Elevated dissolved organic carbon (DOC) concentrations are a major concern for ecosystems and drinking water supply. Data-driven studies revealed variable functioning of different landscape units (upland, riparian zone, and groundwater) in catchment DOC mobilization and export. However, lumped and landscape-explicit (separating upland and riparian zone) model structures are generally calibrated to stream DOC concentrations, while the internal DOC dynamics often do not receive sufficient attention. Here, we developed a flexible model with a lumped and landscape-explicit structure for four headwater catchments in the Harz Mountains, Germany. We evaluated these models under a baseline calibration (only against stream DOC concentration) and a constrained calibration (using stream DOC and internal DOC concentrations). Under the baseline calibration, both model structures reproduced stream DOC dynamics with acceptable performance in some catchments (Kling–Gupta efficiency of behavioural simulations>0.6), but with unrealistically high groundwater DOC. By contrast, the constrained calibration reduces the KGE for stream DOC concentrations but produces internal DOC dynamics consistent with the imposed observational and process-based constraints. Additionally, the landscape-explicit model structure is more robust than the lumped model structure under changing boundary conditions. Our study thus shows that robust catchment DOC modelling requires not only explicit representation of different landscape units, but also calibration constraints based on DOC concentrations within those units.
Rainfall during the snow season plays an increasingly important role in flood risk as climate warms and extreme events become more frequent. However, a given sized rain-on-snow (ROS) event can yield outcomes ranging from flooding to no runoff, depending partly on the snowpack's antecedent cold content and capillary retention forces. Here, we analyze the seasonal evolution of the snowpack's physical state over a 72-year period to assess long-term changes in its capacity to buffer runoff from liquid water input. We use ERA-5 Land data to force Alpine3D, a distributed snowpack model that tracks the layer-by-layer development of heat, mass, and structural framework of the snowpack throughout the snow season. We test our approach in a large Columbia River headwaters basin in NW Montana, USA. We evaluate cold content and total capillary retention of the snowpack to determine long term trends in Liquid Water Buffering Capacity (LWbc) as it evolves throughout the snow season. The LWbcbc, and in fact demonstrated an increase in cold content over the 72 years. Our findings demonstrate that changes in the snowpack's ability to buffer runoff, including dependencies on local basin factors related to snowpack seasonality and elevation, are a key component of evolving ROS risk.
Soil-Vegetation-Atmosphere Transfer (SVAT) models are essential tools for simulating and underploting the dynamic interactions governing water balance components within forest ecosystems. These models are widely employed to predict hydrological responses to environmental change, including the impacts of shifting meteorological conditions on forested landscapes. Despite their usefulness, the reliability of SVAT models is frequently compromised by uncertainties arising from incomplete or imprecise input data. These limitations often result in model assumptions that may lead to over- or underestimation of critical water balance components such as groundwater recharge. In order to improve the accuracy of SVAT models, observed soil moisture data are integrated to enhance parameterization processes by aligning simulated outputs with measured values. However, uncertainties remain regarding the selection of representative soil moisture profiles for calibration and the extent of measurements necessary to robustly characterize a forest plot. To address these challenges, the present study explores the spatial variability of soil moisture across two forested plots with contrasting soil and vegetation conditions by the deployment of an extensive network of soil moisture probes in 11 profiles per plot. The influence of soil moisture variability on the adjustment of model input parameters during the calibration process and its subsequent impact on the computation of groundwater recharge is evaluated. The findings reveal that soil moisture variability at the plot characterized by a heterogeneous soil was greater, both horizontally and in depth, throughout the study period. These patterns of variability are also mirrored in the different parameter sets obtained from the calibration of the LWF Brook90 model, based on the recorded soil moisture time series in each of the 11 profiles per plot. The most significant variation is observed in the infiltration and hydraulic soil parameters, whereby this is more pronounced at the plot with heterogeneous soil structure. When examining the groundwater recharge rates calculated using the 30 best-performing parameter sets for each of the 11 profiles, both plots exhibited comparable temporal patterns but substantial differences in total groundwater recharge volumes across profiles. The results also suggest that model-inherent uncertainties, including parameter interactions, equifinality and dimensional simplifications, have a stronger impact on model outputs than uncertainties arising from variability in soil moisture caused by spatial heterogeneity of soil texture and hydraulic properties within the plot. Taking into account both sources of uncertainty, the application of Monte Carlo based subsampling yielded contrasting results. While groundwater recharge at the Kienhorst plot could be reliably estimated using data from only 3 soil profiles at the investigated 20×20 m plot, eleven soil profiles were required at the Tharandt site to adequately capture the spatial variability of the system. These numbers are indicative rather than universal thresholds, as the required sampling density is strongly site-specific and depends on local soil structure and model related uncertainties.
