The Water Requirements Satisfaction Index (WRSI) – a simplified crop water stress model – is widely used in drought and famine early warning systems, as well as in agro-meteorological risk management instruments such as crop insurance. We developed an adapted WRSI model, as introduced here, to characterise the impact of using different rainfall input datasets, ARC2, CHIRPS, and TAMSAT, on key WRSI model parameters and outputs. Results from our analyses indicate that CHIRPS best captures seasonal rainfall characteristics such as season onset and duration, which are critical for the WRSI model. Additionally, we consider planting scenarios for short-, medium-, and long-growing cycle maize and compare simulated WRSI and model outputs against reported yield at the national level for maize-growing areas in Tanzania. We find that over half of the variability in yield is explained by water stress when the CHIRPS dataset is used in the WRSI model (R² = 0.52–0.61 for maize varieties of 120–160 days growing length). Overall, CHIRPS and TAMSAT show highest skill (R² = 0.46–0.55 and 0.44–0.58, respectively) in capturing country-level crop yield losses related to seasonal soil moisture deficit, which is critical for drought early warning and agro-meteorological risk applications.

水分需求满意度指数（WRSI）是一种简化的作物水分胁迫模型，被广泛用于干旱和饥荒预警系统以及诸如作物保险之类的农业气象风险管理工具中。如本文所述，我们开发了一种适应的WRSI模型，以描述使用不同的降雨输入数据集ARC2，CHIRPS和TAMSAT对关键WRSI模型参数和输出的影响。我们的分析结果表明，CHIRPS可以最好地捕捉季节性降雨特征，例如季节开始和持续时间，这对于WRSI模型至关重要。此外，我们考虑了短，中和长周期玉米的种植情况，并比较了模拟的WRSI和模型产量与坦桑尼亚玉米种植区国家水平报告的单产之间的关系。 对于生长时间为120-160天的玉米品种，R ² = 0.52-0.61）。总体而言，CHIRPS和TAMSAT 在捕获与季节性土壤水分缺乏有关的国家级作物产量损失方面表现出最高的技能（分别为R ² = 0.46-0.55和0.44-0.58），这对于干旱预警和农业气象风险应用至关重要。