Arab J Sci Eng (2016) 41:1375–1390 DOI 10.1007/s13369-015-1915-3 RESEARCH ARTICLE - CIVIL ENGINEERING Floodplain Mapping Using HEC-RAS and ArcGIS: A Case Study of Kabul River Muhammad Shahzad Khattak1 · Faizan Anwar1 · Tariq Usman Saeed2 · Mohammed Sharif3 · Khurram Sheraz1 · Anwaar Ahmed4 Received: 31 May 2014 / Accepted: 13 October 2015 / Published online: 6 November 2015 © King Fahd University of Petroleum & Minerals 2015 Abstract This paper describes the application of HECRAS model to the development of floodplain maps for the part of Kabul river that lies in Pakistan. The intent is to assist policy makers and planners in the development of flood mitigation measures for the Khyber Pakhtunkhwa Province, which experienced unprecedented floods in July/August 2010 exposing the vulnerability of the province to this natural catastrophe. Owing to its reasonable accuracy and free availability, shuttle radar topography mission digital elevation model was chosen for the extraction of geometrical data for the river. Conventional flood frequency analysis, involving log-normal, Gumbel’s, and log-Pearson type III (LP3) distributions, was used to calculate extreme flows with different return periods. Using Kolmogorov–Smirnov (KS) test, LP3 was found to be the best distribution for the Kabul River. The peak floods from frequency analysis were input into HECRAS model to find the corresponding flood levels expected along river reaches extending through Warsak dam to Attock. Results obtained with HEC-RAS model were used in combination with ArcGIS to prepare floodplain maps for different return periods. Through floodplain maps, areas that are vulnerable to flooding hazards have been identified. Analysis B Tariq Usman Saeed [email protected] Muhammad Shahzad Khattak [email protected] 1 Department of Agricultural Engineering, University of Engineering and Technology, Peshawar, Pakistan 2 Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, 47907, USA 3 Department of Civil Engineering, Jazan University, Jazan, Kingdom of Saudi Arabia 4 School of Civil and Environmental Engineering, National University of Sciences and Technology, Islamabad, Pakistan of floodplain maps indicated that more than 400 % area is likely to be inundated as compared to the normal flow of the river. Most of the area found to be vulnerable to flooding is currently used for agriculture. Comparison of simulation of 2010 flood with the image of the flood taken by MODIS clearly shows a close agreement between the two. Keywords Kabul Flood · HEC-RAS · Frequency · Modeling · 1 Introduction Floods are caused by extreme hydrometeorological actions while their evolution depends on geomorphologic agents, such as permeability and soil stability, vegetation cover, and the geometric characteristics of the river basins. Urban expansion and consolidation, changing demographic features within floodplains, changes in flood regime as a result of climate change, and human intervention in the ecological system are the major factors that lead to increased exposure of communities to flood risk [1]. The occurrence of extreme precipitation is a major impact of climate change; this leads to increase in the magnitude and frequency of extreme events such as droughts and floods [2]. Increase in the total amount, frequency and intensity of precipitation will affect the timing and magnitude of runoff, but its decrease will cause droughtlike situations [3]. It is expected that future climatic shifts would cause a great variation in the water accessibility in different regions. As a result, almost every facet of human life including agricultural productivity, wildlife and fish management, energy use, industrial and municipal water supply, and flood control would be affected [4]. As a consequence of climate change, the vulnerability of communities to floods has increased in most parts of the world, including Southeast 123 1376 Arab J Sci Eng (2016) 41:1375–1390 Asia. Khan [5] found an increase in the frequency and magnitude of the occurrence of disastrous floods in South Asia in the past 2–3 decades. Estimation of flood hazard can be carried out using methods of varying complexity depending on data, resources, and time availability [6]. The first step in the flood hazard estimation is to compute discharge for return periods of interest at any given location. To accomplish this step, frequency analysis of annual maximum discharge data may be used, although several other methods are available to estimate flood peaks corresponding to a given return period. Flood frequency analyses involve computing statistical information such as mean values, standard deviations, and skewness of the given annual maximum discharges. Frequency distribution is generated from this statistical data; this distribution gives us the likelihood of various discharges as a function of recurrence interval. Flood frequency distributions are presented in many different ways according to the equations used to conduct the statistical analyses. The Gumbel’s or extreme value type 1 (EV1) distribution, the log-normal (LN) distribution, and the log-Pearson type III distribution (LP3) are standard flood frequency distributions used by US Federal agencies such as Federal Emergency Management Agency (FEMA) and US Geological Survey (USGS) to calculate flood recurrences. Each distribution can be used to estimate design floods with their own advantages and disadvantages. According to the US