Abstract One of the major components of the hydrological cycle, reference evapotranspiration (ET0) represents the maximum amount of water transferred from the land surface to the atmosphere. Vital to quantifying crop water needs, accurate predictions of ET0 are particularly critical in arid regions, where they allow for informed water resources management adjustments through changes to agricultural irrigation rates and scheduling. Drawing upon 84 meteorological stations in northwest China, spatiotemporal variations in present-day ET0 were investigated. Support vector regression (SVR), Extreme learning machine (ELM), and Multivariate adaptive regression spline (MARS) — three machine learning (ML) techniques — served to establish relationships between historical ET0 and the Coordinated Regional Climate Downscaling Experiment – East Asia (CORDEX-EA), drawn from the output data sets of each of three regional climate models (RCM): Weather Research and Forecasting (WRF), Regional Climate Model version 4.0 (RegCM4) and the Mesoscale Model version 5 (MM5). The ML-RCM combinations were calibrated and validated with separate batches (66:34, respectively) of historical ET0 data, and their respective performance and level of uncertainty were assessed statistically. In the historical period (1960–2017) ET0 declined by −0.15, −0.75, and − 0.42 mm y−1 in north Xinjiang, south Xinjiang, and Qinghai region, respectively, and increased in the Hexi Corridor by 0.5 mm y−1. For all four regions, the MARS-WRF and MARS-MM5 combinations performed well and showed greater predictive accuracy than either ELM-WRF or ELM-MM5 combinations. Performances in predicting future (2035–2050) ET0 from CORDEX-EA outputs based on regional climate predictions RCP 4.5 and RCP 8.5 scenarios, depended to a greater extent on the RCM outputs that were selected, rather than the modeling methods. Future ET0 predicted from RCMs generally exhibit increasing trends, and more significantly under the RCP 8.5 scenario. The representation and characterization ability of RCMs to future climate change is crucial for future ET0 projection. Uncertainty analysis, achieved by employing multiple RCMs to predict future ET0, is highly recommended. Knowledge of trends in future ET0 can help guide the management of agricultural irrigation in oases and support decision-makers engaged in water resources management in the future.

中文翻译：

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基于CORDEX-EA的西北参考蒸发量时空变化

摘要 作为水文循环的主要组成部分之一，参考蒸散量 (ET0) 表示从地表向大气转移的最大水量。对于量化作物用水需求至关重要，ET0 的准确预测在干旱地区尤为重要，它们允许通过改变农业灌溉率和调度来调整水资源管理。利用西北地区84个气象站，研究了现今ET0的时空变化。支持向量回归 (SVR)、极限学习机 (ELM) 和多元自适应回归样条 (MARS) — 三种机器学习 (ML) 技术 — 用于建立历史 ET0 与协调区域气候降尺度实验 - 东亚 (CORDEX) 之间的关系-EA), 来自三个区域气候模型 (RCM) 的输出数据集：天气研究和预测 (WRF)、区域气候模型版本 4.0 (RegCM4) 和中尺度模型版本 5 (MM5)。ML-RCM 组合使用不同批次（分别为 66:34）的历史 ET0 数据进行校准和验证，并对其各自的性能和不确定性水平进行了统计评估。历史时期（1960-2017）ET0在北疆、南疆和青海地区分别下降了-0.15、-0.75和-0.42 mm y-1，在河西走廊增加了0.5 mm y-1 . 对于所有四个区域，MARS-WRF 和 MARS-MM5 组合表现良好，并且显示出比 ELM-WRF 或 ELM-MM5 组合更高的预测准确性。基于区域气候预测 RCP 4.5 和 RCP 8.5 情景，从 CORDEX-EA 输出预测未来（2035-2050）ET0 的性能在很大程度上取决于所选的 RCM 输出，而不是建模方法。从 RCM 预测的未来 ET0 通常呈现增加趋势，在 RCP 8.5 情景下更为显着。RCM 对未来气候变化的表征和表征能力对于未来的 ET0 预测至关重要。强烈建议通过使用多个 RCM 来预测未来的 ET0 来实现不确定性分析。了解未来 ET0 的趋势有助于指导绿洲农业灌溉的管理，并支持未来从事水资源管理的决策者。很大程度上取决于所选的 RCM 输出，而不是建模方法。从 RCM 预测的未来 ET0 通常呈现增加趋势，在 RCP 8.5 情景下更为显着。RCM 对未来气候变化的表征和表征能力对于未来的 ET0 预测至关重要。强烈建议通过使用多个 RCM 来预测未来的 ET0 来实现不确定性分析。了解未来 ET0 的趋势有助于指导绿洲农业灌溉的管理，并支持未来从事水资源管理的决策者。很大程度上取决于所选的 RCM 输出，而不是建模方法。从 RCM 预测的未来 ET0 通常呈现增加趋势，在 RCP 8.5 情景下更为显着。RCM 对未来气候变化的表征和表征能力对于未来的 ET0 预测至关重要。强烈建议通过使用多个 RCM 来预测未来的 ET0 来实现不确定性分析。了解未来 ET0 的趋势有助于指导绿洲农业灌溉的管理，并支持未来从事水资源管理的决策者。RCM 对未来气候变化的表征和表征能力对于未来的 ET0 预测至关重要。强烈建议通过使用多个 RCM 来预测未来的 ET0 来实现不确定性分析。了解未来 ET0 的趋势有助于指导绿洲农业灌溉的管理，并支持未来从事水资源管理的决策者。RCM 对未来气候变化的表征和表征能力对于未来的 ET0 预测至关重要。强烈建议通过使用多个 RCM 来预测未来的 ET0 来实现不确定性分析。了解未来 ET0 的趋势有助于指导绿洲农业灌溉的管理，并支持未来从事水资源管理的决策者。