In arid and semi-arid climate zones, understanding the spatial patterns and biogeographical mechanisms of net primary production (NPP) and precipitation use efficiency (PUE) is crucial for assessing the function and stability of ecosystem services, as well as directing ecological restoration. Although the vegetation coverage has changed dramatically after the construction of several ecological restoration projects, due to limited observation data, fewer studies have provided a thorough understanding of NPP and PUE’s recent spatial patterns and the controlling factors of different vegetation types in the Yellow River Basin (YRB). To narrow this gap, we integrated remote-sensing land-cover maps with long-term MODIS NPP and meteorological datasets to comprehend NPP and PUE spatial patterns in YRB. Furthermore, we applied structural equation models (SEM) to estimate the effect intensity of NPP and PUE controlling factors. The results showed that along geographical coordinates NPP and PUE decreased from southeast to northwest and trends were roughly consistent along latitude, longitude, and elevation gradients with segmented patterns of increasing and decreasing trends. As for climate gradients, NPP showed significant linear positive and negative trends across the mean annual precipitation (MAP) and the arid index (AI), while segmented changes for PUE. However, the mean annual average temperature (MAT) showed a positive slope for below zero temperature and no change above zero temperature for both NPP and PUE. SEM results suggested that AI determined the spatial pattern of NPP, whereas PUE was controlled by MAP and NPP. As the AI becomes higher in the further, vegetation tends to have decreased NPP with higher sensitivity to water availability. While artificial vegetation had a substantially lower NPP than original vegetation but increased water competition between the ecosystem and human society. Hence further optimization of artificial vegetation is needed to satisfy both ecological and economic needs. This study advanced our understanding of spatial patterns and biogeographic mechanisms of NPP and PUE at YRB, therefore giving theoretical guidance for ecological restoration and ecosystem function evaluation in the face of further climate change.

The incredible drying of the Aral Sea has resulted in a large area of exposed seafloor with saline soils, which has led to catastrophic consequences. This study investigated ground-truth soil salinity data and used Landsat data to map the soil salinity distribution of the exposed seabed of the Aral Sea from 1960 onwards. The soil salinity distribution, with the depth from 0 cm to 100 cm, was analyzed. The correlation analysis was applied to find the best performance index in describing soil salinity changes. The results showed that ground-truth data of topsoil salinity (depth of 0−5 cm) exhibited a significantly strong correlation with soil salinity index 4 (SI4) among seven indices, where the Pearson correlation coefficient (r) was up to 0.92. Based on the relationship between soil salinity sampling data and SI4, a linear regression model was employed to determine the capability of evaluating the soil salinity distribution of the Aral Sea with the coefficient of determination (R²), root mean squared error (RMSE), and ratio of performance to deviation (RPD) values of 0.84 and 0.86 dS m⁻¹ and 2.36, respectively. The SI4 performed well and was used to predict the soil salinity distribution on the exposed seabed. The distribution showed that soil salinity increased from the former to current shoreline. In the North Aral Sea, compared to 1986, the water area remained stable, accounting for 50.3% in 2020, and the soil salinization level was relatively low. However, the moderately and slightly saline areas dominated 73.8% and 7.5% of the South Aral Sea in 2020, with an increase of 53% and 6% transformed from the water area. The area of salinized soils dramatically increased. The strongly and extremely saline areas were mainly located in the northeastern part of the eastern basin and western part of Vozrozhdeniya Island, respectively, and were the main source of salt-dust storms. These results support the dynamic monitoring and distribution patterns of soil salinization in the Aral Sea.

Terrestrial water storage (TWS) variations are a result of the interconnected impact of various variables including climate, hydrology, ecology, and anthropogenic activities. Previous studies have indicated that climate factors (e.g., precipitation and potential evapotranspiration), vegetation restoration, and water withdrawals (irrigational and industrial water use) are the major determinants of TWS depletion across the Yellow River Basin (YRB). However, few studies have provided explicit information about the main forcing variables that determine spatiotemporal variations in TWS and the synergies among these factors. This study explored the explicit understanding of hydro-climatic and socio-ecological determinants and the key interacting processes that affected the TWS variations across the Yellow River Basin in northern China. The multivariate adaptive regression splines model was employed to establish the relationship function of the long-term trends for the dependent (TWS) and independent (explanatory) variables consisting of normalized difference vegetation index (NDVI), hydro-climate, and human water withdrawal. The long-term trends estimated from the MARS model reproduced the ones calculated by Gravity Recovery and Climate Experiment gravity satellites, with a determination coefficient (R²) of 0.83 and a mean absolute error (MAE) of 1.2 mm. The results showed that precipitation, minimum temperature, runoff, base flow, water withdrawal for electricity, and NDVI were the main drivers of the spatiotemporal variations in the TWS, of which minimum temperature and runoff played a considerable role in TWS variations through the interplay with other variables. The critical values of the trend for interactive variables, which could alter the acting direction of the synergy on the TWS, were also estimated. In view of the connotation of interactive variables, we suggested that spatiotemporal variations in TWS resulted from the coupling of the hydrological energy system, hydrological ecosystem, and hydrological system in the YRB, of which the hydrological system plays the most significant role, followed by the hydrological ecosystem.

Due to the scarcity of field observations and geodetic measurements in catchments in the Karakoram Mountains in Western China, obtaining precipitation data for the high mountains involves large uncertainties and difficulties. In this study, we used a functional relationship between the annual glacier accumulation and summer temperature at the equilibrium line altitude (ELA) to derive precipitation lapse rates (PLAPSs) in a data-scarce watershed. These data were used in a modified Soil and Water Assessment Tool (SWAT) model with a glacier module to simulate glacio-hydrological processes in the Yarkant River basin in the Karakoram. The PLAPS based on the widely-used grid datasets considerably underestimated precipitation, yielding an unreasonable watershed water balance and inaccurate glacier changes. However, the ELA-based PLAPS improved the simulation significantly. In the Yarkant River basin, the annual precipitation reached a peak of 800–1000 mm at approximately 5300 m a.s.l. The model simulations indicated that the contributions of glacier melt and ice melt to total runoff were 52% and 31%, respectively. Moreover, a significant precipitation increase and a non-significant temperature increase during the melt season may be the major reasons for the decreased ice melt and slower glacier shrinkage on the northern slope of the Karakoram during the period of 1968–2007.