Temperature‐based grass‐reference evapotranspiration (ET_o) estimation methods (e.g., Hargreaves−Samani [HS] model) present advantages over combination‐based methods that require full‐suite weather data. The U.S. High Plains region has scarce and short‐term full‐suite weather sites. This data scarcity presents challenges for combination‐based ET_o estimation. The performance of HS model against the American Society of Civil Engineers (ASCE) standardized Penman−Monteith (PM) model was assessed using long‐term data at 124 full‐suite weather sites across nine states in the U.S. High Plains. The HS model underestimated ET_o at arid (mean bias error [MBE] = −1.68 mm d⁻¹), semi‐arid (MBE = −0.34 mm d⁻¹), and dry subhumid sites (MBE = −0.16 mm d⁻¹) and overestimated ET_o at humid sites (MBE = 0.14 mm d⁻¹). There was a significant relationship (p < .01) between HS model performance and aridity index. The HS model performed better (27% lower root mean squared difference [RMSD]) in summer months than the rest of the year at semi‐arid and dry subhumid sites. The model performance was non‐ideal during the summer months in subhumid climates. Spatio‐temporal annual zonal (climate division), monthly zonal, annual site‐specific, and monthly site‐specific calibration resulted in 12, 16, 20, and 26% reduction in RMSD and 11, 16, 17, and 23% reduction in relative error, respectively. Monthly site‐specific calibration performed the best and was used to quantify annual and growing season ET_o across the region. The research characterized performance patterns of the HS model over an important agroecosystem‐dominated region. Practical data‐driven strategies were proposed to better estimate PM ET_o using limited weather data at any given site (with similar aridity) and time of the year.

中文翻译：

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Hargreaves-Samani模型的时空校准以估计美国高平原上的参考蒸散量

基于温度的草参考蒸散量（ET _o）估算方法（例如，Hargreaves-Samani [HS]模型）具有优于需要全套气象数据的基于组合的方法的优势。美国高平原地区的稀缺和短期全套房天气点。这种数据稀缺性给基于组合的ET _o估计提出了挑战。使用美国高平原9个州的124个全套房气象点的长期数据，评估了HS模型相对于美国土木工程师学会（ASCE）标准化Penman-Monteith（PM）模型的性能。HS模型在干旱（平均偏差误差[MBE] = -1.68 mm d ^-1），半干旱（MBE = -0.34 mm d ^-1）时低估了ET _o），干燥的半湿润地区（MBE = -0.16 mm d ^-1）和湿润地区（MBE = 0.14 mm d ^-1）高估了ET _o。有显着的关系（p <.01）在HS模型性能和干旱指数之间。在半干旱和干燥的半湿润地区，HS模型在夏季月份的表现要好于其他月份（均方根差[RMSD]低27％）。在夏季的半湿润气候条件下，模型的表现不理想。时空年度分区（气候分区），每月分区，年度特定地点和每月特定地点的校准导致RMSD降低12％，16％，20％和26％，而RMSD降低11％，16％，17％和23％相对误差。每月的网站特定的校正效果最佳，并用于量化年度生长季ET _Ø整个地区。这项研究描述了在一个重要的农业生态系统为主的地区，HS模型的绩效模式。提出了实用的数据驱动策略，以在任何给定站点（干旱程度类似）和一年中的时间使用有限的天气数据更好地估计PM ET _o。