Hydrology

Yamdrok Lake, a typical closed inland lake located in the southern Tibetan Plateau, is highly sensitive to climate change and human activities. Although previous studies have reported a declining lake-level trend, the mechanisms underlying the sharp decline since 2005 remain debated. In particular, the disturbance effects of human activities, such as pumped-storage hydropower operations, have not been rigorously characterized, and quantitative attribution under the combined influence of multiple factors remains limited. This study aims to systematically identify the drivers of lake-level changes in Yamdrok Lake during 2000–2023 and to quantify the relative statistical explanatory power of climate change and human activities on lake-level fluctuations using Shapley R² decomposition. We integrated 24 years of hydrological, meteorological, remote-sensing, and hydropower-operation data. Cumulative anomaly analysis, multiple linear regression, and Shapley R² decomposition were employed to establish an attribution model incorporating rainfall, evaporation, temperature, glacier meltwater, and hydropower operation intensity, represented by annual electricity generation. The relative statistical explanatory power of these factors with respect to interannual lake-level changes (ΔH) was quantified using Shapley R² decomposition for different periods, representing the proportion of variance in ΔH explained by each factor within the regression framework rather than absolute physical volumetric contributions, including hydropower-operation and non-operation periods. Results show that the annual mean water level of Yamdrok Lake declined significantly during 2000–2023, with a cumulative decrease of 5.1 m and an accelerated decline after 2005. The dominant controls shifted from an early rainfall–evaporation regime to a systematic water deficit dominated by rising temperature. Temperature increased significantly at a rate of 0.03 °C yr⁻¹ (p < 0.05) and constituted the fundamental driver of the long-term lake-level decline. During the hydropower-operation period (2000–2014), the five factors jointly explained 84.51% of the variance in interannual lake-level changes, with hydropower operation intensity (32.32%), temperature (27.98%), and glacier meltwater (26.54%) being the three largest contributors in terms of statistical explanatory power. During the non-operation period (2015–2023), temperature consistently remained the most important individual contributor, accounting for 34.3–49.5% of the explained variance depending on whether missing glacier meltwater data for 2022–2023 were extrapolated or excluded from the analysis. Warming affects lake levels through two pathways: it directly enhances lake-surface evaporation and simultaneously promotes continuous glacier retreat within the basin. Glacier area decreased by approximately 30.5 km² between 2000 and 2021, thereby weakening the long-term resilience of glacier-meltwater recharge. Consequently, the lake system has shifted from a dynamic balance toward a persistent state in which water losses exceed water inputs. Overall, lake-level changes in Yamdrok Lake represent the combined effects of progressive warming-induced water deficits and superimposed disturbances associated with hydropower operations. By extending observations to 2023 and incorporating dynamic glacier-area and hydropower-operation indicators, this study clarifies the temporal shift in dominant drivers, revises the previous interpretation that attributed lake-level decline primarily to reduced rainfall, and provides a scientific basis for lake-water-resource security assessment and climate-change adaptation on the Tibetan Plateau. Full article

Accurate areal rainfall estimation is essential for hydrological modeling and flood forecasting, yet method selection remains uncertain in small catchments with extremely sparse gauge networks. This event-scale study compared arithmetic mean (AM), Thiessen polygon (TP), inverse distance weighting (IDW), precipitation–elevation linear regression (ELR), multiple linear regression (MLR), and a multi-layer perceptron (MLP) in a 0.719 km² catchment monitored by three gauges. Two complementary evaluations were conducted. Station-wise leave-one-out cross-validation (LOOCV) assessed prediction at an omitted gauge, whereas a radar-referenced comparison assessed catchment-average estimates obtained from the complete gauge network. MLR produced the lowest LOOCV error (RMSE = 0.433 mm; CC = 0.777). In the radar comparison, MLP and MLR produced nearly identical RMSE values of 0.668 and 0.669 mm, respectively, and are therefore interpreted as practically similar rather than meaningfully different. All method rankings are conditional on the selected 60 h event, the three-gauge arrangement, and uncertainty in the radar reference. The findings demonstrate that station-omission performance and full-network areal estimation address different operational questions and should be considered together when selecting an interpolation method for extremely sparse networks. Full article