Water Advisory Committee on Water Data [7], the recommended distribution for flood frequency analysis is the log-Pearson type III distribution. The second step involves the “translation” of discharges into water levels using either the rating curves or the hydrodynamic models. Rating curves are based on the relationship between the historical discharges and the corresponding water levels at any given location. These curves are developed using the flows observed over the range of water levels at a given location. Frequently this range does not cover the entire range of flows that can occur. Where flows occur outside of the gauged range, levels are estimated using a direct extrapolation of the rating equation. Also, hydrodynamic models can be used to convert discharges to water levels at any given location if the cross-sectional details are supplied as an input to the model. The final step is to determine the extent of areas likely to be inundated for discharges corresponding to different return periods. This step can be accomplished using an appropriate hydraulic model. Numerous flood estimation studies have been carried out in several parts of the world using different techniques. Ouma and Tateishi [8] describe an integrated model based on geographic information system (GIS) and analytical hierarchy process (AHP) for the assessment of urban flood vulnerability for the Eldoret Municipality in Kenya. Deng [9] used weather research and forecasting (WRF) model to examine the effect of model resolution and parameterization on the simulation 123 of flood-causing events that happened in Jeddah city. The authors found that the occurrence of extreme events is caused by mesoscale convective systems linked with strong moisture convergence. During the last few years, several flood estimation studies have been carried out in the Kingdom of Saudia Arabia (for example, [10–13]). Dawood [11] presented a comparison of curve number and rational methods, in addition to four regression models for the Makkah metropolitan area in Saudi Arabia. Based on the results of their study, these authors recommended the curve number method as an optimum method for flood estimation in Makkah. One of the most widely used models used by hydraulic engineers in the channel flow analysis and floodplain delineation is HEC-RAS [14]. It is a one-dimensional steady flow hydraulic model developed by the US Army Corps of Engineers; it has wide acceptance among government agencies. With HEC-RAS, it is possible to simulate water surface profiles for gradually varied steady flow and the effects of various obstructions such as culverts, bridges, structures in the overbank region and weirs. Several applications of HEC-RAS model to determine the extent of inundation resulting from a given flood have been reported [15–17]. Abdelbasset [18] applied HEC-RAS model to calculate water surface profiles corresponding to selected flood events downstream of Al Wahda Dam in Sebou basin in Northern Morocco. However, numerical instability issues during unsteady analyses, mainly in steep and/or highly dynamic streams and rivers, may be found. Various tools for the analysis and visualization of temporal and spatial data come from Geoinformatics [19]. Software often used in combination with HEC-RAS is ArcGIS. It comprises a set of tools, utilities, and procedures for working with geospatial data. The basic inputs to HEC-RAS include the river discharge, channel, floodplain geometry, and channel resistance. In Pakistan, floods are considered to be a major natural calamity. The Indus River and its tributaries have been a source of a long history of flooding in the country (Fig. 1). The floods of 1928, 1929, 1955, 1957, 1959, 1973, 1976, 1988, 1992, 1995, 1996, and 1997 verify their devastating nature and their adverse impact on infrastructure in Pakistan [20]. Pakistan experienced very heavy rainfall in mid-July 2010 that remained until September 2010. This resulted in the unprecedented floods affecting the entire length of the country. The 2010 floods were the worst in the history of Pakistan [21,22]. A total of 1985 fatalities and 2946 injuries were reported; around 20,251,550 people, 132,000 km2 area, and 1,894,530 houses were affected; 78 out of 141 districts and 2.1 million hectares of cultivated land were damaged; a large number of livestock was perished; 515 health and 10,436 educational facilities were either destroyed or severely damaged. The overall recovery and reconstruction cost as estimated by the World Bank and Asian Develop- Arab J Sci Eng (2016) 41:1375–1390 1377 Fig. 1 Location map of the Indus Basin (Kabul River located at the north-west) ment Bank is of the order of US $ 8.74 to US$ 10.85 billion [23]. Several studies have been carried out in Pakistan to assess the impact of floods. Khan [24] estimated a maximum instantaneous flow of 8205 m3 s−1 having a return period of 1000 years for Kabul River at Nowshera bridge gauging station using Gumbel’s distribution. Hussain [25] conducted a study to determine the extent of flooded area in the Peshawar Valley (Peshawar, Charsadda, and Nowshera districts) of the KP province of Pakistan using Landsat-7 satellite imagery. Kwak [26] estimated the number of people affected and the damage caused by the 2010 flooding using Advanced Land Observing Satellite (ALOS) images and predicted the future risks for the Kabul River. Sayama [27] applied the rainfall–runoff–inundation (RRI) model