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Development of a flood-damage-based flood forecasting technique
Accepted Manuscript Research papers Development of a Flood-Damage-Based Flood Forecasting Technique Eui Hoon Lee, Joong Hoon Kim PII: DOI: Reference:
S0022-1694(18)30401-3 https://doi.org/10.1016/j.jhydrol.2018.06.003 HYDROL 22852
To appear in:
Journal of Hydrology
Received Date: Revised Date: Accepted Date:
29 April 2018 31 May 2018 1 June 2018
Please cite this article as: Lee, E.H., Kim, J.H., Development of a Flood-Damage-Based Flood Forecasting Technique, Journal of Hydrology (2018), doi: https://doi.org/10.1016/j.jhydrol.2018.06.003
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Development of a Flood-Damage-Based Flood Forecasting Technique Eui Hoon Lee a, Joong Hoon Kim b,* a
Research Center for Disaster Prevention Science and Technology, Korea University, 02841, Seoul, South Korea; E-mail: [email protected] b School of Civil, Environmental and Architectural Engineering, Korea University, 02841, Seoul, South Korea * Corresponding author. E-mail: [email protected]
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Abstract Flood forecasting is a pre-emptive non-structural measure used to mitigate inundation. Most current flood forecasting techniques incorporate complex processes, such as training and optimization, before the technique can be applied. Conventional flood forecasting techniques, based on flood volume, provide alerts even if there is no significant risk of flood damage. In this study, a new flood forecasting technique has been developed based on likely flood damage using the multi-dimensional flood damage analysis method. This new flood forecasting technique overcomes the drawbacks of current flood forecasting techniques because it can be easily applied using rainfall data. The studied drainage area was divided into subareas, and the damage functions were obtained for each subarea using the flood volumes and damage information. Using these damage functions, the rainfall intensity when the flood damage initially occurred was calculated for each duration and subarea. The damage graph produced for flood forecasting in each subarea identified the rainfall intensities and durations that resulted from the initial occurrence of flood damage. This new flood forecasting technique could be used to save lives, valuable assets, and manage drainage areas. Keywords: flood forecasting, flood volume, flood damage, damage graph
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1. Introduction Climate change has caused extreme rainfall events to occur and the frequency of these events has increased. The proportion of impermeable areas, as well as the frequency of extreme rainfall events, has increased sharply. The number of floods has increased and various measures such as structural and nonstructural measures for preventing floods have been prepared. To prepare for, or prevent flood damage in drainage areas, one of the nonstructural measures that has been implemented is the application of various types of flood forecasting models. For example, a flood forecasting model was suggested and applied in three UK catchments and real-time flood forecasting was proposed (Beven et al., 1984). A distributed model using digital elevation models (Garrote and Bras, 1995) and short-term rainfall prediction models (Toth et al., 2000) have both been developed for real-time flood forecasting. Jasper et al. (2002) coupled meteorological observations and a distributed hydrological model for advanced flood forecasting. Recursive state-space estimation (Young, 2002) and soil moisture updated by ensemble Kalman filtering (Komma et al., 2008) have been suggested for real-time flood forecasting. It has been difficult to apply these methods to small watersheds, such as single drainage areas, because the time intervals for recording the rainfall data used in previous studies were not small enough to capture the detail required for accurate predictions at smaller scales. Time-consuming detailed analysis using 2D hydrodynamic models is appropriate as part of the preliminary work for flood forecasting, but the time required for real-time forecasting should be minimal. New approaches, such as applying neural networks, distributed hydrological modeling, an ingredients-based methodology, and machine learning, have also been introduced. Neural networks were developed as computer models based on the human brain and nervous system with mathematics and algorithms called threshold logic (McCulloch and Pitts, 1943). Robert Hecht-Niesen defined one as 'a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs' (Caudill, 1989). A neural network has been applied to river flood and flash-flood forecasting (Campolo et al., 1999) and quantitative flood forecasting using multisensory data and neural networks was proposed by Kim and Barros (2001). Research using neural networks combined with other techniques has been suggested for flood forecasting. Machine learning and neural network techniques were combined for flood forecasting by Solomatine and Xue, 2004. Chau et al. (2005) compared two hybrid models, the genetic-algorithm-based artificial neural 3
network and the adaptive-network-based fuzzy inference system. Additionally, the limitations of flash flood forecasting have also been considered (Collier, 2007). Flood forecasting techniques using neural networks require time-consuming training and the application process is complex. Many flood forecasting studies have been conducted since 2010. In Scotland, a new surface water flood forecasting model using 24-hour ensemble rainfall predictions was conducted with static flood risk maps using the Grid-to-Grid hydrological model (Speight et al., 2016). The process of flood forecasting using 24hour ensemble rainfall prediction is complicated to apply but has wide scope for application. Initial state variable correction and particle swarm optimization were used for flood forecasting in Southern China (Li et al., 2017). The application process is so complex because a significant amount of optimization is required. Additionally, the application area and the prediction intervals are large and thus, are unsuitable for single drainage areas. Recently, several techniques have been suggested for flood forecasting. Flash flood modeling and forecasting using a multi-layer third-generation conceptual model were applied to 50 basins in Russia and the results were compared with the Sacramento soil moisture accounting model (Sokolova et al., 2018). As it was applied to the river basins, the size of the target watershed was significantly larger than a single drainage basin. The application process was complicated and required a significant amount of time due to the large application area and the complexity of the techniques. A flood nomograph using the regression curve of the first flooding nodes was suggested for flood forecasting applications in single drainage basins in urban areas (Lee et al., 2018). It is difficult to estimate flood damage in the target watershed because flood forecasting was conducted using only the flood volume. Ensemble flood forecasting using the numerical weather prediction model was applied to the Futatsuno and Nanairo dam catchments in the Shingu river basin (Yu et al., 2018); the model had a two kilometer horizontal resolution and made 30-hour forecasts of quantitative precipitation. The urban coastal flood severity from crowd-sourced flood reports (street flooding record) using Poisson regression and random forest techniques was suggested and applied in Norfolk, Virginia, USA (Sadler et al., 2018). The National Water Model (NWM) with 1 h time steps was developed by National Oceanic and Atmospheric Administration (NOAA) in the Department of Commerce, United States. The NWM consists of three forecast ranges such as short-range (18-hour deterministic forecast), medium-range (10-day deterministic forecast) and long-range (30-day ensemble forecast). These methods are not 4
appropriate for single drainage areas because the application areas and prediction intervals are too large. In the aforementioned studies, the flood forecasting methods used long-term rainfall data that are unsuitable for single drainage areas that require rainfall data on a per-minute scale. Additionally, the applied flood forecasting processes are complex because training and optimization techniques are required (Campolo et al., 1999; Kim and Barros, 2001; Solomatine et al., 2004; Chau et al., 2005; Li et al., 2017). Furthermore, the occurrence and extent of flood damage varies across different areas because properties across each area, such as land use, vary. There is a need for a simple real-time flood forecasting technique that uses rainfall data per minute to be developed and applied to drainage areas to improve the applicability of the method. The rainfall data per minute used in this study are provided by Korea Meteorological Administration and they are used for flood forecasting and the operation of drainage facilities in Korea. In this study, a new flood forecasting method based on damage graphs is introduced. The method only requires rainfall data and predicts flood damage without the need for long training and optimization processes prior to application. The flood damage in each subarea of the target watershed was calculated using multi-dimensional flood damage analysis (MD-FDA). MD-FDA was suggested by Choi et al. (2006) and has been applied in previous studies (Choi et al., 2016; Lee and Kim, 2017). Each subarea was categorized by land use, and the damage functions in each subarea were generated using the MD-FDA. All rainfall-runoff simulations in the target watersheds were conducted using the Storm Water Management Model (SWMM) (United States Environmental Protection Agency, 2010). Damage graphs were generated for each subarea by recording the rainfall intensity when the flood damage initially occurs. After relating historical rainfall events to the damage graph, it was possible to prepare predictions of flood damage to the target watershed.
2. Methodologies 2.1. Overview This study consists of seven parts. First, synthetic rainfall, which is not recorded but artificial generated using selected rainfall distributions, were generated for the rainfall-runoff simulations. Next, damage functions between flood volumes and damage were obtained for each subarea. Then, the results of flood volume were obtained using rainfall-runoff simulations with synthetic rainfall data for selected rainfall durations. Subsequently, the results of flood volume were converted to flood damage using the damage 5
functions. Then, the rainfall intensities that correspond to the total rainfall that caused the initial flood damage for selected rainfall durations (initial flood damage intensities) were recorded. Next, damage graphs for each subarea were generated using the initial flood damage intensities of selected rainfall durations. Finally, a historical rainfall event instead of real-time rainfall data was applied to the new flood forecasting technique. The flood forecasting process workflow for the method proposed in this study is shown in Figure 1. The process shown in Figure 1 was applied to each subarea, resulting in a flood forecasting graph. A flood damage alert occurs for each subarea of the target watershed if the rainfall intensity of a historical rainfall event is higher than the safety threshold in the damage graph. SWMM was developed by the United States Environmental Protection Agency (US-EPA) in 1971. It can be used for rainfall-runoff simulations of combined sanitary sewer conduits and/or drainage facilities in urban as well as rural areas. Flow routing models, such as steady flow routing, kinematic wave routing, and dynamic wave routing models, are available to be selected in SWMM. The steady flow routing model is based on Manning’s equation. The kinematic wave routing model allows flow and area to vary both spatially and temporally in a sewer conduit. The essential elements in flood simulation such as backwater effects, pressurized flow, entrance and exit losses, and runoff analysis in a loop network are not available in both flow routing models. The dynamic wave routing model is based on the complete one dimensional Saint Venant flow equations. It was used for all rainfall-runoff simulations in this study because it is possible to apply all essential elements in the dynamic wave routing model.
