Flood modeling and simulations using hydrodynamic ...

Arab J Geosci (2016) 9:439 DOI 10.1007/s12517-016-2457-z

ORIGINAL PAPER

Flood modeling and simulations using hydrodynamic model and ASTER DEM—A case study of Kalpani River Sana Ullah 1 & Muhammad Farooq 2 & Tahir Sarwar 3 & Mohammad Javed Tareen 1 & Mirza Abdul Wahid 1

Received: 7 September 2015 / Accepted: 30 March 2016 # Saudi Society for Geosciences 2016

Abstract The study was conducted to model a 30-km section of the Kalpani River for flood inundation forecasting with the help of remote sensing, geographical information system (GIS), HEC-RAS (1D), and HEC-Geo RAS. 3D coordinates were extracted from ASTER 30 m digital elevation model (DEM). Model was simulated and calibrated for flood 2010 event of 1901 m3 s−1 (upstream) and 3361 m3 s−1 (downstream) on two parameters, i.e., contraction and expansion coefficients and Manning’s “n” values derived from the landuse map of the study area with the known water surface boundary condition. The average difference between known and computed water surface for model’s pre-calibration was 0.64 m, which was reduced in calibration up to 0.11 m. Calibrated model was then validated for flood 2006 event with known discharges, i.e., 1951 m3 s−1 (upstream) and 2285 m3 s−1 (downstream) with an average difference of 0.10 m between known and computed water surface. Calibrated model was further simulated with critical depth boundary conditions instead of known water surface boundary condition, which confirmed the earlier results of the same study. The model results revealed that the Kalpani River, passing through a flat topography of Mardan, with an average slope of 0.000786 m m−1 had a strong positive correlation coefficient of R2 0.999 and 0.996 between the known and computed water surfaces for calibration and validation, respectively. Finally, the model was simulated for 5-, 10-, 20-, 50- and * Sana Ullah [email protected]

1

Agriculture Research Institute, Saryab Road, Quetta, Pakistan

2

Pakistan Space and Upper Atmosphere Research Commission (SUPARCO), Peshawar, Pakistan

3

Department of Water Management, The University of Agriculture, Peshawar , 25130, Pakistan

100-year return periods with critical depth boundary condition. The risk maps of the study could not be produced owing to the coarser resolution of the DEM followed by area flatness, and river width flood inundation risk factors. It is therefore, suggested that the HEC-RAS model can be used for flood risk management and as decision support tool in the Kalpani River catchment. Keywords ASTER . HEC-RAS . HEC-GeoRAS . RD . DEM . Cross section

Introduction Flood modeling is practically a new practice; attempts to understand and deal with the mechanism at work in floodplains have been made for at least six millennia (Dyhouse et al. 2003). The new developments in computational flood modeling have made the engineers able to step away from the tried and tested approach and its inclination to promote overly engineered structures and to redesign them properly using river hydraulics models (Mehta et al. 2014). For a better management of the hydraulic system, the knowledge of flow dynamic is required (Traore et al. 2015). With the advent of modern technology, the use of sophisticated softwares in flood modeling helps in getting an idea of extent of flood at its submergence (Kute et al. 2014). Flood effects determined by computer model generally require four things, i.e., hydrologic model, which develops rainfall-runoff from designed storm or historical storm event; the hydraulic model, which routes the runoff through stream channels to determine water surface profile at specific locations along the stream network; a tool for floodplain mapping and visualization; and the extraction of geospatial data for use in models (Snead and Baldwin 2000). Spatially explicit hydrodynamic

