Raghavan Srinivasan’s research while affiliated with Texas A&M University and other places

Global warming can change the freeze‐thaw cycles (FTCs) in seasonally frozen ground and influence soil and water conservation. This study employed an enhanced SWAT‐FT (Soil and Water Assessment Tool‐FTCs) model to explore the effects of different future climate change scenarios on the FTCs, soil hydrothermal dynamics, and soil erosion in the Upper Mississippi River Basin (UMRB), a typical black soil region with seasonally frozen ground. Results suggested that SWAT‐FT could more representatively simulate soil hydrothermal dynamics and soil erosion compared to SWAT. The SWAT‐FT simulations revealed that soil temperature in 0–100 cm soil layers of the UMRB could increase by approximately 2°C–4°C during the FTCs period under SSP5‐8.5 in the mid to late 21st century, decreasing the freezing days (FD) and even the absence of FTCs in some southern zones, but an increase in FD for some central zones. These changes were affected by air temperature, soil water content, and snow cover, resulting in three dominant response patterns of soil hydrothermal dynamics to global warming during the FTCs period in the UMRB, which were lag symmetric response in the northern zones, non‐symmetric response in the central zones, and rapid symmetric response in the southern zones. The alterations in soil hydrothermal dynamics due to global warming exacerbated soil erosion in early spring after the FTCs by 2.3 times under SSP5‐8.5 in 2071–2100 compared to the baseline scenario (1985–2014). Moreover, the erosion pattern converted from “dual‐peak” to “single‐peak” in April or May, increasing challenges of spring erosion control.

Global hydrological models are essential tools for understanding water resources and assessing climate change impacts at planetary scales, supporting water management, flood risk assessment, and sustainable development initiatives worldwide. The Soil and Water Assessment Tool (SWAT+) has demonstrated robust performance across various environments and scales, from local to continental applications. However, despite its widespread use, a global implementation of SWAT+ is currently lacking due to computational demands and data management challenges, while existing global models often lack the detailed process representation and high spatial resolution needed for comprehensive hydrological analysis. A global SWAT+ model would offer unique advantages through its integrated simulation of water quantity, quality, and land management processes, while supporting multiple UN Sustainable Development Goals and enhancing research opportunities in global hydrology. This study aimed to develop a High-resolution Global SWAT+ Model and establish a reproducible framework for large-scale SWAT+ applications. We developed the Community SWAT (CoSWAT) modeling framework, an open-source solution that automates data retrieval, preprocessing, and model configuration using Python, while maxmising parallel processing for computational efficiency. The global model was then set up using the framework at 2 km resolution using ASTER DEM, ESA land use data, FAO soil data, and ISIMIP climate data, with performance evaluated against GRDC flow data and GLEAM evapotranspiration dataset. Results without calibration showed reasonable spatial patterns in evapotranspiration simulation with 78.54 % of sampled points showing differences within ±100 mm compared to GLEAM data, though river discharge performance was limited due to lack of reservoir implementation with 23.02 % of stations showing positive Kling-Gupta Efficiency values. The development of this first global SWAT+ model demonstrates the feasibility of high-resolution global hydrological modeling using SWAT+, while the CoSWAT framework provides a robust foundation for reproducible large-scale modeling. These advances enable more detailed analysis of global water resources and climate change impacts, though future work should focus on incorporating water management practices, improving process representation with calibration, and enhancing computational efficiency.

A B S T R A C T Study region: Surface water supply source of Addis Ababa, the capital city of Ethiopia, relies primarily on the small reservoirs in Gefersa and Legedadi water supply systems located upstream of Little and Big Akaki rivers. Thus, Gefersa and Legedadi are the study watersheds of this research. Study focus: This study evaluates the impacts of land use and climate changes on surface water availability and the benefits of nature-based solutions (NbS) to enhance the water supply and the life of the dams in the Gefersa and Legedadi small watersheds that supply water to Addis Ababa city, Ethiopia. Several land use and climate change scenarios have been developed and integrated into baseline hydrological model to assess their impact on water balance components and sediment yield. Extreme climate change scenarios were developed using the combination of the 5th, 50th and 95th percentiles of future precipitation and temperature changes. New hydrological insights for the region: The results of the land use change analysis revealed a shift between 2012, 2022 and 2042, with a significant expansion of urban settlements and a decline in forestland and vegetation cover. Under climate change scenario, the simulations project that drier seasons become drier and wet seasons become wetter. Overall, this study highlights the potential benefits of NbS in enhancing water availability, particularly during the dry season, promoting dry season farming and increasing the water supply to meet the water demand. The approach followed in this study can be adapted to other watersheds with access to more recent and good quality datasets for future research. Keywords: Land use change Climate change; Nature base solution; SWAT; Gefersa watershed; Legedadi watershed

Estimation of the water quality parameters is important to enhance time and cost-effectiveness to that of the conventional approach. This study is aimed to identify the best machine learning (ML) approach to predict concentrations of biochemical oxygen demand (BOD), nitrate, and phosphate. Four ML techniques including decision tree, random forest, gradient boosting, and XGBoost were compared to estimate the water quality parameters based on biophysical (i.e., population, basin area, river slope, water level, and stream flow) and physicochemical properties (i.e., conductivity, turbidity, pH, temperature, and dissolved oxygen) as input parameters. The data were split into training and test sets, and the model performances were evaluated using coefficient of determination (R2), Nash–Sutcliffe efficiency coefficient (NSE), and root mean squared error (RMSE). The mean squared error (MSE) was used as the optimization target. The robust fivefold cross-validation, along with hyperparameter tuning, achieved R2 values of 0.76, 0.67, and 0.71 for phosphate, nitrate, and BOD, and NSE values of 0.73, 0.67, and 0.66, respectively. XGBoost yielded the lowest RMSE across all parameters, showcasing superior performance when considering all metrics performed. In conclusion, ML techniques, particularly with a robust cross-validation technique and hyperparameter optimization, showed good results in water quality parameter prediction.