to simulate 2010 rainfall–runoff–inundation in the Kabul River basin. Ushiyama [28] applied Lagged ensemble rainfall– 123 1378 Arab J Sci Eng (2016) 41:1375–1390 runoff–inundation (RRI) forecasting to the devastating flood of 2010 in the Kabul River basin. Downscaled forecasts made by Ushiyama [28] predicted rainfall amounts with lead times of 1 day and 3-day reasonably well. Given the fury of the floods that Pakistan faces almost each year, there is an urgent need to develop flood mitigation measures in the region. Non-structural flood mitigation steps such as flood forecasting, early warning systems, and awareness programs among the communities likely can be very effective in reducing direct and indirect damages caused by floods [29]. Flood hazard assessment using hydrological models and geographic information system (GIS) can be a valuable tool for the study of historical events. Furthermore, digital flood risk maps for important rivers in Pakistan are not available for any historical events. Therefore, the major focus of the present paper is on the application of HEC-RAS, in combination with ArcGIS, to the development of floodplain maps for the part of Kabul river that lies in Pakistan. The aim is to reduce the exposure of the Khyber Pakhtunwa Province, which experienced unprecedented floods in July/August 2010, to future floods. The implementation of the flood management program based on floodplain maps shall contribute toward a significant reduction in damages to the inhabitants of Peshawar, Charsadda, and Nowshera districts of the Khyber Pakhtunwa Province. The results of the research presented herein have the potential to assist planners and policy makers to develop effective flood management strategies in the region. 2 Description of Study Area The Rivers Indus and Kabul are the two major tributaries passing through the KP province of Pakistan (Fig. 2). The Indus River is one of the world’s longest rivers, having a length of 2900 km and a catchment area of approximately 1.16 million km2 [30]. The Kabul River originates at the base of the Unai Pass in Afghanistan and flows eastward toward Kabul city while enters Pakistan through the hills of Mohmand Agency where its flow is first gauged at Warsak dam. The River has a length of 700 km and a catchment area up to Warsak dam of 8068 km2 . The Kabul River passes through Nowshera and then meets the Indus River at Attock. The river is joined by the Swat River near Charsadda, which drains the Swat and Dir districts—the latter through its tributary Panjkora River The Kabul River basin has complex geologic conditions. Sedimentary limestone and shale are predominant in most of the lower part, whereas the headwaters of the main tributaries rise among very complicated sets of igneous and metamorphic rocks. The drainage area comprises furrowed mountains varying in heights from 300 m above sea level (masl) to more than 6000 m asl with a steady general rise from South to North 123 [31]. Hydrometeorology in the basin is defined by highly complex biophysical environments produced by interaction between geology, terrain, and meteorology. The winter precipitation in the upper basin is mostly observed as snow. The rainfall is mostly concentrated in the middle and lower part of the basin. Several complex mechanisms are responsible for flow generation in Kabul River. It is comprised of contributions from rain, snow, and glaciers; the contribution of each component keeps on varying with the time of the year. During July to September, the river flow is augmented by monsoonal rainfall producing relatively higher discharges in the river. Most often the higher discharges are seen in the month of July and August, and these are mainly due to heavy rain in the middle and lower part of the basin. Glacier-melt contribution is the highest during the months of July and August. Glacier-melt contribution from the upper part of the basin combined with rainfall in the lower part is the most likely cause of flooding in the region. Only the part of Kabul River that lies in the KP province of Pakistan is considered in this study. Of the three branches of the Kabul River, namely Naguman, Shah Alam, and Haji Zai, only the latter branch is used for floodplain mapping. These branches are shown in Fig. 2. 3 Data Used Digital elevation models (DEM) for the entire globe, covering all of the countries of the world, are available from CSI (Consortium for spatial information) Web site. These data were derived using shuttle radar topography mission (SRTM)—an international effort that generated the most complete highresolution digital topographic database of Earth [32]. SRTM consisted of a specially modified radar system that stayed flying on board the Space Shuttle Endeavor during an 11-day mission in February of 2000. Several studies (for example, [33–36]) have employed SRTM data. Owing to its reasonable accuracy and free-of-cost availability, a 90 m × 90 m DEM, which is available from shuttle radar topography mission (Fig. 3), has been used to delineate the boundary of the watershed, to define sub-catchment boundaries and a stream network and to analyze the drainage patterns of the terrain. Using the SRTM-DEM, the river and floodplain geometry was obtained. Colored images from Google Earth were used to classify the land uses, and then theses classes are used for the estimation of Manning’s n values, which were needed by HEC-RAS for performing hydraulic