Integrating short-term weather forecasts with crop growth models has emerged as a valuable decision-support tool for enhancing irrigation water productivity in water-intensive crops. Our primary focus lies in formulating a simplified Quasi-Farmer Behavior Routine to utilize ensemble weather forecasts to provide irrigation guidance. The study proposes the concept of Rainfall Confidence Quotient (RCQ) that is optimized based on the forecast uncertainty levels for rainfall categories that affect the irrigation decision. We utilize two publicly available ensemble weather forecasts from NCEP-GEFS and ECM and a regionally fine-tuned high-resolution Weather Research and Forecasting (WRF) ensemble forecast system with a 3-day lead time. We have refined our methodology by conducting 800 simulation experiments across diverse scenarios, each reflecting distinct climate regimes, soil types, cropping seasons, and management practices. The proposed method consists of a sequential 3-checkpoint algorithm incorporating ensemble rainfall forecasts, antecedent soil moisture, and evapotranspiration. During the wet crop season of 2015–16, the enhanced methodology yielded a substantial increase in irrigation water productivity (IWP), averaging around 20–30 % increase across spatial locations, in contrast to the conventional irrigation practices that do not consider weather forecasts. However, during 2016–17, a dry year, the improvement in IWP only ranged between 2–10 %. On average, water savings (at field scale) of 200–500 mm were recorded in the wet year crop season of 2015–16, while 100–300 mm of water savings were achieved during the dry year crop season of 2016–17, without any considerable reductions in the crop yield. The robustness and adaptability of the developed approach have been established through comprehensive evaluation with field observation and remote sensing techniques, suggesting its potential scalability to similar hydro-climatic typologies across the world.

中文翻译：

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利用流域尺度的短期集合天气预报提高灌溉用水生产力：水文气候变化较大地区的新框架


将短期天气预报与作物生长模型相结合已成为一种有价值的决策支持工具，可提高水密集型作物的灌溉用水生产率。我们的主要重点在于制定简化的准农民行为常规，以利用集合天气预报来提供灌溉指导。该研究提出了降雨置信商（RCQ）的概念，该概念根据影响灌溉决策的降雨类别的预测不确定性水平进行优化。我们利用来自 NCEP-GEFS 和 ECM 的两个公开的集合天气预报，以及一个提前 3 天的区域微调高分辨率天气研究和预报 (WRF) 集合预报系统。我们通过在不同情景下进行 800 次模拟实验来完善我们的方法，每个实验都反映了不同的气候状况、土壤类型、耕作季节和管理实践。所提出的方法由顺序 3 检查点算法组成，该算法结合了集合降雨预报、前期土壤湿度和蒸散量。在 2015-16 年的雨季，改进的方法使灌溉用水生产率 (IWP) 大幅提高，在不同空间位置平均提高约 20-30%，这与不考虑天气预报的传统灌溉做法形成鲜明对比。然而，在 2016-17 年干旱年份，IWP 的改善幅度仅在 2-10% 之间。平均而言，2015-16 年雨季作物节水（田间规模）为 200-500 毫米，而 2016-17 年旱年作物季节则节水 100-300 毫米。农作物产量的大幅下降。 通过现场观测和遥感技术的综合评估，确定了所开发方法的稳健性和适应性，表明其对世界各地类似水文气候类型的潜在可扩展性。