Accurate calibration of Manning’s roughness coefficients is essential for reliable river hydraulic modelling, flood prediction, and water resources management, yet conventional calibration methods often struggle with high-dimensional parameter spaces and nonlinear hydraulic interactions. This study proposes and evaluates a hierarchical artificial intelligence framework for the inverse calibration of spatially distributed Manning’s roughness coefficients across three channel zones (left bank, main channel, and right bank), using a 48 km reach of the Tigris River in Baghdad as a case study. A one-dimensional HEC-RAS hydraulic model based on 30 measured cross-sections generated 18,360 simulations by systematically varying Manning’s roughness coefficients (0.02–0.045). Three calibration strategies were evaluated: (i) a simple Gradient Boosting Regression model based on a weighted composite roughness formula, (ii) conventional machine learning models (Random Forest, Gradient Boosting, and Multi-Layer Perceptron), and (iii) a deep learning framework combining a three-layer neural network (64 → 32 → 16 neurons), Differential Evolution optimisation, and cubic spline interpolation. Calibration accuracy increased with model complexity. The deep learning framework achieved the best performance, reducing the root mean square error by 96.6% (from 1.202 to 0.041 m), with R² = 0.992 and negligible bias (−0.004 m). Conventional machine learning models produced spatially variable Manning’s roughness distributions, with the calibrated main-channel roughness (mean n = 0.0512) being 34.0–57.5% higher than the corresponding bank values. The proposed framework provides an effective approach for calibrating spatially distributed roughness coefficients in one-dimensional hydraulic models, with strong potential to improve river hydraulic simulations and support future applications to flood modelling. Full article

Lumped conceptual models, particularly those in the Génie Rural model family, are widely recognized as benchmark tools in hydrology. Although the final equations of their production function (the ‘Soil’ reservoir) are available in the literature and open-source packages, their original conceptualization and complete mathematical derivation have rarely been formally published. This paper aims to address this issue by reconstructing the complete logical chain linking conceptual assumptions to operational equations. The hydrological conceptualization is revisited and grounded in an analogy with the filling, extraction, and emptying of a physical reservoir. We derive the ordinary differential equations that govern storage dynamics, detailing the transition from temporal to cumulative flux formulation and providing complete analytical solutions. A step-by-step derivation involving separation of variables, partial fraction decomposition, and integration yields canonical closed-form solutions involving the hyperbolic tangent, as implemented in Génie Rural models. Time-step integration using cumulative variables provides fundamental temporal invariance. In addition to historical reconstruction, this formalization emphasizes important structural features such as mass conservation, numerical robustness, and analytical differentiability. By making these mathematical foundations accessible, this paper provides the hydrological community with a solid basis for future methodological extensions. Full article

Mountainous regions often experience a hydrological paradox where high monsoon rainfall coincides with severe dry-season water scarcity. The Chittagong Hill Tracts (CHT) of southeastern Bangladesh represent this challenge, as steep terrain, fractured geology, rapid runoff and limited monitoring constrain sustainable water-resource planning. This review critically evaluated water-resource characteristics, modeling approaches and decision-support strategies for the CHT. The literature shows that standalone models such as SWAT, MODFLOW and SWMM are useful for specific water-cycle components but do not adequately represent cross-domain processes including deep recharge, baseflow and stream–aquifer exchange in steep, complex terrain. International analogue studies indicate that coupled surface water–groundwater models can improve process representation when streamflow, groundwater-level, lithological and recharge data are available. However, their direct calibration and validation in the CHT remain limited by sparse groundwater monitoring and weak hydrogeological characterization. CHT-related and comparable Bangladesh studies indicate that machine-learning (ML) models can improve predictive suitability mapping when representative training data and independent validation are available; however, their relative advantage over the Analytic Hierarchy Process (AHP) is application-specific and depends on data quality, validation design, spatial autocorrelation, and model interpretability. The review identifies three key gaps: inadequate groundwater monitoring, absence of calibrated coupled modeling for fractured aquifers, and limited integration of socio-economic factors in rainwater-harvesting feasibility. Finally, a staged modeling pathway is proposed. Full article