2.2. Generation of synthetic rainfall data for damage graphs The real-time rainfall data based on the actual observations by Korea Meteorological Administration is used for the real application of the new flood forecasting technique. However, the damage graph required for the forecast is based on rainfall-runoff simulations; therefore synthetic rainfall data are required as input data for the simulations. The generation of synthetic rainfall data consists of five steps: 1. Selection of the appropriate regression equation for the target watershed. 2. Selection of rainfall quantities and durations. 3. Generation of the cumulative distribution for the selected regression equation. 6
4. Conversion from cumulative distribution to dispersed distribution. 5. Application of the rainfall quantities to obtain the synthetic rainfall event. The representative distribution for generating synthetic rainfall data in Korea is the Huff distribution, upon which all drainage facilities in Korea are designed (Huff, 1967). The Huff distribution consists of four quartiles based on the locations of the peak values. In the Huff distribution, the peak values of rainfall in the first, second, third and fourth quartiles of the time range are significant. The third quartile in the Huff distribution has been considered appropriate for the design and operation of urban drainage facilities in Korea (Yoon et al., 2013). The new flood forecasting technique in this study is based on the rainfall distribution used in the design of the sewer networks in the target watershed to maintain alignment between the design and forecasting. Since the rainfall distribution used in the new flood forecasting technique is same as that used for the design of the sewer networks in the target watershed, a customized flood forecast was generated in the target watershed. Equation (1) shows the cumulative regression equation using the third quartile of the Huff distribution in Jeongup (Korea Precipitation Frequency Data Server).
y 36 .029 x 6 98 .986 x 5 95 .279 x 4 38 .622 x 3 7 .4086 x 2 0 .1088 x 0 .0002
(1)
where y is the cumulative proportion of the rainfall and x represents the cumulative proportion of time. Cumulative regression equations of the Huff distribution vary at each region. Figure 2 shows the process for generating synthetic rainfall data from the Huff distribution. The cumulative regression equation is used for generating the cumulative distribution. In the cumulative distribution shown in Figure 2, the rainfall quantities are 3.5 % and 8.5 % when the rainfall durations are 10 % and 20 %, respectively. In the dispersed distribution of Figure 2, the rainfall quantity is 5.0 % which is the difference between 8.5 % and 3.5 % when the rainfall duration is 20 %. The rainfall quantity and duration are applied to the dispersed distribution for the generation of the synthetic rainfall distribution. The process is repeated for each duration because a range of synthetic rainfall data is required to generate the threshold of the new flood forecasting using rainfall-runoff simulations.
2.3. Conversion from flood volume to flood damage 7
2.3.1. Concept of flood damage for flood forecasting Flood volumes are region dependent and vary because floods are more likely to occur in low elevation areas. For populated areas at low elevation, flood damage will correlate with flood volumes. Conversely, in the same area, no flood damage will occur in unpopulated areas without houses or buildings. Figure 3 is a schematic diagram comparing areas where flood volumes result in flood damage. In Figure 3(a), the flood volume enters an undeveloped (unpopulated) area, therefore, no flood damage occurs. In this case, a flood alert is generated if the flood forecasting is based solely on the flood volume. Conversely, if the flood forecasting is based on the likelihood of flood damage, a flood alert is not generated. In Figure 3(b), flood alerts based on both flood volumes and the likelihood of flood damage are generated when the flood occurs in the planted area; hence, the new flood forecasting technique differs from the current techniques in this regard. However, current flood forecasting methods are still required because the people can avoid flooded areas and lives can be saved by these techniques. The suggested technique can be used to supplement the current flood forecasting approaches and segment-based flood forecasting can be introduced in several stages.
2.3.2. Calculating flood damage The multi-dimensional flood damage analysis (MD-FDA) method is used for converting flood volumes to flood damage (Choi et al., 2006). MD-FDA has several components, including area and damage classifications. The three area classifications in the analysis are residential, agricultural, and industrial. Damage classifications include damage caused to humans, buildings, farmland, crops, inventory, public facilities, and the contents of buildings. The MD-FDA requires three steps: determination of property value, calculation of the inundated inclusion ratio, and flood damage evaluation. The property value for each asset is based on statistical data and the most important element of MD-FDA is the precise survey of the property value for each asset. The superposition data is generated by administrative district data, land use data, and flood depth data. A 2D drainage model is required to obtain the results of flood depth. The inundated property value (flood damage) in each subarea is based on the superposition data. The results of flood volume and flood damage using rainfall data are used for the generation of damage functions in each subarea. Table 1 shows the property value estimations for residential, industrial, and agricultural areas. The construction industry deflator and consumer price index in Table 1 were based on data from Statistics 8
Korea (2017). The second required factor is the calculation of the inundated inclusion ratio. For calculating the flood damage, the ratio is estimated using the flood depth at each property as well as the flood area. The ratio of the damage to the flood depth for residential, industrial, and agricultural areas is shown in Table 2 (Ministry of Construction and Transportation, 2004). The final factor required for calculating flood damage is the flood area. The flood depth and area can be obtained from historical records or rainfall-runoff simulation models. However, the flood area in this study was conducted by rainfall-runoff simulation using a 2D hydrodynamic model because the flood area is difficult to estimate accurately from historical records in Korea. Therefore, the calculation of flood damage using the MD-FDA is based on the property value, ratio of the flood area, and the ratio of damage to flood depth, as shown in Equation (2).
F d Pv R a R d
(2)
where Fd is the flood damage, Pv is the property value in the area exposed to the flood, Ra represents the ratio of the flood area, and Rd is the ratio of the damage to the flood depth in vulnerable areas. The conversion from flood volume to flood damage is based on the damage functions. The damage functions can be derived from an estimate of the damage threshold using a rainfall-runoff model because the resulting simulations provide the flood volume, area, and depth. The values of flood volume per minute obtained from the rainfallrunoff simulations are converted to a value of flood damage per minute for each subarea. The conversion process workflow for using the damage functions is shown in Figure 4. In Figure 4, the unit of flood volume is m3 and the unit of flood damage is won (Korean currency). The conversion process workflow for using damage functions consists of three steps. In the first step, the study area in the target watershed is divided into subareas according to land use. In the second step, the distributions of flood volume over time in each subarea are obtained using rainfall-runoff simulations. In the third step, the results of flood volume over time are converted to flood damage using damage functions in each area. In cases where there are many flood events based on actual observations and the status of the target watershed does not change during this period, it is possible to create damage thresholds in damage functions 9
based on actual observations. However, flood events do not occur frequently and the status of the target watershed changes during that time. Damage graphs should be revised if the status of the target watershed changes. For this reason, it is challenging to use the flood records based on actual observations. To overcome this shortcoming, synthetic rainfall data used in the design of the sewer network in the target watershed was applied to generate the damage graphs.
2.4. Development of the new flood forecasting technique Flood forecasting used in this study was based on flood damage information obtained from rainfall-runoff simulations using synthetic rainfall data. The rainfall-runoff was simulated for various durations. It was initiated when the total rainfall quantity was 1 mm and continued at 1 mm increments until flood damage occurred. The quantity of rainfall that caused the initial flood damage was identified, and this rainfall quantity was converted to the rainfall intensity that caused the initial flood damage for each period of rainfall. In this process, the rainfall intensity that caused the initial flood damage for each period of rainfall was the quantity of rainfall divided by the duration of the rainfall. A damage graph for each subarea consists of the rainfall intensities that caused the initial flood damage for each duration. The damage graph was generated from the synthetic rainfall data and the historical rainfall data were applied for the flood forecasting. A flood damage alert occurred if the historical rainfall intensities were higher than the threshold in the damage graph, and vice-versa. Therefore, the damage alert is a mechanism for determining safe or at-risk areas. The entire process of conversion from flood volume to flood damage consists of four parts: (1) classification of nodes in each subarea, (2) estimation of the flood volume in each subarea, (3) conversion of the flood volume to damage functions in each subarea, and (4) estimation of flood damage in each subarea. Figure 5 shows the process workflow for generating a damage graph. The safety threshold in the damage graph was generated from the points when the rainfall intensities generated the initial flood damage for each duration. Figure 6 shows the flood forecasting concept using the damage graph. Damage graphs are generated in each subarea and they are the thresholds for the new flood forecasting technique. In Figure 6(a), subarea A1 is safe because all the rainfall intensities are located in the safe zone, i.e., below the solid black line. In Figure 6(b), subarea A2 is dangerous because all rainfall intensities, except 10
the first one, are located in the danger zone. In Figure 6(c), subarea A3 is safe because all rainfall intensities are below the threshold of the damage graph in A3. All rainfall intensities are less than the minimum value on the y-axis in A3. Subarea A4 has a similar pattern to subarea A2 because all rainfall intensities except the first one are located in the danger zone of Figure 6 (d); thus, subarea A4 is unsafe. In Figure 6 (e), only the third rainfall intensity is located in the dangerous zone. The flood forecasting can be conducted using damage graphs in each area. Damage graphs can be generated according to each quartile in the Huff distribution and all types of rainfall data, both real and synthetic, can be applied to a damage graph for flood forecasting. The time interval of the applied rainfall data has a direct correlation with the flood forecasting precision. The damage graph can allow more detailed flood forecasting if the time interval of the applied rainfall data is smaller. Therefore, if the time intervals of the predicted rainfall data are too long, the flood forecasting is less accurate. Furthermore, the predicted rainfall data should be replaced by measured data as it becomes available. Figure 7 shows the process workflow for applying the real-time rainfall data to the target subarea. Figure 7 is an example for showing the real application with real-time rainfall data over time. In Figure 7, all rainfall data, regardless of the time interval, can be applied to the damage graph. However, a new method for truncating the long-term rainfall data is required for application to the damage graph. The intensity of the applied rainfall data over time gradually decreases and becomes too low if long-term rainfall data is applied to the damage graph. When hourly rainfall data is applied to a large watershed, rainfall data can be divided considering the Inter-Event Time Definition (IETD) of the target watershed. When rainfall data per minute is applied to a small watershed, the rainfall data can be divided considering the time of concentration in the target watershed. The appropriate truncation of long-term rainfall data in small watersheds can be determined when the rainless time is longer than the time of concentration because the rainfall is discharged outside of the watershed after the time of concentration.
3. Application and Results 3.1. Study area Korea consists of nine provinces: Gyeonggi-do, Gangwon-do, Chungcheongbuk-do, Chungcheongnam-do, Jeollabuk-do, Jeollanam-do, Gyeongsangbuk-do, Gyeongsangnam-do, and Jeju Island. In 2011, there was 11
inundation and flood damage in Jeollabuk-do Province, Korea; floods also occurred in the Jeongup area. The Sintaein Basin is a representative urbanized area in Jeongup and was selected as a study area to validate our technique. Figure 8 shows the subarea divisions and sewer networks in the target watershed. The Sintaein Basin was divided into five subareas, A1, A2, A3, A4, and A5, according to land use. A1 is a residential area with other facilities, such as schools and apartments. A2 consists of residential and commercial areas. A3 is mostly composed of undeveloped land with a few facilities. A4 is an area vulnerable to inundation because it receives flows from A1, A2, and A3. Public facilities, such as offices, post offices, and schools are located in A5. The drainage area in the Sintaein Basin is 67.9 ha. A1, A2, A3, A4, and A5 are 17.6, 21.5, 0.7, 15.1, and 13.0 ha in area, respectively. The average impermeability of each subarea is 69, 78, 55, 83, and 72 %, respectively. The subarea divisions and sewer networks in the target watershed were modelled with 175 subcatchments, 175 conduits, and 175 nodes.
