Changes in reference crop evapotranspiration (ETo) due to climate change (CC) can severely impact food and water security, emphasizing the need for integrating ETo projections into agricultural water management strategies. In this study, ETo changes were projected for two future time slices with respect to the baseline using several machine learning techniques, incorporating minimum and maximum temperature, diurnal temperature range, and extraterrestrial radiation across Iran. Additionally, an ensemble of 10 CMIP6 Global Climate Models, downscaled by the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6), was employed. The X-means clustering algorithm was also exploited to classify ETo based on various characteristics, including minimum, maximum, average, skewness, and standard deviation, as well as ETo ranges of 0–5, 5–10, and greater than 10 mm d⁻¹. This clustering approach divided the study area into five distinct clusters. Apart from cluster I, where the Support Vector Machine outperformed, the Random Forest technique provided more accurate ETo predictions. The findings project an average ETo increase of 4.8 % and 5.3 % during 2030–2049, and 8.0 % and 13.3 % for 2080–2099 under SSP245 and SSP585, respectively. Geographically, the highest ETo increases are anticipated primarily in the northern and western parts of the country, predominantly within clusters I and II. Notably, the ETo rise will exceed 40 % relative to the baseline during the late century under the SSP585. Furthermore, the most significant ETo increment is expected during winter. Future projections also indicate that cluster V, which already experiences significant daily ETo peaks, will face even more ETo extremes. Given the critical importance of these regions for sustaining food and water security and preserving natural resources, the substantial rise in ETo under future CC poses a significant threat to natural sustainability in Iran. This highlights the critical necessity for adaptive strategies in agricultural water management to mitigate the adverse CC effects. In this context, the current findings can assist decision-makers in identifying hotspots and quantifying CC impacts, thereby enabling the design of crucial adaptations.

中文翻译：

---

基于机器学习算法的参考蒸散发 CMIP6 多模式集成投影


气候变化 （CC） 导致的参考作物蒸散量 （ETo） 变化会严重影响粮食和水安全，因此需要将 ETo 预测纳入农业水资源管理战略。在这项研究中，使用多种机器学习技术相对于基线预测了两个未来时间片的 ETo 变化，包括最低和最高温度、昼夜温度范围和整个伊朗的地外辐射。此外，还采用了 10 个 CMIP6 全球气候模式的集合，这些模式被 NASA 地球交换全球每日缩小预测 （NEX-GDDP-CMIP6） 缩小。还利用 X 均值聚类算法根据各种特征对 ETo 进行分类，包括最小值、最大值、平均值、偏度和标准差，以及 0-5、5-10 和大于 10 mm d⁻¹ 的 ETo 范围。这种聚类方法将研究区域划分为五个不同的集群。除了支持向量机表现优异的集群 I 外，随机森林技术提供了更准确的 ETo 预测。研究结果预测，在 SSP245 和 SSP585 下，2030-2049 年期间 ETo 平均增加 4.8% 和 5.3%，2080-2099 年分别增加 8.0% 和 13.3%。从地理上看，预计 ETo 增幅最高主要在该国北部和西部，主要是第一组和第二组。值得注意的是，在 SSP585 下，ETo 相对于本世纪末的基线将超过 40%。此外，预计冬季的 ETo 增量最显着。未来的预测还表明，已经经历显著的每日 ETo 峰值的集群 V 将面临更多的 ETo 极端事件。 鉴于这些地区对维持粮食和水安全以及保护自然资源至关重要，未来 CC 下 ETo 的大幅增加对伊朗的自然可持续性构成了重大威胁。这凸显了农业水资源管理中适应性策略以减轻 CC 不利影响的迫切必要性。在此背景下，目前的研究结果可以帮助决策者确定热点并量化 CC 影响，从而能够设计关键的适应措施。