Season-ahead forecasting of water storage and irrigation requirements – an application to the southwest monsoon in India

Abstract

Abstract. 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.

Year-ahead predictability of South Asian Summer Monsoon precipitation

“…Prediction could be based on the evolution of the SST field across months, rather than only on a single month's field as done here. For example, [71] found that the difference between spring and winter values of ENSO indices was a better predictor of monsoon-season irrigation requirement for a district in India than the winter or spring index values themselves, and [72,73] found that monsoon precipitation in Nepal shows interannual autocorrelation and correlates with the Pacific Quasi-Decadal Oscillation lagged 2 years. Numerical weather prediction model outputs, which are known to have skill in monsoon prediction at least over lags of up to a few months [74], could be added as predictors in the forecast model.…”

Automatic Detection of Potential Dam Locations in Digital Terrain Models

“…Extensive construction measures like dams, reservoirs and conduits will be necessary to cope with the temporal and spatial discrepancies between availability of and demand for surface water. Numerous similar projects are under way in India and China, where high discrepancies are already present today due to monsoon climate and population distribution [12][13][14].…”