3.2. Generation of damage graphs for each subarea 3.2.1. Standard of calculation for flood damage The calculation of human-related flood damage was not typically considered because of the complex factors and uncertainties, such as the number and ages of transient populations. In this study, human-related flood damage was excluded from the damage calculation so that only factors that can be calculated explicitly would be included. The price per area is an important factor for evaluating flood damage and can be calculated by considering the current status in the study area. The price per area in the study area was obtained from the Korean Appraisal Board website (Korea Appraisal Board). Additionally, construction industry deflators and residence property values should be considered and were obtained from the Korean Development Institute website (Korea Development Institute). In Korea, residences are categorized as detached houses, apartments, and buildings. The value of contents within a residential property was estimated at 12,182,399 won per household (Korea Development Institute). In the study area, there are no agricultural areas; therefore, agricultural area property values were excluded. The price per area values used were 1,385,000 won/m2 for detached houses, 1,690,000 won/m2 for apartments, and 853,000 won/m2 for buildings. The study area was divided into five subareas and the status of each subarea should be considered. The 12
number of types of residence and number of works in each subarea are required in order to calculate the property value of each area. Because there are no agricultural areas in the study area, the property values including only residential and industrial areas were calculated. Table 3 shows the number of residence types, number of works, and property values in each subarea of the study area. Calculating flood damage requires rainfall-runoff simulations from the SWMM model to obtain the flood volume, flood depth, and flood area for each subarea. The 1D drainage model is not appropriate to simulate overland flooding because the results are very approximate, which is problematic for a flood forecasting scheme. A 2D drainage model, such as XP-SWMM, is required for the simulation of overland flooding. The surface (overland) flooding by MD-FDA can be obtained using XP-SWMM (XP solutions, 2013). The 2D simulation of XP-SWMM was developed by the combination between XP-SWMM 1D and the TUFLOW 2D module (Phillips et al., 2005). Damage functions include the property value and flood volume for each subarea. Figure 9 shows the process workflow for generating the damage functions.
3.2.2. Damage functions for generating damage graphs The damage functions for A1, A2, A3, A4, and A5 in the study area were provided from previous studies and are shown in Equations (3) (7), respectively (Choi et al., 2016).
D 1 .107 10 6 (V 63 .9)0 .46012
(3)
D 5 .927 10 5 (V )0 .44332
(4)
D 1 .331 10 7 (V 291 .5)0 .25969
(5)
D 4 .074 10 5 (V )0 .5774
(6)
D 3 .549 10 4 (V )0 .9496
(7)
where D is the flood damage per minute (measured in South Korean won, ₩) and V is the flood volume per 13
minute for the subareas (m3). Rainfall-runoff simulations were conducted using the SWMM model to obtain the flood volumes, which were then converted to flood damage per minute using the damage functions for each subarea. In Equations (4), (6), and (7), the flood volumes were directly linked to the flood damage. In Equations (3) and (5), the flood volume was not directly linked to the flood damage, suggesting that some of the subareas can resist flood damage while other subareas cannot. The difference is due to the varying components within each subarea. The synthetic rainfall data was required to provide a preliminary damage graph in the target watershed. As mentioned previously, the third quartile of the Huff distribution is used to apply the rainfall-runoff simulations and identify the quantities of rainfall and intensities that caused the initial flood damage in the study area. The initial flood damage amounts were verified when the initial flood damage occurred for each duration, and then was converted to rainfall intensities for the damage graph. The damage graph is used as a threshold for the flood forecasting. The calculated quantities of rainfall and average intensities that caused the initial flood damage in each subarea are shown in Table 4. The results in Table 4 were used to generate the damage graphs for the target watershed. As expected, A1 and A3 could withstand flood damage even at high rainfall intensities. A1 was safe from flood damage for rainfall events as high as 100 mm/hour, and A3 was safe from flood damage for rainfall events as high as 200 mm/hour for durations as long as 60 minutes. In contrast, A4 was easily flooded even at low rainfall intensities of less than 70 mm/hour. On the damage graph for each subarea is a threshold that categorizes rainfall events as safe or dangerous. Figure 10 shows the damage graphs for each subarea. The results in Figure 10 show that rainfall intensities causing flood damage in each subarea are different and highlight the necessity of dividing a drainage area into subareas according to land use. The damage graphs for A1 and A3 show that high rainfall intensities for a short duration are not directly linked to flood damage because A1 is at a higher elevation and few structures are located in A3; therefore, rainfall intensities as high as 200 mm/hour for 10-minute durations do not cause flood damage in A1 and A3. The other damage graph results illustrate that rainfall intensities of less than 100 mm/hour do not cause flood damage in A2, A4, and A5. The damage graph provides a threshold for generating flood alerts by applying synthetic or historical rainfall data.
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3.3. Flood forecasting by damage graphs Historical rainfall data from 2011 was selected for flood forecasting using the damage graphs in the target watershed. The historical rainfall volume-per-minute data were converted to rainfall intensity for flood forecasting. A flood alert, based on the risk of flood damage, was generated if the historical rainfall intensity data was located in the dangerous section of the damage graph. In this case, the predicted rainfall data was unnecessary because the historical rainfall data from 2011 was observed. Historical rainfall data gauged at 10 minutes were used for the application of damage graphs because the first damage intensities at each location were calculated at 10-minute intervals. Figure 11 shows the application of the damage graph in each subarea. Figure 11 shows the results of flood forecasting for each subarea. As shown in Figure 11(a), a flood damage alert would not have been generated in 2011 for subarea A1 based on the historical rainfall durations. However, Figure 11(b) illustrates that flood damage started at 30 minutes and continued until 60 minutes had passed in A2; flood damage continued to occur even though the rainfall intensity decreased after 60 minutes. Figure 11(c) indicates that no flood damage occurred in A3, and therefore, no alert would be generated. The threshold from the damage graph in A3 was high, and the rainfall intensity never went above the 200 mm/hour value that would have triggered flood damage. Figure 11(d) shows that the rainfall intensity in A4 produced a similar result to that of A2. Flood damage also started at 30 minutes and continued until 60 minutes had passed. The results of A5 in Figure 11(e) indicate that flood damage occurred between 50 and 60 minutes. The initial flood damage time for A5 was later than for A2 and A4 because A5 is located downstream of the target watershed and receives inflow from A1, A2, and A4. Therefore, flood damage in A5 occurred after receiving the inflows from A1, A2, and A4. In 2011, the historical flood damage (1 person died, 1449 people lost houses, total damage (cost): ₩ 124,397,000) occurred due to the historical rainfall event (total quantity of rain: 420.5 mm); Figure 11(f) shows the historical flood area in the target watershed (Ministry of Public Safety and Security, 2011). Table 5 provides a comparison of the rainfall intensities between the damage graph and the historical rainfall data for each subarea. In Table 5, the historical rainfall data was applied equally to all subareas. Different results would be obtained for varying rainfall intensities when applied to each subarea. The damage graphs were generated from 10minute interval simulations and rainfall intensities; however, more detailed applications are possible if the damage graphs were generated using 1-minute intervals. As shown in this study, new flood forecasting 15
techniques can be conducted by the application of rainfall data and it is based on the thresholds of damage graphs for each subarea in the target watershed.
4. Discussion The practical application of newly developed methods is an important step because the development itself does not necessarily provide a solution to engineering problems. The process of actually applying the new flood forecasting technique based on flood damage to drainage areas should be explained. Figure 12 shows a schematic diagram of how to apply the new flood forecasting technique. In our case-study, the real-time rainfall data were provided by the Korea Meteorological Administration to the rainfall analysis server in the integrated computing system for disaster management. The real-time rainfall data were converted to potential flood damage estimates using the damage functions that were already available for each subarea. The data were applied to the damage graphs and the flood damage occurrences were validated. The results of the flood forecasting were sent to the disaster damage database (DB) server. Additionally, real-time data including the number of transient populations, cars, and other properties were provided to the disaster damage DB server using web logic and extensible markup language (XML) parsing software. The data of the disaster damage DB server were linked to the computer center for management via a proxy server, DB server, and web server. The web server provided information regarding the flood damage forecasts to users via the internet, which makes it particularly accessible to those with smart phones.
5. Conclusions Various structural and non-structural measures are required to prepare for inundation. Conventional measures focus on reducing flood volumes and preparing pre-emptive management procedures, such as forecasting. The purpose of current flood forecasting is to predict flooding and prevent flood damage. The proposed flood forecasting technique in this study investigated flood damage in the study area. An additional feature of the proposed flood forecasting technique is that flood forecasting was conducted in smaller subareas. This study consisted of seven parts: (1) generation of synthetic rainfall data for the rainfall-runoff simulations, (2) calculation of damage functions between flood volumes and damage, (3) acquisition of flood volumes using 16
rainfall-runoff simulations, (4) conversion from flood volume to flood damage, (5) record the rainfall intensities occurring at the time of initial flood damage, (6) generation of damage graphs for each subarea using the initial flood damage intensities, (7) application of a historical rainfall event instead of real-time rainfall data to the new flood forecasting technique. Downscaling is not conducted in this study since predicted and real-time rainfall data provided by Korea Meteorological Administration were used for the suggested technique. The synthetic rainfall data in this study was generated using the third quartile of the Huff distribution, which is appropriate for Korea (Yoon et al., 2013). Damage functions between flood volume and damage for each of the five subareas in the study area were generated using the MD-FDA approach. SWMM was used to generate the rainfall-runoff simulations and the flood volume per minute was obtained at 1 mm increments until the initial flood damage occurred. In each subarea, the rainfall intensities of the initial flood damage were selected every 10 minutes. Individual damage graphs were generated using the rainfall intensities when the initial flood damage occurred in each of the five subareas. These values were then applied as the thresholds for flood forecasting. Historical rainfall data from 2011 were used for the flood forecasting. The results indicate that during flooding, flood damage only occurred in some subareas (A2, A4 and A5), while other areas (A1 and A3) remained undamaged. This method of flood forecasting using damage graphs is a new flood forecasting technique, which may be useful for creating management policies and preparing for flood disasters. Flood forecasting using damage graphs can reduce potential damage to humans and property when applied to urban and rural areas. Damage graphs can be used to determine dangerous or at-risk subareas in target drainage areas and may be useful for taking structural and non-structural measures in dangerous zones. The new flood forecasting technique requires the recalculation of damage functions when the status in the target watershed dramatically changes, for factors as diverse as property values and sewer networks. Damage graphs should be revised to consider the new conditions in each subarea if the status of the target watershed changes because damage graphs in the new flood forecasting technique are based on the current conditions of target watershed. In future studies, this new flood forecasting technique will incorporate resilience into the flood damage prediction. Additionally, studies that rank regional flood risk based on variable rainfall events across a region will be an important next step. Various data about flooding, including flood flow velocity, are required in 17
order to estimate flood damage with more detail and more accuracy. A study of the accurate prediction of rainfall data, which is a fundamental input data, will also be conducted.
Acknowledgements This work was supported by a grant from The National Research Foundation (NRF) of Korea, funded by the Korean government (MSIP) (No. 2016R1A2A1A05005306).
