Research Journal of Recent Sciences _________________________________________________ ISSN 2277-2502 Vol. 4(8), 122-127, August (2015) Res.J.Recent Sci. International Science Congress Association 122 Combination of HEC-HMS and HEC-RAS models in GIS in order to Simulate Flood (Case study: Khoshke Rudan river in Fars province, Iran)Hashemyan F., Khaleghi M.R.and Kamyar M. Torbat-e-Jam Branch,Islamic Azad University, Torbat-e-Jam, IRAN Available online at: www.isca.in , www.isca.me Received 7th January 2014, revised 22nd April 2015, accepted 27th September 2015Abstract Residential and urban development in flood plains beds and riverbanks, regardless of hydrological and hydraulic conditions governing watershed and river, on the one hand, increase the risk of flooding and on the other hand, loss of investment in this area. In the present study to investigate the behavior of the rivers flood and how to expand its range in a reach of khoshke Rudan river, located in the Chaharmahal and Bakhtiari province, it was used from the integration of HEC-HMS hydrological and HEC-RAS hydraulic model. HEC-HMS model was calibrated for the study area by surveying of the cross sections of the rive using the rain gauge and rainfall gauging stations of the neighboring basins. Flood zones associated precipitations with periods of 10, 20, 50 and 100 years was determined by using HEC-GeoRAS extension in GIS environment. The results obtained from the study confirmed the high efficiency of the combination of GIS and HEC-RAS model and while provening the performance the model, recommend its application in program planning and management of residential and agricultural areas. Keywords: Combination, HEC-HMS, GIS, simulate flood.IntroductionFlood plains are association of agricultural and entertainment activities and hence must be protected against risks. Flood is a natural disaster that its damages to human are higher than other natural disasters such as drought and famine and is a major concern in many regions of the world1-8. Therefore, we must to select appropriate and effective methods for management and flood control by understanding the hydraulic flow in floodplains. Land use change can result in change of hydrological conditions such as flood frequency10, base flow11, and annual mean discharge12. Various studies show that lack of attention to rivers and oueds make an exponential growth in flood frequency and the amount of damages and in the meantime urban and semi-urban areas have highest potential risk to flooding. Since complete protection from flood risk is not possible, living in the flood and implementation of new policies about land use management and residential development in riverbank to reduce the impact of its destruction is necessary. Awareness of flood and the study of its behavior require sufficient data about the basin hydrologic condition and river discharge and since achieving this goal is not possible in seasonal rivers and regions where no steady flow, hence implementation of hydrologic models is useful to simulation of rainfall-runoff process and hydraulic models for analysis of the runoff flow in the river and its distribution. Traditional rain gages provide a fine enough resolution for accurate runoff calculations and flood warnings1,13. Hence flood modeling requires distributed model predictions relating to planning and insurance14. Recently, there has been an increase of interest in flood simulation using Geographic information systems (GIS) a powerful tool for managing spatially distributed data and also the distributed catchment model15-21. Using GIS and hydrological watershed parameters, one can to create a hydrological information development system22. The rapid distribution of GIS techniques to a wider population has the potential to make various hydrological models more transparent and to enable the communication of GIS operations and results to a large group of users23. It is possible to facilitate Hydraulic modeling of river systems using GIS. While Data preparation and model interpretation are time-consuming operations, hence using GIS can simplify this process. Also, differences in data models have a significant impact on the computational manipulation of data in the above coupling process23. For predicting effects of bed forms on flow depth with known discharge and slope, a method was derived through dimensional analysis, statistical analysis of an extensive laboratory and field database, and fundamental