ABSTRACT This article introduces a methodology utilizing the Analytical Hierarchy Process (AHP) integrated with a Geographic Information System (GIS) to classify sub-basins within a large river basin in terms of pollution levels. The research uses data from the Haraz River basin, located in northern Iran near the Caspian Sea. The river basin under investigation comprises seven sub-basins. The primary pollution sources in this region include domestic wastewater from urban areas, effluents from fish farms, discharge from sand and gravel mines, sewage from restaurants and tourism centers, as well as land use (agriculture, forests, and rangelands). In addition to these pollution-related factors, other criteria such as basin area, river length, road network length, and slope were also considered. Using scores assigned by experts and leveraging the Expert Choice software, all sub-basins, and factors contributing to river pollution were identified. According to the findings, urban domestic wastewater was the most significant contributor to pollution in the study Basin, accounting for 42.2% of the total pollution. The sub-basins were classified according to their final weighted scores, identifying those with the highest and lowest pollution levels. The studied approach offers substantial advantages in terms of time, cost, and resource efficiency.

ABSTRACT Understanding the hydrological processes of mountainous regions is crucial for watershed management. The increased frequency of floods in the Himalayan region emphasizes the need to set up a hydrological model in this region. The complex topography and climate patterns of the Himalayas, with a few hydrometeorological stations, make modeling the region challenging. Therefore, the study of hydrological responses using a fully distributed hydrological model in this region is very rare. This study aims to address the challenges of data scarcity in mountain regions using alternative data for observed discharge data for the calibration of the hydrological model. We assess the utility of reanalysis surface runoff data (RSRD) from ERA-5 in calibrating a fully distributed hydrological model WATFLOOD. Six water balance components at nine land-cover classes are analyzed using the WATFLOOD model. The results show that the RSRD can be used as an alternative for the discharge data for calibration of the hydrological model. The evaluation of water balance components shows changes corresponding to wet and dry years. We verified simulation results using observed data, revealing the limitations of calibrating a hydrological model with RSRD.

ABSTRACT This study develops and validates a Geomorphological-based Instantaneous Unit Sediment Graph (GIUS) model to estimate the event-based sediment yield of the Talar catchment, a significant sediment source in the southern Caspian Sea. Delivering approximately 11,190 tons of sediment annually, which directly impacts coastal dynamics and port operations, necessitates accurate sediment load estimation to the Caspian Sea. The catchment was divided into three sub-catchments, where the GIUS model was applied. Model calibration and verification utilized paired data of daily rainfall, streamflow, and suspended sediment concentration for 475 events. After separating direct runoff from streamflow, the Soil Conservation Service Curve Number (SCS-CN) model was employed to estimate the excess rainfall, serving as the primary input for the GIUS model. The erosion rate, a critical parameter in the GIUS model, was calibrated as a power function of excess rainfall depth. The results indicate that the sediment delivery ratio for the channel networks ranges from 28% to 44%. The strong correlation (CE = 0.64−0.85, SE = 0.75−1.96, R 2 = 0.85–0.92) between observed and simulated sediment yield values underscores the reliability of the developed model. This research presents a repeatable approach for event scale sediment yield modeling, essential for sustainable water resources management and soil erosion control in the region.

ABSTRACT This study examines the spatial and temporal variations in streamflow due to Landuse/Land cover (LULC) and climate change within four heterogeneous river basins along south-western India: Kuttiyadi and Korapuzha–Kallai (North), Chalakudy (Centre), Karamana–Neyyar (South) River basins. A Soil Water Assessment Tool (SWAT) was used to assess the impact of projected LULC and climate scenarios. Five General Circulation Models (GCMs), CanESM2, BNU-ESM, CNRM-CM5, MPI-ESM-LR and MPI-ESM-MR under two Representative Concentration Pathways (RCP) RCP 4.5 and RCP 8.5 were employed. The combined scenarios include Near future (NF) – 2011-2030-LULC 2030, Mid-future (MF) – 2031-2070-LULC 2070 and Far future (FF) – 2071-2100-LULC 2100. The projected streamflow was then analysed to identify significant trends using the Mann–Kendall Trend test. Under RCP 4.5 and RCP 8.5, average annual streamflow is expected to increase in the NF (8.5% to 20.6% from south to north). However, in the FF, streamflow is expected to decrease (−2.4% to +2.2% from south to north), under RCP 4.5. Trend analysis shows that climate change is unlikely to cause significant trends in the NF, except in the central river basin. However, significant trends are expected in the FF. Water resources planners may consider these future trends while preparing water resources management plans.

ABSTRACT The El Niño-Southern Oscillation (ENSO) has diverse effects on the interannual variability of river flows in Colombia. Given that 70% of the country's electricity supply is provided by hydroelectric dams located in different regions and operated by different companies linked by a nationally integrated system, the decisions taken by operators in response to ENSO can be just as varied. The present study investigates the spatial and temporal distribution of the ENSO effects in four case studies, its relation to hydropower generation and prices, and assesses the reliability of global ENSO indices for monitoring and anticipating local effects. The results show ENSO-driven variations in streamflow and precipitation in all cases, but their intensity and duration are highly dependent on location. La Niña led to positive precipitation and stream flow anomalies, while El Niño led to negative anomalies. In two dams, Hidrosogamoso and Betania, the ENSO influence is stronger, and has a higher correlation with reservoir operations with lags greater than one month. Urra has more stationary annual operations given the reduced ENSO effects in the reservoir’s basin. On the whole, ENSO indices are insufficient in describing the local variations completely. And operator’s size, market share, and installed capacity contribute to increasing flexibility in operations while facing ENSO challenges.