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References Beven, K.J., Kirkby, M.J., Schofield, N., Tagg, A.F., 1984. Testing a physically-based flood forecasting model (TOPMODEL) for three UK catchments. J. Hydrol. 69(14), 119143. Campolo, M., Andreussi, P., Soldati, A., 1999. River flood forecasting with a neural network model. Water Resour. Res. 35(4), 11911197. Caudill, M., 1989. “Neural networks primer, part I.” AI Expert. Chau, K.W., Wu, C.L., Li, Y.S., 2005. Comparison of several flood forecasting models in the Yangtze River. J. Hydrol. Engin. 10(6), 485491. Choi, H., Lee, E.H., Joo, J.G., Kim, J.H., 2016. Determining optimal locations for rainwater storage sites with the goal of reducing urban inundation damage costs. KSCE J. Civil Engin. 113. Choi, S.A., Yi, C.S., Shim, M.P., Kim, H.S., 2006. Multi-dimensional flood damage analysis (I): Principle and procedure. J. Korea Water Resour. Assoc. 50, 19. Collier, C. G., 2007. Flash flood forecasting: What are the limits of predictability? Q. J. Roy. Meteorol. Soc. 133(622), 323. Garrote, L., Bras, R.L., 1995. A distributed model for real-time flood forecasting using digital elevation models. J. Hydrol. 167(14), 279306. Huff, F.A., 1967. Time distribution of rainfall in heavy storms. Water Resour. Res. 3(4), 10071019. Jasper, K., Gurtz, J., Lang, H., 2002. Advanced flood forecasting in Alpine watersheds by coupling meteorological observations and forecasts with a distributed hydrological model. J. Hydrol. 267(1), 4052. Kim, G., Barros, A.P. 2001. Quantitative flood forecasting using multisensor data and neural networks. J. Hydrol. 246(1), 4562. Komma, J., Blöschl, G., Reszler, C., 2008. Soil moisture updating by ensemble Kalman filtering in real-time flood forecasting. J. Hydrol. 357(3), 228242. Korea Appraisal Board. 〈www.kab.co.kr〉 (Feb. 22, 2017). Korea Development Institute. 〈www.kdi.re.kr〉 (Mar. 3, 2017). Korea Meteorological Administration. Korea Precipitation Frequency Data Server. 〈www.k-idf.re.kr〉 Lee, E.H., Kim, J.H., 2017. Development of resilience index based on flooding damage in urban areas. Water 9(6), 428. Lee, E.H., Kim, J.H., Choo, Y.M., Jo, D.J., 2018. Application of flood nomograph for flood forecasting in urban areas. Water 10(1), 53. Li, K., Kan, G., Ding, L., Dong, Q., Liu, K., Liang, L., 2017. A novel flood forecasting method based on initial state variable correction. Water, 10(1), 12. Ministry of Construction and Transportation. 2004. “Study on the Economical Analysis Method of Flood 19
Control Project.” Seoul, Korea. McCulloch, W. S., and Pitts, W., 1943. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5(4), 115133. Ministry of Public Safety and Security. 2011. “The Disaster Year Book” Seoul, Korea. National Oceanic and Atmospheric Administration. 〈https://water. noaa.gov/about/nwm〉 Phillips, B. C., Yu, S., Thompson, G. R., and De Silva, N., 2005. 1D and 2D modelling of urban drainage systems using XP-SWMM and TUFLOW. In 10th International Conference on Urban Drainage, Copenhagen, Denmark. pp 2126. Sadler, J. M., Goodall, J. L., Morsy, M. M., and Spencer, K., 2018. Modeling urban coastal flood severity from crowd-sourced flood reports using Poisson regression and random forest. J. Hydrol. 559, 4355. Sokolova, D., Kuzmin, V., Batyrov, A., Pivovarova, I., Tran, N.A., Dang, D., Shemanaev, K.V., 2018. Use of MLCM3 software for flash flood modeling and forecasting. J. Ecol. Eng. 19(1), 177185. Solomatine, D. P., Xue, Y., 2004. M5 model trees and neural networks: application to flood forecasting in the upper reach of the Huai River in China. J. Hydrol. Engin. 9(6), 491501. Speight, L., Cole, S.J., Moore, R.J., Pierce, C., Wright, B., Golding, B., Cranston, M., Tavendale, A., Dhondia, J., Ghimire, S., 2016. Developing surface water flood forecasting capabilities in Scotland: an operational pilot for the 2014 Commonwealth Games in Glasgow. J. Flood Risk Manag. doi: 10.1111/fr3.122281. Statistics Korea, 〈kostat.go.kr〉 (Feb. 25, 2017). Toth, E., Brath, A., Montanari, A., 2000. Comparison of short-term rainfall prediction models for real-time flood forecasting. J. Hydrol. 239(1), 132147. United States Environmental Protection Agency (2010). “Storm Water Management Model User’s Manual Version 5.0.” EPA: Washington D.C., USA. XP Solutions (2013). “XP-SWMM Stormwater and Wastewater Management Model: Getting Started Manual.” Newbury, UK. Yoon, Y.N., Jung, J.H., Ryu, J.H., 2013. Introduction of design flood estimation. J. Korea Water Resour. Assoc. 46, 5568. Young, P.C., 2002. Advances in real-time flood forecasting. Philos. Trans. Roy. Soc. A 360(1796), 14331450. Yu, W., Nakakita, E., Kim, S., Yamaguchi, K., 2018. Assessment of ensemble flood forecasting with numerical weather prediction by considering spatial shift of rainfall fields. KSCE J. Civ. Engineer. 111, doi: 10.1007/s12205-018-0407-x.
20
Figures
Fig. 1. Flood forecasting process workflow for the method proposed in this study.
21
Fig. 2. Process for generating synthetic rainfall data from the Huff distribution.
22
Fig. 3. Schematic diagrams comparing flood volumes with and without flood damage.
23
Fig. 4. Conversion process workflow for using the damage functions.
24
Fig. 5. Process workflow for generating a damage graph.
25
Fig. 6. Flood forecasting concept using the damage graph.
26
Fig. 7. Process workflow for applying the real-time rainfall data to the target subarea.
27
Fig. 8. Subarea divisions and sewer networks in the target watershed.
28
Fig. 9. Process workflow for generating the damage functions.
29
Fig. 10. Damage graphs for each subarea: (a) A1, (b) A2, (c) A3, (d) A4, and (e) A5.
30
Fig. 11. Application of the damage graph in each subarea: (a) A1, (b) A2, (c) A3, (d) A4, (e) A5 and (f) Historical flood area.
31
Fig. 12. Schematic diagram of the network for the new flood forecasting technique.
32
Tables Table 1. Estimation of property values in residential, industrial and agricultural areas (1) Number of residences (2) Price per area (3) Number of floors (4) Construction industry deflator (5) Assessment of price Residential area Structure Contents (1)×(2)×(3)×(4) (5)×(6)×(7)
Industrial area Tangible assets (5)×(8)×(7)
(6) Number of households (7) Consumer price index (8) Number of works (9) Areas of rice paddy and dry field (10) Types of crops Agricultural area Inventories Farmland (5)×(8)×(7) (5)×(9)
33
Crops (5)×(10)
Table 2. The ratios of damage to flood depth Classification Residences Classification
Contents
Farm
Classification Crops
Depth (m) Types (%) Detached houses Apartments and buildings Depth (m) Types (%) Contents in buildings Tangible assets Inventories Farmland Tangible assets Inventories Duration (days) Depth (m) Types (%) Rice paddy (%) Field (%)
0 ~ 0.5
0.5 ~ 1.5
1.5 ~ 2.5
Over 2.5
Comments
15
40
83
100
15 / n
40 / n
83 / n
100 / n
Residential area n is the number Residential area of floors
0 ~ 0.5
0.5 ~ 1.0
1.0 ~ 2.0
2.0 ~ 3.0
Over 3.0
Areas
15
33
51
93
100
Residential area
80 60 100 79 59
100 100 100 97 90
100 100 100 100 100
Industrial area Industrial area Agricultural area Agricultural area Agricultural area
25 50 15 30 0 0 23 45 13 27 Less than 1 1 ~ 2
3~4
5~6
Over 7
Under 1.0 14 35
All duration Over 1.0
27 51
47 67
34
77 81
95 95
Areas
100 100
Areas Agricultural area Agricultural area
Table 3. Number of residence types, number of works, and property values in the study area Number of residence types Subarea Types Detached houses Apartments Buildings Number of works Subarea Types Manufacturing industry Electronic enterprise gas and water utility Waste water and environmental restoration Construction industry Wholesale business and retail trade Transportation industry Accommodation and restaurant business Broadcasting and information industry Financial and insurance business Real estate industry Science and technology services Business facilities management and business support services Public administration Educational services industry Health care and social welfare Art and leisure service Association and private service Property value Subarea Types Property value of structure (won) Property value of contents (won) Property value of tangible assets (won) Property value of inventories (won) Total property value (won)
A1
A2
A3
A4
A5
89 5 17
36 0 16
44 0 7
41 0 9
49 0 22
A1
A2
A3
A4
A5
0 0 0 0 13 0 4 0 0 0 0
0 3 0 0 18 0 38 2 4 1 11
0 1 0 0 4 1 7 0 0 0 2
0 4 1 2 24 0 18 2 2 2 222
0 1 0 0 38 0 25 0 0 0 5
0
0
0
0
1
0 0 0 0 1
1 3 10 8 3
0 0 2 2 0
2 1 8 6 1
0 3 3 3 1
A1
A2
A3
A4
A5
8,908,320,000 755,308,738 19,293,116 7,200,137 9,690,121,991
2,505,465,000 328,924,773 710,845,218 18,552,083 3,563,787,074
1,855,900,000 243,647,980 225,385,429 4,331,518 2,329,264,927
3,711,800,000 487,295,960 937,125,269 26,725,653 5,162,946,882
2,041,490,000 268,012,778 276,831,416 23,541,351 2,609,875,545
35
Table 4. Initial flood damage rainfall quantities and intensities Subarea Duration (minute) 10 20 30 40 50 60
A1 Quantity (mm) 44 57 68 79 91 103
A2 Intensity Quantity (mm/hour) (mm) 264 12 171 17 136 22 118.5 28 109.2 35 103 41
A3 Intensity Quantity (mm/hour) (mm) 72 44 51 80 44 115 42 150 42 185 41 220
36
A4 Intensity Quantity (mm/hour) (mm) 264 11 240 17 230 21 225 27 222 33.5 220 40
A5 Intensity Quantity (mm/hour) (mm) 66 16 51 26 42 35 40.5 43 40.2 51 40 60
Intensity (mm/hour) 96 78 70 64.5 61.2 60
Table 5. Comparison of rainfall intensities between the damage graphs and the historical rainfall data Subarea Duration (minute) 10 20 30 40 50 60
A1
A2
A3
A4
A5
Historical Damage rainfall graph intensity (mm/hour) (mm/hour) 264 18 171 30 136 49 118.5 61.5 109.2 65.4 103 62
Historical Damage rainfall graph intensity (mm/hour) (mm/hour) 72 18 51 30 44 49 42 61.5 42 65.4 41 62
Historical Damage rainfall graph intensity (mm/hour) (mm/hour) 264 18 240 30 230 49 225 61.5 222 65.4 220 62
Historical Damage rainfall graph intensity (mm/hour) (mm/hour) 66 18 51 30 42 49 40.5 61.5 40.2 65.4 40 62
Historical Damage rainfall graph intensity (mm/hour) (mm/hour) 96 18 78 30 70 49 64.5 61.5 61.2 65.4 60 62
37
Highlights
Development of the new flood forecasting technique based on flood damage in urban areas.
