Prediction of evapotranspiration variance in the Budyko framework with the incorporation of soil storage and runoff

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 (EP), runoff (R), and the change in soil storage (ΔS). Error decomposition is employed to determine the individual contributions of P, R, EP, 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.

Critical influence of vegetation response to rising CO2 on runoff changes

Satellite observations show widespread greening over the global land, which potentially contributes to runoff (R) changes. However, there are discrepancies in the impacts of vegetation greening on R under elevated atmospheric CO2 concentration (eCO2). Here, we proposed an improved Budyko framework with full consideration of the vegetation structural (STR) effect and vegetation physiological (PHY) effect and evaluated runoff changes (ΔR) due to eCO2-induced vegetation variations. We found a better performance of the improved Budyko framework in simulating runoff changes from global climate models (the Nash-Sutcliffe efficiency coefficient (NSE) is 0.82). However, ΔR would be overestimated (underestimated) by 188 % (165 %) when considering the PHY (STR) effect only. Attribution analyses indicated that PHY and STR effects contribute to a ΔR of 12.8 % and - 62 %, respectively, suggesting that PHY and STR effects are indispensable variables in the projection of ΔR. The contribution of the STR effect to future ΔR is 4.8 times larger than the PHY effect, leading to a negative effect of vegetation changes on ΔR in response to eCO2. While the magnitude of PHY appears less than that of STR, the influence of PHY on ΔR follows a faster-increasing tendency in future R projections when compared to STR. Our findings emphasize the critical influence of vegetation response to eCO2 in future R projection.

Analysis of Runoff Variation Characteristics and Influencing Factors in the Wujiang River Basin in the Past 30 Years

Changes in climate and the underlying surface are the main factors affecting runoff. Quantitative assessment of runoff characteristics, and determination of the climate and underlying surface contribution to changes in runoff are critical to water resources management and protection. Based on the runoff data from the Wulong Hydrological Station, combined with the Mann-Kendall test, Indicators of Hydrologic Alteration (IHA), Budyko hypothesis, and changes in climate and the underlying surface, this study comprehensively analyzed the runoff in the Wujiang River Basin (WRB). The results showed that: (1) The annual runoff of Wujiang River showed a downward trend, and an abrupt change occurred in 2005. (2) The overall hydrological change in WRB is 46%, reaching a moderate change. (3) The contribution rates of precipitation (P), potential evaporation (ET₀), and underlying surface to runoff changes are 61.5%, 11.4%, and 26.9%, respectively. (4) After 2005, the WRB has become more arid, human activities have become more active, vegetation coverage has increased, and the built-up land has increased significantly.

Budyko-Based Long-Term Water and Energy Balance Closure in Global Watersheds From Earth Observations

Earth observations offer potential pathways for accurately closing the water and energy balance of watersheds, a fundamental challenge in hydrology. However, previous attempts based on purely satellite-based estimates have focused on closing the water and energy balances separately. They are hindered by the lack of estimates of key components, such as runoff. Here, we posit a novel approach based on Budyko's water and energy balance constraints. The approach is applied to quantify the degree of long-term closure at the watershed scale, as well as its associated uncertainties, using an ensemble of global satellite data sets. We find large spatial variability across aridity, elevation, and other environmental gradients. Specifically, we find a positive correlation between elevation and closure uncertainty, as derived from the Budyko approach. In mountainous watersheds the uncertainty in closure is 3.9 ± 0.7 (dimensionless). Our results show that uncertainties in terrestrial evaporation contribute twice as much as precipitation uncertainties to errors in the closure of water and energy balance. Moreover, our results highlight the need for improving satellite-based precipitation and evaporation data in humid temperate forests, where the closure error in the Budyko space is as high as 1.1 ± 0.3, compared to only 0.2 ± 0.03 in tropical forests. Comparing the results with land surface model-based data sets driven by in situ precipitation, we find that Earth observation-based data sets perform better in regions where precipitation gauges are sparse. These findings have implications for improving the understanding of global hydrology and regional water management and can guide the development of satellite remote sensing-based data sets and Earth system models.

The role of climate change and vegetation greening on the variation of terrestrial evapotranspiration in northwest China's Qilian Mountains

Terrestrial evapotranspiration (ET_a) reflects the complex interactions of climate, vegetation, soil and terrain and is a critical component in water and energy cycles. However, the manner in which climate change and vegetation greening influence ET_a remains poorly understood, especially in alpine regions. Drawing on the Global Land Evaporation Amsterdam Model (GLEAM) ET_a data, the interannual variability of ET_a and its ties to precipitation (P), potential evaporation (ET_p) and vegetation (NDVI) were analysed. The Budyko framework was implemented over the period of 1982 to 2015 to quantify the response of ET_a to climate change's direct (P and ET_p) and indirect (NDVI) impacts. The ET_a, P, ET_p and NDVI all showed significant increasing trends from 1981 to 2015 with rates of 1.52 mm yr^-1, 3.18 mm yr^-1, 0.89 mm yr^-1 and 4.0 × 10^-4 yr^-1, respectively. At the regional level, the positive contribution of increases in P and NDVI offset the negative contribution of ET_p to the change in ET_a (∆ET_a). The positive ∆ET_a between 1982 and 2001 was strongly linked with the concomitant increase in NDVI. Increases in vegetation contributing to a positive ∆ET_a differed among landscape types: for shrub, meadow and steppe they occurred during both periods, for alpine vegetation between 1982 and 2001, and for desert between 2002 and 2015. Climate change directly contributed to a rise in ET_a, with P as the dominant factor affecting forested lands during both periods, and alpine vegetation between 2002 and 2015. Moreover, ET_p was a dominant factor for the desert between 1982 and 2001, where the variation of P was not significant. The contributions of factors having an impact on ∆ET_a are modulated by both the sensitivity of impact factors acting on ET_a as well as the magnitudes of factor changes. The greening of vegetation can influence ET_a by increasing vegetation transpiration and rainfall interception in forest, brush and meadow landscapes. These findings can help in developing a better understanding of the interaction of ecosystems and hydrology in alpine regions.