The Soil Moisture Active Passive (SMAP) mission with high-precision soil moisture (SM) retrieval products provides global daily composites of SM at 3, 9, and 36 km earth grids measured by L-band active and passive microwave sensors. The capability of passive microwave remote sensing has been recognized for the estimation of SM variations. The purpose of this work was to establish an interaction between the highly variable SM spatial distribution on the ground and the SMAP’s coarse resolution radiometer-based SM retrievals. In this work, SMAP Level 3 (L3) and Level 4 (L4) SM products are validated with in situ datasets observed from the different locations of the Soil Moisture Network within the ShanDian River (SMN-SDR) Basin over the period of January 2018 to December 2019. The values of the unbiased root mean square error (ubRMSE) for L3 (SPL3SMP_E) SM retrievals are close to the standard SMAP mission SM accuracy requirement of 0.04 ${\mathrm{m}}^3/{\mathrm{m}}^3$ at the 9-km scale, with an averaged ubRMSE value of 0.041 ${\mathrm{m}}^3/{\mathrm{m}}^3$ (0.050 ${\mathrm{m}}^3/{\mathrm{m}}^3$) for descending (ascending) SM with the correlation (R) values of 0.62 (0.42) against the sparse network sites. The L4 (SPL4SMGP) Surface and Root-zone SM (RZSM) estimates show less error (ubRMSE < 0.04) and high correlation (R > 0.60) values, and are consistent with the previous SMAP-based SM estimations. The SMAP L4 SM products (SPL4SMGP) performed well compared to the L3 SM retrieval products (SPL3SMP_E). In vegetated land, the variability and compatibility of the SMAP SM estimates with the evaluation metrics for both products (L3 and L4) showed a good performance in the grassland, then in the farmland, and worst in the woodlands. Finally, SMAP algorithm parameters sensitivity analysis of the satellite products was conducted to produce time-series and highly precise SM datasets in China.

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.

Glaciers play a vital role in the Asian mountain water towers and have significant downstream impacts on domestic, agricultural, and industrial water usage. The rate of glacier mass loss in the Southeastern Tibetan Plateau (SETP) is among the highest in Asia and has intensified in recent decades. However, a comprehensive quantification that considers both spatial and temporal aspects of glacier mass loss across the entire SETP is still insufficient. This study aimed to address this gap by utilizing geodetic datasets specific to each glacier by calibrating the Open Global Glacier Model (OGGM) driven by HAR v2 and reconstructing the glacier mass balance of 7756 glaciers in the SETP from 1980 to 2019 while examining their spatial variability. The findings reveal that the average mass balance during this period was −0.50 ± 0.28 m w.e. a⁻¹, with an accelerated loss observed in the 2000s (average: 0.62 ± 0.24 m w.e. a⁻¹). Notably, central glaciers in the SETP exhibited relatively smaller mass loss, indicating a gradient effect of increased loss from the central region toward the eastern and western sides. By the end of this century, the area, length, and volume of glaciers in the entire SETP region are projected to decrease by 83.57 ± 4.91%, 90.25 ± 4.23%, and 88.04 ± 4.52%, respectively. Moreover, the SETP glacier melt runoff is estimated to decrease by 62.63 ± 6.16% toward the end of the century, with the “peak water” point of glacier melt runoff predicted to occur in 2023 under the SSP585 scenario. Sensitivity experiments demonstrated that the SETP glaciers are more than three times more sensitive to temperature changes than to precipitation variations, and the observed decrease in monsoon precipitation indicates the weakening magnitude of the Indian summer monsoon in recent years. The spatially refined and high-temporal-resolution characteristics of glacier mass loss presented in this study contribute to a better understanding of specific glacier changes in the SETP. Additionally, the prediction results provide valuable references for future water resources management and policy formulation in the SETP region.

This study examines the separate and combined effects of climate variability and land-use change on historical and future runoff dynamics in the Dongwan Watershed, the largest sub-basin of the Yihe River, China. Runoff was simulated using the Soil and Water Assessment Tool (SWAT), future land-use change was projected with a Cellular Automata-Markov (CA-Markov) model, and climate forcing was derived from five CMIP6 GCMs under SSP126, SSP370, and SSP585. The SWAT model performed well during calibration and validation, with Nash–Sutcliffe Efficiency values of 0.87 and 0.85. Attribution results show climate variability accounted for 121.1% of the historical annual runoff increase between 1971–1990 and 1991–2010, with land-use change exerting a compensating effect (−25.4%). Annual runoff increased by only 3.0%, but seasonal shifts were pronounced: summer runoff rose 21.7% while winter (−32.2%), spring (−8.1%), and autumn (−8.9%) all declined. This pattern reflects higher flood risk in wet months alongside increasing water scarcity in dry ones. Future projections indicate further amplification of these seasonal contrasts, with increasing summer runoff dominance and reduced dry-season buffering capacity. Continued forest expansion and the near loss of grassland and shrubland moderately dampen climate-driven runoff increases but cannot offset intensified climatic forcing. Overall, climate change emerges as the primary control on runoff dynamics, while projected land-use change under business-as-usual trajectories plays a secondary but moderating role. These findings offer insights into seasonal water management and climate adaptation in semi-humid watersheds of China.

The intensification of extreme rainfall events under changing climate regimes has heightened concerns over nutrient losses from paddy agriculture, particularly nitrogen (N), a primary contributor to non-point source pollution. Despite advances in drainage management, limited studies have integrated probabilistic rainfall modeling with N transport simulation to evaluate mitigation strategies in rice-based systems. This study addresses this critical gap by coupling the Log-Pearson Type III (LP-III) distribution with the DRAINMOD-N II model to simulate N dynamics under varying rainfall exceedance probabilities and drainage design configurations in the Kunshan region of eastern China. The DRAINMOD-N II showed good performance, with R² values of 0.70 and 0.69, AAD of 0.05 and 0.39 mg L⁻¹, and RMSE of 0.14 and 0.91 mg L⁻¹ for NO₃⁻-N and NH₄⁺-N during calibration, and R² values of 0.88 and 0.72, AAD of 0.06 and 0.21 mg L⁻¹, and RMSE of 0.10 and 0.34 mg L⁻¹ during validation. Using around 50 years of historical precipitation data, we developed intensity–duration–frequency (IDF) curves via LP-III to derive return-period rainfall scenarios (2%, 5%, 10%, and 20%). These scenarios were then input into a validated DRAINMOD-N II model to assess nitrate-nitrogen (NO₃⁻-N) and ammonium-nitrogen (NH₄⁺-N) losses across multiple drain spacing (1000–2000 cm) and depth (80–120 cm) treatments. Results demonstrated that NO₃⁻-N and NH₄⁺-N losses increase with rainfall intensity, with up to 57.9% and 45.1% greater leaching, respectively, under 2% exceedance events compared to 20%. However, wider drain spacing substantially mitigated N losses, reducing NO₃⁻-N and NH₄⁺-N loads by up to 18% and 12%, respectively, across extreme rainfall scenarios. The integrated framework developed in this study highlights the efficacy of drainage design optimization in reducing nutrient losses while maintaining hydrological resilience under extreme weather conditions.