Abstract:
Despite the remarkable improvements in skillful weather forecasts, their uses in irrigation water management at a farm scale are still limited. This is attributable to the scale mismatch between weather and hydrologic models as well complexities in farmscale ecohydrological processes. Here, we have developed a simulation-optimization algorithm for minimizing irrigation water application using short to medium range forecasts by determining the conditional probability density functions of the rainfall and subsequently the soil moisture for the days in forecast range. With the forecasted soil moisture information, the model ensures that the probability of crops undergoing water stress is less than a prescribed threshold. The optimization model includes a farmscale ecohydrological model, which computes daily evapotranspiration, runoff, and leakage based on a probabilistic description of rainfall through monte-carlo simulations. After calibrating and validating the ecohydrological model with past data obtained from the farmers, this proposed optimization framework was employed to test the outcome for two pilot sites in nashik, maharashtra, india. We found that using the proposed framework, irrigation water use can be reduced by 10%–30% as compared to that resulting from the conventional strategies used by the farmers, without significant loss in crop yields, by almost always maintaining the soil moisture at or above the prescribed threshold. Considering that irrigation accounts for over 80% of the total water use worldwide, the value of such an approach as a decision-support tool for irrigation optimization is self-evident. We also posit that the co-production of this tool with the farmers increases its usability and credibility. © 2021. American geophysical union. All rights reserved.