computations. The hydrometeorological data used in this study were obtained from the Water and Power Development Authority (WAPDA), Pakistan. Discharge data for an adequate number of years are available at two gauging stations: Warsak dam and Nowshera. The maximum instantaneous discharge data at Warsak dam were available from 1965 to 1970, and from Arab J Sci Eng (2016) 41:1375–1390 1379 Fig. 2 Kabul River and its branches in Khyber Pakhtunkhwa province, Pakistan 2005 to 2010. At Warsak dam, the daily mean discharge was available, without any gaps, for the period 1965–2010. Since the maximum instantaneous discharge data at Warsak dam were not available for the entire period of record, the missing values were generated by developing a linear regression model. Twelve pairs of maximum daily and maximum instantaneous discharge data at Warsak dam were used to develop the linear regression model. The derived linear relationship between the maximum instantaneous and maximum daily discharge is given by Eq. (1) and is shown graphically in Fig. 4. The correlation coefficient (r) between the maximum instantaneous and daily maximum flow was found to be 0.94. Q p = 1.238 Q M − 305.47 (1) 123 1380 Arab J Sci Eng (2016) 41:1375–1390 5000 -1 where Instantaneous Disharge (m .s ) Fig. 3 SRTM-DEM of the Kabul River catchment 3 Qp = 1.238QM - 305.48 3 −1 Q p = maximum instantaneous discharge (m s 3 −1 Q M = maximum daily discharge (m s ) ) At Nowshera, the maximum instantaneous discharge data as well as the mean daily discharge are available for the period 1962–2010 (49 years). The values of the generated maximum instantaneous discharges at Warsak dam are shown in Table 1. The entire reach of the river between Warsak dam and Attock was divided into the upper and the lower reaches. The start and end nodes for the upper reach were Warsak dam and the junction of Kabul and Swat Rivers at Charsadda, respectively. The start node of the lower reach corresponds to the end node of the upper reach, whereas the end node of the lower reach was assumed to be at Attock. Assuming steady state conditions, the flow data at Warsak dam were used for the entire upper reach. The discharge at Nowshera was assumed to flow under steady state conditions over the entire lower reach. 4 Methodology The objective of the present study was to develop floodplain maps for the segment of the Kabul River that lies in 123 4000 2 R = 0.8817 3000 2000 1000 0 0 500 1000 1500 2000 2500 3000 3 3500 4000 4500 5000 -1 Daily Discharge (m .s ) Fig. 4 Relationship between maximum daily and maximum instantaneous discharge for the Kabul River at Warsak dam Pakistan. The methodology employed (Fig. 5) to develop floodplain maps comprised of the following steps: (1) flood frequency analysis of the available observed discharge data to obtain floods corresponding to different return periods; (2) preparation of DEM based on SRTM data; (3) delineation of watershed and drainage network using HEC-GeoRAS; (4) preparation of geometric data using HEC-GeoHMS; (5) application of HEC-RAS to a number of potential flow scenarios corresponding to different return-period floods; (6) preparation of floodplain maps Using GIS. To estimate water surface profiles and extent of inundation under different flood intensities, peak discharges for different return periods are required. Flood frequency analysis [37] was carried out to obtain flood peaks for different return Arab J Sci Eng (2016) 41:1375–1390 Table 1 Maximum instantaneous discharge (Q M ) and daily maximum (Q P ) values for Warsak dam and Nowshera bridge gauging stations (m3 s−1 ) 1381 S.No. Year 1 1962 2 1963 3 Nowshera (Q M ) Warsak (Q M ) Warsak (Q P ) 3257 NA NA 4079 NA NA 1964 3852 NA NA 4 1965 6316 4276 2918 5 1966 4588 3285 3121 6 1967 5042 3681 2981 7 1968 4050 3030 2682 8 1969 3031 2271 2170 9 1970 1983 1563 1563 10 1971 2325 1618 1554 11 1972 3937 2911 2598 12 1973 3625 2780 2492 13 1974 2543 1600 1539 14 1975 4843 2537 2296 15 1976 3172 2114 1954 16 1977 2974 3279 2895 17 1978 4758 4642 3996 18 1979 3016 2497 2264 19 1980 2974 2048 1901 20 1981 3342 2402 2187 21 1982 2218 1337 1327 22 1983 2917 2250 2064 23 1984 3229 2661 2396 24 1985 2861 1500 1458 25 1986 3116 2224 2043 26 1987 2713 1976 1843 27 1988 3739 1854 1744 28 1989 3016 1579 1522 29 1990 3368 2553 2309 30 1991 4276 3239 2863 31 1992 3856 2870 2565 32 1993 3496 1838 1731 33 1994 3519 2500 2266 34 1995 4293 2162 2162 35 1996 3615 2435 2435 36 1997 3396 2126 2126 37 1998 3658 2256 2256 38 1999 2149 1432 1432 39 2000 1427 977 977 40 2001 2378 1412 1412 41 2002 2787 1692 1692 42 2003 2677 2008 2008 43 2004 2041 1634 1634 44 2005 4775 3190 3114 45 2006 4451 1398 1364 46 2007 4083 3171 3044 123 1382 Arab J Sci Eng (2016) 41:1375–1390 Table 1 continued S.No. Year Nowshera (Q M ) Warsak (Q M ) Warsak (Q P ) 47 2008 3483 1689 1635 48 2009 3141 2062 1960 49 2010 12707 4324 3825 Q M at Warsak from S.No. 10 to 43 is calculated from the developed regression equation, while the remaining is observed value. The text NA stands for not available data Fig. 5 Methodology flowchart for HEC-RAS modeling periods at Warsak dam and Nowshera bridge. Three commonly used frequency distribution functions for estimation of extreme floods, namely log-Pearson type III distribution [38], Gumbel’s or extreme value distribution [39,40], and log-normal distribution [41,42], were used for frequency analysis. The detailed description of each of the methods is given in Chow [37]. To determine the best fit distribution for the estimation of flood peaks, Kolmogorov–Smirnov (KS) test having a confidence interval of 95 % was used. The procedure of KS test is given in McCuen [43]. The maximum instantaneous observed discharge data of Nowshera and observed and generated data at Warsak dam were used for flood frequency analysis (Table 1). The flood peaks for different return periods were obtained using LP3 and used as an input to HEC-RAS model. HEC-Geo HMS was used to extract basin characteristics, including the river centerline, from DEM. The flow and geometric data such as the bank stations, cross sections, and flow pathlines were prepared using HEC-GeoRAS, and later used 123 as an input to HEC-RAS. The files required in preparing the geometric data were DEM and georeferenced natural color images of the study area. Google Earth was used to acquire the natural color images, and visual inspection method was used to estimate the Manning’s n values. Based upon the type of land cover, the entire area was classified into four groups as given in Table 2. For each group of land cover, separate polygons were drawn, and values of Manning’s roughness coefficient “n” were determined for each polygon using the method suggested by McCuen [44] and Chow [37]. Phillips [45] recommended Manning’s n values of 0.15 each for both single- and double-story dwellings. In this study, n value of 0.15 was used. After assigning n values to each land class and completing all geometric data requirements, RAS GIS Import File was imported into HEC-RAS. This procedure allows the geometric data transfer from ArcGIS into HEC-RAS. The next step was to supply the steady flow data along with boundary conditions to the steady flow editor. Two stations were selected Arab J Sci Eng (2016) 41:1375–1390 1383 Table 2 Land use classes and their estimated Manning’s n values Table 3 Maximum instantaneous discharge for different return periods by LN, Gumbel’s, and LP3 distribution (m3 s−1 ) for Warsak S. No. Land use Manning’s ‘n’ 1. Channel 0.0305 2. Urban 0.15 3. Hilly 0.045 LN 3538 4605 4. Cultivated 0.035 Gumbel’s 3482 4534 LP3 3512 4448 4821 for flow inputs; first station was at some distance downstream of Warsak dam, and the second at the junction of the Kabul and Swat Rivers. The peak flows are input into HEC-RAS to find the expected flood levels along river reaches extending through populated areas of the basin. Flow inputs were supplied for 10-, 50-, 100-, 200-, 500-, and 1000-year flood along with maximum instantaneous discharge value of 2010 flood in Kabul River. Upon completion of data inputs, the HECRAS model was executed to perform a detailed analysis of the flow. A detailed report of the analysis showing the depth of flow, discharge at each cross section, and other details was generated by the model. After removing all errors, the results were exported to ArcGIS in the form of RAS GIS Export File. The RAS GIS Export File was imported into ArcGIS after generating water surface and floodplain delineation, and a raw floodplain map was obtained. This raw map only showed the extents of flood and some islands/reservoirs, so these were removed and a smooth water surface was obtained. The MODIS satellite images of 2010 Kabul River flood captured on July 31, 2010, has been analyzed by the UNITAR Operational Satellite Applications Programme (UNOSAT). This image was used to assess the flood extent and damages over a wide area in Peshwar Valley. A comparison was made between the flood extent simulated by our model and that shown by the MODIS satellite image. 5 Results and Discussion The analysis of the extent of the area inundated under different return-period floods is driven by the peak flows obtained from frequency analysis conducted using the maximum instantaneous discharge available at Warsak dam and Nowshera bridge. The maximum instantaneous discharges at Warsak dam and Nowshera bridge for different periods were obtained using LN, Gumbel’s, and LP3 distributions, and are shown in Tables 3 and 4, respectively. A comparison of these discharge values at Warsak dam and Nowshera obtained using different distributions is presented in Figs. 6 and 7, respectively. As can be seen from Fig. 6, the predicted peak flood, using LN distribution, at Warsak dam is higher than that predicted by the Gumbel’s and LP3 distribution. The smallest values were produced using the LP3 distribution. At Station Warsak Return period 10 50 100 200 500 1000 5053 5502 6099 6557 4979 5422 6006 6448 5182 5648 5993 Table 4 Maximum instantaneous discharge for different return periods by LN, Gumbel’s, and LP3 distribution (m3 s−1 ) for Nowshera Station Nowshera bridge Return period 10 50 100 200 500 1000 LN 5223 6773 7423 8073 8937 9597 Gumbel’s 5714 7788 8664 9537 10,689 11,560 LP3 5317 7845 9160 10,645 12,915 14,902 Nowshera bridge (Fig. 7), the highest values were produced by LP3 distribution and the lowest by the LN distribution. The maximum instantaneous discharge values at Warsak dam estimated by LP3 distribution for return periods of 10, 50, 100, 200, 500 and 1000 years were 3512, 4448, 4821, 5182, 5648, and 5993 m3 s−1 , respectively. The corresponding values for Nowshera bridge as estimated by LP3 distribution were 5317, 7845, 9160, 10,645, 12,915, and 14,902 m3 s−1 . Khan [24] estimated a maximum instantaneous flow of 8205 m3 s−1 having a return period of 1000-years for Kabul River at Nowshera bridge station