Basin Scale Research Articles

Drivers of contaminated sediment dynamics over 80 years at a basin scale (Loire river basin, France): a multifactorial approach.

Snow avalanches pose a significant threat to infrastructure and communities. Avalanches widely affect the Western Himalayan basins every year. This study evaluates avalanche susceptibility and hazard in the Chandra-Bhaga and Upper Beas basins of the Western Himalaya using machine learning and numerical modelling. A variety of machine learning algorithms - including Random Forest, Support Vector Machine, Logistic Regression, and Artificial Neural Network - were tested and compared using a comprehensive set of avalanche predictive factors, to assess avalanche susceptibility at the basin scale. The random forest model achieved 88.73% accuracy and an area under the curve (AUC-ROC) of 0.95. 1,484 potential avalanches were simulated for hazard and exposure analysis. Findings reveal that ~8% of the region is highly susceptible to avalanches, particularly in Lahaul and Spiti. With a snow release-depth of 0.5 m originating from the high and very-high avalanche susceptible slopes, ~161 buildings and 7 lakes are exposed to potential avalanches. In a worst-case scenario with a 3-meter avalanche release-depth, the exposure significantly increases to ~557 buildings and 9 lakes. The findings of the study are crucial for site specific detailed avalanche forecasting and can serve as a base for identifying avalanche hotspots.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-22051-w.

Summary A novel time-lapse modelling scheme for Airborne Electromagnetics (AEM) monitoring datasets is presented, using data from multiple surveys applied to study the hydro-related evolution of the Bookpurnong floodplain in South Australia. Additionally, it introduces a new wide-ranging approach for this type of study, incorporating new processing, validation, and interpretation tools. Time-Lapse studies are widespread in the literature but are not commonly applied to model EM data, particularly AEM data. This is linked to the challenges of performing overlapping data acquisition with inductive systems. The key features of the new time-lapse scheme, which address these issues, include the definition of independent forward and model meshes, essential for considering discrepancies in the location of soundings which arise in multitemporal AEM data acquisition, and the incorporation of system flight height in the inversion. This proved crucial for achieving satisfactory data fitting and limiting artifact propagation in the time-lapse models. Additionally, a novel processing workflow for AEM multitemporal datasets is presented. This has proven important for effectively processing the multitemporal datasets, which presents new challenges in identifying noise coupling arising from the use of different systems across vintages of data, possible variations in acquisition settings operated by different field crews, and changes in subsurface resistivity in the survey area. Results generated from the time-lapse modelling are evaluated with an Independent Hydrogeological Validation (IHV), designed to support the geophysical models validation and interpretation by providing a first-step hydrogeological evaluation. At Bookpurnong, along a sector of the Murray River floodplain, multitemporal AEM surveys were collected in 2015, 2022 and 2024, to study natural and engineered changes in the groundwater system over time. The time-lapse models show significantly smaller variations compared to those determined with individually modelled survey data sets, while delineating sharply bounded changes in resistivity across the floodplain. This demonstrates the effectiveness of the new time-lapse scheme in minimizing inversion variations typically encountered with independently modelled results affected by larger equivalence issues. Here, AEM models are first compared with resistivity borehole measurements, revealing a strong match between the two methodologies and spatial variations in resistivity consistent with a meandering river across the floodplain. These variations are further validated and interpreted using the IHV approach, which revealed a direct correlation between the hydrological stress of the Murray River and the response of shallow aquifers. Additionally, time-lapse geophysical models, combined with a hydrostratigraphic analysis, allow for a direct correlation between shallow and deep hydrogeological responses. We believe that the time-lapse methodology described here can be widely applied to multitemporal studies using AEM datasets, enabling the study of a broad range of natural processes with great accuracy and at the basin scale.