Generation of damage graphs in each subarea by damage functions between flood volumes and damages.
Conversion from flood volumes per minute to flood damage per minute.
Determination of the initial flood damage rainfall amount for each rainfall duration.
Application of a historical rainfall event to the new flood forecasting technique.
38
S0022-1694(18)30401-3 https://doi.org/10.1016/j.jhydrol.2018.06.003 HYDROL 22852
To appear in:
Journal of Hydrology
Received Date: Revised Date: Accepted Date:
29 April 2018 31 May 2018 1 June 2018
Please cite this article as: Lee, E.H., Kim, J.H., Development of a Flood-Damage-Based Flood Forecasting Technique, Journal of Hydrology (2018), doi: https://doi.org/10.1016/j.jhydrol.2018.06.003
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Development of a Flood-Damage-Based Flood Forecasting Technique Eui Hoon Lee a, Joong Hoon Kim b,* a
Research Center for Disaster Prevention Science and Technology, Korea University, 02841, Seoul, South Korea; E-mail: [email protected] b School of Civil, Environmental and Architectural Engineering, Korea University, 02841, Seoul, South Korea * Corresponding author. E-mail: [email protected]
1
Abstract Flood forecasting is a pre-emptive non-structural measure used to mitigate inundation. Most current flood forecasting techniques incorporate complex processes, such as training and optimization, before the technique can be applied. Conventional flood forecasting techniques, based on flood volume, provide alerts even if there is no significant risk of flood damage. In this study, a new flood forecasting technique has been developed based on likely flood damage using the multi-dimensional flood damage analysis method. This new flood forecasting technique overcomes the drawbacks of current flood forecasting techniques because it can be easily applied using rainfall data. The studied drainage area was divided into subareas, and the damage functions were obtained for each subarea using the flood volumes and damage information. Using these damage functions, the rainfall intensity when the flood damage initially occurred was calculated for each duration and subarea. The damage graph produced for flood forecasting in each subarea identified the rainfall intensities and durations that resulted from the initial occurrence of flood damage. This new flood forecasting technique could be used to save lives, valuable assets, and manage drainage areas. Keywords: flood forecasting, flood volume, flood damage, damage graph
2
1. Introduction Climate change has caused extreme rainfall events to occur and the frequency of these events has increased. The proportion of impermeable areas, as well as the frequency of extreme rainfall events, has increased sharply. The number of floods has increased and various measures such as structural and nonstructural measures for preventing floods have been prepared. To prepare for, or prevent flood damage in drainage areas, one of the nonstructural measures that has been implemented is the application of various types of flood forecasting models. For example, a flood forecasting model was suggested and applied in three UK catchments and real-time flood forecasting was proposed (Beven et al., 1984). A distributed model using digital elevation models (Garrote and Bras, 1995) and short-term rainfall prediction models (Toth et al., 2000) have both been developed for real-time flood forecasting. Jasper et al. (2002) coupled meteorological observations and a distributed hydrological model for advanced flood forecasting. Recursive state-space estimation (Young, 2002) and soil moisture updated by ensemble Kalman filtering (Komma et al., 2008) have been suggested for real-time flood forecasting. It has been difficult to apply these methods to small watersheds, such as single drainage areas, because the time intervals for recording the rainfall data used in previous studies were not small enough to capture the detail required for accurate predictions at smaller scales. Time-consuming detailed analysis using 2D hydrodynamic models is appropriate as part of the preliminary work for flood forecasting, but the time required for real-time forecasting should be minimal. New approaches, such as applying neural networks, distributed hydrological modeling, an ingredients-based methodology, and machine learning, have also been introduced. Neural networks were developed as computer models based on the human brain and nervous system with mathematics and algorithms called threshold logic (McCulloch and Pitts, 1943). Robert Hecht-Niesen defined one as 'a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs' (Caudill, 1989). A neural network has been applied to river flood and flash-flood forecasting (Campolo et al., 1999) and quantitative flood forecasting using multisensory data and neural networks was proposed by Kim and Barros (2001). Research using neural networks combined with other techniques has been suggested for flood forecasting. Machine learning and neural network techniques were combined for flood forecasting by Solomatine and Xue, 2004. Chau et al. (2005) compared two hybrid models, the genetic-algorithm-based artificial neural 3
network and the adaptive-network-based fuzzy inference system. Additionally, the limitations of flash flood forecasting have also been considered (Collier, 2007). Flood forecasting techniques using neural networks require time-consuming training and the application process is complex. Many flood forecasting studies have been conducted since 2010. In Scotland, a new surface water flood forecasting model using 24-hour ensemble rainfall predictions was conducted with static flood risk maps using the Grid-to-Grid hydrological model (Speight et al., 2016). The process of flood forecasting using 24hour ensemble rainfall prediction is complicated to apply but has wide scope for application. Initial state variable correction and particle swarm optimization were used for flood forecasting in Southern China (Li et al., 2017). The application process is so complex because a significant amount of optimization is required. Additionally, the application area and the prediction intervals are large and thus, are unsuitable for single drainage areas. Recently, several techniques have been suggested for flood forecasting. Flash flood modeling and forecasting using a multi-layer third-generation conceptual model were applied to 50 basins in Russia and the results were compared with the Sacramento soil moisture accounting model (Sokolova et al., 2018). As it was applied to the river basins, the size of the target watershed was significantly larger than a single drainage basin. The application process was complicated and required a significant amount of time due to the large application area and the complexity of the techniques. A flood nomograph using the regression curve of the first flooding nodes was suggested for flood forecasting applications in single drainage basins in urban areas (Lee et al., 2018). It is difficult to estimate flood damage in the target watershed because flood forecasting was conducted using only the flood volume. Ensemble flood forecasting using the numerical weather prediction model was applied to the Futatsuno and Nanairo dam catchments in the Shingu river basin (Yu et al., 2018); the model had a two kilometer horizontal resolution and made 30-hour forecasts of quantitative precipitation. The urban coastal flood severity from crowd-sourced flood reports (street flooding record) using Poisson regression and random forest techniques was suggested and applied in Norfolk, Virginia, USA (Sadler et al., 2018). The National Water Model (NWM) with 1 h time steps was developed by National Oceanic and Atmospheric Administration (NOAA) in the Department of Commerce, United States. The NWM consists of three forecast ranges such as short-range (18-hour deterministic forecast), medium-range (10-day deterministic forecast) and long-range (30-day ensemble forecast). These methods are not 4
appropriate for single drainage areas because the application areas and prediction intervals are too large. In the aforementioned studies, the flood forecasting methods used long-term rainfall data that are unsuitable for single drainage areas that require rainfall data on a per-minute scale. Additionally, the applied flood forecasting processes are complex because training and optimization techniques are required (Campolo et al., 1999; Kim and Barros, 2001; Solomatine et al., 2004; Chau et al., 2005; Li et al., 2017). Furthermore, the occurrence and extent of flood damage varies across different areas because properties across each area, such as land use, vary. There is a need for a simple real-time flood forecasting technique that uses rainfall data per minute to be developed and applied to drainage areas to improve the applicability of the method. The rainfall data per minute used in this study are provided by Korea Meteorological Administration and they are used for flood forecasting and the operation of drainage facilities in Korea. In this study, a new flood forecasting method based on damage graphs is introduced. The method only requires rainfall data and predicts flood damage without the need for long training and optimization processes prior to application. The flood damage in each subarea of the target watershed was calculated using multi-dimensional flood damage analysis (MD-FDA). MD-FDA was suggested by Choi et al. (2006) and has been applied in previous studies (Choi et al., 2016; Lee and Kim, 2017). Each subarea was categorized by land use, and the damage functions in each subarea were generated using the MD-FDA. All rainfall-runoff simulations in the target watersheds were conducted using the Storm Water Management Model (SWMM) (United States Environmental Protection Agency, 2010). Damage graphs were generated for each subarea by recording the rainfall intensity when the flood damage initially occurs. After relating historical rainfall events to the damage graph, it was possible to prepare predictions of flood damage to the target watershed.
2. Methodologies 2.1. Overview This study consists of seven parts. First, synthetic rainfall, which is not recorded but artificial generated using selected rainfall distributions, were generated for the rainfall-runoff simulations. Next, damage functions between flood volumes and damage were obtained for each subarea. Then, the results of flood volume were obtained using rainfall-runoff simulations with synthetic rainfall data for selected rainfall durations. Subsequently, the results of flood volume were converted to flood damage using the damage 5
functions. Then, the rainfall intensities that correspond to the total rainfall that caused the initial flood damage for selected rainfall durations (initial flood damage intensities) were recorded. Next, damage graphs for each subarea were generated using the initial flood damage intensities of selected rainfall durations. Finally, a historical rainfall event instead of real-time rainfall data was applied to the new flood forecasting technique. The flood forecasting process workflow for the method proposed in this study is shown in Figure 1. The process shown in Figure 1 was applied to each subarea, resulting in a flood forecasting graph. A flood damage alert occurs for each subarea of the target watershed if the rainfall intensity of a historical rainfall event is higher than the safety threshold in the damage graph. SWMM was developed by the United States Environmental Protection Agency (US-EPA) in 1971. It can be used for rainfall-runoff simulations of combined sanitary sewer conduits and/or drainage facilities in urban as well as rural areas. Flow routing models, such as steady flow routing, kinematic wave routing, and dynamic wave routing models, are available to be selected in SWMM. The steady flow routing model is based on Manning’s equation. The kinematic wave routing model allows flow and area to vary both spatially and temporally in a sewer conduit. The essential elements in flood simulation such as backwater effects, pressurized flow, entrance and exit losses, and runoff analysis in a loop network are not available in both flow routing models. The dynamic wave routing model is based on the complete one dimensional Saint Venant flow equations. It was used for all rainfall-runoff simulations in this study because it is possible to apply all essential elements in the dynamic wave routing model.