hydraulic principles24. However, little research has been undertaken to study the problem of flood zoning. The present study aims at highlighting some meteorological and hydrological factors which could enhance the hydro-meteorological modeling of such hazardous events. At this purpose, two different hydrological models have been used over the upper Khoshk Rud river basin: HEC-HMS model – run in a semi-distributed and event-based configuration) and HEC-RAS (the distributed and physically-based). These models differ in their physical parameterizations and structure. HEC-HMS utilizes a graphical interface to build the semi-distributed watershed model and to set up precipitation and control variables for the simulations. The two-way nesting allows a feedback upscale of the small-scale features from the fine to the coarse domain, and therefore, it influences the features in the Research Journal of Recent Sciences _____________________________________________________________ ISSN 2277-2502Vol. 4(8), 122-127, August (2015) Res.J.Recent Sci. International Science Congress Association 123 large-scale25. Therefore, both nesting techniques could lead to rather different results on the simulated precipitation fields. It would be thus of interest to investigate the behavior of the rivers flood. The present study was aimed to investigate the behavior of the rivers flood and how to expand its range in a reach of the Khoshke Rudan River using the integration of hydrological model HEC-HMS and hydraulic model HEC-RAS. Material and Methods Case Study: The study area with 2.9 km long of khoshke Rudan river is located in the in Chaharmahal and Bakhtiari Province. Using only published data sources and used to simulate a large flood event that occurred in January 2012. According to UTM system, the coordinates of the study area is as flow; Min=455031/91, XMax = 456981/2 YMin = 351947/39, Max=3569757 The mean annual rainfall of the region is estimated to 500 mm by Isohyets method and 516.2 mm by Theissen method. The main part of the basin has semi arid climate by De-marten method. The concentration time of the watershed is about 3.2 hour. In practice, a flood event at such a river section is defined when the water level, recorded by the gauge station, reaches or exceeds the value of 0.8m (in terms of discharge, a value of about 80 m3s 1), corresponding to the warning threshold. The pre-alarm level is set to 1.6m (corresponding to a discharge value of about 630 (m3s 1)25. To recognize the khoshke Rudan river watershed, some of information such as the vegetation, geology, soil and other information were prepared in the form of maps. Also the evaluation of the correctness has been performed to compare the results of hydrological model HEC-HMS and hydraulic model HEC-RAS with observed flow data for calibration of the model. Some of GIG techniques such as; HEC-GeoRAS extension v.3.1, ArcView software, 3D Analysis and Spatial Analysis has used in order to the regional modeling, simulation and flooding. To determine the water velocity in the selected reaches, a cross section was surveyed between the highest and lowest main channels in each hydrological unit26. Also, these sections should be regular and constant in shape. After estimation of roughness coefficient by Chow method27, Manning equation was used to calculation of the water velocity for the main river and each tributary. Finally, 7 return periods of 2, 5, 10, 25, 50, 100 and 200 years has selected in order to the flood zonation of the 2.9 km section of the khoshke Rudan river watershed28. The hydrological models: The hydro-meteorological model inter-comparison study proposed in the present work is carried out by using two different physically-based rainfall-runoff models to generate simulated discharges. These are: (i) HEC-HMS and; (ii) HEC-RAS25. The modeling features in a hydrologic or hydraulic simulation model must be uniquely identified so that input and output time series associated with them can be labeled appropriately. HEC-HMS model: HEC-HMS model for flood simulation utilizes a graphical interface to build the semi-distributed watershed model. For each sub-basin in the mentioned watershed, the hydrological model is forced by using a single hyetograph.. Firstly, using kriging method, rainfall spatial distributions were generated from hourly values recorded at the automatic rain-gauge station located in the upper section of the khoshke Rudan river. Then, for each sub-basin the hourly