Hydro-meteorological correlations and relationships for estimating streamflow for Gangotri Glacier basin in Western Himalayas

Abstract

This study investigates the relationships between hydrological and meteorological data collected near the snout of Gangotri Glacier, Indian Himalayas for the different ablation seasons (May - October). The complete analysis employs a hydro- meteorological data series for a period of 7 years (2000 - 2006). The first 5 years data (2000 - 2004) was used for determining correlations, lag-cross-correlations and multivariate regression analyses between daily mean discharge, daily mean temperature and daily rainfall , whereas, last 2 years data (2005 - 2006) was used to simulate the daily discharge using established relationship. Changes in correlations between discharge and meteorological variables, lagged by 0 - 3 days, were determined. Variations in the physical features of the glacier, weather conditions, and precipitation and its distribution with time over the basin account for changes in correlations. The analysis suggests a very high discharge auto-correlation for each individual year and for the combined data series of 5 years. The substantial storage of melt water in the glacier body and its delayed response of the runoff attribute to the high dependency of a particular day discharge on its pervious day’s discharge. A comparison of correlations between discharge and temperature, and discharge and rain shows that temperature has a better correlation with discharge series for all the years. To estimate the discharge for Gangotri Glacier basin, multiple linear regression equations were developed separately for each ablation season and a combined data set of 5 ablation seasons. The generalized regression equation developed using stepwise regression approach for the data set of 5 years (2000 - 2004) was adopted to estimate daily mean discharge for 2005 and 2006. For both simulation years, the simulation efficiency was very high (R 2 = 0.96). It is found that discharge of study basin is well represented Qi-1 (1-day lagged discharge) and Ti, Ti-1, Ti-2 (0 - 2 days lagged temperature) and Ri (0-day lagged rain). Such relationships can be used for filling the missing discharge data as well as for forecasting of discharge.