District to subdistrict scale optimum irrigation water management planning at multi-week lead time
摘要
The growing population, climatic change with the declining monsoon, increased extreme rainfall, and high temperature have impacted India’s agriculture, threatening water and food security. Suitable adaptation involving strategies to produce ‘more crop per drop’ is essential. In earlier works, we demonstrated that soil moisture monitoring at farm scale, downscaled weather forecasts (1–7 days), and extended predictions (2–3 weeks) together can save 10–30% of water compared to the traditional irrigation method. Here, we attempt to upscale such an approach at district levels for regions without in-situ soil moisture sensors. We estimate district-level irrigation requirements using satellite soil moisture extended to the root zone using an analytical relationship. The extended range predictions are used for meteorological predictions for multiple weeks. We used these inputs to the optimization model for irrigation decisions. We applied the methodology to the district of Bankura in West Bengal. We considered five crops: Two cereal (maize and wheat), two oilseeds (sunflower and groundnut), and one commercial water-intensive crop (sugarcane) for different crop seasons. The model computed optimal irrigation requirements at a subdistrict level. Such upscaled analysis at the district to subdistrict level can guide water managers to plan for canal water allocation for irrigation, which may substantially reduce groundwater usage.
Research highlightsDistrict to subdistrict level irrigation water management planning. Bottom-up approach from farm scale crop specific to district scale irrigation planning. Use of a simulation–optimization framework for irrigation water management and its applicability across different crops. Incorporating satellite soil moisture and S2S predictions in irrigation water management.