rainfall series were calculated. The Soil Conservation Service Curve Number method (SCS-CN) was used to calculation of runoff volumes in the rainfall-runoff model. Development of HEC-RAS flood simulation Model (Geometric simulation of river): The HEC-RAS model when presented with the appropriate hydraulic and geometric data, calculates water-surface profiles. The original reference for the method to determining the roughness coefficient27 in reaches is Cowan method because it includes several factors control the roughness coefficient29-32. Then HEC-Geo RAS extension was used to preparing and inputting geometric information about the reach that these data are include flow path, left and right bank and cross sections that in the form of new data layers was entered into HEC-RAS model. Then while importing of the output hydrographs resulted from HEC-HMS and introducing the roughness, channel convergence and divergence coefficients, HEC-RAS model was run and the results of the hydraulic analysis and extracting of flood zones and flood depth was done in ARCVIEW software and the flood plains was determined for return periods of 10, 20 and 50 years. Figure-1 presents the floodplain zone map with different return periods. Hydrological model and choose of suitable software: In this study to simulation of rainfall and runoff and to determining the output flood hydrograph, the HEC-HMS model version 3.5 was used. Model inputs were identified according to selective methods in this model. To convert the rainfall data to the runoff data, curve number method or CN was used. CN map was prepared by integrating the maps of vegetation, hydrologic soil groups and land use in GIS and Arc View 3.3 software. Figure-2 shows CN map in Cheharmehal-o-bakhteyari province. Model calibration and validation was done using rainfall-runoff events in the region stations that to investigation of spatial distribution of rainfall, IDW method in GIS was used and to determining of the temporal pattern of precipitation for each sub-basin, precipitation pattern belong to the nearest register station was used. After calibration and validation of model extraction of optimal parameters, precipitation of return periods 10, 20, 50 and 100 years was extracted from intensity, duration; frequency curves (IDF) of synoptic station in the basin and was entered into the model. Then flood hydrograph was computed for various return periods using HEC-RAS software for Jonghan sub-basin and was presented in figure 2. To calculation of average reach velocity, a spatial harmonic mean travel time was computed33. Research Journal of Recent Sciences ______ ______________________________ Vol. 4(8), 122-127, August (2015) International Science Congress Association Results and Discussion Discharge Estimation for Different Return Period probability maximum discharges between different statisti distributions of discharges, using Hyfa software was prepared and then the best one with different return period for each station was determined. The result shows that in this study, Pearson type III has the best statistical distribution. The flow rate s used in simulation of flood discharge in the khoshke Rudan river watershed are shown in table-1. Determination of Manning roughness coefficient determination of manning`s coefficient, all measurable characteristics in right, left and the ma in bed of the river channel of the selected sections, separately have measured in the field (table-2). The results of this study indicate the ability of HEC The best statistical distribution data used for simulation of flood discharge in khoshke Rudan river watershed Analysis method Distr./Ret.(yr) Log. Normal (2 parameter) 17.2 Log. Normal (3 parameter) 20.15 Pearson III 12.56 Log. Pearson III 25.63 Gamble 18.6 Selected Pearson III 15.243 Determination of coefficient for each section No. Section Right Bank 1 0.050 2 0.035 Flood hydrograph for various return periods ______________________________ __________ _______________ International Science Congress Association Discharge Estimation for Different Return Period : The probability maximum discharges between different statisti cal distributions of discharges, using Hyfa software was prepared and then the best one with different return period for each station was determined. The result shows that in this study, Pearson type III has the best statistical distribution. The flow s used in simulation of flood discharge in the khoshke Determination of Manning