ESTIMATION OF RUNOFF AND SEDIMENT YIELD FROM A SMALL UNGAUGED WATERSEHED USING GIS AND HEC-HMS

International Journal of Civil Engineering and Technology (IJCIET) Volume 8, Issue 6, June 2017, pp. 517 527, Article ID: IJCIET_08_06_057 Available online at http://www.iaeme.com/ijciet/issues.asp?jtype=ijciet&vtype=8&itype=6 ISSN Print: 0976-6308 and ISSN Online: 0976-6316 IAEME Publication Scopus Indexed ESTIMATION OF RUNOFF AND SEDIMENT YIELD FROM A SMALL UNGAUGED WATERSEHED USING GIS AND HEC-HMS Dillip Kumar Barik Assoc. Prof. Dept. of Water Resources and Environmental Engineering, SCALE, VIT University, Vellore Campus, Tamil Nadu, India Aakash Deep Singh, Mandeep Singh Sra, Raja Student, Dept. of Water Resources and Environmental Engineering, SCALE, VIT University, Vellore Campus, Tamil Nadu, India ABSTRACT Runoff and soil erosion are two important factors in water resources management. These factors should be considered while planning for watershed management. But, researchers face difficulties in the modeling of runoff and soil erosion in the watershed due to their nonlinearity and scaling effect. The hydrological connectivity may decrease due to infiltration and deposition of sediment in watershed. Hence, it is important to use tools like rainfall-runoff simulation model to estimate runoff and soil erosion from the small watershed. There are a lots of models are available to predict runoff and sediment from the small watershed. In this study, Natural Resources Conservation Service Curve Number (NRCS-CN) method is used to predict the depth of runoff and peak discharge from an ungauged small watershed by coupling remote sensing and GIS software with HEC-HMS. By using the peak discharge and depth of runoff the sediment yield has been estimated by using Modified Universal Soil Loss Equation (MUSLE). The regression analysis method has been used to derive the relationship between the depths of rainfall sediment yield from the small watershed. The frequency analysis has been performed to predict the depth of runoff and sediment yield in the different return period. Key words: Runoff, Soil erosion, Simulation model, Curve number, HEC-HMS, GIS, Modified Universal Soil Loss Equation, Ungauged watershed, Frequency analysis. Cite this Article: Dillip Kumar Barik, Aakash Deep Singh, Mandeep Singh Sra and Raja, Estimation of Runoff and Sediment Yield From A Small Ungauged Watersehed Using GIS and HEC-HMS. International Journal of Civil Engineering and Technology, 8(6), 2017, pp. 517 527. http://www.iaeme.com/ijciet/issues.asp?jtype=ijciet&vtype=8&itype=6 http://www.iaeme.com/ijciet/index.asp 517 editor@iaeme.com

