenon known as “clogging,” where machining chips adhere to the tool surface, obstructing the abrasive grains. To combat this issue, an auxiliary system was developed to complement MQL. This system directs a compressed air jet at a 30° angle onto the grinding wheel, dislodging the lodged materials from the tool. Hence, the objective of this study was to assess the effectiveness of these lubrication-cooling methods in machining VP50IM steel—a high-hardness material used in mold die manufacturing. For this purpose, the VP50IM ring-shaped workpieces were machined on a CNC cylindrical grinder RUAP515H, under different feed rates (0.25, 0.50, and 0.75 mm/min), using various lubricooling methods (flood, MQL, and MQL + WCJ). After each machining operation, measurements of roughness, roundness error, tool wear, and acoustic emission were conducted. Additionally, G-ratio, cost, and pollution analyses were also carried out to determine the performance for each case. Across various feed rates, the conventional system showed superior efficiency in most cases. However, it also exhibited the highest application cost and associated pollution. In contrast, the MQL + WCJ system emerged as a highly competitive alternative to the flood method, with comparable surface finish and roundness error, along with lower costs and a significantly reduced environmental impact. In terms of feed rates, the 0.25 mm/min feed rate provided the best surface finish for the workpieces; however, this slowness in the process led to an increase in cost and pollutant emissions. On the other hand, the 0.5 mm/min feed rate yielded the most balanced results. Meanwhile, for the 0.75 mm/min feed rate, the disparity between the lubricooling methods became more pronounced.

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Acknowledgements

The authors are thankful to the companies Abrasipa Indústria de Abrasivos Ltda. for supplying the grinding wheel and Quimatic Tapmatic Ltda. for the donation of MWF.

The authors are thankful to the Coordination for the Improvement of Higher Level Education Personnel (CAPES), National Council for Scientific and Technological Development (CNPq) (Grant PIBIC 2022/2023), and São Paulo Research Foundation (FAPESP) (Grant 2023/00741–2).

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Guilherme Bressan Moretti, Jorge Luiz Cuesta, Bianca Marcusso Perili Noronha, Anthony Gaspar Talon, Luiz Eduardo de Angelo Sanchez, Eduardo Carlos Bianchi & José Claudio Lopes

Department of Control and Industrial Processes, Federal Institute of Education, Science and Technology of Paraná, Jacarezinho Campus, Jacarezinho, Paraná, Brazil

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G.B.M: writing the original draft; visualization; conceptualization; formal analysis; investigation; validation. J.L.C.: writing the original draft; resources; methodology. B.M.P.N.: writing, review; investigation; validation. Fernando S.F.R.: writing, review and editing; visualization; conceptualization. A.G.T.: writing, review and editing; conceptualization; project administration. L.E.D.A.S.: software; supervision; project administration. E.C.B.: writing, review and editing; conceptualization; supervision. J.C.L.: funding acquisition; conceptualization; resources; supervision; review and editing; project administration.

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Moretti, G.B., Cuesta, J.L., Noronha, B.M.P. et al. Wheel cleaning jet (WCJ) strategy for green grinding: mitigating greenhouse impact in VP50IM steel machining with green silicon carbide wheel. Int J Adv Manuf Technol 129 , 2125–2137 (2023). https://doi.org/10.1007/s00170-023-12395-w

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