Copernicus Land Monitoring Service (CLMS) produces biogeophysical maps of the global land surface. The CLMS portfolio so far did not include actual evapotranspiration (ETa), despite it being a direct link between the energy, water and carbon cycles and its importance for global food security, efficient water resources management and weather forecasting. However, a global CLMS ETa product was recently developed and entered operational production at the end of 2025. It consists of water flux (ETa, evaporation and transpiration) and energy flux (latent and sensible heat) sub-products, has a spatial resolution of 300 m, dekadal (10 d mean) temporal resolution for water fluxes and daily temporal resolution for instantaneous energy fluxes, is produced in near-real-time, and (like all other CLMS products) is distributed under free and open data policy. It is based mainly on Copernicus input data with primary satellite imagery coming from the observations of OLCI and SLSTR sensors on board of Sentinel-3 satellites. Such product fills a gap in previously existing global and operational ETa products, thus satisfying a wide range of potential users' needs. In this paper, we describe, evaluate and justify the various design choices taken during the development of the ETa product, ranging from cloud masking and gap-filling, through derivation of biophysical traits, radiation components and weather forcings to spatial sharpening of the land surface temperature observations. Those data were then used to drive two ETa models: TSEB-PT and ETLook. A prototype implementation of the ETa processing chain was used to produce ETa data (using both models individually and their ensemble) across a globally representative range of climatic zones and plant functional types, which was validated against measurements from 206 Eddy Covariance flux tower sites. The ensemble resulted in the all-site root mean squared error (RMSE) of 0.87 mm d−1−1), bias of 0.01 mm d−1−1.39–1.50 mm d−1) and correlation coefficient of 0.81 (site mean of 0.77), which compares well with WaPOR global ETa dataset and therefore supports the operational production of global, near-real time, Copernicus-based 300 m ETa maps. Finally, we propose a number of potential evolutions of the CLMS ETa product, including enlarging and enhancing the model ensemble, producing a reanalysis version of the product with more robust meteorological forcing and improved gap-filling, and focusing on aspects of modelling related to influence of surface roughness and soil moisture on energy and water fluxes.
Groundwater is vital but increasingly stressed by climate change and rising water demand. This study estimates how quickly aquifers respond to changes in recharge using a signal-analysis method. Most aquifers reacted within one to ten months, while deeper responded more slowly and proved more resilient to short droughts. The method uses existing monitoring data, offering a practical way to identify vulnerable aquifers and guide groundwater management under climate change.