Dillip Kumar Barik, Aakash Deep Singh, Mandeep Singh Sra and Raja 1. INTRODUCTION Land and water resources are the limited resource. If these resources are not properly utilized it becomes an important issue especially for dynamically growing countries like India. Due to continuous pressure of population growth, development is the main agenda for India [13]. When the focus is to develop, the proper utilization of land and water resources is a challenging job. Hence, there is a huge pressure on natural resources like land and water. Water is needed for domestic use, irrigation use, and industrial use and is the most important requirement to meet food requirement of this nation. Still, in many parts of this country, the adequate emphasis is not being given with respect to conserve, develop and judiciously utilization of these resources. The recent Chennai flood and drought in large parts of Maharashtra are the good evidence of it. This indicates that effective planning is required for the future. These problems can be well addressed if we know the characteristics of our catchments i.e. from a particular rainfall event how much excessive rainfall as well as sediment loss we get. Destruction is cause for soil erosion as it has seen that about 16.4 tons/ha of soil is detached annually in our country because of destruction [15]. The mainstay of our present generation is to develop land and water resources on a sustainable basis as well as to increase food productivity. To overcome aforesaid conditions at the national level, an effective planning and development of watershed are essential [4]. The factors like crop production, soil and water conservation and management, wasteland reclamation and protection from losses has importance to increase the overall efficiency of the watershed. It is better to start from micro level i.e. from smaller watershed to bigger. Hence, a small watershed from Palar river basin in Vellore district of Tamil Nadu has been chosen for this study to estimate depth of runoff and sediment loss due to rainfall. The main reason to choose this area is population growth. It has been observed that in past decades the population of Vellore district is increasing at a tremendous rate. The available natural resources should be judiciously used to cope up this pressure. As the study watershed is small and ungauged there is no extensive research has done to determine the loss of rainfall water as runoff and corresponding sediment loss from this watershed. But, runoff and sediment yield from a watershed are the main criteria for assessing the vulnerability of a watershed in term of soil erosion and water loss, provided, there must be continuous monitoring of runoff and sediment data for different time intervals after precipitation at the outlet of the watershed. Majority of the small watersheds are ungauged in India, hence, such data are hardly available in India [8]. The yield of sediment from large basins can be obtained from such observations, but, it is not possible to ascertain the vulnerability of small watersheds within a basin. Further, there are inherent difficulties in acquiring field data to generate information on runoff and soil erosion. In a watershed, setting up discharge and sediment gauging station to monitor spatial behavior is not practically feasible for a number of reasons. Hence, there is an utmost need to assign relative priorities to a different region within a catchment by using low cost and readily available tools and technologies. GIS (Geographical Information System) technology is very effective tools now a day in the field of water resources to extracts data from satellite image. It is widely used by many researchers in their research work [5]. In the present study, geomorphological features of the watershed have been determined by using GIS tools such as ArcGIS. These features used to get the information regarding runoff and sediment loss from the study area. Watershed has been delineated by using 30 m ASTER GLOBAL DEM data from the USGS database (code no. 4C2A3). The geomorphological characteristics such as area, perimeter, bifurcation ratio, stream order etc. have determined [14]. Runoff has been estimated by using Curve Number (CN) Method. The sediment loss from the watershed has been determined by using the Modified Universal Soil Loss Equation (MUSLE). The analysis has been done for 10 years i.e. from year 2000 to 2010. By keeping http://www.iaeme.com/ijciet/index.asp 518 editor@iaeme.com

Estimation of Runoff and Sediment Yield From A Small Ungauged Watersehed Using GIS and HEC- HMS other parameters constant the relationship between depths of rainfall and sediment yield has been established. A frequency analysis has been performed to know the relationship between return period and sediment yield for extreme events by using Gumbel frequency distribution function. The best fit graph has been obtained in logarithmic form and it has found that sediment loss is directly proportional to return period. 2. DESCRIPTION OF STUDY AREA There is demand for land and water resources to be managed properly in the district of Vellore in Tamil Nadu state as well as due to easily availability of hydrological, meteorological, soil, and other collateral data, a sub watershed in Cheyyar Nagavadi watershed was selected as the study area for the present study (Fig. 1). The study area is situated between 12 59 24 N Latitude & 79 18 50 E Longitude at elevation of 164 m above MSL. Major River passing through this is Cheyyar Nagavadi. The total area of the watershed is 140 km 2. 3. METHODOLOGY 3.1. WATERSHED DELINEATION Watershed has been delineated from 30 m ASTER GLOBAL DEM which has downloaded from the USGS database (code no. 4C2A3) by using spatial analyst tool of ArcGIS (Fig. 2). To delineate the watershed commands such as Fill, Flow Direction, Flow Accumulation, Basin, Basin Polygon and Raster Calculator have been used in ArcGIS. The blue lines depict the drainage path followed by the water in the watershed in Fig.2. Figure 1 Location of study area Figure 2 Delineated watershed with drainage lines 3.2. GEOMORPHOLOGICAL PARAMETERS The shape file of the watershed has been used to calculate the geomorphological parameters i.e. area and perimeter, stream order and bifurcation ratio by using ArcGIS. To calculate these parameters the geographical coordinate system has been changed to projected coordinate system by using project option in Projections and Transformations tool [1]. The area and perimeter are calculated as 140.98 km 2 and 79.57 km respectively [10]. Straler s stream ordering scheme was used to get the order of the drainage network in spatial analyst tool [16]. The watershed is having 5 th order stream and it has shown in Fig.3. The bifurcation ratio depicts the distribution of a tributary in an area. Higher bifurcation ratio denotes lower probability of flooding in that region. Stream order 1 has the higher bifurcation ratio, followed by stream order 5 [3]. Hence, the upper part of the watershed which has the tributaries of order 1 has lower http://www.iaeme.com/ijciet/index.asp 519 editor@iaeme.com