Understanding and quantifying the coupled effects of land cover change and climate change on hydrological regimes is critical for sustainable water management in tropical mountainous regions. The Río Negro Basin in eastern Antioquia, Colombia, has undergone rapid urban expansion, agricultural intensification, and deforestation over recent decades, profoundly altering its hydrological dynamics. This study integrates advanced satellite image processing, AI-based land cover modeling, climate change projections, and distributed hydrological simulation to assess future streamflow responses. Multi-sensor satellite data (Landsat, Sentinel-1, Sentinel-2, ALOS) were processed using Random Forest classifiers, intelligent multisensor fusion, and probabilistic neural networks to generate high-resolution land cover maps and scenarios for 2060 (optimistic, trend, and pessimistic), with strict area constraints for urban growth and forest conservation. Future precipitation was derived from MPI-ESM CMIP6 outputs (SSP2-4.5, SSP3-7.0, SSP5-8.5) and statistically downscaled using Empirical Quantile Mapping (EQM) to match the basin scale and precipitation records from the national hydrometeorological service of the Colombia IDEAM (Instituto de Hidrología, Meteorología y Estudios Ambientales, Colombia). The TETIS hydrological model was calibrated and validated using observed streamflow records (1998–2023) and subsequently used to simulate hydrological responses under combined land cover and climate scenarios. Results indicate that urban expansion and forest loss significantly increase peak flows (Q90, Q95) and flood risk while decreasing baseflows (Q10, Q30), compromising water availability during dry seasons. Conversely, conservation-oriented scenarios mitigate these effects by enhancing flow regulation and groundwater recharge. The findings highlight that targeted land management can partially offset the negative impacts of climate change, underscoring the importance of integrated land–water planning in the Andes. This work provides a replicable framework for modeling hydrological futures in data-scarce mountainous basins, offering actionable insights for regional authorities, environmental agencies, and national institutions responsible for water security and disaster risk management.

Abstract Flood hazards across High Mountain Asia present pronounced complexity due to the combined influence of rainfall, meltwater, and glacial lake outburst floods (GLOFs). The eastern Himalayan Brahmaputra basin is a critical hotspot of this integrated risk. However, a basin-wide quantification of the individual contributions of these drivers to flood exposure has been lacking. Here, we employ a calibrated coupled hydrological-hydraulic model to provide the first comprehensive assessment. We show that meltwater (snow and glacier melt) contributions to annual maximum floods increase with altitude. At the basin scale, meltwater amplifies population and gross domestic product (GDP) exposure to flooding by 1.3% and 1.5%, respectively. This amplification rises dramatically to 27.9% for population and 33.3% for GDP in high-altitude regions (≥2000 m). Rainfall-derived discharge constitutes the dominant source of flood exposure, followed by meltwater; GLOFs present the least contribution. Nonetheless, GLOFs increase basin-wide population and GDP exposure by 1.2% and 1.3%, and notably intensify to 31.1% and 33.5% above 2000 m. Our findings deliver critical insights for developing targeted flood management strategies in the Brahmaputra basin.

Identification of potential snow drought (SD) hotspots is critical, especially considering seasonal snow imbalances in the recent years, with snow being one of the significant water resource in the Hindu Kush Himalayas (HKH). The critical linkage between the spatio-temporal anomalies of snow cover days (SCD) and SD remains under-observed in the HKH region. To investigate this linkage, we identified the SD at basin (11 basins) using the snow water equivalent index (SWEI) based on the High Mountain Asia Snow Reanalysis (HMASR) data from 1999 to 2016 water cycle. The declined snow cover days (DSCD) and snow cover persistence anomalies (SCPA) at sub-km spatial resolution from 2002 to 2018 were estimated from the improved MOYDGL06 snow cover data, respectively. Our basin scale findings indicate moderate to severe snow droughts were observed in 2008, 2011, 2015 and 2016 in the North-West (NW), Amu-Darya (AD), Indus (IN), and Salween (SA) and Mekong (MK) basins with strong linkages to DSCD and SCPA. The observed frequencies of snow-droughts were 25, 16, 14, 5, and 3 in the NW, AD, IN, MK, SA basins, respectively. These basins also exhibit significant DSCD, approximately 12, 11, 12 in NW, AD, IN, respectively, and 14 days in MK and SA basins each. Further, it is noted that significantly higher negative SCPA coincides with the SD episodes in drought years. Both SD and DSCD were more prominent between 3000 and 6000m elevations in the HKH, which are often considered under elevation dependent warming (EDW) scenarios in various studies. Overall, we observed that a higher frequency of drought events corresponds to a greater DSCD and higher negative magnitudes of SCPA. These insights indicate the urgent need for snow-conservation strategies and development and enforcement of strong policies.

