Publications and Research
Season-ahead forecasting of water storage and irrigation requirements – an application to the southwest monsoon in India
Water risk management is a ubiquitous challenge faced by stakeholders in the water or agricultural sector. We present a methodological framework for forecasting water storage requirements and present an application of this methodology to risk assessment in India. The application focused on forecasting crop water stress for potatoes grown during the monsoon season in the Satara district of Maharashtra. Pre-season large-scale climate predictors used to forecast water stress were selected based on an exhaustive search method that evaluates for highest ranked probability skill score and lowest root-mean-squared error in a leave-one-out cross-validation mode. Adaptive forecasts were made in the years 2001 to 2013 using the identified predictors and a non-parametric k-nearest neighbors approach. The accuracy of the adaptive forecasts (2001–2013) was judged based on directional concordance and contingency metrics such as hit/miss rate and false alarms. Based on these criteria, our forecasts were correct 9 out of 13 times, with two misses and two false alarms. The results of these drought forecasts were compared with precipitation forecasts from the Indian Meteorological Department (IMD). We assert that it is necessary to couple informative water stress indices with an effective forecasting methodology to maximize the utility of such indices, thereby optimizing water management decisions.
Agriculture Commons, Applied Statistics Commons, Hydrology Commons, Natural Resources Management and Policy Commons, Other Civil and Environmental Engineering Commons, Other Statistics and Probability Commons, Probability Commons, Statistical Methodology Commons, Statistical Models Commons, Sustainability Commons, Water Resource Management Commons
This article was originally published in Hydrolology and Earth System Sciences, available at https://doi.org/10.5194/hess-22-5125-2018.
This work is distributed under the Creative Commons Attribution 4.0 License