Dillip Kumar Barik, Aakash Deep Singh, Mandeep Singh Sra and Raja probability of flooding and stream orders 4 and 5 have higher probability of flooding (Table 1). Bifurcation ratio is defined as the ratio of number of streams of the given order to the number of streams of the next order. Figure 3 Stream Order Table 1 Bifurcation ratio for different stream order Stream Order Number of Streams Bifurcation Ratio 1 2072 2.59 2 798 1.2 3 665 2.29 4 290 1.54 5 188-3.3. SOIL AND LAND USE LAND COVER CLASSIFICATION The tiff format of soil classification file of the entire world has been downloaded from the FAO database. This tiff formatted soil file has imported in ArcGIS and the shape file of the watershed is overlapped [2]. The soil profile of the study area has been clipped and presented in Fig. 4. Based on the infiltration capacity of the soil, it has been classified into B and D hydrological soil group [6]. To get the information regarding land use and land cover Landsat imagery has been used. According to the shape of the study watershed Landsat image has been clipped in ArcGIS and clipped image has been imported in (Earth Resources Data Analysis System) ERDAS IMAGINE for further analysis. Out of 11 band images 2, 3 and 4 bands has been overlapped in ERDAS IMAGINE. http://www.iaeme.com/ijciet/index.asp 520 editor@iaeme.com

Estimation of Runoff and Sediment Yield From A Small Ungauged Watersehed Using GIS and HEC- HMS Figure 4 Soil Classification The unsupervised classification is done and the watershed has classified according to land use classifications [9]. The land use and land cover are Barren, Urban and Built up, Agricultural, Distributed vegetation, Shrubs and Water body as shown in Fig. 5. The detail percentages of the land use and land cover are presented in Table 2 for 8 different sub watersheds. The curve number has been assigned to each sub catchment based on hydrologic soil groups and the land use classification from the curve number table (Table 3). The area, river length and slope of each catchment have been presented in Table 3, which are obtained from ArcGIS. The curve number has been used to estimate the time of concentration (Tc), lag time, soil moisture retention (S) and initial abstraction (Ia) by using the following equations [11]: T c=... (1) L = length of watershed ft; Sg = ground slope in %, Tc in hour Lag time = 0.6 T c (2) S = 10 (3) I a = 0.2S, S in inch (4) Figure 5 Land Classification http://www.iaeme.com/ijciet/index.asp 521 editor@iaeme.com