Abstract. Permafrost thaw profoundly changes landscapes in the Arctic-boreal region, affecting ecosystem composition, structure, function and services and their hydrological controls. The water balance provides insights into water movement and distribution within a specific area and thus helps understand how different components of the hydrological cycle interact with each other. However, the water balances of small- (<101 km2) and meso-scale basins (101–103 km2) in thawing landscapes remain poorly understood. Here, we conducted an observational study in three small-scale basins (0.1–0.3 km2) of a thawing boreal peatland complex. The three small-scale basins were situated in the headwater portion of Scotty Creek, a meso-scale low-relief basin (drainage area estimated to range between 130–202 km2) near the southern permafrost limit in the Taiga Plains ecozone in northwestern Canada. By measuring water losses (discharge and evapotranspiration [ET]), inputs (rainfall [R] and snow water equivalent [SWE]) and storage change (ΔS), and by calculating runoff (Q), we (1) aimed to quantify the growing season water balances (May–September, 2014–2016) of the three small-scale headwater sub-basins. After (2) comparing monthly sub-basin and corresponding basin water losses through ET and Q, we aimed to (3) assess the long-term (1996–2022) annual basin water balances using publicly available observations of discharge (and thus calculated Q), R and SWE in combination with simulated ET. (1) Growing season water balance residuals (RES) for the sub-basins ranged from −81 to +122 mm. The monthly growing season water balance for the sub-basin for which all water balance components throughout the 3 year study period were recorded exhibited large positive RES for May (+117 to +176 mm), since it included late-winter SWE routinely estimated in late March right before snowmelt. In contrast, lower monthly and negative RES were obtained for June–September (−41 to 0 mm). For two sub-basins, we provide two different drainage area estimates, highlighting the challenges associated with automated terrain analysis using digital elevation models (DEMs) in low-relief landscapes. Drainage areas were similar for one sub-basin, but they exhibited a fivefold difference for the other. This discrepancy was attributed to the high degree of landscape heterogeneity and resulting hydrological connectivity, with implications for Q calculations and RES. (2) Spring freshet contributed 41 % to 100 % (sub-basins) and 50 % to 79 % (basin) of the April–September Q. Spring freshet peaks were comparable, except for the driest year (2014), when the basin Q was more than 10 times lower than in the sub-basins. At both scales, ET was the dominant source of water loss, more than twice Q. (3) Over the long term (1996–2022), the increase in the basin runoff ratio (the ratio of runoff to precipitation) from 1996 to 2012 (0.1 to 0.5) has been attributed to the increased connectivity of wetlands to the drainage network due to permafrost thaw. However, the smaller mean and more variable runoff ratio from 2013 to 2022 may be due to wetland drying and/or changes in precipitation patterns. Overall, we demonstrate how the hydrological responses of rapidly thawing boreal peatland complexes – at both the sub-basin and basin scales – are shaped by complex factors that extend beyond year-to-year changes in precipitation and ET. Long-term hydrological monitoring is crucial to identify and understand potential threshold effects (e.g. changes in land cover and hydrological connectivity) and ecohydrological feedbacks affecting the local (e.g. subsistence activities), regional (e.g. water storage) and global ecosystem services (e.g. carbon storage) provided by thawing boreal peatland complexes.

Spatial variability of lacustrine groundwater discharge at basin scale

Robust data-driven approach evolution for multi-factor driving effect understanding of nutrient loading variations at reservoir-basin scale.

Distribution patterns and assembly mechanisms of microbial communities in the riverine waters of southeastern Tibet.

Abstract Sensor-based oxygen (O2) measurements in the ocean have become the dominant data source for in situ O2 since 2010. We examine the overall quality of the shipborne conductivity–temperature–depth (CTD)- and Argo-measured O2 in the World Ocean Database (WOD) and evaluate biases by comparing them to independently measured reference bottle-sampled profiles. No significant O2 difference is found between CTD and bottle data for the global ocean, suggesting high quality of CTD O2 observations generally. A negative residual bias is found in both postcorrected delayed-mode (−1.69 ± 5.15 μmol kg−1, fitted mean ± standard deviation) and real-time-adjusted (−4.68 ± 6.99 μmol kg−1) Argo O2. Residual biases have both spatial (basin scale and vertical) and temporal variations, indicating the complexity of residual biases. We also examine Argo O2 biases based on the type of calibration methods. The Argo delayed-mode O2 profiles calibrated using the World Ocean Atlas (WOA), on average, are offset by −1.60 ± 5.30 μmol kg−1, while a larger bias (−3.29 ± 4.86 μmol kg−1) is found for profiles calibrated using in-air measurements for the global ocean. The Argo delayed-mode data calibrated with O2 profiles from the WOD have a slight positive bias (0.33 ± 4.16 μmol kg−1). Further analysis demonstrates that the O2 profiles without matched reference profiles also have negative bias similar to those with matched reference profiles when they are compared to WOA. This analysis suggests the negative residual biases in Argo postcorrected O2 should be carefully considered when using Argo O2 data to draw conclusive inferences about anthropogenic impacts on ocean oxygen concentrations and/or derived quantities in a changing ocean. Significance Statement This study evaluates the quality of dissolved oxygen (O2) measurements from CTD and Argo profiling floats in the World Ocean Database, comparing them to reference bottle data. Results show that CTD measurements are highly accurate and serve as a reliable reference for Argo O2 evaluations. Argo data reveal negative residual biases, both in real-time-adjusted and delayed-mode measurements, with variations across spatial, vertical, and temporal scales. Biases are also influenced by calibration methods, with larger discrepancies found in profiles calibrated using in-air measurements compared to those calibrated with the World Ocean Atlas. These findings suggest the presence of global negative biases in Argo O2 data, highlighting the need for further corrections to improve the calibration methods of Argo O2.