Groundwater resources represent Germany's most important source of freshwater but they are increasingly under pressure. Climate change, societal developments, and rising abstraction rates are impacting subsurface storage in ways that are currently difficult to predict, affecting both the quantity and quality of groundwater. To ensure sustainable groundwater management, it is crucial to evaluate the intrinsic and spatially variable vulnerability of groundwater systems, especially to prepare for the effects of hydrological extremes. In this context, the groundwater response time, generally defined as the timescale over which a groundwater system responds or adjusts to changes in external or internal conditions and derived here as the characteristic timescale of the aquifer's low-pass filtering behavior, serves as a valuable indicator for vulnerability assessments. Unlike traditional methods, we propose estimating response times through spectral analysis of groundwater level data. Time series from around 200 selected observation wells across Bavaria in Southern Germany were processed and transformed into the spectral domain. Corresponding recharge time series were extracted from high-resolution hydrological model outputs. By integrating these data with hydrogeomorphic information, we fitted a semi-analytical model to the groundwater level spectra to obtain aquifer response times. The semi-analytical solution for the spectral domain accurately reproduced the majority of observed groundwater level spectra. Half of the estimated response times fall between 30 and 100 d. Significant correlation were found between the response time and the depth of the groundwater table. Groundwater systems exhibiting longer response times are interpreted as more resilient to drought conditions and therefore potentially better suited for groundwater abstraction than aquifers with shorter response times.
Precise seasonal prediction of groundwater nitrate concentrations in intensive agricultural areas faces challenges such as data sparsity, strong spatiotemporal heterogeneity, and complex hydro-biogeochemical processes. To address these issues, this study proposes an integrated prediction framework combining hybrid quantum-classical machine learning, advanced virtual sample generation (t-SNE-GMM-KNN), and remote sensing foundation model semantic embedding (AEF). Modeling was conducted across the 2022–2023 normal, dry, and wet seasons in Xiong'an New Area. Hydrochemical types were dominated by Ca-Mg-HCO
Despite the widespread use of physically based groundwater models, their deployment at the catchment scale remains challenging and time-consuming. HydroModPy was developed to address this gap by enabling automated and streamlined multi-site development of hydrogeological models at the catchment scale. This open-source Python toolbox facilitates the construction, execution, calibration, and analysis of unconfined shallow groundwater models. The current version integrates well-established geospatial tools, such as Whitebox Tools, and hydrogeological libraries with FloPy-driven MODFLOW-NWT simulations, along with optional particle-tracking and solute transport modules (MODPATH and MT3DMS), to provide a fully scriptable, end-to-end workflow. Automation is achieved through dedicated functions and classes capable of performing watershed delineation from digital elevation models, preparing spatial and temporal recharge forcings, generating computational meshes and vertical discretization schemes, assigning model parameters, and running simulations in steady-state or transient modes. The overall framework supports systematic and reproducible calibration routines that leverage subsurface data such as groundwater head measurements, as well as surface observations – including stream network maps and stream intermittency patterns – to constrain model estimates of aquifer hydraulic properties. Model outputs and provenance metadata are exported in standard geospatial formats to ensure interoperability and alignment with FAIR data principles. Built-in visualization tools and integration with Jupyter Notebooks support interactive exploration, teaching applications, and fully reproducible analyses. In this technical note, we present the HydroModPy architecture and its core functionalities, demonstrate model deployment across various hydrogeological contexts, and discuss ongoing and planned developments for future versions of this collaborative tool. The codebase is modular and extensible, making it suitable for adoption by a broad user community. Planned enhancements include tighter coupling with land-surface or ecohydrological models adding new numerical solvers,
Accurately simulating overland flow in vegetated landscapes remains a challenge in hydrological modeling due to the complex interactions between vegetation, surface roughness, and soil infiltration. This study evaluates multiple methods for estimating Manning's roughness coefficient and explores the influence of vegetation on infiltration parameters, namely saturated hydraulic conductivity (Ksat) and wetting front suction (Psi), using the OpenLISEM model. Based on 132 artificial rainfall experiments across 22 sites in southwest Germany, the model was calibrated and validated against observed runoff data, incorporating both depth-independent and depth-dependent roughness formulations. Incorporating water depth-dependent roughness into the model can improve its performance in simulating overland flow. Beyond roughness effects, vegetation was shown to significantly alter soil hydraulic properties, particularly saturated hydraulic conductivity (Ksat). Paired site comparisons revealed that increased vegetation cover corresponded with higher infiltration capacities, emphasizing vegetation's role not only in surface resistance but also in enhancing subsurface water fluxes. The findings demonstrate that models must account for both surface and subsurface impacts of vegetation to improve runoff predictions.
