Understanding hydrological effects of ecological restoration (ER) is fundamental to develop effective measures guiding future ER and to adapt climate change in China's Loess Plateau (LP). Streamflow (Q) is an important indicator of hydrological processes that represents the combined effects of climatic and land surface conditions. Here 14 catchments located in the LP were chosen to explore the Q response to different driving factors during the period 1961–2009 by using elasticity and decomposition methods based on the Budyko framework. Our results show that (1) annual Q exhibited a decreasing trend in all catchments (−0.30 ∼ −1.71 mm yr⁻²), with an average reduction of −0.87 mm yr⁻². The runoff coefficients in flood season and nonflood season were both decreasing between two periods divided by the changing point in annual Q series; (2) the precipitation (P) and potential evapotranspiration (E₀) elasticity of Q are 2.75 and −1.75, respectively, indicating that Q is more sensitive to changes in P than that in E₀; (3) the two methods consistently demonstrated that, on average, ER (62%) contributing to Q reduction was much larger than that of climate change (38%). In addition, parameter n that entails catchment characteristics in the Budyko framework showed positive correlation with the relative area of ER measures in all catchments (eight of them are statistically significant with p < 0.05). These findings highlight the importance of ER measures on modifying the hydrological partitioning in the region. However, ER actions over the sloping parts of the landscape weakened the impact of those in channels (i.e., check‐dams) on Q, especially after the implementation of the Grain‐for‐Green project in 1999.

Quantifying the effects of climate change and human activities on runoff changes is the focus of climate change and hydrological research. This paper presents an integrated method employing the Budyko-based Fu model, hydrological modeling, and climate elasticity approaches to separate the effects of the two driving factors on surface runoff in the Luan River basin, China. The Budyko-based Fu model and the double mass curve method are used to analyze runoff changes during the period 1958~2009. Then two types of hydrological models (the distributed Soil and Water Assessment Tool model and the lumped SIMHYD model) and seven climate elasticity methods (including a nonparametric method and six Budyko-based methods) are applied to estimate the contributions of climate change and human activities to runoff change. The results show that all quantification methods are effective, and the results obtained by the nine methods are generally consistent. During the study period, the effects of climate change on runoff change accounted for 28.3~46.8% while those of human activities contributed with 53.2~71.7%, indicating that both factors have significant effects on the runoff decline in the basin, and that the effects of human activities are relatively stronger than those of climate change.

Despite the fact that gross primary productivity (GPP) and water use efficiency (WUE) have been widely used as indicators to evaluate the water-carbon cycle, uncertainties exist in the patterns of GPP and WUE responses to climate variability along different aridity gradients. In this study, the aridity index was used to divide China into four arid-humid zones. The spatiotemporal variability of multiple vegetation types GPP and WUE in response to climate change under different arid-humid zones were investigated based on remote sensing data. The results indicated that the increasing trend of WUE in the four arid-humid zones of China was less pronounced than GPP from 2001 to 2021. The GPP value decreased gradually from the humid to the arid zone, and the WUE value in the arid zone was slightly higher than in the semi-arid zone. The GPP of all vegetation types in China showed a tendency to increase, while shrubland and wetland WUE tended to decrease. The major vegetation types (e.g., forest, cropland and grassland) in each aridity gradient contributed to the changes in local GPP and WUE. However, in individual arid-humid zones, wetland and shrubland also exhibited high GPP and WUE values that were not inferior to forest and cropland. Temperature and precipitation were the main climatic factors responsible for the increase in vegetation GPP in different aridity gradients, with a higher positive correlation for temperature than precipitation. WUE showed a distinct positive and negative correlation with the thermal factors (temperature and net radiation) and the moisture factors (precipitation and relative humidity); this pattern was more pronounced in the humid and semi-humid zones. Net radiation and precipitation may be the main climatic factors causing a slight upward trend in WUE across the arid-humid zones, while the decrease in shrubland and wetland WUE may be related to relative humidity and precipitation.

Urbanization and associated land use changes significantly alter the energy and radiation balance, land surface characteristics, and regional climates, posing challenges to natural ecosystems and human society. The combined effects of changes in air temperature (T), relative humidity (RH), and wind speed (WS) profoundly influence human-perceived temperature and the corresponding human thermal comfort, especially in urban areas with large population. This study analyzes the spatiotemporal changes in human-perceived temperatures in the North China Plain, represented by heat index (HI) in summer and wind chill temperature (WCT) in winter, and quantifies the effects of urbanization on temperature changes, based on the observational data of 56 meteorological stations during 1976–2016. The results show a significant warming trend, with human-perceived temperatures increasing faster than T. The warming trend in WCT is higher than that in HI, indicating more thermal discomfort in summer and more thermal comfort in winter. However, the warming trend moderately slows after 1996, partly due to the global surface warming hiatus. Urban areas experience stronger warming trends than non-urban areas, demonstrating the notable effects of urbanization. For the entire study area, urbanization and associated urban land expansion accelerate the increase in HI by 26% and the increase in WCT by 17%.