The Samara River is a left-bank tributary of the Volga with a total basin area of 46,500 km², of which 22,800 km² is the gauged area analyzed in this study. This river system serves as a case study for examining hydrological transformations in the extreme southeastern region of European Russia (the Southern Cis-Urals) driven by progressive climate change and historical land-use shifts. To achieve this, the study analyzes long-term, station-based gauge observations of monthly river runoff for the period 1933–2022. The methodology employs a baseline set of standard statistical criteria combined with specialized hydromathematical procedures to evaluate runoff structure. The results demonstrate that while the river’s annual runoff lacked a statistically significant trend, its intra-annual structure underwent profound transformations. While April and May exhibited non-significant declines, the vast majority of months demonstrated prominent upward runoff trends. This systematic increase compressed the amplitude between hydrologically contrasting months by a factor of 2.5 to 3 from the historical climatological baseline (1933–1960) period to the modern baseline (1991–2020) period. Since 1989, the intra-annual variability coefficient has decreased by approximately one-third, especially during low-flow years. Concurrently, the spring flood contribution decreased by approximately 25%, and the frequency of prolonged, three- to four-month snowmelt-driven freshets during the 1987–2022 period doubled to ~22% compared to the preceding period, whereas the summer–autumn and winter low-flow shares increased substantially. Spectral analysis demonstrates a structural asymmetry in runoff generation mechanisms: low-flow dynamics are predominantly governed by long-term macrocycles (spanning 11 years or more), whereas maximum discharge exhibits a complex spectral configuration rather than a purely stochastic white noise process, reflecting an underlying statistical ambiguity between the applied criteria. Finally, five alternating multiyear flow phases occurred since 1933, characterized by low-flow conditions during 1933–1945, 1966–1984, and 2014–2022, and high-flow conditions during 1946–1965 and 1985–2013, with the respective contributions of the spring snowmelt flood and low-flow runoff differing substantially across these phases. This research advances the understanding of regional river runoff transformations in the Anthropocene, providing an objective basis for long-term water balance forecasting in the Southern Cis-Urals, strategic environmental management, climate change adaptation, and environmental risk assessment. Full article

Large-scale atmospheric teleconnections, including the North Atlantic Oscillation (NAO), East Atlantic (EA), Scandinavia (SCAND), and East Atlantic/Western Russia (EAWR) patterns, as well as synoptic weather patterns, are strongly associated with streamflow extremes across Great Britain. This study examines the statistical association between these circulation patterns and monthly streamflow extremes, represented by the Standardised Streamflow Index (SSI-1), across 131 catchments. Correlation analysis first quantifies the relationships through lagged correlation analysis and then identifies associated lag structures, and multiple linear regression models finally quantify the joint association of teleconnections and weather patterns with streamflow extremes, enabling direct comparison between the two predictor sets. The associations are highly heterogeneous, with pronounced spatial and temporal variability. Wet extremes are more spatially coherent and better explained than dry extremes, particularly across Atlantic-facing western and northern catchments, whereas dry extremes show greater regional and seasonal heterogeneity. Weather patterns characterise these atmospheric associations more effectively than teleconnections: the weather-pattern regression models explain up to adjusted R² = 0.56 (Scotland W) of the variance in extreme occurrence, compared with up to adjusted R² = 0.44 (Scotland W, February) for the teleconnection models, and several hundred weather-pattern associations survived false-discovery-rate correction compared with virtually none for the teleconnections. Distinct anticyclonic circulation is associated with dry extremes and cyclonic, westerly circulation with wet extremes. These findings improve understanding of the climatic associations of hydrological extremes and provide precursor information relevant to water-resource management across Great Britain. Full article