roughness coefficient : In order to determination of manning`s coefficient, all measurable in bed of the river channel of the selected sections, separately have measured in the field The results of this study indicate the ability of HEC -RAS model to simulate the hydraulic behavior of a catchment and determination of spatial parameters are easy by hydrological model systems and the results can be generalized. Also, the ability of GIS to accurately preparation of the input and output of model data shows their combining performance. Also detailed surveying of the river bed and flood plain residential areas can increase the efficiency and accuracy of the output map. Hence we can to recommen method in flood plain management and flood insurance and also planning for residential development for similar areas and especially for urban areas. If the present study be accompanied with hydrometric data, it is capable of simul consistent with some researchers 1, GeoRAS extension is reach TIN1 that is considered as the base of extraction of geometric properties of the reach that has shown in figure-2. Table-1 best statistical distribution data used for simulation of flood discharge in khoshke Rudan river watershed Discharges with different return periods (m3/s) 2 5 10 20 25 50 17.2 52.3 72.5 97.88 124.35 157.28 20.15 63.5 74.3 99.36 118.51 162.3 12.56 46.5 69.7 94.32 114.88 168.32 25.63 52.6 65.96 75.26 129.65 181.12 18.6 67.2 89.21 88.94 134 155 15.243 41.898 60.898 83.95 113.25 137.32 Table-2 Determination of coefficient for each section Left Bank Main Channel Number for each section 0.050 0.040 0.035 0.030 Figure-1 Flood hydrograph for various return periods _______________ ISSN 2277-2502 Res.J.Recent Sci. 124 to simulate the hydraulic behavior of a catchment and determination of spatial parameters are easy by hydrological model systems and the results can be generalized. Also, the accurately preparation of the input and output of model data shows their combining performance. Also detailed surveying of the river bed and flood plain residential areas can increase the efficiency and accuracy of the output map. Hence we can to recommen d the application of this method in flood plain management and flood insurance and also planning for residential development for similar areas and especially for urban areas. If the present study be accompanied with hydrometric data, it is capable of simul ating flood. This is 1, 34,35. The application HEC- that is considered as the base of extraction of geometric properties of the reach that has shown best statistical distribution data used for simulation of flood discharge in khoshke Rudan river watershed Discharges with different return periods (m3/s) 50 100 200 157.28 195 223 162.3 191 212 168.32 184 197 181.12 172 183 155 169.22 177 137.32 150.243 182.898 Number for each section 1 to 46 46 to 93 Research Journal of Recent Sciences _____________________________________________________________ ISSN 2277-2502Vol. 4(8), 122-127, August (2015) Res.J.Recent Sci. International Science Congress Association 125 Figure-2 Floodplain zone map with different return periodsConclusionThe results show that model performs satisfactorily for both test watersheds and performance is much better with HEC-RAS model option of flood calculation as compared to HEC-HMS model. Thus, HEC-RAS model can be adopted as a standard tool for modeling rainfall runoff transformation process in scantily gauged watersheds. The surface flood dominates any flood event36. The different model responses to friction parameterizations cause the differences in predictive performance37. Hence, the results of the experiment question/undermine the previous research. The results presented here indicate that the HEC-RAS model is an efficient model for this reach. Hence, the mentioned model will have the capability to perform hydrological studies on a regional scale. Also, the successful incorporation of the HEC-RAS and HEC-HMS models at a regional scale demonstrates the versatility of this technique for flood inundation studies at the city, county, and regional levels. This is consistent with some researchers38. The catastrophic effects of flooding disaster can be mitigated by integrating scientifically reliable information obtained from a risk assessment studies developed using this hybrid approach. Flood risk assessed by simulating higher magnitude flood events magnifies the vulnerability of the region and reinforces the fact that any land use