Abstract Throughout the Tibetan Plateau (TP), water supplies are fed primarily through rainfall and snowmelt. A warmer climate modifies the supply due to a change in precipitation patterns from snow to rain. This work introduces a novel analytical approach to examine the cumulative snowfall anomalies (csa) throughout snow year (from September to the subsequent August), aiming to reevaluate the impact of the seasonality on reduced snowfall and water supply at the basin scale of TP. Analysis of the csa metric using ERA5 data (1979–2023) reveals significant declining trends linked to snowfall seasonality. In various basins and seasons, the key physical processes behind csa decrease, related to warming and large-scale circulation anomalies, can be identified. The most substantial reduction of csa in the Indus basin gradually emerges during the cold season and persists throughout the subsequent warm season. In the Inner basin, decreases of csa in September and November are compensated until the following May. Over the eastern TP, decreases of csa prevail in the warm season because of the shift to rainfall. The analysis of cumulative rainfall anomalies (cras) further confirms the precipitation shift from snow to rain in the warm and rainy season. Due to warmer and wetter trends in the central and southern TP, the runoff is likely to increase from late summer through the following cold and dry season. More than 30 years of streamflow observations in the upper Brahmaputra, the upper Yangtze, and the subbasin of Hexi confirm this change. Meanwhile, the csa reduction may trigger water shortage in the Indus basin, particularly in early summer.

Divergence in anthropogenic activities contributed to the eutrophication heterogeneity between river and lake at basin scale: new insights from trophic state index in Asia's largest basin (Yangtze River).

Per- and polyfluoroalkyl substances (PFAS) are persistentorganicpollutants that are subject to increasingly restrictive regulations.This study characterized the occurrence of 77 PFAS compounds in rawand treated water from 15 drinking water treatment plants (WTPs) inthe Greater Montreal Area, including an urban creek receiving airportrunoff. A total of 32 compounds were detected at least once, representingdiverse classes and carbon chain lengths. This helped to identifytrends and precursor impacts on the PFAS profiles. Perfluoroalkylcarboxylic acids (PFCA) and perfluoroalkyl sulfonic acids (PFSA) werethe most frequently detected. The highest concentrations occurredin WTPs drawing from the St. Lawrence River, while the Ottawa andL’Assomption Rivers demonstrated the occurrence of localizedcontamination. Conventional treatment showed negligible PFAS removal.WTPs drawn from the same water source were generally correlated. Correlationanalyses also demonstrated that some plants are influenced by boththe Ottawa and St. Lawrence Rivers. Airport-related PFAS compounds,such as those from aqueous firefighting foam and hydraulic fluids,were detected in downstream WTPs. Seasonal trends suggest that temperatureand flow variations might affect PFAS concentrations. These findingsillustrate the challenges when protecting water sources against PFASat a basin scale while offering insights into how their patterns canassist with the identification of local contamination sources.

The impact of land use on the composition of dissolved organic matter and its relationship with microbes in a river basin in Northwestern China: Insights into microbial community structure and metabolic function.

ABSTRACT Global warming has been intensifying the water cycle, thereby altering regional climate systems and hydrological processes. This is particularly the case for the Poyang Lake Basin (PLB) in monsoon-controlled southeast China, where climate changes and human activities are evident. Our study aims to quantify the contributions of climate change and human activities to the spatiotemporal variations of the relevant variables across meteorological and hydrological compartments on the basin scale. This study applies the moving t-test, Mann–Kendall test, and linear regression models to quantify the impacts of climate change and human activities on changes in streamflow and lake level from 1960 to 2019. Results show that precipitation, streamflow, and air temperature have increased, but Poyang Lake level has declined. Change points in streamflow trends are identified in 1991 and 2002 and in lake level in 2003. Contribution analysis indicates that climate change is the primary driver of increased streamflow. However, after 2002, the contribution of climate change declined, while that of human activities increased. The abrupt decline in lake level is mainly attributed to anthropogenic interventions. These findings identify the dominant factors of hydrological change and provide guidance for ensuring water security and sustainable water resource management in the basin.