The simulation of streamflow is a complex task due to its intricate formation process. Existing single models struggle to accurately capture the stochastic, non-stationary, and nonlinear dynamics of basin streamflow in changing environments. This study combined process-driven hydrological mechanism models with data-driven deep learning models, considering various factors like hydrology, meteorology, environment, and the interconnected effects of upstream and downstream rivers, to develop an interpretable hybrid streamflow simulation model. The study collected multiple external variables to better understand the hydrologic system complexity and used the Maximum Information Coefficient (MIC) to analyze their relationship with streamflow. Subsequently, the Xinanjiang (XAJ) model with physical mechanisms was employed, alongside the TCN-GRU model integrating Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU), for separate streamflow simulations. Furthermore, a robust integration method was adopted, realizing nonlinear ensemble through Random Forest (RF), thus establishing the hybrid XAJ-TCN-GRU model. This model exhibits promising results in simulating streamflow in four different basins in China, achieving high Nash-Sutcliffe Efficiency (NSE) values with 0.991, 0.971, 0.984, and 0.986 for the Wuding River, Chu River, Jianxi River, and Qingyi River respectively. In terms of streamflow simulation, flood simulating, and interval simulation, this model outperforms other benchmark models. Additionally, the study quantified the contributions of each hydro-meteorological variable to the long-term streamflow trend using mean absolute SHAP values (SHAPABS), Feature Importance (FI), and Permutation Feature Importance (PFI), thereby enhancing the model's external interpretability. The results of this study are of significant importance for optimizing water resource management and mitigating flood disasters.
We evaluate the performance of the Super-Fast INundation of CoastS (SFINCS) hydrodynamic model for simulating riverine floods, combined with a fully automated open-source data preprocessing pipeline. To do this, we assessed the simulated extent of 499 historic flood events against the satellite derived flood extents using the Critical Success Index (CSI) as a performance metric. We utilised simulated discharges from the Global Flood Awareness System (GloFAS) hydrological model and found that SFINCS performance improved with upstream basin size, with a global mean CSI of 0.42 for basins with large upstream area (>1000 km2) and a CSI of 0.29 for basins with small upstream area (<50 km2). Our results illustrate the importance of accurate discharge data input to flood hazard simulations. When the (globally simulated) GloFAS data is replaced with observed discharge data for ten events in the US, the CSI improved from 0.39 to 0.67. These results suggest that global hydrological model performance limits the accuracy of the flood hazard simulations. Our findings also showed a significant improvement in the CSI (from 0.37 to 0.57) when changing to a higher-resolution elevation input by contrasting a ∼1 m digital elevation model (DEM; 3DEP) with our default ∼30 m global DEM (FABDEM) in six US events. Sensitivity analysis of bathymetric calculations revealed a systematic underestimation of the default 2-year return period estimated by GloFAS discharge, likely driven by underrepresentation of annual block maxima, which resulted in underestimated channel dimensions. All of these factors resulted in a loss of detail, which impacted model performance, especially in smaller headwater rivers. We recommend to improve the estimation of bathymetry, for instance by employing the “gradually varying solver” method or using data from the SWOT mission. Furthermore, incorporating additional validation data which ideally includes flood depth measurements can largely enhance our understanding of the model performance.