using Gumbel’s distribution. For each of the three distributions used in the frequency analysis, higher discharge values were produced than those reported by Khan [24]. Table 5 contains the results of the K–S test for Nowshera bridge gauging stations and Warsak dam. The K–S test is a nonparametric test that can serve as a goodness-of-fit test. The critical value of K–S test statistic ‘D’ for Warsak dam and the Nowshera bridge station was 0.196 and 0.194, respectively. Since the values of the test statistic for each of the three distributions are less than the critical value, all the distributions used in this study are considered to be fit for use at both the gauging stations. Based upon the values of K–S statics, LP3 distribution was used for the estimation of flood peaks for both Warsak dam and Nowshera bridge. Maximum instantaneous flows derived from LP3 distribution for the return periods of 10, 50, 100, 200, 500, and 1000 years and 2010 floods were used as steady flow inputs in HEC-RAS at two sections. The first section was at some distance downstream of Warsak dam where the topography was relatively plain compared to the sections upstream, whereas the second section was at the junction of Kabul and Swat Rivers. A comparison of water surface profiles at 123 1384 Arab J Sci Eng (2016) 41:1375–1390 Fig. 6 Discharges at Warsak dam obtained using different distributions Fig. 7 Discharges at Nowshera bridge obtained using different distributions Table 5 K–S test results for the Warsak dam and Nowshera bridge Distribution Warsak dam Test statistic (D) Fit Ranking Test statistic (D) Fit Ranking LN 0.060 Yes 2 0.124 Yes 3 Gumbel’s 0.068 Yes 3 0.057 Yes 2 LP3 0.052 Yes 1 0.046 Yes 1 Warsak dam under 10-, 100-, and 1000-year return-period floods and under 2010 flood is shown in Fig. 8. As illustrated by Fig. 8, both the 100-year and 1000-year return periods produce higher water levels than that produced by the 2010 flood. The water surface elevations produced at the junction of Charsadda and Nowshera districts under 10-, 100-, and 1000-year return-period floods and under 2010 floods are shown in Fig. 9. The water surface elevations produced by 2010 flood are higher than that produced by 100year return-period flood, thus indicating the severity of the 2010 floods. However, the levels were lower than 1000-year return-period flood. Figure 10 shows water surface elevations 123 Nowshera bridge near Nowshera bridge under different return-period floods. Comparison of water surface elevations clearly indicates the severity of the 2010 flood at the Nowshera bridge as the levels are found to be higher than the 100-year return-period flood at the bridge site. The Kabul River runs through steeper slopes in the upper reaches, thereby leading to smaller depths of flows. When the river passes through the plains (Charsadda and Nowshera), the river becomes slower as compared to the upstream with the higher depth of flow. Flow profiles for different return floods and for 2010 flood are shown in Figs. 11 and 12 for the upper and the lower reach, respectively. As expected, at all the sections in the upper and Arab J Sci Eng (2016) 41:1375–1390 1385 Fig. 8 Water surface elevations at some distance downstream of Warsak dam under different floods D/S Warsak Dam .045 .0305 .035 348 Legend WS 1000Y WS 100Y 346 WS 2010_FL WS 10Y Elevation (m) Ground 344 Bank Sta 342 340 338 200 400 600 800 1000 1200 Station (m) Fig. 9 Water surface elevations at the junction of Peshawar, Charsadda and Nowshera districts under different floods Districts' Junction .035 .0305 .035 306 Legend WS 1000Y 304 WS 2010_FL WS 100Y 302 WS 10Y Elevation (m) 300 Ground Bank Sta 298 296 294 292 290 288 0 2000 4000 6000 8000 10000 12000 14000 Station (m) Fig. 10 Water surface elevations near Nowshera bridge under different floods Nows hera Bridge .15 .035 .15 .0305 .15 296 Legend WS 1000Y 294 WS 2010_FL WS 100Y WS 10Y Elevation (m) 292 Ground Bank Sta 290 288 286 284 1000 2000 3000 4000 5000 6000 Station (m) the lower reach, the water surface elevation under 2010 flood is higher than that under 100-year return-period flood. 5.1 Floodplain Maps Floodplain maps were produced using elevation, land cover and geological, physiographic, and basin network data. Water surface profile data were extracted from HEC-RAS through HEC-GeoRAS and then were incorporated into a floodplain map through GIS. Using the water surface data and DEM created for the basin, the flooded area under different returnperiod floods was delineated. Although floodplain maps were prepared for various return periods, namely 10, 50, 100, 200, 500, and 1000 years, but only the 100-year floodplain maps are presented. For presenting floodplain maps, the entire reach was divided into the upper and the lower reaches, and separate floodplain maps were presented for each reach. Figures 13 and 14 show the areas that are likely to be inundated 123 1386 Arab J Sci Eng (2016) 41:1375–1390 Fig. 11 Water surface profile under different floods for the upper reach (Warsak dam to junction of Kabul and Swat River) 350 Legend 345 WS 1000Y WS 2010_FL 340 WS 100Y 335 WS 10Y Ground 330 Elevation (m) 325 320 315 310 305 D/S Warsak Dam 300 295 290 285 60 65 70 75 80 85 90 95 Main Channel Distance (km) Fig. 12 Water surface profile under different floods for the lower reach (Kabul and Swat River Junction to Kabul and Indus River Junction) 300 Legend WS 1000Y WS 2010_FL 295 WS 100Y WS 10Y Ground