Abstract: In this study surface runoff is estimated using the USDA Soil Conservation Service curve number (SCS-CN) method and Autoregressive Time Series Model for Maharuwa micro-watershed. The total geographical area of the micro-watershed is 1028.00 ha, located between 830 54’ 29’’ to 820 54’ 32” North latitude and 250 64’ 44” to 250 55’ 38” East longitude which is situated at Maharuwa village of Ambedkar Nagar district, Uttar Pradesh. Hydrological modeling is a powerful technique of hydrologic system investigation for both the research hydrologists and the practicing water resources engineers involved in the planning and development of integrated approach for management of water resources. The present study involved two hydrologic runoff model viz. SCS-Curve Number method and autoregressive time series model. In these study SCS-CN method has been applied for the estimation of surface runoff, CN and AMC condition for Maharuwa micro watershed. The Maharuwa micro watershed area is about 1028.00 ha. and is located under Ambedkar Nagar district of Uttar Pradesh.A total of 20 rainfall-runoff events were selected between the years 2001 and 2020 for the study. The SCS-CN is applied to generate curve number and to estimate the surface runoff with help of potential maximum retention the curve number of the watershed was estimated comparison of measured and estimation runoff show that the value is in close agreement with each other. The regression model between measured and estimated runoff was developed and also measured that the correlation coefficient was found to be 0.970450243. Autoregressive models of order 0,1 and 2 were tried for annual stream flow series and the annual stream flow was predicted. The goodness of fit and adequacy of models were tested by box- pierce portmanteau test, Akaike information criterion and by comparison of historical and generated datacorrelogram. For runoff the AIC value for AR(O) model 150.7629 which is lyingbetween 149.75 and 150.6924. Which one for AR(I) and AR(2) respectively. The mean forecast error is also very less in case of runoff in AR (l) models on the bases of the statistical test, AIC the AR (l) models with estimated model parameters was estimated for the best future prediction in Maharuwa micro watershed. This is also proved by graphical representation between measured and predicted correlogram, where is runoff there is a very close agreement. A comparison was mad between the estimated runoff from the SCS-CN method and the predicted runoff from the Autoregressive time series model with the measured data that were collected from the Maharuwa micro watershed. From comparison the measured data were is close agreement with the predicted data that was collected from the auto regressive time series model.
Index Terms: runoff, autoregressive time series model, micro-watershed, rainfall, AMC and SCS-CN method
| DOI: 10.17148/IARJSET.2022.9808