Check dams, globally built for controlling soil erosion, form complex cascading systems that pose significant challenges for assessing spatiotemporal dynamics of Sediment Yield (SY) at large basin scale. This study proposed an integrative framework combining dynamic sediment trapping efficiency of cascading check dams with the Revised Universal Soil Loss Equation (RUSLE), Index of Connectivity (IC), and Sediment Delivery Ratio (SDR). This model was applied to evaluate grid-cell-based distribution of SY and sediment trapped by check dams during 1970–2020 in the Middle Yellow River Basin (with over 47 000 check dams). The Nash-Sutcliffe efficiency of proposed model increased to 0.71 compared to model ignoring sediment trapping of check dams (0.59). Check dams reduced the multi-year average SY by 50.01 % in dam-controlled areas. Totally 3.84 × 109 t of sediment was trapped over the 50 years, constituting 41.49 % of designed storage capacity. The Sediment Reduction Contribution by check dams (SRCdam) exhibited considerable spatial heterogeneity, ranging from 41.3 % to 0.9 % among sub-basins, and the proportion of accumulated sediment to storage capacity of check dams (SARdam) varied from 78.1 % to 1.1 %. The SRCdamR2R2=0.76, respectively; P < 0.001), whereas SARdamR2P < 0.001). A trade-off between SRCdamdam
Vegetation restoration on the Loess Plateau has raised an important question of whether enhanced land–atmosphere coupling can alleviate regional water scarcity in this water-limited environment. In this study, we integrate the WAM-2layers atmospheric moisture-tracking model with a random forest–enhanced Budyko framework and introduced a vegetation-weighted leaf area index (LAIw) to examine vegetation-associated precipitation recycling and its hydrological effects during the peak growing season (July–August during 2000–2022). Results show that precipitation is dominated by terrestrial moisture sources (86.5 %), while oceanic contributions account for 13.5 %. Internal moisture recycling contributes 14.4 % of total precipitation but exhibits a trend of −0.073 mm yr−1. At the regional mean scale, vegetation-associated recycled precipitation has a limited effect on precipitation, evapotranspiration, and surface water availability (0.059 %, 0.02 %, and 0.107 %, respectively), reflecting strong compensation between positive and negative vegetation effects and the limited fraction of internal recycling. However, a clear regime shift emerges along the LAIw
Evaporative fraction (EF), defined as the ratio of latent heat flux to the sum of latent and sensible heat fluxes, is a key metric of surface energy partitioning and an indicator of plant water stress. To investigate the mechanisms underlying EF dynamics, we developed an explainable machine learning (ML) model based on a Long Short-Term Memory (LSTM) architecture that explicitly incorporates memory effects. The model was trained using data from 90 eddy-covariance sites across diverse plant functional types (PFTs) from the ICOS, AmeriFlux, and FLUXNET2015 Tier 1 datasets. Using only routinely available meteorological inputs (e.g., precipitation, incoming shortwave radiation, air temperature, and vapor pressure deficit) together with static site attributes (e.g., PFT and soil properties), the model accurately captured EF dynamics, particularly during post-rainfall pulses and soil moisture dry-down events. Across all test periods, ensemble mean predictions showed good agreement with observations, with a median site-level NSE of 0.63 across sites spanning broad climate and ecosystem gradients. Explainable ML analyses identified precipitation and vapor pressure deficit as the dominant drivers of EF in woody savanna, savanna, open shrubland, and grassland ecosystems, whereas air temperature emerged as the dominant factor in deciduous broadleaf, evergreen needleleaf, and mixed forests. Expected Gradients further revealed substantial variation in memory effect contributions across PFTs, with evergreen broadleaf forests and savannas showing stronger influences from antecedent conditions than grasslands. These memory effects were closely associated with rooting depth, soil water-holding capacity, and plant water-use strategies, which together regulate drought-response timescales. The learned memory patterns also suggest potential for inferring information related to rooting-zone water storage capacity and plant water stress from surface observations. Overall, our findings underscore the critical role of memory effects in EF prediction and highlight their relevance for anticipating vegetation water stress under increasing drought frequency and intensity.
