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

Applicability of attribution methods for identifying runoff changes in changing environments

Under the dual impacts of global climate change and human activities, how to accurately and quantitatively assess and identify changes in watershed runoff is crucial to the rational development and utilization of water resources. Different methods of identifying runoff change attribution are applicable to different environments. In this study, we used five different methods, including the double mass curve (DMC), the soil and water assessment tool (SWAT), and the three Budyko hypotheses based on Fu, Choudhury‒Yang, and Milly‒Zhang formulas to explore the impact of human activities and climate change on runoff change in the Jinghe River basin (JRB). The results show that annual runoff of the JRB decreased considerably, and human activities accounted for more than 90%. Specifically, the results of the SWAT model and the three Budyko hypotheses are highly consistent except for the DMC method, which is not recommended in basins with significant changes in factors other than precipitation. More importantly, the Budyko hypothesis based on the Milly‒Zhang formula uses the vegetation water utilization coefficient to consider the impact of vegetation change on runoff, which is most consistent with the simulation results of the SWAT model and is recommended for the attribution analysis of changes in the Loess Plateau represented by the JRB.

A deep learning-based hybrid model of global terrestrial evaporation

Terrestrial evaporation (E) is a key climatic variable that is controlled by a plethora of environmental factors. The constraints that modulate the evaporation from plant leaves (or transpiration, Et) are particularly complex, yet are often assumed to interact linearly in global models due to our limited knowledge based on local studies. Here, we train deep learning algorithms using eddy covariance and sap flow data together with satellite observations, aiming to model transpiration stress (St), i.e., the reduction of Et from its theoretical maximum. Then, we embed the new St formulation within a process-based model of E to yield a global hybrid E model. In this hybrid model, the St formulation is bidirectionally coupled to the host model at daily timescales. Comparisons against in situ data and satellite-based proxies demonstrate an enhanced ability to estimate St and E globally. The proposed framework may be extended to improve the estimation of E in Earth System Models and enhance our understanding of this crucial climatic variable.

Diurnal Evapotranspiration and Its Controlling Factors of Alpine Ecosystems during the Growing Season in Northeast Qinghai-Tibet Plateau

It is generally believed that evapotranspiration at night is too miniscule to be considered. Thus, few studies focus on the nocturnal evapotranspiration (ETN) in alpine region. In this study, based on the half-hour eddy and meteorological data of the growing season (from May to September) in 2019, we quantified the ETN of alpine desert (AD), alpine meadow (AM), alpine meadow steppe (AMS), and alpine steppe (AS) in the Qinghai Lake Basin and clarified the different response of evapotranspiration to climate variables in daytime and nighttime with the variation of elevation. The results show that: (1) ETN accounts for 9.88~15.08% of total daily evapotranspiration and is relatively higher in AMS (15.08%) and AD (12.13%); (2) in the daytime, net radiation (Rn), temperature difference (TD), vapor pressure difference (VPD), and soil moisture have remarkable influence on evapotranspiration, and Rn and VPD are more important at high altitudes, while TD is the main factor at low altitudes; (3) in the nighttime, VPD and wind speed (WS) control ETN at high altitudes, and TD and WS drive ETN at low altitudes. Our results are of great significance in understanding ETN in the alpine regions and provide reference for further improving in the evapotranspiration estimation model.