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flood models can take part in risk reduction of natural hazards (Zerger and Wealands 2004). Farooq (2007) tested hydrodynamic model for flood inundation forecasting with the use of Manning roughness coefficient and channel contraction and expansion coefficient with high resolution dataset to determine the inundation extent. The integration of geographical information system (GIS) and hydrodynamic model is a well-organized way to get flood inundation for emergency planning and degree of risk concern to local community (Yang et al. 2002). The advancement in GIS technology and improved graphical computer interface for the output of flood inundation through computer models provides easy access to stakeholder community, and therefore, predictions made for flood risk management from such models can be regularly used as a tool for communication between the engineers and stakeholders (Pender and Neelz 2007), and these computer-based flood models which are user friendly can also provide an easy mean of understanding, visualization, and building query, conducting repetitive and multiple analytical tasks through graphic user interface for the model output in the GIS environment as an effective tool for flood risk analysis and flood mapping (Sinnakaudan and Abu Bakar 2005; Farooq 2007; Khattak et al. 2015). Patro et al. (2009) successfully performed flood inundation simulation by coupling of 1D and 2D Mike Flood hydrodynamic model during the year of 2001, and thus, its inundation extent simulated by the model was compared with the actual inundation area extracted from IRS-ID WIFS imagery. Mike 11 GIS is specifically tailored for presenting and analyzing model output which provides a unique tool for flood mapping and flood impact assessment (Corria et al. 1998). A key aspect of these flood models is the ability to provide time series inundation about the beginning, interval, and passing of a hazard event which makes them appropriate for risk reduction management (Zerger and Wealand 2004).

Material and methods Study area Kalpani River is a major river with a catchment area of about 2813 km2 that passes through the district Mardan, and it was modeled using HEC-RAS 1D Model. The Kalpani River is 70 km long out of which 30 km was selected for the present study and it flows from North to South. Floods in the Kalpani River occur more often and cause substantial losses to the lives and properties of the dwellers of the area. Precipitation in the catchment area ranges from 350 mm in the North West of Mardan District to 750 mm in Malakand Agency, and it generates high surface runoff, thereby causing the ravaging of Mardan City along with its suburbs resulting in the inundation of thousands of acres of

fertile lands (Govt. of Khyber Pakhtunkhwa, 2005). The floods in 1973, 1975, 1976, 1978, 1997, 2005, 2006, 2008, 2009, and 2010 caused severe damages to human lives, houses, livestock, infrastructure, and communication. HEC-RAS model HEC-RAS model is a one-dimensional hydraulic model which measures the water surface along the stream channel. The system consist of a graphical user interface (GUI) with separate components for hydraulic analysis along with data management, storage capabilities, and graphical and reporting facilities. The HEC-RAS system provides four different analysis facilities, i.e., steady and unsteady flow computations, movable boundary with sediment transport computation, and water quality analysis. A common feature between all four types of analysis is the use of geometric inputs and hydraulic computation routines. HEC-RAS calculates water surface for each cross section. Water depth is calculated between cross sections by interpolating the water surface at upstream and downstream cross sections by using energy Eq. 1 with an iterative process called standard step method (Hydraulic Engineering Center (HEC), 2010a). Z2 þ Y 2 þ

α2 V 22 α1 V 21 ¼ Z1 þ Y 1 þ þ he 2g 2g

ð1Þ

where Z1 and Z2 are the elevations of the main channel inverts, Y1 and Y2 are the depths of water at each cross section, V1 and V2 are the average velocities (total discharge/total flow area), a1 and a2 are the velocity weight coefficients, g is the gravitational acceleration, and he is the energy head loss between the two cross sections. The energy head loss between the two cross sections is calculated from Eq. 2 ((Hydraulic Engineering Center (HEC), 2010a). α2 V 22 α1 V 21 he ¼ LS f þ C − ð2Þ 2g 2g Energy equation is valid for gradually varied flow conditions when the water surface passes through the critical depth. Then, this equation cannot be used for computation. Flow regime changes due to changes in channel slope, bridges, drop structures, and stream junctions. The transformation from subcritical to supercritical or supercritical to subcritical is a condition of rapidly varied flow condition. So, in that scenario, the momentum Eq. 3 is used instead of energy equation (Hydraulic Engineering Center (HEC), 2010b). P2 −P1 þ W x −F f ¼ QρΔV x

ð3Þ

where P is the hydrologic pressure force at locations 1 and 2, Wx is the force due to weight of water in the x direction, Ff is the force due to external friction loss from 1 and 2, Q is the