Hydrological impact assessments under climate change commonly rely on conventional modeling chains where climate projections are bias-corrected before being used in hydrological simulations. While this improves agreement with historical observations, it can introduce methodological uncertainties, reduce the diversity of climate ensembles, and smooth out extreme events. Asynchronous methods have been proposed as an alternative, allowing hydrological models to be calibrated directly with raw climate model outputs. However, fully asynchronous methods often fail to capture the timing of key hydrological processes, especially in snow-affected regions.This study introduces and evaluates a semi-asynchronous calibration on monthly observed discharge distributions to address these limitations. Using the physically based WaSiM model, we compare the semi-asynchronous, fully asynchronous, and conventional methods across ten snow-influenced catchments in southern Quebec, Canada, under historical and future climate conditions.The results show that while the fully asynchronous and semi-asynchronous methods perform well in preserving streamflow distributions and high-flow extremes, only the semi-asynchronous method succeeds in restoring the seasonal timing of key processes such as snowmelt and low flows. The semi-asynchronous method notably reduces intermodel variability in streamflow and snow water equivalent compared to the fully asynchronous approach. It also exhibits seasonal dynamics that closely align with observations and the conventional method, despite relying on uncorrected climate inputs. In contrast, the fully asynchronous method shows signs of desynchronization, with unrealistic snowmelt timing and elevated variability across projections. The conventional method, while more stable in the historical period, exhibits an increase in intermodel variability under future conditions, likely due to divergent magnitudes of projected change across climate models.Compared to the conventional method, which benefits from stable and consistent simulations but tends to dampen extremes through bias correction, the semi-asynchronous approach offers a compelling alternative. It strikes a different balance between realism, ensemble diversity, and the ability to represent extreme flood events, making it particularly valuable for future-oriented climate impact assessments.This study highlights the potential of the semi-asynchronous method as an innovative and robust tool for hydrological modeling under climate change. As climate model simulations continue to improve and their biases are progressively reduced, the semi-asynchronous approach is poised to benefit significantly, enhancing its potential for future hydrological projections.
Geological carbon sequestration (GCS) mitigates climate change by storing anthropogenic carbon dioxide (CO2) in geological formations. CO222, while geochemical reactions generate preferential pathways for CO2-rich flow. For the scenarios we analyze, after 500 years of mass transfer, dissolved CO2222γ22
Accurate simulation of snowmelt runoff (SMR) is critical for water resource management. However, despite the abundance of global hydrological models, little is known about their SMR performance. This study presents a comprehensive evaluation of SMR across 15 state-of-the-art large-scale models and runoff products by focusing on their biases in first-order indices, i.e., the total volume (Qsum), peak flow (Qmax), and centroid timing (CTQ) of runoff in the snowmelt period. Then by introducing 1455 snow-dominated basins with diverse topography and vegetation complexities, we further proposed a novel model robustness metric to test how different models perform under increasing basin complexity, thereby allowing for a quantification on how they adapt to complex environmental conditions. Our results reveal that (1) most models exhibit underestimated QsumQmaxQsumQmaxQsumQmax, land surface models show a clear advantage in simulating CTQ, highlighting their structural strength in capturing melt timing rather than runoff magnitude. This study provides a large-sample benchmark for SMR evaluation and complements existing model assessment approaches by examining model performance across basin complexity gradients, offering useful insights for future model development and uncertainty reduction.