planning decisions in floodplains should make informed choices and the corresponding developmental activities should be carried out in a sensible and careful manner38. Equipping the watershed to the register rain gauge stations to analysis the time pattern of rainfall in the case study and increasing the accuracy of the studies there. Due to the high economic value of the land area, it is recommended for flood control to use structures that occupy a smaller width. Use of Gabion check dams’ structures for controlling river erosion in sensitive areas and the combination of structural and non-structural methods based on various results is the best way to flood control and management. Acknowledgment The authors thank Chaharmahal and Bakhtiari Regional Water Company and TAMAB (Researches Organization of Water Resources) for providing the data of discharge, maps and for helping us with data preprocessing. References 1.Knebl M.R., Yang Z.L., Hutchison K. and Maidment D.R., Regional scale flood modeling using NEXRAD rainfall, GIS, and HEC-HMS/RAS: a case study for the San Antonio River Basin Summer 2002 storm event, Journal of Environmental Management, 75, 325–336 (2005) Doi:10.1016/j.jenvman.2004.11.024, (2005)2.Townsend P.A. and Walsh S.J., Modeling floodplain inundation using an integrated GIS with radar and optical remote sensing, Geomorphology, 21(3-4), 295–312 (1998)Doi: 10.1016/s0169-555x (97)00069-x, (1998)3.Dutta D., Herath S. and Musiake K., Flood inundation simulation in a river basin using a physically based distributed hydrologic model, Hydrological Processes,14(3), 497–519 (2000) DOI: 10.1002/ (SICI) 1099-1085(20000228), (2000)4.Dolcine L., Andrieu H., Sempre-Torres D. and Creutin D., Flash flood forecasting with coupled precipitation model in mountainous Mediterranean basin, Journal of Hydrologic Engineering,6(1), 1–10 (2001) Doi: 10.1061/ (ASCE) 1084-0699(2001)6:1(1)), (2001)5.Sheng Y., Gong P. and Xiao Q., Quantitative dynamic flood monitoring with NOAA AVHRR, International Journal of Remote Sensing,22(9), 1709–1724 (2001) DOI: 10.1080/01431160118481, (2001) Research Journal of Recent Sciences _____________________________________________________________ ISSN 2277-2502Vol. 4(8), 122-127, August (2015) Res.J.Recent Sci. International Science Congress Association 126 6.Bryant R.G. and Rainey M.P., Investigation of flood inundation on playas within the Zone of Chotts, using a time-series of AVHRR, Remote Sensing of Environment,82(2-3), 360–375 (2002)Doi: 10.1016/S0034-4257(02)00053-6, (2002)7.Lee K.S. and Lee S.I., Assessment of post-flooding conditions of rice fields with multi-temporal satellite SAR data, International Journal of Remote Sensing,24(17), (2003) 3457–3465 DOI: 10.1080/ 0143116021000021206, (2003)8.Hudson P.F. and Colditz R.R., Flood delineation in a large and complex alluvial valley, lower Panuco basin, Mexico, Journal of Hydrology,280, 229–245 (2003)DOI: http://dx.doi.org/ 10.1016/S0022-1694(03) 00227-0, (2003)9.Li Z., Liu W., Zhang X. and Zheng F., Impacts of land use change and climate variability on hydrology in an agricultural catchment on the Loess Plateau of China, Journal of Hydrology, Elsevier, 377, 35-42 (2009) Doi:10.1016/j.jhydrol.2009.08.007, (2009)10.Brath A., Montanari A. and Moretti G., Assessing the effect on flood frequency of land use change via hydrological simulation (with uncertainty), Journal of Hydrology,324(1–4), 141–153 (2006)DOI: 10.1016/j.jhydrol. 2005.10.001, (2006)11.Wang G.X., Zhang Y., Liu G.M. and Chen L., Impact of land-use change on hydrological processes in the Maying River basin, China, Science in China Series D: Earth Sciences, 49(10), 1098–1110 (2006) DOI: 10.1007/s11430-006-1098-6, (2006)12.Costa M.H., Botta A. and Cardille J.A., Effects of large-scale changes in land cover on the discharge of the Tocantins River, Southeastern Amazonia, Journal of Hydrology, 283, 206–217 (2003) Doi: 10.1016/S0022-1694(03)00267-1, (2003)13.Bedient P.B., Holder A., Benavides J.A. and Vieux B.E., Radar-based flood warning system applied to tropical storm Allison, Journal of Hydrologic Engineering,8(6), 308–318 (2003) DOI: 10.1061/ (ASCE) 1084-0699, (2003)14.Bates P.D., Remote sensing and flood inundation modeling, Hydrological Processes,18, 2593–2597 (2004) DOI: 10.1002/hyp.5649, (2004)15.Bates P.D. and De Roo A.P.J., A simple raster-based model for flood inundation simulation, Journal of Hydrology,236(1–2), 54–77 (2000) Doi: 10.1016/S0022-1694(00)00278-X, (2000)16.Sanders B.F., Evaluation of on-line DEMs for flood inundation modeling, Advances in Water Resources, 30(8), 1831–1843 (2007) Doi:10.1016/j.advwatres. 