Dillip Kumar Barik, Aakash Deep Singh, Mandeep Singh Sra and Raja Table 2 Detail land use classification of watershed in different sub catchment Sub Catchment Area (km 2 ) CN Slope (%) River length(m) 1 27.25 75.94 29.16 9473.68 2 20.9 75.64 15.19 1160.25 3 41.27 74.26 29.11 5055.03 4 25.87 79.45 8.13 4942.98 5 13.87 73.12 8.13 5756.62 6 22 86.56 8.13 3287.13 7 29.87 82.33 8.13 12094.10 8 16.97 89.02 15.19 6452.10 Sub Catchment Table 3 Curve number, area, river length and slope of different sub catchment. Barren Land (%) Urban/Built up (%) Agricultural Land (%) Vegetation (%) 1 40 27 11 15 7 2 5 42 2 47 4 3 38 18 12 22 10 4 10 35 10 30 15 5 8 28 14 48 2 6 33 18 10 27 12 7 50 20 17 8 5 8 22 35 10 20 13 Water body (%) 3.4. PREPARATION OF BASIN MODEL FOR HEC-HMS Basin model which is as shown in Fig. 6 has been prepared by using HEC-HMS. The inputs for HEC-HMS are stream grid, stream link grid, catchment and adjoin catchment. These parameters have been obtained by using Arc-hydro tool. In HEC-HMS the sub basins, junctions and outlet have been defined for each sub basins and shown in Fig. 7. Basin characteristics such as initial abstraction, curve number, lag time etc. have been entered for each sub basin [7]. The depth of runoff and peak runoff obtained by entering depth of annual precipitation. Figure 6 Basin and river profile Figure 7 HEC-HMS Model http://www.iaeme.com/ijciet/index.asp 522 editor@iaeme.com

Estimation of Runoff and Sediment Yield From A Small Ungauged Watersehed Using GIS and HEC- HMS 3.5. SOIL LOSS ESTIMATION Soil loss is a major problem that the world is facing. Not only the vegetation is affected, it affects water quality, water retention etc. It is difficult to manage soil erosion to protect water resources and maintain land productivity. In ideal case, areas most prone to severe erosion should be prioritized for conservation practices. Soil erosion is a hydrological driven process and it depends on sediment being discharged with runoff. The soil loss (Y) in tons has been estimated from this watershed by using the following equations [17]: =11.8!."# % &' ( ) (5) where, K is the erodibility factor; LS is the dimensionless length slope factor; C is the dimensionless cropping factor; P is the dimensionless support practice factor. Vr and Qp are the total volume of runoff and peak runoff; The erodibility factor K is calculated by [17]: %=7.594.0.0034+0.0405234 5." 6789 8:.;< =. > (6) where, Dg is the mean particle diameter of particles in mm. The annual soil loss has been estimated from year 2000 to 2010. The peak discharge and runoff depth obtained from HEC-HMS are used as input to this soil loss model. 3.6. FREQUENCY ANALYSIS Like flood the rainfall is also hydrological extreme event. There is a probability of getting such extreme events. Hence, frequency analysis is performed to know the amount of the depth of runoff and sediment yield from the different return period. The probabilities of occurrence of these events are worked out by performing frequency analysis. Knowing the frequency, risk associated with that event can be worked out. 4. RESULTS AND DISCUSSION From the year 2000 to 2010 annual rainfall data have been used to estimate depth of runoff and peak discharge from the ungauged watershed. To get these results, geomorphological parameters of the ungauged water have been determined by using the geo spatial tools such as ArcGIS, ERDAS IMAGINE and HEC-HMS. The depth of runoff and peak discharge values has been used to estimate the sediment yield by using the MUSLE. The results of depth of runoff, peak discharge and sediment yield in annual basis have been presented here. The results of frequency analysis of extreme event have also presented here to know the relationship between runoff and sediment yield to the return period. 4.1. BASIN MODELLING AND SEDIMENT LOSS The annual precipitation data for 10 years (2000-2010) have entered manually to this model. The other parameters i.e. time of concentration, lag time and the initial abstraction have been calculated for each sub catchments which are the required inputs parameters to run the HEC- HMS model successfully [12]. The units of these parameters converted into SI units and presented in Table 4. The variation of depth of rainfall, runoff and sediment yield different year has been obtained from basin modeling and presented in Figs. 8. http://www.iaeme.com/ijciet/index.asp 523 editor@iaeme.com

