The Yarlung Zangbo River Basin (YZRB), located in the Qinghai-Tibetan Plateau, has been significantly impacted by global warming and greening. Serving as an indicator of coupled vegetation growth and climate variation, the spatiotemporal land surface temperature (LST) has undergone substantial changes in recent decades. In this study, we evaluated the components of the water and energy cycle from 1980 to 2015 using the VIC model, a widely recognized and applied distributed hydrological model, to obtain continuous 35-year daily LST data. The results demonstrated that the VIC model exhibited high adaptability in the YZRB. Then, the fluctuation of LST was examined, and the influence of environmental elements on LST was identified. Our modeling indicated that climate factors were increasing, while human activities remained stable in the YZRB. In YZRB, the greening was witnessed while LST showed an increasing trend. By distinguishing the impacts of climate and human activities on LST, LST was mainly affected by climate with contribution rate at 70.36% from 1980 to 1995. After 1995, LST was mainly affected by human activities, and its contribution rate was 55%. Grassland with medium cover showed the potential of a cooling influence. Among all environmental factors, albedo showed a negative and delayed effect on LST. Temperature, precipitation, and evapotranspiration were positively correlated with LST and displayed relatively synchronous changes. Soil moisture and NDVI were detected as leading positive changes in LST. Our study contributes to clarifying the mechanisms influencing LST in high-altitude and high-latitude regions under global greening, providing fundamental insights for socio-economic development in alpine mountainous regions.

The Yarlung Zangbo River Basin (YZRB), located in the Qinghai-Tibetan Plateau, has been dramatically affected by global warming. In recent decades, serving as the indicator of coupled vegetation growth and climate variation, the spatiotemporal land surface temperature has been changed substantially by changes in environmental factors while greening spreading. In this study, we evaluated the components of water and energy cycle during 1980-2015 based on the VIC model, one of the widely recognized and applicated distributed hydrological model. The fluctuation of LST was examined and the influence of environmental elements on LST was identified. The results showed that VIC model performed a high adaptability in applying and conducting in YZRB with R 2 over 0.7 and Er at 5.03%. Climate factors were increasing while human activities stayed stable in YZRB by our modeling. In addition, climate factors (precipitation, evapotranspiration, temperature) and underlying factors (soil moisture, NDVI, Albedo) were detected as influencing factors of LST. In YZRB, the greening was witnessed while LST showed an increasing trend. By distinguishing the climate and human activities on LST, ET and NDVI are two dominant factors effecting LST. From 1980 to 1995, LST was mainly affected by climate and its contribution rate was 70.36%. After 1995, LST was affected by human activities, and its contribution rate was 55%. Grassland with medium cover showed the potential of a cooling influence. Among all the environmental factors, Albedo showed a negative and a lagged behind effect on LST. Temp, P and ET were positively related to LST and displayed changes that are relatively in phase. SM, NDVI, were detected as leading the changes in LST, positively. Our study contributes to clarifying the mechanisms influencing LST in high-altitude and high-latitude regions under the global greening and is fundamental for socio-economic development in alpine mountainous regions.

Technology has greatly promoted ecohydrological model development, but runoff generation and confluence simulations have fallen behind in ecohydrological model development due to limited innovations. To fully understand ecohydrological processes and accurately describe the coupling between ecological and hydrological processes, a distributed ecohydrological model was constructed by integrating multisource information into MYEH. We mainly describe runoff generation and convergence modules. Based on the improved HBV model and degree-3 hour factor method, runoff generation and snow routines were constructed for semiarid grassland basins. In view of meandering and variable steppe river channels and steep hydrological relief characteristics, a confluence module was constructed; the 1-km bend radius equivalent concept was innovatively proposed to unify river channel bend degrees. The daily runoff simulation validation results obtained using two datasets were R2=0.947 and 0.932, NSE=0.945 and 0.905, and KGE=0.029 and 0.261. In the 3-hour flood simulations, the MYEH model could better restore small long-distance water flows than the confluence method that did not consider actual river lengths or bend energy losses; the MYEH model more accurately simulated the flood peak arrival time than the confluence method that did not consider overflow. The simulated mainstream overflow frequency increased by 0.84/10 years, and significant interaction periods of 10 to 13 years occurred with local precipitation, ecological status and global climate change. An approximately 2-year lag occurred in the global climate change response. This study helps us further understand and reveal the ecohydrological processes of steppe rivers in semiarid regions.

Key Points: • Ecological and evapotranspiration characteristics of ten typical vegetation communities in semi-arid steppe were refined and decomposed. • Sensitive parameters of dynamic evapotranspiration improve the regional simulation effect. • Deep learning was used to downscale regional evapotranspiration at the 3-hour scale. Abstract Reports on ecohydrological models for semi-arid steppe basins with scarce historical data are rare. To fully understand the ecohydrological processes in such areas and accurately describe the coupling and mutual feedback between ecological and hydrological processes, a distributed ecohydrological model was constructed , which integrates multi-source information into the MY Ecohydrology (MYEH) model. This paper mainly describes the evapotranspiration module (Eva module) based on sensitive parameters and deep learning. Based on multi-source meteorological, soil, vegetation, and remote sensing data, the historical dynamic characteristics of ten typical vegetation communities in the semi-arid steppe are refined in this study and seven evaporation (ET) components in the Xilin River Basin (XRB) from 1980 to 2018 are simulated. The results show that the Naive Bayesian model constructed based on the temperature and three types of surface reflectance can clearly distinguish between snow-covered or-free conditions. Based on the refinement of typical vegetation communities, the ET process characteristics of different vegetation communities in response to climate change can be determined. Dynamic sensitive parameters significantly improve the regional ET simulation. Based on the validation with the Global Land Evaporation Amsterdam Model product and multiple models in multiple time scales (year, quarter, day, 3 h), a relatively consistent and reliable ET process 1 was obtained for the XRB at the 3-hour scale. The uncertainties of adding and dynamizing more ET process parameters and adjusting the algorithm structure must be further studied.

Meteorological elements have different lag periods for solar activities (SA), climatic oscillation (CO) and other influencing factors at different spatiotemporal scales. To further understand the “solar-climate-water resource” system, this study considers China as the study area and investigates the monthly data of temperature (T) and precipitation (P) during 1900–2020 that were obtained from 3836 grid stations. The strong interaction and lag distribution between T or P with SA and CO were studied and influence weights of SA, CO, and geographical factors (GF) of each grid station were calculated. A multivariate hysteretic decomposition model was established to simulate and quantitatively decompose the periodic lag considering the factors of the earth’s revolution. The results indicate the existence of two dividing lines in the distribution of T and P lag periods. Additionally, the underlying surface conditions and urbanisation were observed to have significant effects on the periodic lag of meteorological elements.