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discharge, ρ is the density of water, and ΔVx is the change in the velocity from 1 and 2 in the x direction. HEC-RAS model is one dimensional in nature, and it calculates the friction losses using Eq. 4 and considering the channel contraction and expansion coefficients all by default. It also calculates the single mean energy level and the single water surface at each cross section by weighing the flow at the three subsection divisions, i.e., from the left overbank, main channel, and right over bank. Thus, the channel contraction and expansion coefficient values were changed according to the geometry of the cross sections (Hydraulic Engineering Center (HEC), 2010a). α1 V 21 α2 V 22 hec ¼ C ð4Þ − 2g 2g where C is the contraction or expansion coefficient in the channel. The HEC-RAS model assumes that a contraction is occurring whenever the velocity head downstream is greater than the velocity head upstream. Similarly, when the velocity head upstream is greater than the velocity downstream, the HEC-RAS model assumes that a flow expansion is occurring.

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and thermal infrared spectral bands. Stereo image data are recorded only in band 3, which is the near-infrared wavelength region from 0.78 to 0.86 μm, using both nadir and after looking telescopes. From the nominal Terra altitude of 705 km, the “push broom” linear array sensor covers a 60km-wide ground track at a 15-m spatial resolution. A major advantage of the along-track mode of data acquisition (as compared to cross track) is that the images forming the stereopairs are acquired a few seconds (rather than days) apart under uniform environmental and lighting conditions, resulting in stereo pairs of consistent quality that are well suited for digital elevation model (DEM) generation by automated stereo correlation techniques (Colvocoresses 1982; Fujisada 1994). The ASTER DEM is a product that is generated from a pair of ASTER Level 1A images. This level 1A input includes bands 3N (nadir) and 3B (aft-viewing) from the visible and near-infrared telescope’s along-track stereo data that is acquired in the spectral range of 0.78 to 0.86 μm having a vertical accuracy of 7 m and spatial resolution of 30 m. It was also available in public domain, but in our case, it was provided by Pakistan Space and Upper Atmosphere and Research Commission (SUPARCO).

Modeling of the Kalpani River HEC-GeoRAS HEC-GeoRAS is an extension for HEC-RAS, which comprised of a set of procedures, tools, and utilities for geospatial data processing in ArcGIS through a graphical user interface (GUI). The interface allows the user to import the initially prepared geometric data from ArcGIS into HEC-RAS and exports back HEC-RAS simulated results into ArcGIS. The created file can be imported with the help of a digital elevation model (DEM) in TIN or grid format of the river system in the ArcInfo. User can create a series of line themes relevant to the development of geometric data for HEC-RAS, which includes stream centerline (necessary), flow path centerlines (optional), main channel bank line (optional), and a cross-section cutline, which is referred to as RAS themes. Additional RAS themes might also be created for additional geometric data to import into HEC-RAS. These themes consist of landuse, levee alignment, ineffective flow areas, and storage areas. HEC-GeoRAS involves the processing of water surface profile data and velocity data, which are exported back from HEC-RAS simulations for floodplain mapping, flood damage computations, ecosystem restoration, and flood warning response and preparedness in ArcGIS (Hydraulic Engineering Center (HEC), 2010b). ASTER DEM The ASTER sensor is designed to provide image data in 14 bands from visible, near-infrared, short-wavelength infrared,

HEC-RAS analyzes the stream flow as a series of cross sections along the channel and simulates a steady flow perpendicular to the channel. However, it works very well for those channels, where water remains inside the channel or remains parallel to the channel. HEC-RAS requires two types of data, i.e., geometric data and hydrological data for river modeling. Data preparation for HEC-RAS was initially prepared in ArcGIS which was then edited in the HEC-RAS for further computation. Geometric data is the most important and necessary input data to the HEC-RAS model as it provides spatial information of the river and its floodplain. One of the most important components of the geometric data is the floodplain 30 m ASTER DEM, which was used for 3D coordinates’ extraction for layers such as cross sections, river centerline, and bridge cross sections in HEC-GeoRAS environment, an extension of HEC-RAS for ArcGIS. Then data was exported to HEC-RAS for simulation. HEC-RAS reads the RAS GIS import file in “sdf” and “xml” format, which contains all the prepared data in ArcGIS through HEC-GeoRAS. The export file from HEC-GeoRAS to HEC-RAS was not completely imported especially channel boundary condition, bridge/weir geometry. Those were edited through actual paper-based cross sections in HEC-RAS model. Geometric data prepared for the model includes river centerline; bank line, x section cutline, and bridge cutlines were entered in all cross sections. Figure 1 shows the methodology for model pre-processing data in HEC-GeoRAS.

