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The Kaligandaki River Basin is highly influenced by monsoon rainfall and its complex topography, resulting in pronounced spatial and seasonal variations in water availability. Soil and Water Assessment Tool (SWAT) was applied, for simulating water balance components under data limited conditions. The model was calibrated and validated using observed discharge data from the Manglaghat and Kotagaon stations, employing the SUFI-2 algorithm. The model showed satisfactory performance, with NSE and R² values exceeding 0.7 and 0.5, respectively, at both monthly and daily time scales as hydrological model performance is acceptable for NSE and R2 more than 0.5. The simulation effectively captured key hydrological processes, including evapotranspiration, surface runoff, lateral flow, and baseflow, and revealed significant spatial and seasonal variability across the basin. Results indicate that approximately 24% of total precipitation contributes to surface runoff, another 24% to lateral flow, 16% to groundwater recharge, and about 22% is lost through evapotranspiration. Surface and lateral flows were found to dominate in the mid-hill regions, while baseflow contributions were more prominent in the lowland plains. Overall, the findings provide valuable insights into the hydrological dynamics of the Kaligandaki River Basin and offer a strong scientific basis for improved water resource management and policy planning.

Climate change has intensified water-related challenges, particularly in regions that rely on seasonal rainfall and groundwater for agriculture and daily needs. Among nature-based solutions, interlinking lakes has emerged as a promising approach to improve groundwater recharge, manage surface runoff, and enhance long-term water resilience. This review explores lake connectivity as a decentralized and adaptive strategy for sustainable water resource management under changing climatic conditions. It compiles and analyzes existing research on the hydrological, ecological, and climatic impacts of interlinked lake systems, with particular emphasis on their contribution to groundwater replenishment. The paper aims to synthesize the current state of research on lake interlinking, focusing on its potential to enhance groundwater recharge and contribute to climate change adaptation. It draws on academic literature, technological approaches, case studies, and institutional practices to offer a comprehensive perspective on this nature-based solution. The synthesis of findings indicates that interlinking lakes can serve as a nature-based solution to mitigate the adverse effects of climate change, enhance groundwater sustainability, and support integrated watershed development. The paper does not aim to test a specific hypothesis or introduce a new predictive model. Instead, it serves as a foundation by synthesizing existing knowledge, examining current research directions, and outlining areas that require further exploration.

Accurate prediction of streamflow and understanding its influential features are crucial for ensuring the reliability of models used in managing water resources effectively at a basin level. This study conducts a comparative analysis of different models including univariate deep learning, process-based model (PBM), and a combination of PBM and deep learning, to enhance the accuracy of streamflow prediction. The study introduces the univariate N-BEATS model, renowned for its proficiency in analyzing single-variable time series, and a standard LSTM model to predict daily streamflow at three river gauge stations in the Ponnaiyar River Basin. Despite the N-BEATS univariate model showing promising accuracy during testing at Gummanur (R2 = 0.75), Vazhavachanur (R2 = 0.71), and Villupuram (R2 = 0.67) stations compared to the LSTM model, the study underscores the importance of considering the intricate relationships among multiple variables in the river basin. Ignoring these complexities could result in suboptimal predictive accuracy for the models. Therefore, the PBM SWAT model was established to predict daily streamflow, exhibiting lower accuracy during calibration at Gummanur (R2 = 0.38), Vazhavachanur (R2 = 0.32), and Villupuram (R2 = 0.26) stations. To enhance predictability while maintaining accuracy and reliability, this study proposed integrating influential features from calibrated SWAT-generated features using Pearson correlation analysis and the interpretable SHAP technique with XGBoost. Incorporating significant positive and negative impact features identified through SHAP analysis, the study developed coupled PBM and deep learning SWAT-N-BEATS and SWAT-LSTM, along with the SWAT-XGBoost model, to improve daily streamflow prediction. The SWAT-N-BEATS model exhibited superior accuracy during testing at Gummanur (R2 = 0.82), Vazhavachanur (R2 = 0.75), and Villupuram (R2 = 0.74) stations compared to the SWAT-LSTM, SWAT-XGBoost, univariate LSTM, N-BEATS and calibrated SWAT model. This research emphasizes the significance of comprehending the features that influence streamflow prediction, highlights the effectiveness of N-BEATS in streamflow prediction, and demonstrates the benefits of the coupled model compared to univariate streamflow prediction using deep learning techniques, especially in regions with limited data availability.