A new simulation − optimization framework based on multi-layer Green-Ampt infiltration model and genetic algorithm for developing planning guidelines of low impact development measures at river basin scales

The Ili River Basin is characterized by complex topography and diverse climatic zones with limited in situ observations. This study evaluates the performance of six widely used precipitation datasets, CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), ERA5_Land (European Centre for Medium-Range Weather Forecasts—ECMWF Reanalysis 5_Land), GPCC (Global Precipitation Climatology Centre), IMERG (Integrated Multi-satellite Retrievals for GPM), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), and TerraClimate, against ground-based data from 2001 to 2023. The evaluation is conducted across multiple spatial scales and temporal resolutions. At the basin scale, most datasets exhibit strong correlations with in situ observations across all temporal scales (r > 0.7), except for PERSIANN, which demonstrates a relatively weaker performance during summer and winter (r < 0.6). All datasets except ERA5_ Land show low annual and monthly bias (<5%), although larger errors are observed during summer, particularly for IMERG and PERSIANN. Dataset performance generally declines with increasing elevation. Basin-wide gridded evaluations reveal distinct spatial variations across all elevation zones, with CHIRPS showing the strongest ability to capture orographic precipitation gradients throughout the basin. All datasets correctly identified 2008 as a drought year and 2016 as a wet year, even though the magnitude and spatial resolution of the anomalies varied among them. These findings highlight the importance of selecting precipitation datasets that are suited to the complex topographic and climatic characteristics of transboundary basins. Our study provides valuable insights for improving hydrological modeling and can be used for water sustainability and flood–drought mitigation support activities in the Ili River Basin.

ABSTRACT The Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On missions hold a pivotal role in exploring global mass change and migration. However, inconsistencies in terrestrial water storage (TWS), arising from variations in geophysical background models and processing techniques among different products, hinder the global and regional applicability of GRACE. The absence of independent observations at the commensurate scale compromises detection accuracy and reliability when relying on a single-institution solution. Urgently addressing this issue by optimizing different products with rigorous algorithms is crucial to enhance the comprehension of global and regional TWS variability. We employ the generalized three-cornered hat (GTCH) method to quantify uncertainties of different GRACE products and integrate it with the Bayesian model averaging (BMA) method to construct a new fusion algorithm, GTCH+BMA, which generates integrated TWS products. At the basin scale, fused TWS products exhibit excellent reliability in capturing annual amplitude and trend variations. In comparison with the single-institution solution, the fusion products effectively attenuate noise and enhance the SNR, enabling a 59.7% reduction in the average uncertainty and an 8.05-fold improvement in the SNR, especially in regions with weak hydrological signals. A 62.8% reduction in average uncertainty and a 1.54-fold improvement in the SNR are similarly achieved globally. The fusion methodology significantly improves the effectiveness of probing TWS changes globally and regionally, helping to provide a valuable basic dataset for future exploration of related environmental changes and water resources management based on reliable data.

Abstract. In cold regions, snow cover and seasonally frozen ground (SFG) exert a substantial influence on hydrological processes, yet their effects – especially at the scale of large basins – remain insufficiently understood due to limited observations and process-based analyses. To address this, we extended the widely used Grid Xinanjiang (GXAJ) hydrological model by developing two physically meaningful yet computationally efficient modules: (i) the GXAJ-S model, which incorporates snowmelt processes, and (ii) the GXAJ-S-SF model, which additionally accounts for freeze–thaw cycles of SFG. These modules strike a balance between physical representation and simplicity, making them applicable in data-sparse cold regions. The model performance was evaluated using multi-source remote sensing/reanalysis data and observed daily runoff, enabling a systematic investigation of how snow and SFG jointly regulate key hydrological processes. The results demonstrate that: (1) including both snowmelt and freeze–thaw processes significantly improves runoff simulation, especially during cold seasons; (2) snow dynamics directly modulates the development of soil freeze–thaw cycles, thereby altering the hydrothermal state of the vadose zone; and (3) the inclusion of the SFG module in the model variant, which already accounted for snowmelt, increased the predicted surface runoff by 39 %–77 % during cold months, reduced evapotranspiration by approximately 85 %, and substantially modified interflow processes, particularly during the early-spring thaw period. These findings provide quantitative evidence of the critical role of SFG in shaping the seasonal hydrological regime of large cold-region basins. Moreover, the modular and transferable design of the snow and SFG components allows for straightforward integration into other hydrological models, offering a valuable tool for hydro-climatic assessments and water resource management in mountainous regions under changing climate conditions.