Understanding water dynamics and nutrient transport in structured soils remains a critical challenge in hydrology, particularly due to the influence of preferential flow pathways. This systematic literature review synthesises evidence from 47 studies identified through screening 770 papers by title, abstract, and full text using a PRISMA-based workflow. This review only focused on eligible studies investigating water flow, solute transport, or modelling approaches in structured soil, while studies without an experimental or modelling component were excluded. This review included 39 studies, coupled with 25 and 28 studies using field, laboratory, and modelling approaches, to analyse preferential flow and nutrient transport. The most common method for field experiments is soil physical measurement (42%), whereas HYDRUS (28%) is the primary tool used in modelling approaches. Despite advances in physically based modelling, accurately representing preferential flow remains challenging due to limitations in model conceptualisation, parameterisation, and the representation of spatial and temporal soil heterogeneity. Modelling strategies such as dual-domain formulations and inverse modelling improve representation but introduce additional complexity and uncertainty. The synthesis highlights a critical gap between experimental evidence and model representation, emphasising the need for improved integration of soil structural dynamics and non-equilibrium transport processes in hydrological modelling. Full article

Short-term discharge forecasting remains challenging under highly variable, nonlinear, and data-constrained hydrological conditions, where conventional machine learning and deep learning approaches require large datasets and extensive parameterisation, and offer limited interpretability. This study proposes a Singh-based fuzzy logic framework for one-hour-ahead discharge and flood peak prediction using hourly streamflow from the Schwarzbach catchment (135 km², Hesse, Germany). Four storm-driven flood events were analysed under contrasting hydrological conditions. Temporal dependence was characterised using autocorrelation function (ACF), partial autocorrelation function (PACF), and fuzzy-cluster transition analysis to link event-specific discharge dynamics with predictive behaviour. The results revealed substantial heterogeneity in hydrological memory, autocorrelation decay, and state-transition complexity across events, ranging from smooth memory-dominated responses to oscillatory multi-peak behaviour. Despite these differences, the framework reproduced all flood events with Nash–Sutcliffe efficiency (NSE) above 0.99 while capturing peak timing and magnitude, including complex double-peak dynamics. Because NSE is referenced to the variance of the observed series, forecasts were benchmarked against a persistence predictor, which itself attains a NSE of 0.958–0.976 at this lead time and time step; relative to persistence the framework reduced the root mean squared error by 38–68%, corresponding to skill scores of 0.612–0.897, whereas an AR(1) benchmark yielded no meaningful improvement. Prediction uncertainty increased with hydrological complexity but remained free of systematic bias. The results demonstrate that fuzzy logic provides an interpretable and computationally efficient alternative for short-term discharge forecasting that improves measurably on a naïve benchmark, offering potential for flood early warning systems and water resource management in data-scarce catchments. Full article

Drought persistence and delayed recovery remain major challenges for drought monitoring in semi-arid environments, where precipitation variability interacts with slowly evolving land-surface conditions. Although precipitation-based indicators effectively characterize meteorological drought, they often provide limited information on the persistence and recovery of integrated dry land-surface states. This study investigates soil moisture persistence and coupled drought-state variability in a semi-arid mountainous region of the southern Peruvian Andes using long-term satellite and reanalysis datasets of precipitation (CHIRPS), root-zone soil moisture (ERA5-Land), vegetation conditions (NDVI), and land surface temperature (LST). Soil moisture persistence was characterized using percentile-based metrics, duration analysis, and temporal persistence diagnostics to evaluate the continuity of dry conditions beyond precipitation anomalies. To synthesize hydroclimatic forcing and land-surface variability within a common framework, an Integrated Drought State (IDS) was developed by combining standardized precipitation, soil moisture, vegetation, and thermal indicators into a simplified diagnostic representation of coupled drought-state variability. The results show measurable temporal persistence in soil moisture together with distinct temporal responses in vegetation and surface thermal conditions, indicating that drought-state continuity reflects coupled land-surface behavior beyond precipitation anomalies alone. Persistent thermal anomalies and reduced vegetation activity co-evolve with soil moisture depletion, indicating that land-surface responses adjust more slowly than atmospheric forcing. The IDS captures the coherent temporal evolution of precipitation, soil moisture, vegetation, and surface thermal conditions during prolonged dry periods, providing an integrated representation of drought-state variability across the study region. Overall, the findings emphasize the importance of incorporating slowly varying land-surface conditions, particularly soil moisture persistence, alongside hydroclimatic forcing to improve the interpretation of drought persistence and recovery in semi-arid mountainous environments. Full article