2.2. Generation of synthetic rainfall data for damage graphs The real-time rainfall data based on the actual observations by Korea Meteorological Administration is used for the real application of the new flood forecasting technique. However, the damage graph required for the forecast is based on rainfall-runoff simulations; therefore synthetic rainfall data are required as input data for the simulations. The generation of synthetic rainfall data consists of five steps: 1. Selection of the appropriate regression equation for the target watershed. 2. Selection of rainfall quantities and durations. 3. Generation of the cumulative distribution for the selected regression equation. 6
4. Conversion from cumulative distribution to dispersed distribution. 5. Application of the rainfall quantities to obtain the synthetic rainfall event. The representative distribution for generating synthetic rainfall data in Korea is the Huff distribution, upon which all drainage facilities in Korea are designed (Huff, 1967). The Huff distribution consists of four quartiles based on the locations of the peak values. In the Huff distribution, the peak values of rainfall in the first, second, third and fourth quartiles of the time range are significant. The third quartile in the Huff distribution has been considered appropriate for the design and operation of urban drainage facilities in Korea (Yoon et al., 2013). The new flood forecasting technique in this study is based on the rainfall distribution used in the design of the sewer networks in the target watershed to maintain alignment between the design and forecasting. Since the rainfall distribution used in the new flood forecasting technique is same as that used for the design of the sewer networks in the target watershed, a customized flood forecast was generated in the target watershed. Equation (1) shows the cumulative regression equation using the third quartile of the Huff distribution in Jeongup (Korea Precipitation Frequency Data Server).
y 36 .029 x 6 98 .986 x 5 95 .279 x 4 38 .622 x 3 7 .4086 x 2 0 .1088 x 0 .0002
(1)
where y is the cumulative proportion of the rainfall and x represents the cumulative proportion of time. Cumulative regression equations of the Huff distribution vary at each region. Figure 2 shows the process for generating synthetic rainfall data from the Huff distribution. The cumulative regression equation is used for generating the cumulative distribution. In the cumulative distribution shown in Figure 2, the rainfall quantities are 3.5 % and 8.5 % when the rainfall durations are 10 % and 20 %, respectively. In the dispersed distribution of Figure 2, the rainfall quantity is 5.0 % which is the difference between 8.5 % and 3.5 % when the rainfall duration is 20 %. The rainfall quantity and duration are applied to the dispersed distribution for the generation of the synthetic rainfall distribution. The process is repeated for each duration because a range of synthetic rainfall data is required to generate the threshold of the new flood forecasting using rainfall-runoff simulations.
2.3. Conversion from flood volume to flood damage 7
2.3.1. Concept of flood damage for flood forecasting Flood volumes are region dependent and vary because floods are more likely to occur in low elevation areas. For populated areas at low elevation, flood damage will correlate with flood volumes. Conversely, in the same area, no flood damage will occur in unpopulated areas without houses or buildings. Figure 3 is a schematic diagram comparing areas where flood volumes result in flood damage. In Figure 3(a), the flood volume enters an undeveloped (unpopulated) area, therefore, no flood damage occurs. In this case, a flood alert is generated if the flood forecasting is based solely on the flood volume. Conversely, if the flood forecasting is based on the likelihood of flood damage, a flood alert is not generated. In Figure 3(b), flood alerts based on both flood volumes and the likelihood of flood damage are generated when the flood occurs in the planted area; hence, the new flood forecasting technique differs from the current techniques in this regard. However, current flood forecasting methods are still required because the people can avoid flooded areas and lives can be saved by these techniques. The suggested technique can be used to supplement the current flood forecasting approaches and segment-based flood forecasting can be introduced in several stages.
2.3.2. Calculating flood damage The multi-dimensional flood damage analysis (MD-FDA) method is used for converting flood volumes to flood damage (Choi et al., 2006). MD-FDA has several components, including area and damage classifications. The three area classifications in the analysis are residential, agricultural, and industrial. Damage classifications include damage caused to humans, buildings, farmland, crops, inventory, public facilities, and the contents of buildings. The MD-FDA requires three steps: determination of property value, calculation of the inundated inclusion ratio, and flood damage evaluation. The property value for each asset is based on statistical data and the most important element of MD-FDA is the precise survey of the property value for each asset. The superposition data is generated by administrative district data, land use data, and flood depth data. A 2D drainage model is required to obtain the results of flood depth. The inundated property value (flood damage) in each subarea is based on the superposition data. The results of flood volume and flood damage using rainfall data are used for the generation of damage functions in each subarea. Table 1 shows the property value estimations for residential, industrial, and agricultural areas. The construction industry deflator and consumer price index in Table 1 were based on data from Statistics 8
Korea (2017). The second required factor is the calculation of the inundated inclusion ratio. For calculating the flood damage, the ratio is estimated using the flood depth at each property as well as the flood area. The ratio of the damage to the flood depth for residential, industrial, and agricultural areas is shown in Table 2 (Ministry of Construction and Transportation, 2004). The final factor required for calculating flood damage is the flood area. The flood depth and area can be obtained from historical records or rainfall-runoff simulation models. However, the flood area in this study was conducted by rainfall-runoff simulation using a 2D hydrodynamic model because the flood area is difficult to estimate accurately from historical records in Korea. Therefore, the calculation of flood damage using the MD-FDA is based on the property value, ratio of the flood area, and the ratio of damage to flood depth, as shown in Equation (2).
F d Pv R a R d
(2)
where Fd is the flood damage, Pv is the property value in the area exposed to the flood, Ra represents the ratio of the flood area, and Rd is the ratio of the damage to the flood depth in vulnerable areas. The conversion from flood volume to flood damage is based on the damage functions. The damage functions can be derived from an estimate of the damage threshold using a rainfall-runoff model because the resulting simulations provide the flood volume, area, and depth. The values of flood volume per minute obtained from the rainfallrunoff simulations are converted to a value of flood damage per minute for each subarea. The conversion process workflow for using the damage functions is shown in Figure 4. In Figure 4, the unit of flood volume is m3 and the unit of flood damage is won (Korean currency). The conversion process workflow for using damage functions consists of three steps. In the first step, the study area in the target watershed is divided into subareas according to land use. In the second step, the distributions of flood volume over time in each subarea are obtained using rainfall-runoff simulations. In the third step, the results of flood volume over time are converted to flood damage using damage functions in each area. In cases where there are many flood events based on actual observations and the status of the target watershed does not change during this period, it is possible to create damage thresholds in damage functions 9
based on actual observations. However, flood events do not occur frequently and the status of the target watershed changes during that time. Damage graphs should be revised if the status of the target watershed changes. For this reason, it is challenging to use the flood records based on actual observations. To overcome this shortcoming, synthetic rainfall data used in the design of the sewer network in the target watershed was applied to generate the damage graphs.
2.4. Development of the new flood forecasting technique Flood forecasting used in this study was based on flood damage information obtained from rainfall-runoff simulations using synthetic rainfall data. The rainfall-runoff was simulated for various durations. It was initiated when the total rainfall quantity was 1 mm and continued at 1 mm increments until flood damage occurred. The quantity of rainfall that caused the initial flood damage was identified, and this rainfall quantity was converted to the rainfall intensity that caused the initial flood damage for each period of rainfall. In this process, the rainfall intensity that caused the initial flood damage for each period of rainfall was the quantity of rainfall divided by the duration of the rainfall. A damage graph for each subarea consists of the rainfall intensities that caused the initial flood damage for each duration. The damage graph was generated from the synthetic rainfall data and the historical rainfall data were applied for the flood forecasting. A flood damage alert occurred if the historical rainfall intensities were higher than the threshold in the damage graph, and vice-versa. Therefore, the damage alert is a mechanism for determining safe or at-risk areas. The entire process of conversion from flood volume to flood damage consists of four parts: (1) classification of nodes in each subarea, (2) estimation of the flood volume in each subarea, (3) conversion of the flood volume to damage functions in each subarea, and (4) estimation of flood damage in each subarea. Figure 5 shows the process workflow for generating a damage graph. The safety threshold in the damage graph was generated from the points when the rainfall intensities generated the initial flood damage for each duration. Figure 6 shows the flood forecasting concept using the damage graph. Damage graphs are generated in each subarea and they are the thresholds for the new flood forecasting technique. In Figure 6(a), subarea A1 is safe because all the rainfall intensities are located in the safe zone, i.e., below the solid black line. In Figure 6(b), subarea A2 is dangerous because all rainfall intensities, except 10
the first one, are located in the danger zone. In Figure 6(c), subarea A3 is safe because all rainfall intensities are below the threshold of the damage graph in A3. All rainfall intensities are less than the minimum value on the y-axis in A3. Subarea A4 has a similar pattern to subarea A2 because all rainfall intensities except the first one are located in the danger zone of Figure 6 (d); thus, subarea A4 is unsafe. In Figure 6 (e), only the third rainfall intensity is located in the dangerous zone. The flood forecasting can be conducted using damage graphs in each area. Damage graphs can be generated according to each quartile in the Huff distribution and all types of rainfall data, both real and synthetic, can be applied to a damage graph for flood forecasting. The time interval of the applied rainfall data has a direct correlation with the flood forecasting precision. The damage graph can allow more detailed flood forecasting if the time interval of the applied rainfall data is smaller. Therefore, if the time intervals of the predicted rainfall data are too long, the flood forecasting is less accurate. Furthermore, the predicted rainfall data should be replaced by measured data as it becomes available. Figure 7 shows the process workflow for applying the real-time rainfall data to the target subarea. Figure 7 is an example for showing the real application with real-time rainfall data over time. In Figure 7, all rainfall data, regardless of the time interval, can be applied to the damage graph. However, a new method for truncating the long-term rainfall data is required for application to the damage graph. The intensity of the applied rainfall data over time gradually decreases and becomes too low if long-term rainfall data is applied to the damage graph. When hourly rainfall data is applied to a large watershed, rainfall data can be divided considering the Inter-Event Time Definition (IETD) of the target watershed. When rainfall data per minute is applied to a small watershed, the rainfall data can be divided considering the time of concentration in the target watershed. The appropriate truncation of long-term rainfall data in small watersheds can be determined when the rainless time is longer than the time of concentration because the rainfall is discharged outside of the watershed after the time of concentration.
3. Application and Results 3.1. Study area Korea consists of nine provinces: Gyeonggi-do, Gangwon-do, Chungcheongbuk-do, Chungcheongnam-do, Jeollabuk-do, Jeollanam-do, Gyeongsangbuk-do, Gyeongsangnam-do, and Jeju Island. In 2011, there was 11
inundation and flood damage in Jeollabuk-do Province, Korea; floods also occurred in the Jeongup area. The Sintaein Basin is a representative urbanized area in Jeongup and was selected as a study area to validate our technique. Figure 8 shows the subarea divisions and sewer networks in the target watershed. The Sintaein Basin was divided into five subareas, A1, A2, A3, A4, and A5, according to land use. A1 is a residential area with other facilities, such as schools and apartments. A2 consists of residential and commercial areas. A3 is mostly composed of undeveloped land with a few facilities. A4 is an area vulnerable to inundation because it receives flows from A1, A2, and A3. Public facilities, such as offices, post offices, and schools are located in A5. The drainage area in the Sintaein Basin is 67.9 ha. A1, A2, A3, A4, and A5 are 17.6, 21.5, 0.7, 15.1, and 13.0 ha in area, respectively. The average impermeability of each subarea is 69, 78, 55, 83, and 72 %, respectively. The subarea divisions and sewer networks in the target watershed were modelled with 175 subcatchments, 175 conduits, and 175 nodes.
