Abstract
Abstract. Snowmelt-dominated streamflow of the Western Himalayan rivers is an important water resource during the dry pre-monsoon spring months to meet the irrigation and hydropower needs in northern India. Here we study the seasonal prediction of melt-dominated total inflow into the Bhakra Dam in northern India based on statistical relationships with meteorological variables during the preceding winter. Total inflow into the Bhakra Dam includes the Satluj River flow together with a flow diversion from its tributary, the Beas River. Both are tributaries of the Indus River that originate from the Western Himalayas, which is an under-studied region. Average measured winter snow volume at the upper-elevation stations and corresponding lower-elevation rainfall and temperature of the Satluj River basin were considered as empirical predictors. Akaike information criteria (AIC) and Bayesian information criteria (BIC) were used to select the best subset of inputs from all the possible combinations of predictors for a multiple linear regression framework. To test for potential issues arising due to multicollinearity of the predictor variables, cross-validated prediction skills of the best subset were also compared with the prediction skills of principal component regression (PCR) and partial least squares regression (PLSR) techniques, which yielded broadly similar results. As a whole, the forecasts of the melt season at the end of winter and as the melt season commences were shown to have potential skill for guiding the development of stochastic optimization models to manage the trade-off between irrigation and hydropower releases versus flood control during the annual fill cycle of the Bhakra Reservoir, a major energy and irrigation source in the region.
Highlights
The Satluj River is one of the five main tributaries of the Indus river that traverse the Punjab region of northern India and Pakistan, whose name translates as “the land of five rivers”
Spring seasonal streamflow distribution of the Satluj River flow was found to be Gaussian; we proposed using a best-subset selection technique using AIC (Akaike information criteria) and BIC (Bayesian information criteria) from all variable combinations within a multiple linear regression framework and compare this with principal component regression (PCR) and partial least squares regression (PLSR) techniques, which will be discussed later
Since total spring seasonal (MAMJ) inflow to Bhakra Dam is largely contributed by the winter snow melt, winter precipitation and temperature data available from the Indian side of Satluj Basin in addition to inflow itself were used as predictors
Summary
The Satluj River is one of the five main tributaries of the Indus river that traverse the Punjab region of northern India and Pakistan, whose name translates as “the land of five rivers”. With an improved data network, more recent studies have simulated the “daily” flow of the Satluj River based on daily precipitation, temperature and snow cover information from the satellite images (Singh and Quick, 1993; Jain et al, 1998, 2010; Singh and Jain, 2003). While these studies have reported better results with time, these conceptual rainfall-runoff models are not useful for longer-term (seasonal) forecasting since they are based on near-real-time daily weather data.
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