Machine learning for hydrologic modeling has seen significant recent development and has been suggested as a valuable augmentation to physical hydrological modeling, especially in data-scarce catchments. In Pakistan, surface water flows predominantly originate from the transboundary Upper Indus sub-catchments of Chenab, Jhelum, Indus, and Kabul rivers. These catchments have large drainage areas, climate-driven streamflows, high variations in elevation, and limited streamflow gauge coverage. Hence, using machine learning models for data-driven river flow modeling may be well-suited for these catchments. However, hydrologic interpretability of machine learning models is important for the practical use of such models for these catchments. Thus, the current study besides evaluating the potential of three machine learning models (XGBOOST, Classification and Regression Trees(CART), and RandomForest) for streamflow simulation also focused on the hydrologic interpretation of machine learning models using SHapley Additive exPlananations (SHAP). All of these models performed well and the range of $$\textrm{R}^2$$R 2 and Nasche-Efficiency for all three models lies between 0.61 to 0.90. Moreover, SHAP correctly identified minimum temperature as the most critical feature in glacier-fed Indus and Chenab catchments. It also provides logical insights into interactions between minimum temperature and precipitation for the indus and Chenab catchment. The findings of this study strongly illustrate the usefulness of SHAP analysis in interpreting the behavior of data-scarce high-elevation climate-sensitive catchments using tree-based machine learning models.

Heavy metal (HM) contamination in marine sediments is a critical environmental concern due to its potential ecological and human health impacts. This study provides a comprehensive assessment of the spatial and temporal evolution of HM contamination in Arabian Gulf sediments over a 33-year period (1991–2024). Contamination levels were evaluated using key sediment quality indices such as the contamination factor (CF), geoaccumulation index (I_gₑₒ), pollution load index (PLI), and ecological risk index (ERI) based on available meta-data. Spatial analysis revealed that marine sediments along the Iranian and Saudi Arabian coasts exhibited higher contamination levels compared to those of other Gulf countries. This elevated contamination is likely attributed to industrial discharges, oil spills and effluent releases from desalination plants. Arsenic (As), cadmium (Cd), copper (Cu), nickel (Ni), and lead (Pb) emerged as the primary toxic contributors to sediment pollution. Overall, HM contamination status in the region is predominantly low to moderate, with localized hotspots near industrialized coastal zones. The study highlights the need for scientifically informed waste management strategies to mitigate HM contamination, particularly in nearshore environments impacted by industrial activities. Furthermore, it recommends the implementation of continuous monitoring programs to track temporal trends and identify emerging contamination sources. These findings provide critical insights for environmental managers and policymakers in developing sustainable pollution mitigation frameworks and enhancing the resilience of marine ecosystems in the Arabian Gulf.

Effective drought monitoring, prediction and early warning systems are crucial for optimized management of human activities associated with water use in a climate affected world. In Pakistan, surface water flows predominantly originate from the transboundary Upper Indus sub-catchments of Chenab, Jhelum, Indus and Kabul rivers. Hence, impact of droughts manifested through water deficits in these catchments are strongly felt by downstream users. Use of different drought indicators is limited in Pakistan's operational drought monitoring system. Moreover, there is very limited prior literature that explores the use of multiple indicators for unearthing relationships between different drought types. This study aims to explore the relationship between meteorological and hydrological droughts in the Upper Indus catchments of Pakistan using the Standard Precipitation and Evaporation Index (SPEI) and the Standard Streamflow Index (SSI). Since there are no previous studies for the Indus that compare different distributions for SSI computation, we compare five distributions to adequately compute SSI values at catchment outlets. Our most crucial contribution in this study is analysis of seasonal cross-correlations and lagged cross-correlations between SSI and SPEI for the above-mentioned four catchments. The cross-correlation analysis shows strong lagged (with up to 2 lag months) cross-correlations between SPEI and SSI for Chenab, Jhelum and Kabul catchments in early Kharif months. These correlations may be used in operational drought monitoring and forecasting systems, and also in reservoir planning and operations (for the Mangla reservoir in Jhelum) in drought conditions. We strongly believe that the findings of this study can be used in future to collectively explore hydrological and meteorological drought perspectives in Pakistan and to successfully incorporate multiple indicators into operational drought management.