Soil moisture (SM) plays an important role in regulating terrestrial–atmospheric water circulation and energy balance. Most of the existing studies have explored the dynamic patterns of SM based on experimental methods. However, the analysis of large-scale regions and long-term SM sequences was limited. Alternatively, satellite remote sensing data is a potential source for SM analysis for large-scale basins. Therefore, the SM data from the European Space Agency (ESA) Climate Change Initiative (CCI) from 2000 to 2015 is used in this paper to analyze the SM spatial-temporal changes in the Yellow River Basin (YRB). Further, the Normalized Difference Vegetation Index (NDVI) and meteorological data are used to explore the relationships between SM and NDVI, precipitation, air temperature, and wind speed, respectively. The results showed that the overall trend of SM in the YRB was decreasing from southeast to northwest during the past 16 years. The upper reaches of the YRB had shown a humid trend, with a value of 0.00047 m³·m⁻³·year⁻¹, mainly due to the increase in precipitation; there was an obvious drought trend in the middle reaches of the YRB, especially in Shanxi Province and Henan Province, with a value of −0.00030 m³·m⁻³·year⁻¹, which may be owed to vegetation greening increasing the soil evaporation. Overall, it is determined that the main factors influencing SM changes were NDVI and precipitation, followed by air temperature and wind speed. This study can provide a scientific basis for the spatial-temporal distribution characteristics and attributions of SM in the YRB over a long time series.

High-resolution hydrological modeling is crucial for detecting extreme hydrological events and understanding fundamental terrestrial processes. However, spatial resolutions in current hydrological modeling studies have been mostly constrained to relatively coarse resolution (~10–100 km), and they therefore have a difficult time addressing flooding or drought issues with fine resolutions. In this study, a continental-scale high-resolution hydrological modeling framework (0.0625°, ~6 km) driven by remote sensing products was used to detect extreme hydrological event occurrences in China and evaluated based on the Variable Infiltration Capacity (VIC) model. The results showed that the developed model provided more detailed information than the coarser resolution models (a 0.25° and 1°), thereby capturing the timing, duration, and spatial extent of extreme hydrologic events regarding the 2012 Beijing flood and 2009/10 drought in Hai River Basin. Here, the total water storage changes were calculated based on the VIC model (−0.017 mm/year) and Gravity Recovery and Climate Experiment (GRACE) satellite (−0.203 mm/year) to reflect the water availability caused by climate change and anthropogenic factors. This study found that the 0.0625° dataset could capture detailed changes, thereby providing reliable information during occurrences of extreme hydrological events. The high-resolution model integrated with remote sensing products could be used for accurate evaluations of continental-scale extreme hydrological events and can be valuable in understanding its long-term occurrence and water resource security.

Global urban expansion has altered surface aerodynamics and hydrothermal dynamics, aggravating environmental challenges such as urban heat and urban dry islands. To identify such environmental responses, various physical models, including urban canyon models (UCMs) and land surface models (LSMs), have been developed to represent surface hydrothermal processes. However, UCMs often treat a city as a unified entity and overlook subcity heterogeneity. LSMs are generally designed for natural land cover types and lack the capability to capture urban characteristics. To address these limitations, the aim of this study is to couple an urban module with a sophisticated LSM, i.e. the Variable Infiltration Capacity (VIC) model. This coupled model, i.e. the VIC-urban model, is characterized by its ability to coordinate certain critical urban features, including urban geometry, radiative interactions, and human impacts. Adopting Beijing as an evaluation site, the VIC-urban model shows higher performance than the original version, with excellent accuracy in simulating sensible heat, latent heat, runoff, and land surface temperature (LST). The absolute error is smaller than 25 % for the sensible heat and latent heat and smaller than 12 % and 30 % for the LST and runoff, respectively, which indicates that VIC-urban can effectively simulate hydrological and thermal fluxes in urban systems. Sensitivity analysis reveals that the roof emissivity and interception capacity exert the greatest impact on the roof temperature and evaporation and the height-to-width ratio has the greatest influence on the canyon. Our work introduces a reliable option for large-scale land surface simulations that accounts for urban environments and is among the first attempts to establish a systematic urban modelling framework of the VIC model. The VIC-urban model enables the analysis of urbanization-induced environmental changes and quantification of environmental variations among different urban configurations. The proposed model can thus offer invaluable insights for urban planners and landscape designers.