Elevation (m) 290 285 Kabul and Swat Junction 275 Districts' Junction Nowshera Bridge 280 270 0 10 20 30 40 50 60 70 Main Channel Dis tance (km ) Table 6 Percentage area inundated for different return periods relative to Normal flow Return period Normal flow Percentage w.r.t normal flow (%) 64.24 100 10-Year 252.00 392 50-Year 266.28 415 100-Year 270.09 420 200-Year 271.86 423 500-Year 275.26 428 1000-Year 279.02 434 under 100-year return-period flood in the upper and the lower reach, respectively. Each map shows contour lines at 20-m interval as smaller intervals tend to make the maps crowded and difficult to understand. The details shown by the floodplain maps include area inundated under normal flow, flood extent for the given return period, district boundaries, names of different villages, towns and roads in close proximity to floodplain, and gauging stations. The area inundated under normal flow refers to the area occupied in between the banks and is delineated using areal images. It can be observed from Figs. 11 and 12 that the slope of Kabul River in the upper reach is about 1 in 600, which 123 Area affected (km2 ) is steep as compared to the slope of the lower reach, which is about 1 in 4200. Consequently, the inundated area and depth of flow in the upper reach is relatively less as compared to the lower reach. The maximum affected area lies at the junction of Peshawar, Charsadda, and Nowshera districts. It can be seen from Table 6 that more than 400 % area is predicted to be inundated under 100-year return period. Results of simulation under 100-year return-period flood indicate that the most severely affected villages in Peshwar district are Bela momandan, Banda Farid, Bunyadi Kalyay, Agra Payan and Kudi Kalay (Fig. 13), whereas the areas most severely affected villages in Charsadda district are Sardaryab, Mohib Arab J Sci Eng (2016) 41:1375–1390 1387 Fig. 13 100-year floodplain map for the Kabul River in Peshawar, Charsadda, and Nowshera districts Fig. 14 100-year floodplain map for the Kabul River in Charsadda and Nowshera districts Banda, Paxton Gari, and Mohala Khera Khel (Fig. 14). The villages that are most affected in Nowshera districts are Pir Piai, Mohabat Khel, and Peer Sabaq. It can be seen from Fig. 15 that most of the areas shown to be affected by the simulated 100-year return-period floods were also affected by 2010 historical floods. The majority of the inundated areas as shown in Fig. 15 are currently used for cultivation purposes, while the inundated areas in district Nowshera mostly comprise of urban areas. Since flood is a wave process, the satellite images showing inundated areas may relate to images taken during the flood at different times. A MODIS satellite images of 2010 123 1388 Arab J Sci Eng (2016) 41:1375–1390 Fig. 15 Comparison between simulated and actual (MODIS image taken on July 31, 2010) flood extents Kabul River flood captured on July 31, 2010, were used in this study to assess the reliability of the HEC-RAS model in simulating floods of a given magnitude. Although the time of taking satellite image may not necessarily coincide with the time of flood peak or maximum inundated area, but fields visits and surveys conducted in the basin confirm that the peak flood occurred around July 31, 2010. Therefore, a comparison of the extent of inundated areas shown by MODIS image and that simulated by the HEC-RAS model for 2010 flood was made. Figure 15 shows the extent of inundation shown by MODIS image and by the model. The floodplain map prepared by using GIS and HEC-RAS for 2010 flood matched closely to those reported by Hussain [25] and Kwak [26] using different satellite images. The boundaries of the inundated areas simulated by our model showed close agreement to the MODIS image of July 31, 2010, but there were small deviations at some places. These deviations could be attributed to the manner in which the water surface is generated by ArcGIS. The water surface produced by ArcGIS had several islands and reservoirs, probably due to the reason that the elevations in the DEM had an interval of 1 m, whereas the water surface assumed continuous values of elevations. Overall, the performance of HEC-RAS model in producing 2010 flood inundation maps is quite promising, considering that channel resistance was based on very preliminary values and no calibrating adjustments were made. Calibration of channel roughness could possibly improve the accuracy of the model. Review of literature presented in this paper clearly indicated that a large number of different techniques have been 123 employed by researchers for the simulation of flooding events. However, due to the highly complex nature of meteorological and hydrological processes, it is not possible to forecast extreme precipitation events that lead to catastrophic floods. In developing countries such as Pakistan, humans are particularly vulnerable to flooding because of high population density, absence of adequate flood control measures, and lack of zoning regulations and emergency preparedness systems. Industrialized countries are equipped with better flood control measures. However, instances of flood-related casualties have been reported from industrialized countries as well because it is not possible to provide absolute protection against floods. Moreover, global climate change has given an impetus to studies aimed at improving flood forecasting procedures, and reduction in damages caused by floods. 