Vegetation‐related processes, such as evapotranspiration (ET), irrigation water withdrawal, and groundwater recharge, are influencing surface water (SW)—groundwater (GW) interaction in irrigation districts. Meanwhile, conventional numerical models of SW‐GW interaction are not developed based on satellite‐based observations of vegetation indices. In this paper, we propose a novel methodology for multivariate assimilation of Sentinel‐based leaf area index (LAI) as well as in‐situ records of streamflow. Moreover, the GW model is initially calibrated based on water table observations. These observations are assimilated into the SWAT‐MODFLOW model to accurately analyze the advantage of considering high‐resolution LAI data for SW‐GW modeling. We develop a data assimilation (DA) framework for SWAT‐MODFLOW model using the particle filter based on the sampling importance resampling (PF‐SIR). Parameters of MODFLOW are calibrated using the parameter estimation (PEST) algorithm and based on in‐situ observation of the GW table. The methodology is implemented over the Mahabad Irrigation Plain, located in the Urmia Lake Basin in Iran. Some DA scenarios are closely examined, including univariate LAI assimilation (L‐DA), univariate streamflow assimilation (S‐DA), and multivariate streamflow‐LAI assimilation (SL‐DA). Results show that the SL‐DA scenario results in the best estimations of streamflow, LAI, and GW level, compared to other DA scenarios. The streamflow DA does not improve the accuracy of LAI estimation, while the LAI assimilation scenario results in significant improvements in streamflow simulation, where, compared to the open loop run, the (absolute) bias decreases from 75% to 6%. Moreover, S‐DA, compared to L‐DA, underestimates irrigation water use and demand as well as potential and actual crop yield. Key Points Using source code modification, SWAT‐MODFLOW is connected to sequential DA Multivariate assimilation of streamflow, GW‐level and leaf area index (LAI) shows the best results Streamflow data assimilation does not improve LAI simulation, while LAI data assimilation improves streamflow simulation

Climate change influences streamflow availability and dependability in watersheds due to its effect on the hydrologic cycle. This study assessed the quantitative impacts of climate change on dependable flow in the Lasang River watershed in southern Philippines using the Soil and Water Assessment Tool (SWAT) model. Three climate change scenarios including the baseline conditions and moderate and extreme conditions were formulated based on CMIP6 climate projections in the Philippines. The SWAT model was adequately calibrated and validated (NSE of 0.54 to 0.56) and was subjected to sensitivity and uncertainty analyses to ensure accurate representation of the watershed's hydrological behavior. Results of model simulation and flow duration analysis showed that moderate increases in precipitation and temperature due to climate change have negligible effects on dependable flow (1.1% increase), while extreme climate change conditions would result in relatively greater impacts on the watershed's dependable flow at a 14% increase. Results suggest that the river system may remain practically normal under a moderate climate change scenario, while greater water availability and dependability could be expected from the watershed under an extreme climate change scenario, particularly during the dry season for irrigation and other purposes. However, increased streamflow may still cause seasonal variability and potential dry-season water availability constraints under future climate conditions. Results obtained in this study could be used for proper irrigation system planning, design, and management and at the same time could serve as a basis for policy formulation geared towards sustainable water resources management under changing climatic conditions in the Lasang River watershed.