Dillip Kumar Barik, Aakash Deep Singh, Mandeep Singh Sra and Raja Table 4 Inputs for HEC modelling Sub Catchment Tc (mins) Lag Time (mins) 1000/CN S(in) Initial Abstraction (mm) 1 103.93 62.358 13.168 3.168 16.09 2 170.98 102.588 13.22 3.22 16.358 3 65.99 39.594 13.46 3.46 17.576 4 105.08 63.048 12.58 2.58 13.106 5 143.027 85.82 13.67 3.67 18.64 6 59.82 35.892 11.55 1.55 7.874 7 196.47 117.882 12.146 2.146 10.922 8 68.23 40.9 11.233 1.233 6.35 Fig. 8 shows that, there is linear relationship between depth of rainfall and runoff, though in reality they are in nonlinear nature. In this study, only rainfall is the input and the geospatial parameters like, soil type and land use and land cover. But, the effects of these parameters are not commendable in this case. Hence, it s become linear in nature. The results shows that sediment yield also directly proportional to the runoff and as well as depth of rainfall. That means, there is relationship exist between depth of rainfall and sediment yield. Here, it has been tried to get the relationship by doing a simple regression analysis and the equation is found to be Sediment yield = 3.6033* Depth of rainfall +803. 42 and the coefficient of determination R 2 = 0.99. Then, to see the effectiveness of this regression analysis, a graph has been plotted between estimated sediment yield (MUSLE) and predicted sediment yield (Regression) (Fig. 9). The R 2 value found to be 0.99. Hence, this equation can be used to predict the yield of sediment from this watershed, if depth of rainfall is known. But, the limitation of this analysis is that, it is valid only if there is rainfall, otherwise, it will be zero 1300 Depth of Rainfall and Runoff (mm) 1200 1100 1000 900 800 700 600 500 Rainfall Runoff 400 2000 2000 2002 2004 2006 2008 2010 2012 Year Figure 8 Depth of rainfall, runoff and sediment yield in different year 5500 5000 4500 4000 3500 3000 2500 Sediment Yield (Tons) http://www.iaeme.com/ijciet/index.asp 524 editor@iaeme.com

Estimation of Runoff and Sediment Yield From A Small Ungauged Watersehed Using GIS and HEC- HMS Sediment Yield, tons (Predicted ) 5500 5000 4500 4000 3500 3000 2500 R² = 0.9999 2000 2000 3000 4000 5000 6000 Sediment Yield, tons (MUSLE) Figure 9 Predicted and estimated sediment yield from the watershed. 4.2. FREQUENCY ANALYSIS This extreme value distribution was introduced by Gumbel (1941) and is commonly known as Gumbel s distribution. It is one of the most widely used probability-distribution functions to determine return period of extreme values in hydrological and meteorological studies. The return period of 10, 25, 50 and 100 years has been chosen and depth of rainfall, depth of runoff, peak discharge and sediment yield have been calculated for each return period. The results of sediment yield Vs return period has been plotted which is shown in Fig. 10. To get the relationship between sediment yield and return period the best fit graph has been drown in logarithm scale and its fitted well with R 2 value 1. That indicates that for any desired return period, sediment yield can be estimated for this watershed. This will be very much helpful for planning water resources management purposes. Sediment loss (tons) 9000 8500 8000 7500 7000 6500 6000 5500 5000 y = 1174.1ln(x) + 3336.5 R² = 1 10 30 50 70 90 110 Return Period (Years) Sediment Yield Figure 10 Sediment yield with respect to different return period 5. CONCLUSION In the present study, rainfall-runoff modelling is carried out using HEC-HMS hydrologic model, and remote sensing and GIS techniques in the Cheyyar River basin of Vellore district. The soil and land use maps have been collected for this the study area. The input file for the proposed hydrologic models was prepared using remote sensing and GIS techniques. For simulating stream flow by the HEC-HMS model, the SCS unit hydrograph transform method was used to compute direct surface runoff hydrographs, the SCS curve number loss method to compute runoff volumes. http://www.iaeme.com/ijciet/index.asp 525 editor@iaeme.com