6 Conclusions The major focus of this study was on assessing the suitability of HEC-RAS model in simulating water surface profiles and in determining the extent of inundation under different return-period floods for Kabul River in Pakistan. It involved routing the flood by making use of natural channel geometry in the sub-reaches whererin approximate geometry between these sites and flood levels is required. Analysis carried out using Kolmogorov–Smirnov test indicated that LP3 is the best fit distribution for both Warsak dam and Nowshera bridge gauging station on River Kabul. Using LP3 distribution, the values of 10-, 50-, 100-, 200-, 500-, and 1000-year Arab J Sci Eng (2016) 41:1375–1390 return-period floods at Warsak dam were found to be 3512, 4448, 4821, 5182, 5648 and 5993 m3 s−1 , respectively. At Nowshera, the corresponding values were 5317, 7845, 9160, 10,645, 12,915, and 14,902 m3 s−1 , respectively. Using the HEC - RAS model, in combination of ArcGIS, an analysis of the extent of areas likely to be inundated under different return-period floods was carried out. Analysis of results clearly indicated that the 100-year return-period flood at Warsak (4821 m3 s−1 ) would inundate 400 % more area than the normal flow. The extent of inundation with 100-year return-period flood at Nowshera (9160 m3 s−1 ) would be around the same magnitude. Floodplain maps showed that areas prone to flooding are mostly used for cultivation. Urban areas such as those of Nowshera are also vulnerable to flooding, which was also evident in July/August 2010 floods. The extent of inundation shown by the satellite image of the 2010 flood was very well reproduced when the 2010 flood was input into the HEC-RAS model, thereby demonstrating the capability of the model to simulate open water floods and produce water levels at the desired locations with reasonable accuracy. It is clearly visualized from the floodplain maps that with one in 100-year return-period flood, the levels of inundation are roughly four times that due to normal flow. Therefore, it is extremely important to provide protection to cities such as Charsadda and Nowshera on both banks of the river primarily through raising of embankments. The reduction in the magnitude of damages could be achieved through de-silting at vulnerable reaches on the River. During the field visits, it was noticed that certain sections of the marginal embankment need repairing as the villagers have cut these at several locations and constructed passages for their cattle and tractors. This has resulted in the weakening of embankment structures. Repair and maintenance of existing embankments at regular intervals should be at the top of the agenda for the governmental agencies entrusted with the responsibility of river maintenance. Another associated problem with floodplain management in the basin is its encroachment by the people. In recent years, there has been increased encroachment of floodplains because of development and population pressures in the basin. To ensure protection of people and property from flooding, the floodplains shall remain preserved in or restored to an undeveloped natural state. Keeping the floodplain free from encroachment can reduce flood damages significantly and provide recreation benefits through the development of parks and other recreational facilities. The results of this case study indicate that the simulation of flood levels for a given floods can easily be performed using the HEC-RAS —a public domain model. Application of the model to simulate floods need not require the expensive acquisition of channel geometry data between cities. Employing the approach presented herein would enable the government agencies in Pakistan to achieve significant reduc- 1389 tion in flood damages in the Kabul River basin. Though unsteady flow hydraulic models is known for being difficult to apply, it is not surprising that water system managers and planners are questioning about the value of their operational use. However, many agencies in Pakistan already have familiarity with HEC-RAS models that can be effectively utilized to improve and simplify the forecasts of areas likely to be inundated under a given flood. As it is a quasi-hydrodynamic model, preparing input data and their reliability are critical. The value of resistance parameter, namely Manning’s “n”, keeps on varying with water level. This value decreases with increasing water level as the effective relative roughness diminishes and then increases again as the flow spills overbank, as the channel roughness is lower than floodplain roughness [46]. Although the HEC-RAS model was found to perform well in the absence of calibration of Manning’s n, it is worth investigating the sensitivity of model performance to this parameter in future works. Acknowledgments We would like to acknowledge all the guiding studies that helped us in finishing this research. Any use of trade, product, or firm name is for descriptive purposes only. References 1. Dang, N.M.; Babel, M.S.; Luong, H.T.: Evaluation of floods risk parameters in the day river flood diversion area, Red River Delta, Vietnam. J. Nat. Hazard 56, 169–194 (2011) 2. Intergovernmental Panel on Climate Change (IPCC): Impacts, adaptation and vulnerability. Asia climate change 2007. In: Parry, M.L.; Canziani, O.F.; Palutikof, J.P.; van der Linden, P.J.; Hanson, C.E. (eds.) 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