This article presents a scoping review of the literature on digital twins (DTs) for sustainable groundwater resources management, which constitutes a very recent and rapidly expanding research field, with literature moving quickly from conceptual frameworks to application-oriented systems. The literature, selected through the PRISMA 2020 methodology, is organized into seven sub-topics: AI and ML foundations, digital twin architectures and frameworks, aquifer-scale DT applications, agricultural and water–energy–food (WEF) nexus DTs, basin and urban water DTs, sensing, monitoring and IoT infrastructures, and governance, resilience and socio-hydrology. This structure shows how the field is shifting from monitoring and prediction toward integrated decision support, where process-based models, machine learning surrogates, real-time sensing and optimization are combined to support drought mitigation, saltwater intrusion control, irrigation management, climate adaptation and basin-scale planning. Across the reviewed studies, the most recurrent contributions are the construction of hybrid model architectures, the use of DTs to close the loop between observation and control, and the growing recognition that groundwater management must incorporate governance, stakeholder decision-making and socio-hydrological feedbacks. At the same time, the literature still faces key limitations, especially uncertainty quantification, interoperability between models and data streams, transferability to data-scarce settings and limited validation under real operational conditions. Future research should therefore focus on physics-informed and explainable AI, federated and scalable DT architectures, stronger coupling with socio-hydrological and governance frameworks, and more field-tested implementations that can demonstrate robust performance across diverse hydrogeological and institutional contexts. Full article

To elucidate the recharge relationships and hydrological processes among different water bodies in the Qinghai Lake Basin, precipitation, river water, groundwater, and lake water samples were collected from April to November 2024. The hydrogen and oxygen stable isotope compositions (δ²H and δ¹⁸O) were determined to characterize their spatiotemporal variations, and the MixSIAR model was applied to quantitatively evaluate the recharge contributions among different water bodies. The results show significant differences in stable isotope compositions among the various water bodies. Overall, lake water exhibited the most enriched isotopic signatures, whereas groundwater was the most depleted and isotopically stable, while precipitation displayed the largest variability. Precipitation isotopes exhibited pronounced seasonal effects. River water showed a depletion–enrichment–depletion pattern, reflecting the important recharge contributions from wet season precipitation and frozen-soil meltwater. Groundwater exhibited relatively weak seasonal variations and a distinct smoothing effect. In contrast, the isotopic composition of lake water was jointly controlled by evaporative fractionation and multiple recharge sources, with the highest enrichment occurring in spring and gradual depletion during wet season and dry season. Spatially, significant isotopic differences were observed among rivers. Groundwater was relatively enriched in the western part of the basin and depleted in the eastern part, whereas lake water exhibited an opposite pattern, characterized by enrichment in the east and depletion in the west. The Local Meteoric Water Line (LMWL) of the Qinghai Lake Basin was defined as δ²H = 8.07δ¹⁸O + 37.48 (R² = 0.96). Both the slope and intercept were higher than those of the Global Meteoric Water Line (GMWL), indicating that locally recycled evaporated moisture played an important role in regional precipitation formation. The river water line showed characteristics similar to those of the groundwater line, suggesting strong hydraulic connectivity between river water and groundwater. In contrast, the lake water line deviated markedly from the meteoric water line, indicating significant evaporative fractionation of lake water. The MixSIAR results indicate that the contributions of river water, groundwater, and precipitation to lake water were 37.5%, 33.9%, and 28.6%, respectively. These findings reveal the complex hydrological connections and transformation processes among multiple water bodies in the Qinghai Lake Basin and provide a scientific basis for water resource management and ecological environmental protection in inland basins of the Qinghai–Tibet Plateau. Full article