Dillip Kumar Barik, Aakash Deep Singh, Mandeep Singh Sra and Raja Using these readily available computational tools rough idea can be established and the ungauged catchments can be prioritize for any part of the country and then accordingly thorough investigation can be carried out by concerned government bodies and corresponding steps can be taken for conservation of resources. The unsupervised classification has been used to classify the land use and land cover, which may include little error. This error can be minimized if supervised classification can be done. In this study depth of runoff and sediment yield has been estimated for this ungauged watershed. But, this results can be verified with observe data, if data are available to know the accuracy of the result. It may need more manpower and modern equipment s of survey. This study provides a base for further extensive survey for the organizations can take appropriate steps for conservation and management of this watershed. By using the regression equation it can be easily determine the sediment yield from a particular depth of rainfall as well as for a particular return period sediment loss can be calculated by using the derived equation which is in logarithm form. REFERENCES [1] Archer, R. and Clarke, K. Measuring and monitoring long term disaster recovery using remote sensing. AutoCarto 2012 International Research Symposium. September 16-18, 2012, pp.16-18. [2] Bhattacharya T. Mukhopadhyay, S. Baruah, U. Chamuah, GS. Need of soil study to determine degradation and landscape stability. Current Science 74, 1998, pp. 42 47. [3] Choubey, VK. and Jain, SK. (1992). Morphometric analysis of Sabarmati basin using remotely sensed data. Hydrol. J. Indian Assoc. Hydrologists, XV (3 and 4), 1992, pp. 21-30. [4] Dilip, K., Bhattacharyya, R. K. Distributed Rainfall Runoff Modelling. International Journal of Earth Sciences and Engineering, 2011, pp. 270-275. [5] Gautam, M. R., Watanabe, K., Saegusa, H. Runoff analysis in humid forest catchment with artificial neural network. J. Hydrol. 2000, pp.117-136. [6] GSI. Geological and Mineralogical Map of Tamil Nadu, Geological Survey of India. 1981 [7] HEC. HEC-HMS flood hydrograph package User's manual. Davis: U.S. Army Corps of Engineers. 1998. [8] Jain SK., Goel MK. Assessing the vulnerability to soil erosion of the Ukai dam catchments using remote sensing and GIS. Hydrol Sci J 47 (1), 2002, pp. 31-40. [9] Jain MK., Kothyari UC. Estimation of soil erosion and sediment yield using GIS. Hydrol Sci J 45(5), 2000, pp. 771-786. [10] Karuppannan S., Venkateswaran S. Vijaya P. M. Morphometric Analysis using GIS in Pambar Sub Basin, Krishnagiri and Vellore District, Tamil Nadu, India. International Journal of Advanced Earth Science and Engineering, 2015, pp. 293-307. [11] NEH. Time of Concentration, Part 630 Hydrology, Chapter 15, US Department of Agriculture and NRCS, 2010, pp. 5-7. [12] Prasad, T. D., Gupta, R. Prakash, S. Determination of optimal loss rate parameters & unit hydrograph. Journal of Hydrologic Engineering, 4(1), 1999, pp. 83 87. [13] Sharma, S.K., Gajbhiye, S., Nema, R.K., Tignath, S. Assessing Vulnerability to Soil Erosion of a Watershed of Tons River Basin in Madhya Pradesh using Remote Sensing and GIS. International Journal of Environmental Research and Development, 2014, pp. 153-164. [14] Sharma SK., Tignath, S., Gajbhiye, S., Patil, RJ. Use of Geographical Information System in Hypsometric analysis of Kanhiyanala watershed. International Journal of Remote Sensing and Geosciences 2(3), 2013, pp. 30-35. http://www.iaeme.com/ijciet/index.asp 526 editor@iaeme.com

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