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Fig. 1 Methodology of geometric data processing in HEC-GeoRAS

River centerline and bank line River centerline and bank line provide the shape of the river which was digitized from SPOT 2.5 m pan-sharpened satellite imagery while ASTER 30 m DEM was used for the extraction of river 3D coordinates in grid format. River centerline and bank line were used for river station allocations which are important for cross-section location. Cross sections Cross sections are the main components of the geometric data for 1D flood model which characterizes the floodplain. Surveyed cross sections from RD 3000 to RD 107500 with 152 m step in a hard copy format acquired from the Provincial Irrigation Department of Khyber PakhtunKhwa were used in the case study, and besides that, there are six bridges located on the Kalpani River. Bridge structure causes contraction and expansion of the river which directly affects the flood water behavior, i.e., from gradually varied flow to rapidly varied flow (which is a supercritical condition). Digitizing and updating of those cross sections at the right location into the model was a hectic job. Those cross sections were not geo referenced; only river stations were known from the longitudinal cross sections. 2D cross-section cutlines were drawn in ArcMap from left to right bank while looking downstream using the longitudinal cross-section data for accurate location of cross sections. ASTER DEM, 30 m grid (Fig. 2), was used for the extraction of 3D coordinates from 2D cross-section cutlines at river stations. The

digitized cross sections were then exported to HECRAS, where the same were edited using paper-based cross sections for correct stations. By default, HEC-RAS inundates all low-lying areas, even though there is no overbank flow and this problem was solved by considering cross sections’ left and right banks as a levee. Model calibration Model calibration is a process of matching the computed water surface with known water surface at each cross section for the entire study area. Model was calibrated for flood 2010 event of 1901 m3 s−1 (stage of 9.45 m) and 3361 m3 s−1 (stage of 9.45 m) for two different locations of a single reach because there is a change in flow where the Muqam River joins Kalpani River at downstream of Mardan City by Manning’s “n” with Known Water Surface Boundary condition as known water surface for all cross sections for the year 2010 event was available on paper-based cross sections. The Federal Emergency Management Authority (FEMA) standards were followed for the model calibration. The model was initially run for Manning’s “n” of 0.025, and the model was calibrated on the fifth run with Manning’s “n” value of 0.06. Manning’s “n” values used in various runs for different cross sections are shown in Table 1. Although the Kalpani River passes through the city center of Mardan, but most of the floodplains are surrounded by agricultural lands. Manning’s “n” values for the left and right banks were selected according to land use. The calibrated model was also simulated for the same flood event at both locations with critical depth boundary condition

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Fig. 2 Longitudinal profile of geometric data extraction

in order to check the model’s behavior and its impacts on the results. The HEC-RAS model employed Eq. 5 for water Table 1

surface depth computation through critical depth boundary condition (Hydraulic Engineering Center (HEC), 2010b).