3.2. Generation of damage graphs for each subarea 3.2.1. Standard of calculation for flood damage The calculation of human-related flood damage was not typically considered because of the complex factors and uncertainties, such as the number and ages of transient populations. In this study, human-related flood damage was excluded from the damage calculation so that only factors that can be calculated explicitly would be included. The price per area is an important factor for evaluating flood damage and can be calculated by considering the current status in the study area. The price per area in the study area was obtained from the Korean Appraisal Board website (Korea Appraisal Board). Additionally, construction industry deflators and residence property values should be considered and were obtained from the Korean Development Institute website (Korea Development Institute). In Korea, residences are categorized as detached houses, apartments, and buildings. The value of contents within a residential property was estimated at 12,182,399 won per household (Korea Development Institute). In the study area, there are no agricultural areas; therefore, agricultural area property values were excluded. The price per area values used were 1,385,000 won/m2 for detached houses, 1,690,000 won/m2 for apartments, and 853,000 won/m2 for buildings. The study area was divided into five subareas and the status of each subarea should be considered. The 12
number of types of residence and number of works in each subarea are required in order to calculate the property value of each area. Because there are no agricultural areas in the study area, the property values including only residential and industrial areas were calculated. Table 3 shows the number of residence types, number of works, and property values in each subarea of the study area. Calculating flood damage requires rainfall-runoff simulations from the SWMM model to obtain the flood volume, flood depth, and flood area for each subarea. The 1D drainage model is not appropriate to simulate overland flooding because the results are very approximate, which is problematic for a flood forecasting scheme. A 2D drainage model, such as XP-SWMM, is required for the simulation of overland flooding. The surface (overland) flooding by MD-FDA can be obtained using XP-SWMM (XP solutions, 2013). The 2D simulation of XP-SWMM was developed by the combination between XP-SWMM 1D and the TUFLOW 2D module (Phillips et al., 2005). Damage functions include the property value and flood volume for each subarea. Figure 9 shows the process workflow for generating the damage functions.
3.2.2. Damage functions for generating damage graphs The damage functions for A1, A2, A3, A4, and A5 in the study area were provided from previous studies and are shown in Equations (3) (7), respectively (Choi et al., 2016).
D 1 .107 10 6 (V 63 .9)0 .46012
(3)
D 5 .927 10 5 (V )0 .44332
(4)
D 1 .331 10 7 (V 291 .5)0 .25969
(5)
D 4 .074 10 5 (V )0 .5774
(6)
D 3 .549 10 4 (V )0 .9496
(7)
where D is the flood damage per minute (measured in South Korean won, ₩) and V is the flood volume per 13
minute for the subareas (m3). Rainfall-runoff simulations were conducted using the SWMM model to obtain the flood volumes, which were then converted to flood damage per minute using the damage functions for each subarea. In Equations (4), (6), and (7), the flood volumes were directly linked to the flood damage. In Equations (3) and (5), the flood volume was not directly linked to the flood damage, suggesting that some of the subareas can resist flood damage while other subareas cannot. The difference is due to the varying components within each subarea. The synthetic rainfall data was required to provide a preliminary damage graph in the target watershed. As mentioned previously, the third quartile of the Huff distribution is used to apply the rainfall-runoff simulations and identify the quantities of rainfall and intensities that caused the initial flood damage in the study area. The initial flood damage amounts were verified when the initial flood damage occurred for each duration, and then was converted to rainfall intensities for the damage graph. The damage graph is used as a threshold for the flood forecasting. The calculated quantities of rainfall and average intensities that caused the initial flood damage in each subarea are shown in Table 4. The results in Table 4 were used to generate the damage graphs for the target watershed. As expected, A1 and A3 could withstand flood damage even at high rainfall intensities. A1 was safe from flood damage for rainfall events as high as 100 mm/hour, and A3 was safe from flood damage for rainfall events as high as 200 mm/hour for durations as long as 60 minutes. In contrast, A4 was easily flooded even at low rainfall intensities of less than 70 mm/hour. On the damage graph for each subarea is a threshold that categorizes rainfall events as safe or dangerous. Figure 10 shows the damage graphs for each subarea. The results in Figure 10 show that rainfall intensities causing flood damage in each subarea are different and highlight the necessity of dividing a drainage area into subareas according to land use. The damage graphs for A1 and A3 show that high rainfall intensities for a short duration are not directly linked to flood damage because A1 is at a higher elevation and few structures are located in A3; therefore, rainfall intensities as high as 200 mm/hour for 10-minute durations do not cause flood damage in A1 and A3. The other damage graph results illustrate that rainfall intensities of less than 100 mm/hour do not cause flood damage in A2, A4, and A5. The damage graph provides a threshold for generating flood alerts by applying synthetic or historical rainfall data.
14
3.3. Flood forecasting by damage graphs Historical rainfall data from 2011 was selected for flood forecasting using the damage graphs in the target watershed. The historical rainfall volume-per-minute data were converted to rainfall intensity for flood forecasting. A flood alert, based on the risk of flood damage, was generated if the historical rainfall intensity data was located in the dangerous section of the damage graph. In this case, the predicted rainfall data was unnecessary because the historical rainfall data from 2011 was observed. Historical rainfall data gauged at 10 minutes were used for the application of damage graphs because the first damage intensities at each location were calculated at 10-minute intervals. Figure 11 shows the application of the damage graph in each subarea. Figure 11 shows the results of flood forecasting for each subarea. As shown in Figure 11(a), a flood damage alert would not have been generated in 2011 for subarea A1 based on the historical rainfall durations. However, Figure 11(b) illustrates that flood damage started at 30 minutes and continued until 60 minutes had passed in A2; flood damage continued to occur even though the rainfall intensity decreased after 60 minutes. Figure 11(c) indicates that no flood damage occurred in A3, and therefore, no alert would be generated. The threshold from the damage graph in A3 was high, and the rainfall intensity never went above the 200 mm/hour value that would have triggered flood damage. Figure 11(d) shows that the rainfall intensity in A4 produced a similar result to that of A2. Flood damage also started at 30 minutes and continued until 60 minutes had passed. The results of A5 in Figure 11(e) indicate that flood damage occurred between 50 and 60 minutes. The initial flood damage time for A5 was later than for A2 and A4 because A5 is located downstream of the target watershed and receives inflow from A1, A2, and A4. Therefore, flood damage in A5 occurred after receiving the inflows from A1, A2, and A4. In 2011, the historical flood damage (1 person died, 1449 people lost houses, total damage (cost): ₩ 124,397,000) occurred due to the historical rainfall event (total quantity of rain: 420.5 mm); Figure 11(f) shows the historical flood area in the target watershed (Ministry of Public Safety and Security, 2011). Table 5 provides a comparison of the rainfall intensities between the damage graph and the historical rainfall data for each subarea. In Table 5, the historical rainfall data was applied equally to all subareas. Different results would be obtained for varying rainfall intensities when applied to each subarea. The damage graphs were generated from 10minute interval simulations and rainfall intensities; however, more detailed applications are possible if the damage graphs were generated using 1-minute intervals. As shown in this study, new flood forecasting 15
techniques can be conducted by the application of rainfall data and it is based on the thresholds of damage graphs for each subarea in the target watershed.
4. Discussion The practical application of newly developed methods is an important step because the development itself does not necessarily provide a solution to engineering problems. The process of actually applying the new flood forecasting technique based on flood damage to drainage areas should be explained. Figure 12 shows a schematic diagram of how to apply the new flood forecasting technique. In our case-study, the real-time rainfall data were provided by the Korea Meteorological Administration to the rainfall analysis server in the integrated computing system for disaster management. The real-time rainfall data were converted to potential flood damage estimates using the damage functions that were already available for each subarea. The data were applied to the damage graphs and the flood damage occurrences were validated. The results of the flood forecasting were sent to the disaster damage database (DB) server. Additionally, real-time data including the number of transient populations, cars, and other properties were provided to the disaster damage DB server using web logic and extensible markup language (XML) parsing software. The data of the disaster damage DB server were linked to the computer center for management via a proxy server, DB server, and web server. The web server provided information regarding the flood damage forecasts to users via the internet, which makes it particularly accessible to those with smart phones.
5. Conclusions Various structural and non-structural measures are required to prepare for inundation. Conventional measures focus on reducing flood volumes and preparing pre-emptive management procedures, such as forecasting. The purpose of current flood forecasting is to predict flooding and prevent flood damage. The proposed flood forecasting technique in this study investigated flood damage in the study area. An additional feature of the proposed flood forecasting technique is that flood forecasting was conducted in smaller subareas. This study consisted of seven parts: (1) generation of synthetic rainfall data for the rainfall-runoff simulations, (2) calculation of damage functions between flood volumes and damage, (3) acquisition of flood volumes using 16
rainfall-runoff simulations, (4) conversion from flood volume to flood damage, (5) record the rainfall intensities occurring at the time of initial flood damage, (6) generation of damage graphs for each subarea using the initial flood damage intensities, (7) application of a historical rainfall event instead of real-time rainfall data to the new flood forecasting technique. Downscaling is not conducted in this study since predicted and real-time rainfall data provided by Korea Meteorological Administration were used for the suggested technique. The synthetic rainfall data in this study was generated using the third quartile of the Huff distribution, which is appropriate for Korea (Yoon et al., 2013). Damage functions between flood volume and damage for each of the five subareas in the study area were generated using the MD-FDA approach. SWMM was used to generate the rainfall-runoff simulations and the flood volume per minute was obtained at 1 mm increments until the initial flood damage occurred. In each subarea, the rainfall intensities of the initial flood damage were selected every 10 minutes. Individual damage graphs were generated using the rainfall intensities when the initial flood damage occurred in each of the five subareas. These values were then applied as the thresholds for flood forecasting. Historical rainfall data from 2011 were used for the flood forecasting. The results indicate that during flooding, flood damage only occurred in some subareas (A2, A4 and A5), while other areas (A1 and A3) remained undamaged. This method of flood forecasting using damage graphs is a new flood forecasting technique, which may be useful for creating management policies and preparing for flood disasters. Flood forecasting using damage graphs can reduce potential damage to humans and property when applied to urban and rural areas. Damage graphs can be used to determine dangerous or at-risk subareas in target drainage areas and may be useful for taking structural and non-structural measures in dangerous zones. The new flood forecasting technique requires the recalculation of damage functions when the status in the target watershed dramatically changes, for factors as diverse as property values and sewer networks. Damage graphs should be revised to consider the new conditions in each subarea if the status of the target watershed changes because damage graphs in the new flood forecasting technique are based on the current conditions of target watershed. In future studies, this new flood forecasting technique will incorporate resilience into the flood damage prediction. Additionally, studies that rank regional flood risk based on variable rainfall events across a region will be an important next step. Various data about flooding, including flood flow velocity, are required in 17
order to estimate flood damage with more detail and more accuracy. A study of the accurate prediction of rainfall data, which is a fundamental input data, will also be conducted.
Acknowledgements This work was supported by a grant from The National Research Foundation (NRF) of Korea, funded by the Korean government (MSIP) (No. 2016R1A2A1A05005306).
