Spatiotemporal Heterogeneity of Surface Evapotranspiration and its Driving Mechanisms in a Significant Ecological Recovery Area

Suhua Liu, Zhenfeng Zang (), Xiangchao Qin, Xudong Huang, Chunying Wang, Hongbo Su and Yuping Han
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Suhua Liu: North China University of Water Resources and Electric Power, College of Water Resources
Zhenfeng Zang: North China University of Water Resources and Electric Power, College of Water Resources
Xiangchao Qin: Eco-environmental Monitoring and Scientific Research Center, Yellow River Basin Ecology and Environment Administration
Xudong Huang: North China University of Water Resources and Electric Power, College of Water Resources
Chunying Wang: North China University of Water Resources and Electric Power, College of Water Resources
Hongbo Su: Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering
Yuping Han: Zhejiang University of Water Resources and Electric Power

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2025, vol. 39, issue 15, No 14, 8135-8155

Abstract: Abstract Evapotranspiration (ET) plays a crucial role in both the hydrological and energy cycles, and it is closely influenced by environmental factors. Changes in environmental factors affect ET by altering energy distribution and canopy conductance. Hence, exploring the driving mechanisms of environmental factors on ET is a fundamental issue in ecohydrology. The study focuses on the Yanhe River Basin located in the Loess Plateau of China, which is a typical hilly and gully region undergone large-scale and high-standard ecological restoration projects since 1999. By developing a dynamic potential canopy conductance equation that accounts for the dynamic changes in land cover types, the ET model is improved. Based on the analysis of spatiotemporal patterns of annual ET from 2000 to 2023, this study employs the GeoDetector method to elucidate the driving mechanisms of multiple environmental factors on ET formation. Findings can be categorized into three main aspects. First, the improved ET model greatly minimizes simulation errors at both site and watershed scales. A strong linear relationship is observed between ET estimates from the improved model and those from the water balance model, with a coefficient of determination of 0.97 at the 0.01 significance level. Second, the interannual ET exhibits an overall increasing trend from 2000 to 2023. In terms of ET spatial distribution, the evolutionary pattern indicates a significant growth trend in ET across most regions, while certain small areas (e.g., villages and towns) show declining trends due to sparse vegetation coverage and surface hardening. Third, the GeoDetector-based analysis demonstrates that fractional vegetation cover (FVC), precipitation (P), vapor pressure deficit (VPD) and land use and cover change (LUCC) exhibit explanatory powers (q-values) greater than 0.10 (p = 0.01) across the periods of 2000—2010, 2011—2023 and 2000—2023, and these four variables are identified as the primary drivers of the spatiotemporal differentiation in ET, with FVC showing the highest explanatory power. Moreover, the interaction between any two factors yields higher explanatory power than individual factors alone, and all interactions are characterized as bi-factor enhancement or nonlinear enhancement. Results provide a theoretical foundation and practical frameworks for promoting ecosystem rehabilitation and implementing sustainable water resources management strategies.

Keywords: Evapotranspiration; Driving mechanism; GeoDetector; Ecological recovery (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11269-025-04334-z

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