The Soil and Water Assessment Tool (SWAT) model has been widely applied for simulating the water cycle and quantifying the influence of climate change and anthropogenic activities on hydrological processes. A major uncertainty of SWAT stems from poor representation of vegetation dynamics due to the use of a simplistic vegetation growth and development module. Using long-term remote sensing-based phenological data, we improved the SWAT model’s vegetation module by adding a dynamic growth start date and the dynamic heat requirement for vegetation growth rather than using constant values. We verified the new SWAT model in the Han River basin, China, and found its performance was much improved in comparison with that of the original SWAT model. Specifically, the accuracy of the leaf area index (LAI) simulation improved notably (coefficient of determination (R2) increased by 0.193, Nash–Sutcliffe Efficiency (NSE) increased by 0.846, and percent bias decreased by 42.18%), and that of runoff simulation improved modestly (R2 increased by 0.05 and NSE was similar). Additionally, we found that the original SWAT model substantially underestimated evapotranspiration (Penman–Monteith method) in comparison with the new SWAT model (65.09 mm (or 22.17%) for forests, 92.27 mm (or 32%) for orchards, and 96.16 mm (or 36.4 %) for farmland), primarily due to the inaccurate representation of LAI dynamics. Our results suggest that accurate representation of phenological dates in the vegetation growth module is important for improving the SWAT model performance in terms of estimating terrestrial water and energy balance.

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.

Hot extremes may adversely impact human health and agricultural production. Owing to anthropogenic and climate changes, the close and dynamic interaction between drought and hot extremes in most areas of China need to be revisited from the perspective of nonstationarity. This study therefore proposes a time-varying Copula-based model to describe the nonstationary dependence structure of extreme temperature (ET) and antecedent soil moisture condition to quantify the dynamic risk of hot extremes conditioned on dry/wet condition. This study proposed a new approach to identify the soil moisture driving law over extreme temperature from the point view of tail monotonicity and nonstationary risk assessment. Owing to the LTI-RTD (left tail increasing and right tail decreasing) tail monotonicity for dependence structure of these two extremes derived from most areas, the driving laws of soil moisture over ET follows DDL1-WDL1 laws (DDL1: drier antecedent soil moisture condition would trigger a higher risk of ET; WDL1: wetter antecedent soil moisture condition would alleviate the occurrence risk of ET). Because of the spatiotemporal divergence of sensitivity index derived from tail monotonicity (SITM), we can conclude that the spatial and temporal heterogeneity of response degree of ET over the variations of antecedent dry/wet conditions is evident. Incorporation of nonstationarity and tail monotonicity helps identify the changes of driving mechanism (laws) between soil moisture and hot extremes. From the comparison of different kinds of nonstationary behaviours over the spatial distribution of conditional probability of ET (CP1), the dependence nonstationarity can impose greater variations on the spatial distribution of conditional risk of ET given antecedent dry condition (CP1).

A coupled atmospheric-hydrologic system models the complex interactions between the land surface and the atmospheric boundary layer, and the water-energy cycle from groundwater across the land surface to the top of the atmosphere. A regional climate model called WRF (Weather Research Forecasting) was coupled with a land surface scheme (Noah) to simulate intensive storms in central Alberta, Canada. Accounting for the land-atmosphere feedback enhances the predictability of the fine-tuned WRF-Noah system. Soil moisture, vegetation, and land surface temperature influence latent and sensible heat fluxes, and modulate both thermal and dynamical characteristics of land and lower atmosphere. WRF was set up in a two-way, three-domain nested framework so that the output of the outermost domain (D1) was used to run the second domain (D2) and the output of D2 was used to run the innermost domain (D3). In two-way nesting, D3 and D2 provide the feedback to their outer domains (D2 and D1), respectively. D3 was set at a 3-km resolution adequate to simulate convective storms. WRF-Noah was forced with climate outputs from Global Climate Models (GCMs) for the baseline period 1980–2005. A quantile-quantile bias correction method and a regional frequency analysis were applied to develop intensity-duration-frequency (IDF) curves from precipitation simulated by WRF-Noah. The simulated baseline precipitation of central Alberta agreed well with observed rain gauge data of Edmonton. The 5th‐generation NCAR mesoscale atmospheric model (MM5) was also set up in a 3-domain, but one-way nesting configuration. As expected, after bias correction, precipitation simulated by MM5 was less accurate than that simulated by WRF-Noah. For storms of short durations and return periods of more than 25 years, both MM5 driven by SRES climate scenarios of CMIP3 and WRF-Noah driven by RCP climate scenarios of CMIP5 projected storm intensities in central Alberta to increase from the base period to the 2050s, and to the 2080s.

While evapotranspiration (ET) is normally measured as one hydrologic component, evaporation (E) and transpiration (T) result from different physical-biological processes. In the two-source model, a trapezoidal framework has been widely applied in recent years. The key to applying the trapezoidal framework is the determination of the dry/wet boundaries of the land surface temperature-vegetation coverage trapezoid (LST-fc). Although algorithms have been developed to characterize the two boundaries, , there still, however, exists a significant uncertainty near the wet boundary which scatters in a discrete and uneven manner. It thus is difficult to precisely locate the wet boundary. To tackle the problem, a Wet Boundary Algorithm (WBA) was developed in this study and the algorithm was applied in the region of Huang-Huai-Hai plain of China by using Pixel Component Arranging and Comparing Algorithm (PCACA) to retrieve ET from MODIS Data. The latent heat flux (LE) observed by eddy covariance (EC) measurements from China FLUXNET was used to verify the modified model where the coefficient of determination (R2) was found to 0.81 and the root-mean-square-error (RMSE) was 22.8 W/m2. The ratio of transpiration to evapotranspiration (T/ET) varied between 0.5-0.75 over the region of Huang-Huai-Hai plain. The spatial and temporal distribution of ET revealed that agriculture practices had a great influence on the hydrological cycle.