Application of regression in seasonal flow forecasting for Upper Indus Basin of Pakistan

Water managers in Pakistan need timely and accurate seasonal flow forecasts like in several areas of the world to allocate water for various kinds of water usages like canal operations for irrigation, reservoir operations, and strategies to respond to extreme cases. Various hydrological models are deployed that include University of British Columbia Watershed Model (UBCWM) and snowmelt runoff model (SRM) to seasonal streamflow forecasts in Pakistan. Here, I assess the approach that employed snow water equivalent (SWE), temperature (T), precipitation (P), and February month flow (Feb) to forecasts Kharif (April–September) season with no need for intensive hydrological modeling in a skillful way for Upper Indus Basin (UIB) at Tarbela Dam. For Indus, I compare this approach results with Indus River System Authority (IRSA), UBCWM, and SRM. This approach uses multiple linear regression (MLR) to develop regression function for forecast seasonal flow volume. This regression approach provides much skillful flow forecasts that is consistent in uncertainty spread for the Indus. The regression forecast accuracy with a mean absolute percentage error (MAPE) is 7.95% with regard to statistical approach used by IRSA, UBCWM, and SRM as 10.25%, 11.05%, and 8.91%, respectively. This approach even allows improvement of 1% (volume ~ 0.6 km 3 ) and is very simple to apply with requirement of four input data that are easy to procure or download and process. With this information in hand, a regression function can generate seasonal forecast for the authorities in Pakistan.