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. Plain Language Summary This study examines how global warming could impact the freeze‐thaw cycles (FTCs) in the Upper Mississippi River Basin (UMRB) and its implications for soil erosion. Researchers used an improved SWAT model to simulate the effects of different future climate scenarios on FTCs, soil hydrothermal dynamics, and soil erosion. The findings indicated geographical differences in the UMRB caused FTCs to be influenced by air temperature, snow cover, and soil water content. These factors led to great changes in the freezing days of all soil layers during the mid to late 21st century. Therefore, researchers summarized three response patterns of freezing days to global warming: a lag symmetric response in northern zones, a non‐symmetric response in central zones, and a rapid symmetric response in southern zones. Additionally, changes in soil hydrothermal dynamics during the FTCs period would alter soil erosion patterns, with annual soil erosion risk converting from a “dual‐peak” to a “single‐peak”, posing greater challenges for early spring erosion control. These findings highlight the need for effective soil and water management strategies to mitigate the adverse effects of changing FTCs on erosion and agriculture.

Abstract The aims of the present research was to find out the diversity of family Araneidae in district Swat Pakistan and to provide updated checklist of the family Araneidae from Pakistan. Also their occurrence throughout the year was given from District Swat Khyber Pakhtunkhwa, Pakistan. Data was collected from January 2018 to December-2018 from seven different Tehsils of District Swat by using different methods like pitfall trap, ground hand collection, air hand collection and were then preserved in plastic vials and appendorf tubes by using 70% ethanol. Camera mounted on microscope was used for photography. By using literature from World Spider Catalog, 2022, spiders were identified to species level. In a ttal of 1243 specimens of family Araneidae 4 genera and 7 species were identified. Dominant species with great number of specimen collected was Cyrtophora citricolla with 229 (18.4%) samples, followed by Bojaranius mitificus (15.7%), Neoscona Scylla (15.4%), Argiope lobata (14.8), Neoscona theisi (14.6%) and Neoscona polyspinippes (13.8%) respectively. While lowest collection was done of Argiope versicolor with 90 (7.3%) samples. High occurrence of spiders was studied during July 187 samples. Fluctuation in temperature can affect the diversity of spiders observed and recorded in present study with lowest collection done in low temperature. Moreover, the humidity also play a great role in spiders’ population and occurrence. Resumo Os objetivos da presente pesquisa foram descobrir a diversidade da família Araneidae no distrito Swat, Paquistão, e fornecer uma lista atualizada da família Araneidae nesse país. Sua ocorrência ao longo do ano também foi dada no Distrito Swat, Khyber Pakhtunkhwa, Paquistão. Os dados foram coletados de janeiro de 2018 a dezembro de 2018 de sete diferentes Tehsils, do Distrito Swat, usando métodos diferentes, como armadilha de queda, coleta no solo com a mão e coleta no ar com a mão, e foram então preservados em frascos de plástico e tubos eppendorf usando etanol 70%. Foi usada câmera montada em microscópio para fotografia. Utilizando a literatura do World Spider Catalog (2022), as aranhas foram identificadas em nível de espécie. Em um total de 1.243 exemplares da família Araneidae, foram identificados 4 gêneros e 7 espécies. A espécie dominante com grande número de espécimes coletados foi Cyrtophora citricola com 229 (18,4%) amostras, seguida por Bojaranius mitificus (15,7%), Neoscona scylla (15,4%), Argiope lobata (14,8%), Neoscona theisi (14,6%) e Neoscona polyspinippes (13,8%), respectivamente. Já a menor coleta foi feita de Argiope versicolor com 90 (7,3%) amostras. A alta ocorrência de aranhas foi estudada em julho, com 187 amostras. A oscilação na temperatura pode afetar a diversidade de aranhas observadas e registradas no presente estudo com menor coleta feita em baixa temperatura. Além disso, a umidade também desempenha um grande papel na população e ocorrência de aranhas.

