HYDROLOGIC RESPONSE OF. KANKAIMAI WATERSHED IN EASTERN NEPAL

Abstract

Soil erosion is a serious environmental problem in Nepal where more than 80 percent of the land area is mountainous and tectonically active. Anthropogenic causes such as deforestation, overgrazing and intensive farming have accelerated the erosion problem. Unscientific cultivation, haphazard construction and intensive monsoon have further aggravated the situation. Thus, it is very important to understand the erosion process and assess the magnitude of the problem so that effective counter measures and appropriate sediment management method can be implemented. The rapid growth in population, urbanization and industrialization, and economic and social changes during last few years has resulted in an increased and diversified demand of water. At the same time, quantity of available water from surface and ground water sources has remained constant. So water has progressively emerged to become one of the most pressing national issues of present time on the development and environment. Thus there is need to use recent advancement in technology and modeling tool to develop optimal water resources system and manage sediment problems. Rainfall-runoff and sediment yield models are the key components of the overall modeling framework for sustainable watershed development and Management. The process of runoff and sediment erosion is influenced by the spatial controls exerted by the land surface such as elevation, slope, drainage network and vegetation cover. So the knowledge of geomorphology and vegetation of watershed can enhance one's understanding and capability to model the processes. The present study has been taken up in the above background to develop rainfall-runoff and sediment yield models to predict runoff and sediment yield from Kankaimai watershed in eastern Nepal. Geomorphologic and vegetational analysis was carried out using remote sensing and geographic information system. The study has revealed that the Kankaimai watershed is fairly good with moderately high peak flow of shorter duration giving quick response of sediment yield and runoff. iii With this knowledge of watershed characteristic and statistical analysis, eight different combinations of daily rainfall, runoff and temperature for runoff models and four combinations of daily runoff and sediment yield for sediment yield prediction models were selected as input data. Artificial Neural Network (ANN) models were developed using selected combinations of data as input to a three layered back-propagation feed-forward neural network. The regression models were also developed using all above combinations of input data and compared with the results obtained from ANN models. Nash coefficient (R2), coefficient of correlation (CC), root mean square error (RMSE) and recovery ratio (RR) were . estimated to assertion the model performance. ANN model validation statistics resulted in R2 values ranging from 0.35 to 0.82, CC values from 0.60 to 0.91, RMSE values from 192.29 m3/s to 103.67 m3/s and RR from 0.59 to 0.96 for runoff prediction and R2 values from 0.80 to 0.93, CC values from 0.90 to 0.97, RMSE values from 19058 t/day to 34345 t/day and RR values from 0.93 to 1.18 for sediment yield prediction. Using regression models, R2 values for the same data set varied from 0.10 to 0.60 (0.81>CC>0.384, 226.33>RMSE>157.45, 0.758>RR> 0.614) for runoff prediction and -0.85 to -0.16 (CC-0.97, 106607>RMSE>83770, 1.65>RR>1.46) for sediment yield prediction. Performance evaluation of different models suggests that antecedent runoffs of time step ' t-1 and 't-2` as an additional inputs variable alongwith concurrent rainfall improve the performance of runoff model for this watershed. Similarly the concurrent runoff has very high correlation with sediment yield. Use of antecedent runoff and sediment yield as additional input variables to concurrent runoff does not improve the model performance. Furthermore ANN model performed better than the regression equations. iv