Model calibration with Manning’s “n” values Manning’s n 0.025

0.030

0.035

0.039

0.060

1st run 2nd run

RD 19000–28000 RD 43000–45000 ———–

———– ———–

———– ———–

———– ———–

3rd run

———–

———– RD 7000–18500 RD 29000–42500 RD 46000–47500 RD 50500–54000 RD 59500–69000 RD 74500–90000 RD 94500–107500 ———–

———–

———–

4th run 5th run

———– ———–

———– ———–

RD 48500–50000 RD 90500–94000 ———– ———–

RD 54500–59000 ———–

———– RD 3000–6500

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Fig. 3 Bed profile of the Kalpani River

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(a) Known and computed water surfaces from

(b) Known and computed water surfaces from

model pre-calibration

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(c) Water surface profiles of known water surface and critical depth boundary Fig. 4 a Known and computed water surfaces from model pre-calibration. b Known and computed water surfaces from calibrated model. c Water surface profiles of known water surface and critical depth boundary

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Page 7 of 11 439 315.00 y = 1.0001x - 0.1497

310

y = 1.0051x - 1.6379

310.00

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Observed water surface (m)

315

R = 0.9998

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R = 0.9996

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(a) Relationship between known and computed

(b) Relationship between known water surface

water surfaces

and critical depth boundary

Fig. 5 a Relationship between the known and computed water surfaces. b Relationship between the known water surface and critical depth boundary

H ¼ WS þ

αV 2 2g

ð5Þ

Critical depth boundary condition does not require known water surface height, as it calculates water surface height using total energy head equation, where H is the total energy head, 2 WS is water surface elevation, and αV 2g is the velocity head at each cross section.

Result and discussion Modeling of the Kalpani River The river from its source moves from a high slope gradient and reduces as it passes through the study area. Figure 3 shows the bed profiles of the Kalpani River

passing through the study area. Vertical bars in the plot show the bridge location on the river. The average slope of the river was 0.000786 m m−1 which signifies the flat topography of the said river. HEC-RAS model works very well for low slope gradient (May et al. 2000). At RD 19000–50000, the river passes through the Mardan City. Average slope in that particular section of the river was 0.000772 m m−1 which was found comparatively lower as compared to overall average slope. If the channel slope is about 0.002 m m−1 (0.2 %) or more, there should be an addition of extra cross sections. However, if the slope is gentle between the cross sections having the similar shape of distance, i.e., less than 300 m apart, then additional cross sections are not needed (Dyhouse et al. 2003). The observed slope of the river was in a permissible limit, and thus, it was found good for flood simulations in having good results with HEC-RAS.

311

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Observed water surface (m)

Elevation (m)

y = 0.9945x + 1.544 301 296 291 286 281

30 00 12 50 0 22 00 0 31 50 0 41 00 0 50 50 0 60 00 0 69 50 0 79 00 0 88 50 0 98 00 0 10 75 00

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RDs Channel bed Computed water surface

Known water surface FEMA estimated surface

2

R = 0.9996

305.00 300.00 295.00 290.00 285.00 280.00 280.00 285.00 290.00 295.00 300.00 305.00 310.00 315.00 Known water surface (m)

(a) Relationship between known and computed

(b) Known and computed water surfaces for

water surface

validated output

Fig. 6 a Relationship between the known and computed water surfaces. b Known and computed water surfaces for validated output

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Page 8 of 11 Kalpani Nullah Mardan section

315

Legend WS Q100 years Crit Q100 years

310 WS Q50 years Crit Q50 years WS Q20 years

305

Crit Q20 years WS Q10 years Crit Q10 years

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WS Q5 years Crit Q5 years Ground

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(a) Water surface profiles of 100-years return periods

(b) 100-years flood water surface at Bughdada Bridge Fig. 7 a Water surface profiles of 100 years return periods. b 100-years flood water surface at Bughdada Bridge

Model calibration Pre-calibrated model did not give the satisfactory result in the first run by assigning Manning “n” value of 0.025 for all cross sections. However, by slightly varying the Manning’s “n” values, the model was calibrated in the fifth run which brought some good results in bringing the simulated water surface to the known water surface according to the FEMA standards, i.e., 0.2 m or 20 cm (FEMA 2007). The average known water surface for the year 2010 flood was 302.07 m from mean sea level (MSL) while average computed water surface for precalibration simulation was 301.42 m with an average

difference of 0.64 m between the known and computed water surfaces as shown in Fig. 4a. For this purpose, different Manning’s “n” values were used for proper calibration of the model. The average computed water surface of 301.95 m was observed at the average difference of 0.11 m from the calibrated model data as shown in Fig. 4b. Therefore, the calibrated model results showed that the difference between the known and computed water surfaces was in acceptable limits of FEMA. But some cross-section results of RD 3000–4000, 53500–56500, 60000, and 98500–102000 showed slightly higher limits than the acceptable range. However, the overall

