Projected snow cover and river flows are important for planning and managing water resources in snow-dominated basins of the Himalayas. To quantify the impacts of climate change in the data scarce Panjshir River basin of Afghanistan, this study simulated present and future snow cover area (SCA) distributions with the snow model (SM), and river flows with the snowmelt runoff model (SRM). The SRM used the degree-day factor and precipitation gradient optimized by the SM to simulate river flows. Temperature and precipitation data from eight kinds of general circulation models (GCMs) were used for bias correction. The SM and SRM were first calibrated and validated using 2009–2015 data, and then bias-corrected future climate data were input to the models to simulate future SCA and river flows. Under both the representative concentration pathways (RCP) 4.5 and 8.5, the annual average SCA and river flow were projected to decrease in the mid and late 21st century, although seasonal increases were simulated in some instances. Uncertainty ranges in projected SCA and river flow under RCP 8.5 were small in the mid 21st century and large in the late 21st century. Therefore, climate change is projected to alter high-altitude stream sources in the Hindukush mountains and reduce the amount of water reaching downstream areas.

Assessment of Terrestrial Water Storage (TWS) components is crucial for understanding regional climate and water resources, particularly in arid and semi-arid regions like Afghanistan. Given the scarcity of ground-based data, this study leverages remote sensing datasets to quantify water storage changes. We integrated Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on (GRACE-FO) data with WaterGap, Global Land Water Storage (GWLS), Catchment Land Surface Model (CLSM), and climate variables (precipitation, temperature, potential evapotranspiration) using artificial neural networks (ANN) and random forests (RF). Additionally, Ice, Cloud, and Land Elevation Satellite (ICESat-1,2) data were utilized to estimate glacier mass changes. Seasonal trend decomposition using Loess (STL) was applied to assess TWS changes from 2003 to 2022. Our methodology reveals a high correlation (R = 0.90-0.97) between reconstructed and observed TWS anomalies across Afghanistan's major basins. Glacier mass decreased by -0.59 and -1.17 Gt/year during 2003-2009 and 2018-2022, respectively, while overall TWS declined by -2.46 Gt/year. The HRB experienced the largest TWS loss (-1.47 Gt/year), primarily due to groundwater depletion (-1.18 Gt/year). These findings underscore the importance of our approach for assessing water resources, providing vital insights for sustainable management under a changing climate in a data-scarce country.

Moderate Resolution Imaging Spectroradiometer (MODIS) products were used in this study which covers a period of 2000 − 2014. Comparison of the SCA derived from MODIS and Landsat NDSI showed a significant correlation, the resultant coefficient of determination (R²) was 0.85. For analyzing the trend in the SCA, the Mann-Kendall test was applied; the results show an increasing non-significant trend in the mean annual SCA with a Sen’s slope value of +16.99 (km²/year), and a significant increasing trend in the winter SCA was identified. The annual mean SCA was 8.92 km² and 564 km² for elevation ranges of 5001 − 5694 m and 4001 − 4500 m respectively. The spatial extent of snow cover was maximum in 70^° − 78.3^° slope class and minimum in 30^° − 40^° slope class. The results highlight the MODIS snow cover products’ capability to assess the spatiotemporal dynamics of snow cover in complex mountainous watersheds.

Effective water and soil management is crucial for crop productivity, particularly in rice cultivation, where poor soil quality and water scarcity pose challenges. The response of deeper-rooted rice grown in soils amended with different soil amendments (SAs) to Internet of Things (IoT)-managed alternate wetting and drying (AWD) irrigations remains undetermined. This study explores the effects of various SAs on DRO-1 IR64 rice plants under IoT-based soil moisture monitoring of AWD irrigation. A greenhouse experiment executed at the Tokyo University of Agriculture assessed two water management regimes—continuous flooding (CF) and AWD—alongside six types of SAs: vermicompost and peat moss (S + VC + PM), spirulina powder (S + SPP), gypsum (S + GS), rice husk biochar (S + RHB), zeolite (S + ZL), and soil without amendment (S + WA). Soil water content was continuously monitored at 10 cm depth using TEROS 10 probes, with data logged via a ZL6 device and managed through the ZENTRA Cloud application (METER GROUP Company). Under AWD conditions, VC + PM showed the greatest decline in volumetric water content due to enhanced root development and water uptake. In contrast, SPP and ZL maintained consistent water levels. Organic amendments like VC + PM improved soil properties and grain yield, while AWD with ZL and GS optimized water use. Strong associations exist between root traits, biomass, and grain yield. These findings highlight the benefits of integrating SAs for improved productivity in drought-prone rice systems.