Assessing Variations in Water Use Efficiency and Linkages with Land-Use Changes Using Three Different Data Sources: A Case Study of the Yellow River, China

Abstract

The dependence of water use efficiency (WUE) on changes in land cover types is crucial for understanding of long-term water availability and assessment of water-saving strategies. Investigating the impact of land cover types on ecosystem WUE has important implications when revealing water dynamics and land management. However, the determination of WUE and its dominant factors have always been subject to high data dependency and large calculation consumption within large basins. This paper proposes a framework for processing actual evapotranspiration (AET) and WUE calculation by coupling the Maximum Entropy Production (MEP) method with the Google Earth Engine (GEE). By employing the proposed framework and three data sources available in the GEE platform, results for actual ET and WUE from 2001 to 2020 were obtained in the Yellow River Basin (YRB). The results show that the proposed framework provides an acceptable estimation of actual ET via validation with Eddy Covariance flux sites in the YRB. The calculated WUE values varied greatly in different sub-basins within the YRB, indicating a cumulative growth rate of about 56% during the past 20 years. The dominant factor that led to these changes was the transition from Grasslands into other land-use types. Our results suggest that the use of the GEE platform coupled with the MEP method offers new possibilities for advancing understanding of water exchange and water resource management.

Dissecting the Characteristics and Driver Factors on Global Water Use Efficiency Using GLASS Data Sets

“…This study investigated the impact of latitude, elevation, SM, and four meteorological variables (PRE, TEM, PDSI, and VPD) on WUE. It is worth noting that WUE can also be influenced by additional factors, such as rising atmospheric CO 2 (Battipaglia et al., 2020; Law, 2013; N. Liu et al., 2020; X. Liu et al., 2020), nitrogen deposition (Adams et al., 2021; Gharun et al., 2021; Law, 2013; X. Lu et al., 2019), solar radiation (H. Zheng et al., 2019; P. F. Zheng et al., 2019), nutrient cycling (Ding et al., 2022; Keenan et al., 2013; Morecroft et al., 1992), and change in land use (Guo et al., 2019; H. Sun et al., 2022). Previous research has demonstrated that CO 2 concentration can affect WUE by influencing plant physiology and stomatal conductance (L. He et al., 2017; X. Liang et al., 2023; Umair et al., 2020; Zhu et al., 2011).…”

Projection of Precipitation Extremes and Flood Risk in the China–Pakistan Economic Corridor

“…It should be noted that the role of precipitation extremes in shaping flood risk depends on land cover, region, and environmental conditions (Wang et al, 2017;Sun et al, 2022). Benito et al (2015) used the flood risk assessment model established by the ancient flood data to assess the risk in Europe.…”