ABSTRACT This study addresses the challenge of accurately estimating groundwater-surface water fluxes, essential for sustainable water resource management, by improving traditional temperature time series methods. Existing approaches often struggle with distinguishing natural temperature variations from significant water exchanges, especially across different spatial and temporal scales under varying conditions like recharge and discharge. To overcome these issues, advanced statistical tools were applied to temperature data, and COMSOL Multiphysics was used to simulate interactions by coupling fluid flow and heat transport. Multisite and multivariate calibration techniques refined parameters like hydraulic conductivity and porosity, while models accounted for external factors such as climate variability and land-use changes. The findings reveal that the combined method was effective in estimating groundwater-surface water fluxes under recharge conditions. The COMSOL model achieved an MAE of 0.71, MAP has 0.92, RMSE of 0.80, and RMSLE has 0.30, indicating consistent performance across evaluation metrics. This advancement improves groundwater-surface water model precision, especially in distinguishing natural temperature variations from significant water exchanges. It offers more accurate flux estimation, aiding better water resource management.

ABSTRACT This study evaluates several machine learning (ML) models for estimating streamflow in the Osage River, Missouri, USA, and the Severn River, UK, using hydraulic input variables. These input variables are the flow section area (A), wetted perimeter (Pw ), water surface width (W), and velocity parameter (U) derived from isovel contours. Before using these models, the influential variables are selected using the sequential feature selection (SFS) method to reduce model complexity while maintaining performance. The two most important input variables identified were A and U, reducing the number of inputs from four to two. The results showed that the river’s flow conditions affect the accuracy of ML models used for estimating streamflow. Linear models such as MLR and ANFIS perform better in steady flow conditions (Osage River). In contrast, decision tree-based and non-linear models such as SVR with a radial basis kernel function (SVR_RBF) are better for unsteady flow conditions (Severn River). The finding suggests simpler models outperform complex deep-learning approaches (such as LSTM) for estimating streamflow by hydraulic variables. By selecting appropriate and efficient ML models, hydrometer stations can significantly improve the accuracy of streamflow estimation, leading to more informed decision-making in water management.

ABSTRACT Flow and environmental dispersion are necessary in water management systems. To better understand contaminant transport in wetland flows with adjacent aquatic vegetation and riparian buffers, a two-zone-model wetland is considered to characterize the water flow and environmental dispersion. Many researchers developed a dispersion model to characterize the mean concentration subject to the first-order degradation effect, present study attempts to derive an analytical dispersion model to discuss the transverse mean concentration of pollutants subject to second-order degradation effects in a width-dominated wetland flow. In addition, this study attempts to highlight the effect of environmental parameters like vegetation, viscous friction, and relative width on flow velocity. Also, the transverse mean concentration in the longitudinal direction is derived based on Mei’s multi-scale perturbation approach and analyzed with different degradation reaction rates. In a second-order reaction, higher degradation rate at a high concentration zone, contaminants degrade more effectively at the source than in a first-order reaction. It is conveyed that the pollutant transport is influenced by various parameters such as Peclet number, tortuosity, vegetation constraint, and dispersion time. For a specific water quality standard in wetlands, the maximal length and duration of the affected area for the common pollutant lead (Pb) is assessed and presented graphically; also, compared with a single zone.

ABSTRACT The study intends to bridge the lack of baseline information on microplastic (MPs) contamination in two river systems (Periyar: the longest river, lifeline of the Kerala State and Chalakudy: the fifth longest river) and hence is highly relevant. Sampling was carried out twice during December 2020 and January 2021. Elevated level of contamination was recorded at midstream sites in Chalakudy and downstream sites at Periyar. Small-sized particles (<100 µm) constituted the majority (>45%) in both river systems resulting from the disintegration of larger particles or direct release of primary MPs. Prevalence of fragment shape, accounting for ≈48–71% across all sites in both rivers, resulted from the disintegration of meso or macro plastics. Preponderance of blue-coloured particles (≈33–67%) inferred potential risk to aquatic organisms. Low-density polyethylene emerged as the dominant polymer group, originated from packaging materials and bottles. Overall abundance and recovery rate in both rivers indicate that MPs occur at all sites, irrespective of land use patterns, sourced to the degradation of abandoned fishing gears, shipping, tourism activities, and untreated waste disposal. The high risk of microplastic pollution in the study area was endorsed by polymer hazard index and coefficient of microplastic impact index.

ABSTRACT Information on the riverbanks can improve our ability to monitor water quality and generate adequate management strategies. This research seeks insights into the riverbanks of eight Colastiné River Basin (Argentina) streams, which have been influenced by intensive agricultural land use for decades. We aim to (a) describe the temporal and spatial distribution patterns of riparian vegetation, (b) assess their current riparian quality through riverbank quality indices, and (c) estimate whether the riparian quality is linked to the water quality. Results of the Normalized Difference Vegetation Index (NDVI) showed an increasing trend in the vegetation cover with seasonal periodicities during the last 22 years in only 2 streams. Overall, 41% of plant species registered were exotic although native species dominated in most streams. The overall riverbank quality, based on the mean values of four riverbank quality indexes, was regular-to-bad. The overall water quality of the streams was low and significantly correlated to the Riparian Quality Index, suggesting a link between both compartments. More studies are needed to determine the main variables that establish this connection. Further effort is also needed to generate appropriate indices for this region, as no current ones are still developed.