Evapotranspiration is a necessary input and one of the most uncertain hydrologic variables for quantifying the water balance. Key to accurately predicting hydrologic processes, particularly under data scarcity, is the development of an understanding of the regional variation of the impact of potential evapotranspiration (PET) data inputs on model performance and parametrization. This study explores this impact using four different potential evapotranspiration products (of varying quality). For each data product, a lumped conceptual rainfall–runoff model (GR4J) is tested on a sample of 57 catchments included in the MOPEX data set. Monte Carlo sampling is performed, and the resulting parameter sets are analyzed to understand how the model responds to differences in the forcings. Test catchments are classified as energy- or water-limited using the Budyko framework and by eco-region, and the results are further analyzed. While model performance (and parameterization) in water-limited sites was found to be largely unaffected by the differences in the evapotranspiration inputs, in energy-limited sites model performance was impacted as model parameterizations were clearly sensitive to evapotranspiration inputs. The quality/reliability of PET data required to avoid negatively impacting rainfall–runoff model performance was found to vary primarily based on the water and energy availability of catchments.

蒸散是必要的输入，也是量化水平衡的最不确定的水文变量之一。准确预测水文过程（尤其是在数据匮乏的情况下）的关键是，对潜在蒸散量（PET）数据输入对模型性能和参数化的影响的区域变化的理解的发展。这项研究使用四种不同的潜在蒸散产品（质量各不相同）探索了这种影响。对于每个数据产品，都对MOPEX数据集中包含的57个集水区的样本进行了集总概念降雨径流模型（GR4J）的测试。执行蒙特卡洛采样，然后分析所得的参数集，以了解模型如何响应强制差异。使用Budyko框架和生态区域将集水区分为能量限制或水限制，并对结果进行进一步分析。虽然发现水受限站点的模型性能（和参数化）在很大程度上不受蒸散输入的差异的影响，但在能量受限站点中，模型参数显然对蒸发蒸腾输入很敏感，因此模型性能受到影响。发现避免对降雨-径流模型的性能产生负面影响所需的PET数据的质量/可靠性主要取决于集水区的水和能源供应情况。在能量受限的地点，由于模型参数化对蒸发蒸腾输入显然敏感，因此模型的性能受到影响。发现避免对降雨-径流模型的性能产生负面影响所需的PET数据的质量/可靠性主要取决于集水区的水和能源供应情况。在能量受限的地点，由于模型参数化对蒸发蒸腾输入显然敏感，因此模型的性能受到影响。发现避免对降雨-径流模型的性能产生负面影响所需的PET数据的质量/可靠性主要取决于集水区的水和能源供应情况。