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References Beven, K.J., Kirkby, M.J., Schofield, N., Tagg, A.F., 1984. Testing a physically-based flood forecasting model (TOPMODEL) for three UK catchments. J. Hydrol. 69(14), 119143. Campolo, M., Andreussi, P., Soldati, A., 1999. River flood forecasting with a neural network model. Water Resour. Res. 35(4), 11911197. Caudill, M., 1989. “Neural networks primer, part I.” AI Expert. Chau, K.W., Wu, C.L., Li, Y.S., 2005. Comparison of several flood forecasting models in the Yangtze River. J. Hydrol. Engin. 10(6), 485491. Choi, H., Lee, E.H., Joo, J.G., Kim, J.H., 2016. Determining optimal locations for rainwater storage sites with the goal of reducing urban inundation damage costs. KSCE J. Civil Engin. 113. Choi, S.A., Yi, C.S., Shim, M.P., Kim, H.S., 2006. Multi-dimensional flood damage analysis (I): Principle and procedure. J. Korea Water Resour. Assoc. 50, 19. Collier, C. G., 2007. Flash flood forecasting: What are the limits of predictability? Q. J. Roy. Meteorol. Soc. 133(622), 323. Garrote, L., Bras, R.L., 1995. A distributed model for real-time flood forecasting using digital elevation models. J. Hydrol. 167(14), 279306. Huff, F.A., 1967. Time distribution of rainfall in heavy storms. Water Resour. Res. 3(4), 10071019. Jasper, K., Gurtz, J., Lang, H., 2002. Advanced flood forecasting in Alpine watersheds by coupling meteorological observations and forecasts with a distributed hydrological model. J. Hydrol. 267(1), 4052. Kim, G., Barros, A.P. 2001. Quantitative flood forecasting using multisensor data and neural networks. J. Hydrol. 246(1), 4562. Komma, J., Blöschl, G., Reszler, C., 2008. Soil moisture updating by ensemble Kalman filtering in real-time flood forecasting. J. Hydrol. 357(3), 228242. Korea Appraisal Board. 〈www.kab.co.kr〉 (Feb. 22, 2017). Korea Development Institute. 〈www.kdi.re.kr〉 (Mar. 3, 2017). Korea Meteorological Administration.
Control Project.” Seoul, Korea. McCulloch, W. S., and Pitts, W., 1943. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5(4), 115133. Ministry of Public Safety and Security. 2011. “The Disaster Year Book” Seoul, Korea. National Oceanic and Atmospheric Administration. 〈https://water. noaa.gov/about/nwm〉 Phillips, B. C., Yu, S., Thompson, G. R., and De Silva, N., 2005. 1D and 2D modelling of urban drainage systems using XP-SWMM and TUFLOW. In 10th International Conference on Urban Drainage, Copenhagen, Denmark. pp 2126. Sadler, J. M., Goodall, J. L., Morsy, M. M., and Spencer, K., 2018. Modeling urban coastal flood severity from crowd-sourced flood reports using Poisson regression and random forest. J. Hydrol. 559, 4355. Sokolova, D., Kuzmin, V., Batyrov, A., Pivovarova, I., Tran, N.A., Dang, D., Shemanaev, K.V., 2018. Use of MLCM3 software for flash flood modeling and forecasting. J. Ecol. Eng. 19(1), 177185. Solomatine, D. P., Xue, Y., 2004. M5 model trees and neural networks: application to flood forecasting in the upper reach of the Huai River in China. J. Hydrol. Engin. 9(6), 491501. Speight, L., Cole, S.J., Moore, R.J., Pierce, C., Wright, B., Golding, B., Cranston, M., Tavendale, A., Dhondia, J., Ghimire, S., 2016. Developing surface water flood forecasting capabilities in Scotland: an operational pilot for the 2014 Commonwealth Games in Glasgow. J. Flood Risk Manag. doi: 10.1111/fr3.122281. Statistics Korea, 〈kostat.go.kr〉 (Feb. 25, 2017). Toth, E., Brath, A., Montanari, A., 2000. Comparison of short-term rainfall prediction models for real-time flood forecasting. J. Hydrol. 239(1), 132147. United States Environmental Protection Agency (2010). “Storm Water Management Model User’s Manual Version 5.0.” EPA: Washington D.C., USA. XP Solutions (2013). “XP-SWMM Stormwater and Wastewater Management Model: Getting Started Manual.” Newbury, UK. Yoon, Y.N., Jung, J.H., Ryu, J.H., 2013. Introduction of design flood estimation. J. Korea Water Resour. Assoc. 46, 5568. Young, P.C., 2002. Advances in real-time flood forecasting. Philos. Trans. Roy. Soc. A 360(1796), 14331450. Yu, W., Nakakita, E., Kim, S., Yamaguchi, K., 2018. Assessment of ensemble flood forecasting with numerical weather prediction by considering spatial shift of rainfall fields. KSCE J. Civ. Engineer. 111, doi: 10.1007/s12205-018-0407-x.
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Figures
Fig. 1. Flood forecasting process workflow for the method proposed in this study.
21
Fig. 2. Process for generating synthetic rainfall data from the Huff distribution.
22
Fig. 3. Schematic diagrams comparing flood volumes with and without flood damage.
23
Fig. 4. Conversion process workflow for using the damage functions.
24
Fig. 5. Process workflow for generating a damage graph.
25
Fig. 6. Flood forecasting concept using the damage graph.
26
Fig. 7. Process workflow for applying the real-time rainfall data to the target subarea.
27
Fig. 8. Subarea divisions and sewer networks in the target watershed.
28
Fig. 9. Process workflow for generating the damage functions.
29
Fig. 10. Damage graphs for each subarea: (a) A1, (b) A2, (c) A3, (d) A4, and (e) A5.
30
Fig. 11. Application of the damage graph in each subarea: (a) A1, (b) A2, (c) A3, (d) A4, (e) A5 and (f) Historical flood area.
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Fig. 12. Schematic diagram of the network for the new flood forecasting technique.
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Tables Table 1. Estimation of property values in residential, industrial and agricultural areas (1) Number of residences (2) Price per area (3) Number of floors (4) Construction industry deflator (5) Assessment of price Residential area Structure Contents (1)×(2)×(3)×(4) (5)×(6)×(7)
Industrial area Tangible assets (5)×(8)×(7)
(6) Number of households (7) Consumer price index (8) Number of works (9) Areas of rice paddy and dry field (10) Types of crops Agricultural area Inventories Farmland (5)×(8)×(7) (5)×(9)
33
Crops (5)×(10)
Table 2. The ratios of damage to flood depth Classification Residences Classification
Contents
Farm
Classification Crops
Depth (m) Types (%) Detached houses Apartments and buildings Depth (m) Types (%) Contents in buildings Tangible assets Inventories Farmland Tangible assets Inventories Duration (days) Depth (m) Types (%) Rice paddy (%) Field (%)
0 ~ 0.5
0.5 ~ 1.5
1.5 ~ 2.5
Over 2.5
Comments
15
40
83
100
15 / n
40 / n
83 / n
100 / n
Residential area n is the number Residential area of floors
0 ~ 0.5
0.5 ~ 1.0
1.0 ~ 2.0
2.0 ~ 3.0
Over 3.0
Areas
15
33
51
93
100
Residential area
80 60 100 79 59
100 100 100 97 90
100 100 100 100 100
Industrial area Industrial area Agricultural area Agricultural area Agricultural area
25 50 15 30 0 0 23 45 13 27 Less than 1 1 ~ 2
3~4
5~6
Over 7
Under 1.0 14 35
All duration Over 1.0
27 51
47 67
34
77 81
95 95
Areas
100 100
Areas Agricultural area Agricultural area
Table 3. Number of residence types, number of works, and property values in the study area Number of residence types Subarea Types Detached houses Apartments Buildings Number of works Subarea Types Manufacturing industry Electronic enterprise gas and water utility Waste water and environmental restoration Construction industry Wholesale business and retail trade Transportation industry Accommodation and restaurant business Broadcasting and information industry Financial and insurance business Real estate industry Science and technology services Business facilities management and business support services Public administration Educational services industry Health care and social welfare Art and leisure service Association and private service Property value Subarea Types Property value of structure (won) Property value of contents (won) Property value of tangible assets (won) Property value of inventories (won) Total property value (won)
A1
A2
A3
A4
A5
89 5 17
36 0 16
44 0 7
41 0 9
49 0 22
A1
A2
A3
A4
A5
0 0 0 0 13 0 4 0 0 0 0
0 3 0 0 18 0 38 2 4 1 11
0 1 0 0 4 1 7 0 0 0 2
0 4 1 2 24 0 18 2 2 2 222
0 1 0 0 38 0 25 0 0 0 5
0
0
0
0
1
0 0 0 0 1
1 3 10 8 3
0 0 2 2 0
2 1 8 6 1
0 3 3 3 1
A1
A2
A3
A4
A5
8,908,320,000 755,308,738 19,293,116 7,200,137 9,690,121,991
2,505,465,000 328,924,773 710,845,218 18,552,083 3,563,787,074
1,855,900,000 243,647,980 225,385,429 4,331,518 2,329,264,927
3,711,800,000 487,295,960 937,125,269 26,725,653 5,162,946,882
2,041,490,000 268,012,778 276,831,416 23,541,351 2,609,875,545
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Table 4. Initial flood damage rainfall quantities and intensities Subarea Duration (minute) 10 20 30 40 50 60
A1 Quantity (mm) 44 57 68 79 91 103
A2 Intensity Quantity (mm/hour) (mm) 264 12 171 17 136 22 118.5 28 109.2 35 103 41
A3 Intensity Quantity (mm/hour) (mm) 72 44 51 80 44 115 42 150 42 185 41 220
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A4 Intensity Quantity (mm/hour) (mm) 264 11 240 17 230 21 225 27 222 33.5 220 40
A5 Intensity Quantity (mm/hour) (mm) 66 16 51 26 42 35 40.5 43 40.2 51 40 60
Intensity (mm/hour) 96 78 70 64.5 61.2 60
Table 5. Comparison of rainfall intensities between the damage graphs and the historical rainfall data Subarea Duration (minute) 10 20 30 40 50 60
A1
A2
A3
A4
A5
Historical Damage rainfall graph intensity (mm/hour) (mm/hour) 264 18 171 30 136 49 118.5 61.5 109.2 65.4 103 62
Historical Damage rainfall graph intensity (mm/hour) (mm/hour) 72 18 51 30 44 49 42 61.5 42 65.4 41 62
Historical Damage rainfall graph intensity (mm/hour) (mm/hour) 264 18 240 30 230 49 225 61.5 222 65.4 220 62
Historical Damage rainfall graph intensity (mm/hour) (mm/hour) 66 18 51 30 42 49 40.5 61.5 40.2 65.4 40 62
Historical Damage rainfall graph intensity (mm/hour) (mm/hour) 96 18 78 30 70 49 64.5 61.5 61.2 65.4 60 62
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Highlights
Development of the new flood forecasting technique based on flood damage in urban areas.
Generation of damage graphs in each subarea by damage functions between flood volumes and damages.
Conversion from flood volumes per minute to flood damage per minute.
Determination of the initial flood damage rainfall amount for each rainfall duration.
Application of a historical rainfall event to the new flood forecasting technique.
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