This paper aims to improve the Soil and Water Assessment Tool (SWAT) model performance across the Major River Basins in Madagascar (MRBM), specifically for SWAT simulation in the Manambolo, Onilahy, Mananara, and Mandrare basins. A multi-gauge calibration was carried out to compare the performance of SWAT+ Toolbox, and R-SWAT, SWAT+ Editor Hard calibration on a monthly time step for the periods 1982–1999. We found that the SWAT+ model generated greater surface runoff, while the SWAT model resulted in higher groundwater flow in both CSFR and CHIRPS datasets. It has been demonstrated that the SWAT+ Toolbox had more potential in calibrating runoff across the MRBM compared to R-SWAT. Calibration in both methods led to a reduction in surface runoff, percolation, water yield, and curve number but increased the lateral flow, evapotranspiration (ET), and groundwater flow. The results showed that the multi-gauge calibrations did not significantly enhance simulation performance in the MRBM compared to single-site calibration. The performance of the SWAT+ model for runoff simulation within the SWAT+ Toolbox and R-SWAT was unsatisfactory for most basins (NSE < 0) except for Betsiboka, Mahavavy, Tsiribihina, Mangoro, and Mangoky basins (NSE = 0.40–0.70; R2 = 0.45–0.80, PBIAS≤ ±25), whether considering the CHIRPS or CSFR datasets. Further study is still required to address this issue.

Assessing the spatiotemporal dynamics of land use land cover (LULC) change on water resources is vital for watershed sustainability and developing proper management strategies. Evaluating LULC scenarios synergistically with hydrologic modeling affords substantial evidence of factors that govern hydrologic processes. Hence, this study assessed the spatiotemporal effects and implications of LULC dynamics on groundwater recharge and surface runoff in Gilgel Gibe, an East African watershed, using the Soil and Water Assessment Tool (SWAT) model. Three different LULC maps (2000, 2010, and 2020) were derived from Landsat images, and the comparisons pointed out that the land-use pattern had changed significantly. The agricultural land and grassland cover increased by 3.76% and 1.36%, respectively, from 2000 to 2020. The implications acquired for 2000 show that forested land covers decreased by 5.49% in 2020. The SWAT simulation process was executed using a digital elevation model, soil, LULC, and weather data. The model was calibrated and validated using streamflow data to understand the surface runoff and groundwater recharge responses of each Hydrologic Response Units on reference simulation periods using the Calibration and Uncertainty Program (SWAT-CUP), Sequential Uncertainty Fitting (SUFI-2) algorithm. The observed and simulated streamflows were checked for performance indices of coefficient of determination (R2), Nash–Sutcliffe model efficiency (NSE), and percent bias (PBIAS) on monthly time steps. The results show that there is good agreement for all LULC simulations, both calibration and validation periods (R2 & NSE ≥ 0.84, −15 < PBIAS < +15). This reveals that for the LULC assessment of any hydrological modeling, the simulation of each reference period should be calibrated to have reasonable outputs. The study indicated that surface runoff has increased while groundwater decreased over the last two decades. The temporal variation revealed that the highest recharge and runoff occurred during the wet seasons. Thus, the study can support maximizing water management strategies and reducing adverse driving environmental forces.

Karst water resources represent a primary source of freshwater supply, accounting for nearly 25% of the global population water needs. Karst aquifers have complex recharge characteristics, storage patterns, and flow dynamics. They also face a looming stress of depletion and quality degradation due to natural and anthropogenic pressures. This prompted hydrogeologists to apply innovative numerical approaches to better understand the functioning of karst watersheds and support karst water resources management. The Soil and Water Assessment Tool (SWAT) is a semi-distributed hydrological model that has been used to simulate flow and water pollutant transport, among other applications, in basins including karst watersheds. Its source code has also been modified by adding distinctive karst features and subsurface hydrology models to more accurately represent the karst aquifer discharge components. This review summarizes and discusses the findings of 75 SWAT-based studies in watersheds that are at least partially characterized by karst geology, with a primary focus on the hydrological assessment in modified SWAT models. Different karst processes were successfully implemented in SWAT, including the recharge in the epikarst, flows of the conduit and matrix systems, interbasin groundwater flow, and allogenic recharge from sinkholes and sinking streams. Nonetheless, additional improvements to the existing SWAT codes are still needed to better reproduce the heterogeneity and non-linearity of karst flow and storage mechanisms in future research.