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Model validation Model was validated for the flood 2006 event using the calibrated n values. Model result was slightly on the higher side for some cross sections, but overall model result was found in the acceptable limits of FEMA standards. The average known water surface and computed water surface (Fig. 6a) for the year 2006 flood was 302.22 and 301.12 m, respectively, while the FEMA estimated water surface for the year 2006 flood was 302.02 m. The average difference between the known and computed water surfaces was 0.10 m, while the FEMA-estimated difference between the known and computed was 0.2 m. To analyze the gap between the known and computed water surfaces, a correlation graph between the data was plotted. However, Fig. 6b showed a correlation of R2 = 0.996 between the known and computed water surfaces and it was found in the acceptable limits, as compared to the individual cross sections and it was found good for model validation as well.

Velocity (ms-1)

10 8 6 4 2 0 30 0 11 0 00 0 19 00 27 0 00 0 35 00 43 0 00 0 51 00 59 0 00 0 67 00 75 0 00 0 83 00 91 0 00 0 99 00 10 0 70 00

model result showed a strong correlation, R2 = 0.999 (Fig. 5a), between the known to computed water surface, and it revealed that the model was calibrated carefully. The calibrated model results were also checked through critical depth boundary condition. Figure 4c shows the relationship between the output of computed water surface from critical depth boundary condition and known water surface. Average water surface with critical depth boundary condition for the year 2010 was 301.98 m with an average difference of 0.09 m between the known and computed water surfaces, and it further showed that the results are in acceptable limits of the FEMA standards. Model results were also plotted in order to check the correlation between the known and computed water surfaces (Fig. 5b) which also showed a strong correlation, i.e., R2 = 0.999. However, the “n” values were kept the same with the critical depth boundary condition in model simulations. Therefore, on the basis of these analyses, the model was finally simulated for 5-, 10-, 20-, 50-, and 100-year return period peak flood with critical depth boundary condition.

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RDs Velocity

Average velocity

Fig. 8 Velocity profiles of a 100-year return period

19000–50000) was observed at 305.52, 305.95, 307.15, 308.01, and 308.82 m, respectively. There are six bridges on the Kalpani River in the study area. Water surface for historical floods was available only for the Bughdada and Chowki bridges. It was verified through field observation that water surface height at the Bughdada Bridge was 309.75 m from mean sea levels (MSL) for the year 2006 peak flood having a discharge of 1950 m3 s−1. Flood in 2006 was a 30-year return period and submerged the bridge deck by 0.60 m, while gauge height at this point was measured at 9.45 m; thus, the total head was at 10.05 m for that flood. Moreover, this was further compared with 5-, 10-, 20-, 50-, and 100-year return periods’ peak flood of 1104, 1558, 1925, 2406, and 2835 m3 s−1 with water surface height of 307.91, 308.75, 309.36, 310.24 and 311.13 m, respectively, as shown in Fig. 7b for 100-year peak water surface on the Bughdada Bridge. Flood in the year 2010 was a 50-year return period at Chowki Bridge, and the water surface height was observed at 284 m with a discharge rate of 3358 m3 s−1. The gauge height at this point was recorded at 8.23 m with a total head of 9.45 m. Flood water submerged the gauge about 1.2 m and was lower about 1 m from the bridge’s lower deck, which was verified from water marks on the piers of the Bridge. The estimated water depth of the model at Chowki Bridge for 5-, 10-, 20-, 50-, and 100-year return periods was recorded at 281, 306.00

Model simulation for return periods

296.00 291.00 286.00 281.00

00 16 00 22 0 50 0 29 00 0 35 50 42 0 00 0 48 50 55 0 00 0 61 50 0 68 00 0 74 50 81 0 00 0 87 50 94 0 00 10 0 05 10 00 70 00