The use of remote sensing data in hydrological applications has increased, especially in regions with limited ground-based observations. Satellite precipitation products (SPPs) provide extensive temporal and spatial coverage but may contain biases that can affect their performance in hydrological simulations. This study evaluates the performance of four near-real-time SPPs for daily streamflow modeling in the Tulijá River Basin (TRB), Mexico: Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS), PERSIANN-Dynamic Infrared Rain Rate near real-time (PDIR-Now), and the Early Run and Late Run products of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG). The SPPs were first compared with meteorological station precipitation data and subsequently bias-corrected using the Linear Scaling (LS) method. The CEQUEAU hydrological model simulated streamflow using three precipitation datasets: meteorological stations, original SPPs, and bias-corrected SPPs. For simulations using observed precipitation, the model was calibrated for 1991–2014 and validated for 1968–1990; for SPP-based simulations, calibration and validation were performed for 2003–2011 and 2012–2014, respectively. Model performance was assessed using the Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), and coefficient of determination (R²). The results show that CEQUEAU performance varies by precipitation dataset. Simulations using observed precipitation yielded NSE values close to 0.70 during both calibration and validation, whereas the original SPPs yielded NSE values below 0.18, including negative values. After bias correction, IMERG-Early and IMERG-Late yielded NSE values of approximately 0.55 during both periods. These findings highlight the importance of analyzing the performance of near-real-time SPPs in hydrological applications, especially in tropical regions with complex topography. Full article

Groundwater discharge contributes to stream baseflow, but coupling strength varies among catchments and transferable quantification methods are limited. We present GWBASE, an open-source Python algorithm that pairs U.S. Geological Survey (USGS) wells and gages within hydrographic catchments and ranks coupling at each gage by linear regression and mutual information (MI, capturing nonlinear and lagged dependence) on monthly $\Delta$WTE–$\Delta Q$ records from baseflow-dominated months. We apply it to the Great Salt Lake Basin (Utah; ∼$\mathrm{93,000}{\mathrm{km}}^2$ with 8752 USGS wells, 1906–2025). GWBASE ranked four terminal-gage catchments using a seasonally corrected, within-well regression as the primary estimate; three of the four show statistically significant groundwater–baseflow coupling. The ranking depends on the metric: absolute magnitude (cfs per foot) is dominated by the large Bear River catchment, whereas size-normalized sensitivity and coupling tightness identify the smaller Little Cottonwood Creek. The basin-scale aggregate (∼4 cfs per foot of basin-averaged decline) is dominated by Bear River and, once catchment-level uncertainty is propagated, is not distinguishable from zero. Because national groundwater records are predominantly intermittent, GWBASE resolves seasonal-to-interannual storage coupling rather than event-scale exchange. It is best used for screening and ranking catchment-scale coupling rather than yielding a single basin-scale coefficient. Full article

Rivers are vulnerable to hydrometeorological and climatic pressures, while effective water resources management during low-flow season can be challenging. This paper studies the impacts of extreme hydrometeorological events and climate change on Greek rivers, and presents a framework for the incorporation of these pressures into the Program of Measures (PoMs) of the River Basin Management Plans (RBMPs) for the implementation of the Water Framework Directive 2000/60 (WFD). The final aim is the adaptation to drought and water scarcity. The proposed methodological approach incorporates a process-based model, climate change projections, drought indices and low-flow boundaries, so as to assess the impact of hydrometeorological and climatic pressures on the quantitative quality status of rivers. Then, the quantitative response of rivers under different predicted climate scenarios was examined. Based on the results, a water management plan was adopted and tested regarding its effectiveness, so as to promote evidence-based water strategies. Additionally, comprehensive measures and an operational scheme and adaptations actions for the mitigation of the hydrometeorological and climate related pressures in rivers of Greece were proposed. This operational framework is based on quantitative thresholds that trigger actions and measures for adaptation and can be used by decision-makers and stakeholders as guidelines for mitigation actions. The framework proposed was applied to the Spercheios river basin, which was selected as a demonstration site. Full article

Hydrological modeling across heterogeneous watersheds remains a key challenge for water resource assessment in regions with strong hydroclimatic gradients. This study presents and evaluates the Chilean Hydrological Model for Climate Change (MHiDROC3) that integrates hydrometeorological and geospatial inputs, data imputation, sensitivity analysis, parameter calibration, distributed hydrological simulation, water-demand representation, and climate scenario analysis within a single workflow. MHiDROC3 was applied to five contrasting watersheds in south-central Chile over the 1980–2021 historical period. Across calibration, validation, and full-period simulations, KGE ranged from 0.51 to 0.77, while natural-streamflow simulations for the complete period showed KGE values of 0.52–0.74 and NSE values of 0.15 to 0.64, indicating variable model performance among basins. Mean simulated streamflow differed from observations by −32.2% to 17.7% across watersheds. Future simulations for 2022–2100 projected lower streamflow under SSP5–8.5 relative to SSP1–2.6, particularly during fall (−33.1% to −57.5%) and winter (−16.0% to −35.9%). By combining distributed process representation, demand effects, and climate-scenario testing in a Chilean framework, MHiDROC3 provides a practical basis for basin-specific water resource assessment, while its variable performance indicates that local evaluation remains necessary before operational application. Full article

