Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, India; Sustainable and Resilient Food Production, International Water Management Institute, Pusa, New Delhi, 110012, India.
Interdisciplinary Program in Climate Studies, Indian Institute of Technology Bombay, Mumbai 400 076, India.
Sci Total Environ. 2023 Jun 25;879:163003. doi: 10.1016/j.scitotenv.2023.163003. Epub 2023 Mar 23.
The enormous progress in weather and extended range predictions for the Indian monsoon over the last decade has not been translated to operationalized irrigation water management tools despite many agricultural advisories from operational agencies. The limited implementation is mainly due to the resolution mismatches of forecasts and decision-needs and a lack of soil moisture monitoring networks. Sustained soil moisture monitoring suffers from the high cost to farmers in installing distributed sensors. Here we develop an irrigation water management tool for the farmers at farm scale, which starts with utilizing and merging a few available soil moisture sensors and L-band satellite observations of surface soil moisture using machine learning. Such derived soil moisture field is used as the initial condition with the multi-ensemble future rainfall for the following few weeks given the weather/extended range forecasts in a farm-scale ecohydrological model. This ecohydrological model is integrated with Monte-Carlo simulations within a stochastic optimization framework to minimize water use while not allowing the soil moisture to drop below a threshold level with a certain probability. The optimization results in water arrangement decisions 2 weeks in advance and water application decisions 1-7 days in advance. We also estimate the water storage capacity needed at farm scale for effective water utilization. We find that 20-45 % and 17-35 % water savings were achievable for Kharif and Rabi seasons, respectively, without losing any yield when applied to grape farms of Nashik, Maharashtra, India. The proposed framework is co-developed with the farmers and can be used in any region for any crops, since it is generalized and easy to transfer. This is an extension of our earlier work to an end-to-end system using satellite data for soil moisture.
尽管业务机构提出了许多农业建议，但过去十年中，印度季风的天气预报和延伸期预报取得了巨大进展，但并没有转化为可操作的灌溉水资源管理工具。实施程度有限，主要是因为预测和决策需求的分辨率不匹配，以及缺乏土壤湿度监测网络。持续的土壤湿度监测因农民在安装分布式传感器方面的高成本而受到限制。在这里，我们为农民开发了一种农场规模的灌溉水资源管理工具，该工具首先利用和合并一些可用的土壤湿度传感器以及 L 波段卫星观测的表层土壤湿度，使用机器学习。根据天气/延伸期预报，将由此产生的土壤湿度场作为初始条件，与接下来几周的多集合未来降雨一起用于农场尺度生态水文模型。该生态水文模型与蒙特卡罗模拟集成在随机优化框架内，以在不允许土壤湿度以一定概率降至阈值以下的情况下最小化用水量。优化结果可提前 2 周做出水资源安排决策，并提前 1-7 天做出水资源应用决策。我们还估计了在农场规模上进行有效水资源利用所需的储水能力。我们发现，在不损失任何产量的情况下，印度马哈拉施特拉邦纳西克的葡萄农场可以在 Kharif 和 Rabi 季节分别节省 20-45%和 17-35%的水。该框架是与农民共同开发的，可以在任何地区用于任何作物，因为它是通用的，易于转移。这是我们使用卫星数据进行土壤湿度的端到端系统的早期工作的扩展。