2007.02.005, (2007)17.Adarsa J., Shamina S. and Arkoprovo B., Morphological Change Study of Ghoramara Island, Eastern India Using Multi Temporal Satellite Data, Res. J. Recent Sci., 1(10), 72-81 (2012)18.Manimaran D., Groundwater geochemistry study using GIS in and around Vallanadu Hills, Tamilnadu, India, Res. J. Recent Sci., 1(6), 32–37 (2012)19.Biswas A., Jana A. and Sharma S.P., Delineation of groundwater potential zones using satellite remote sensing and geographic information system techniques: A case study from ganjam district, Orissa, Res. J. Recent Sci, 1(9), 59– 66 (2012)20.Mayavan N. and Sundaram A., Statistical Analysis for Landslide in Relation to Land use, In Sirumalai Hill, Dindigul District, Tami Nadu, India, using GI Technologies, Res. J. Recent Sci.,1(12), 36– 39 (2012)21.Saher F.N., Nasly M.A., Abdul Kadir T.A.B., Yahaya N.K.E.M. and Wan Ishak W.M.F., Harnessing Floodwater of Hill Torrents for Improved Spate Irrigation System Using Geo-Informatics Approach, Res. J. Recent Sci.,3(1), 14– 22 (2014)22.Smith P., Hydrologic Data Development System, Master Thesis, Department of Civil Engineering, University of Texas at Austin (1995) 23.Kang, S.H., Tight coupling UFM ArcGIS for simulating inundation depth in densely area, Nat. Hazards Earth Syst. Sci.,10, 1523–1530 (2010) Doi: 10.5194/nhess-10-1523-2010, (2010)24.Brownlie, W.R., Flow Depth in Sand-Bed Channels. Journal of Hydraulic Engineering, ASCE, 109(7), 959-990 (1983) Doi: 10.1061/ (ASCE) 0733-9429, (1983)25.Lee, A.J. and Ferguson, R.I., “Velocity and flow resistance in step-pool streams,” Geomorphology, 46, 59- 71 (2002) Doi: 10.1016/S0169-555X (02)00054-5, (2002)26.Amengual A., Diomede T., Marsigli C., Martin A., Morgillo A., Romero R., Papetti P. and Alonso S., A hydro- meteorological model inter-comparison as a tool to quantify the forecast uncertainty in a medium size basin, Nat. Hazards Earth Syst. Sci.,8, 819–838 (2008) www.nat-hazards-earth-syst-sci.net/8/819/2008/, (2008)27.Chow V.T., Maidment D.R. and Mays L.W., Applied Hydrology, McGraw-Hill, New York, (1988)28.Mohseni M. and Solimani K., Flood Hazard Zonation Using Hydraulic Model of HEC-RAS in GIS, (2005) http://www.gisdevelopment.net/application/natural_hazards/floods/ma06_181.htm, (2005)29.Cowan W.L., Estimating Hydraulic Roughness Coefficients, Agricultural Engineering, ASAE, August, (1956) Research Journal of Recent Sciences _____________________________________________________________ ISSN 2277-2502Vol. 4(8), 122-127, August (2015) Res.J.Recent Sci. International Science Congress Association 127 30.Jarvela, J., Effect of Submerged Flexible vegetation on Flow Structure and Resistance, Journal of Hydrology, Elsevier, 307 (2005) Doi: 10.1016/j.jhydrol.2004.10.013, (2005)31.Nepf, H.M., Drag, Turbulence, and Diffusion in Flow through Emergent Vegetation, Water Resources Research, AGU,35(2) (1999) DOI: 10.1029/1998WR900069, (1999)32.Wilson, C.A.M.E., Flow Resistance models for Flexible Submerged Vegetation, Journal of Hydrology, Elsevier,342, 213-222 (2007) Doi: 10.1016/j.jhydrol.2007.04.022, (2007)33.Walden, M.G., Estimation of average stream velocity, Journal of Hydraulic Engineering,130(11), 1119- 1122 (2004) Doi: 10.1061/ (ASCE) 0733-9429(2004)130:11(1119), (2004)34.Tate, E.C., Floodplain mapping using HEC-RAS and ArcView GIS, Master’s Thesis, Department of Civil Engineering, University of Texas at Austin. 137., (1998) 35.Radwan, A., Flood Analysis and Mitigation for Area in Jordan, J. of Water Resources and Manag,125(3), 170-177 (1999) DOI: 10.1061/ (ASCE) 0733-9496(1999)125:3(170), (1999)36.Sommer, T., Karpf, C., Ettrich, N., Haas, D., Weichel, T., Peetz, J.V., Steckel, B., Eulitz, K. and Ullr, K., Coupled modelling of subsurface water flux for an integrated flood risk management, Nat. Hazards Earth Syst. Sci.,, 1277–1290 (2009) www.nat-hazards-earth-syst-sci.net/9/1277/2009/, (2009)37.Horritt, M.S. and Bates, P.D., Evaluation of 1D and 2D numerical models for predicting river flood inundation, Journal of Hydrology,268, 87–99 (2002) Doi: 10.1016/S0022-1694(02)00121-X, (2002)38.Yerramilli, S., A Hybrid Approach of Integrating HEC-RAS and GIS towards the Identification and Assessment of Flood Risk Vulnerability in the City of Jackson, MS, American Journal of Geographic Information System,1(1), 7-16 (2012) doi: 10.5923/j.ajgis.20120101.02, (2012)