Jun Changhyun, Narimani Roya, Yeh Pat J-F, Kim Sang Yeob, Wu Chuanhao
Department of Civil and Environmental Engineering, Chung-Ang University, Seoul 06974, Republic of Korea.
Discipline of Civil Engineering, School of Engineering, Monash University (Malaysia Campus), Malaysia.
Sci Total Environ. 2024 May 15;925:171839. doi: 10.1016/j.scitotenv.2024.171839. Epub 2024 Mar 19.
Water availability needs to be accurately assessed to understand and effectively manage hydrologic environments. However, the estimation of evapotranspiration (ET) is prone to errors due to the complex interactions that occur between the atmosphere, the Earth's surface, and vegetation cover. This paper proposes a novel approach for analyzing the sources of inaccuracy in estimating the annual ET using the Budyko framework (BF), particularly temporal variability in precipitation (P), potential evapotranspiration (E), runoff (R), and the change in soil storage (ΔS). Error decomposition is employed to determine the individual contributions of P, R, E, and ΔS to the ET error variance at 12 locations in the state of Illinois using a dataset covering a 22-year period. To the best of our knowledge, this study represents the first BF-based investigation that considers R in the error decomposition of the predicted ET variance. The ET error variance increases with the variance in the P and R in Illinois and decreases with the covariance between these two variables. In addition, when accounting for ΔS in the BF, the scenario in which ΔS affects the total available water (i.e., P) is reliable, with a low prediction error and a 13.87 % lower root mean square error compared with the scenario in which the effect of ΔS is negligible. We thus recommend the inclusion of ΔS and R as key variables in the BF to improve water budget estimations.
为了理解并有效管理水文环境，需要准确评估水资源可利用量。然而，由于大气、地球表面和植被覆盖之间发生的复杂相互作用，蒸散量（ET）的估算容易出现误差。本文提出了一种新方法，用于分析使用布迪科框架（BF）估算年ET时误差的来源，特别是降水（P）、潜在蒸散量（E）、径流（R）和土壤储水量变化（ΔS）的时间变异性。利用涵盖22年的数据集，采用误差分解方法确定P、R、E和ΔS对伊利诺伊州12个地点ET误差方差的各自贡献。据我们所知，本研究是基于BF的首次调查，在预测ET方差的误差分解中考虑了R。伊利诺伊州的ET误差方差随P和R的方差增加而增加，随这两个变量之间的协方差减小而减小。此外，在BF中考虑ΔS时，ΔS影响总可用水量（即P）的情景是可靠的，预测误差较低，与ΔS影响可忽略不计的情景相比，均方根误差低13.87%。因此，我们建议将ΔS和R作为BF中的关键变量纳入，以改进水平衡估算。
