Exploring evapotranspiration stress in China: A blending approach employing multi-source remote sensing proxies

Abstract

Study region: China Study focus: An effective evapotranspiration stress assessment system requires reliable estimates actual evapotranspiration (ETa), and potential evapotranspiration (ETp), and should consider multiple influencing indicators. This study explores the consistency of multiple evaporation reanalysis datasets across mainland China and fuses them with multi-model ensemble method. To address the uncertainty inherent in the traditional evapotranspiration stress index (ESI) that only considers the ETa and ETp relationship, we employ Pearson Correlation Analysis and a Random Forest-based Boruta Algorithm to propose new evapotranspiration stress proxies blending multiple remote sensing indicators, named as MESI-P and MESI-B, respectively. Then, we verified their performance in depicting vegetation production capacity based on Solar-Induced Chlorophyll Fluorescence (SIF) and Gross Primary Productivity (GPP). New hydrological insights for the region: The patterns of ETa from GLDAS, GLEAM, and Harvard Dataverse in China show consistent, but their threshold values differ. The synthesized ETa obtained after using a multi-model ensemble (MME) method is more adaptable in China, with a range of 0.45–1485.31 mm and an increase of 11.34 mm/10a. Following the same pattern as GLDAS and CRU, the synthetic ETp experiences an increasing trend (11.24 mm/10a) during 2000–2019. Compared with the traditional ESI, the MESI-P and MESI-B proposed in this study have superior signal convergence with ETa, SIF, and GPP due to the higher correlation coefficients (＞0.95) in China. Furthermore, during the calculation of MESI-B, soil moisture and solar radiation are identified as dominate factor in Northwest and Southern China respectively.

Keywords

• Multi-source remote sensing products
• Machine learning model
• Hydrological factors
• Potential evapotranspiration
• Actual evapotranspiration