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The model was simulated for five return periods with critical depth boundary condition. Therefore, five water surfaces were calculated using the model for each cross section for all return periods. Figure 7a shows the water surface profile for all return periods. Red marks near the bridges in the plots showed a critical depth due to water surface contraction and expansion near the bridges. The average estimated water surface for 5-, 10-, 20-, 50-, and 100-year return periods was observed at 300.27, 300.89, 301.72, 302.40, and 302.89 m, respectively, whereas the average water surface in Mardan City (RD

Elevation (m)

301.00

RDs Cross sections surface

Fig. 9 Cross section vs. DEM surface

DEM surface

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281.08, 283.20, 284.02, and 284.33 m for the discharge of 1420, 2130, 2635, 3400, and 3940 m3 s−1, respectively. Velocity profile of different return periods HEC-RAS model being one dimensional in nature calculates the velocity along the channel for each cross section. As the river moves with gentle slope in the study area, the velocity also decreases. The average velocity observed for 5-, 10-, 20-, 50-, and 100-year return periods was 1.51, 1.59, 1.64, 1.72, and 1.78 ms−1, respectively, for the entire channel. Since the Kalpani River passes through the downtown of Mardan City which is a populated area in the district, so it was necessary to observe the velocity in that portion of the river. Mardan City lies between RD 19000 to 50000. The average velocities in that reach for 5-, 10-, 20-, 50-, and 100-year return periods was 1.51, 1.54, 1.59, 1.63, and 1.65 ms−1, respectively. Figure 8 shows the velocities for 100-year return period. Model output The output of HEC-RAS model was exported for postprocessing in ArcGIS 9.3, where analysis had to be performed with the help of HEC-GeoRAS. Two types of maps can be generated, i.e., depths and velocity. Inundation maps can be created by extracting water surface grid from HEC-RAS output and DEM (grid), whereas risk maps can be created, if the building heights are added to DEM. However, flood inundation map could not be prepared in the ArcGIS environment, because HEC-GeoRAS failed to extract the grid from ASTER 30 m DEM. An error message appeared at the floodplain extraction during raster grid conversion to vector. The reason for that error was due to the flatness of the geographical extent of the study area in ASTER 30 m DEM. The floodplain error was further verified by constructing the graphical relationship (Fig. 9) between DEM surface extent (TIN) and paper-based cross sections for each river station. The paper-based cross sections remained to continue in a more or less liner pattern with the gradual slope from upstream to downstream, while DEM surface showed abrupt changes along the channel length and on many point dimensions of paper-based cross sections beyond the DEM surface which was the real cause of the error. However, water inundation extent map was developed for 100-year return period’s water surface at each cross section with the help of contours in ArcGIS.

Conclusions The Kalpani River was successfully modeled using HECRAS due to low slope gradient of the channel. The observed slope of the river was good enough for flood simulations. The

model was calibrated using Manning’s “n” and calibration results were within the acceptable limits of the FEMA standards (±0.20 m). The model was validated for the year 2006 peak flood using the calibrated n values. Model result was slightly on the higher side for some cross sections, but the overall model result was within the acceptable limits of FEMA standards. The model was simulated for a 100-year return period with critical depth boundary condition. The average estimated water surface for the 100-year return period was observed at 302.89 m, while the average water surface in Mardan City (RD 19000–50000) was observed at 308.82 m. Flood inundation map could not be prepared, because the HEC-GeoRAS failed to extract water surface grid from ASTER 30 m DEM. However, flood water inundation extent map was developed for water surface at each cross section with the help of contours in ArcGIS, which can be used as a decision support tool for flood risk reduction for a certain return period, i.e., 100-year return period. Acknowledgments The authors would like to acknowledge SUPARCO, the National Space Agency, for providing SPOT satellite imagery, technical support, and guidance during the study as well as giving an opportunity for utilizing the SUPARCO lab for data analysis. Thanks are also extended to the US Corps of Engineering for free availability of HEC-RAS and HEC-GeoRAS Software on the Internet.

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