In the Ramis River basin of the Peruvian Andes, flood events have become increasingly frequent and intense due to climate variability. However, the basin has limited hydro-meteorological observation records, and its flood generation mechanisms are extremely complex. This situation not only hinders the accurate identification of flood-prone areas, but also limits the effective implementation of flood risk management measures. This study sets three core objectives: to assess flood sensitivity across the basin, identify the dominant factors that influence flood sensitivity, and verify the flood detection performance of multispectral indices. The study adopts two core methods. First, a multi-criteria framework that integrates the Analytic Hierarchy Process (AHP) and Geographic Information System (GIS) is used, incorporating seven flood-related environmental factors and one precipitation triggering variable. Second, the performance of four spectral indices—NDVI, NDWI, SAVI, and MSAVI2 is verified through Spearman correlation analysis, Moran’s I index, and the random forest algorithm. The study finds that landform and geology are the core factors controlling flood sensitivity, with weights of 0.35 and 0.23, respectively. Moderately flood-sensitive areas account for the largest share of the basin, reaching 66% and covering 236.54 km². The flood extent estimated by the spectral indices ranges from 36.73 km² to 101.87 km². Among these indices, NDVI has the strongest spatial correlation with flood-prone areas. The random forest model used in this study has an AUC of 0.6935 and an overall accuracy of 63.51%. The analytical framework proposed in this study is applicable to data-scarce Andean River basins, and the combined use of multispectral indices can provide support for flood risk management and decision-making in this region. Full article

The reactive transport of hexavalent chromium (Cr(VI)) in anthropogenically disturbed sites (e.g., mine waste rock dumps, chromium salt industrial sites) is critically influenced by physico-chemical heterogeneity, yet the interplay between physical and chemical heterogeneities remains poorly understood. This study employed a series of experiments and numerical modeling to investigate the transport of Cr(VI), focusing on the implications of physical heterogeneity—represented by preferential flow paths—and chemical heterogeneity—characterized by reductive mineral lenses. Key findings indicate that physical heterogeneity accelerates Cr(VI) breakthrough by 1.4 to 2.1 pore volumes (PV) relative to homogeneous columns. The presence of pyrite lenses delays breakthrough by 0.6–1.2 PV under neutral pH and 1.6–2.0 PV under acidic pH. At a flow rate of 3.0 m/day, the apparent sorption capacity decreases by ~62.5% compared to 0.3 m/day, indicating that physical advection largely suppresses chemical retention under high-flux conditions. The above results demonstrate that physical heterogeneity governs flow paths and advection rates, whereas chemical heterogeneity impedes transport through heterogeneous adsorption and reduction in Cr(VI) to Cr(III) along these pathways. Furthermore, the presence of preferential paths leads to greater spatial variability, which subsequently influences the interaction dynamics between Cr(VI) and reactive minerals in the aqueous environment. The dominance shifts between physical/chemical controls based on flow rates and pH. At higher flow rates, the influence of physical heterogeneity becomes more pronounced, diminishing chemical reactions due to insufficient residence time of Cr(VI). Conversely, a lower pH environment enhances pyrite dissolution, which decouples the dependency on physical heterogeneity by promoting homogeneous reactions. Further evidence was obtained through X-ray photoelectron spectroscopy (XPS) analysis. The experimental observations are complemented by TOUGHREACT-based reactive transport simulations, which further reveal that the apparent dominance shifts arise from competing timescales between advection and surface reaction. The insights gained from the study emphasize the necessity of integrating both physical and chemical spatial variability in risk assessments, transport modeling, and designing targeted remediation strategies. Full article