In complex hydrological systems, flow path dynamics, water storage and mixing, and biogeochemical processing vary in space and may change rapidly during events. Understanding source areas, connectivity and short-term dynamics in stream water quality therefore requires high-temporal-frequency, multi-source observations both within and across catchments. Revolutions in field-deployable analysers and sensors, together with advancement in automation techniques, now make such observations feasible via true “labs-in-the-field”. This paper details the technical realisation and proof-of-concept for the Water Analysis Trailer for Environmental Research (WATER). The WATER is a mobile, trailer-based platform for environmental sensing and automated, high-temporal-frequency sampling and analysis of water from multiple (currently up to 11) sources. It offers two analytical pathways – a Throughflow Pathway for measurement devices using a flow cell and a 5 µm-filtered Reservoir Pathway for devices requiring filtered water – and is currently equipped to measure stable water isotopes, nitrate, electrical conductivity, pH and temperature. Integration of additional measurement devices in the future is supported by the modular design of the WATER. A field test in the 1.03 km22×3×1×
The White Nile from Lake Victoria through Lakes Kyoga and Albert to the Sudd wetlands forms a complex connected lake-river-wetland system where flood propagation, storage, and attenuation remain poorly quantified. Following unprecedented and persistent flooding across South Sudan in 2022, this study quantified system-scale flood-wave transit time and examined how long it takes a flood wave to travel from Lake Victoria to the Sudd and how upstream storage and connectivity shape multi-year flood behaviour. Using daily lake levels, discharge, CHIRPS rainfall, and MODIS-derived inundation for 2002–2024, we tracked sequential flood peaks through the Victoria–Kyoga–Albert–Sudd cascade and mapped monthly wetland dynamics across five South Sudan sub-catchments. Flood-wave tracking showed a mean system transit time of approximately 17 months (16.84±1.95 months; range 13.0–20.9 months), substantially longer than the commonly inferred four-to-five-month timescale based on seasonal peak alignment. Segmental analysis revealed rapid transmission from Victoria to Kyoga (mean 4.2 months) but strong attenuation through the Albert–Sudd reach (mean 9.3 months), consistent with extensive floodplain storage and backwater control. Correlations between Lake Victoria peaks and downstream wetland extents strengthened markedly after 2019, with r2The 2019–2024 high-water regime was not a series of isolated rainfall events but a multi-year propagation of excess storage initiated by the 2019 positive Indian Ocean Dipole anomaly and consecutive rainfall seasons. Lake Victoria reached exceptional peak levels of 1136.48 m a.s.l. in 2020, 1136.50 m a.s.l. in 2021, and 1136.66 m a.s.l. in 2024, each exceeding the historical 1964 maximum (1136.42 m a.s.l.). Over the same period, the Sudd Wetland exceeded its previous MODIS-era maximum extent (81 496 km22, respectively. Flood-persistence mapping shows a shift from rainfall-driven activation in the eastern Sudd (Baro-Akobbo-Sobat–White Nile) in 2019–2020 to sustained, inflow-driven inundation across the central and western Sudd (Bahr el Jebel–Bahr el Ghazal–Bahr el Arab) during 2020–2022, consistent with water pathway activation and backwater expansion under high antecedent storage. When compared with historical episodes in the 1870s and 1960s, the persistence and spatial reach of the 2019–2024 floods rank among the most extensive in the modern record. These results redefine the White Nile as a long-memory system in which upstream storage governs downstream flood risk over multi-season timescales and challenge interpretations of flood propagation based on seasonal timing alone, offering a new empirical basis for flood forecasting, wetland
Runoff threshold behavior is widely reported in event-based hydrological studies, but its interpretation and cross-catchment variability remain unresolved because threshold metrics, values, and process interpretations vary among studies, climates and landscape settings. This study synthesizes reported storm-runoff thresholds from 138 experimental catchments worldwide, as well as reported dominant runoff mechanisms, documented wetness-dependent mechanism transitions, and soil-geology-hydrogeology associations. Across the reviewed literature, threshold-like responses were identified using rainfall metrics (e.g., event rainfall amount and rainfall intensity), hydrological-state metrics (e.g., antecedent or within-event soil moisture, storage, and groundwater level), and composite rainfall–state indicators. Hydrological-state and composite indicators were reported more frequently than rainfall-only metrics. Subsurface- and saturation-related mechanisms were most frequently reported, particularly among studies in humid catchments. Among the catchments with explicitly documented event-scale transitions in runoff generation mechanisms, shifts from surface-dominated responses toward saturation-, subsurface-, or shallow-groundwater-influenced responses were more frequently reported as catchment wetness increased within the reported transition subset, although reverse and context-dependent pathways are hydrologically possible. Co-occurrence analysis indicates that reported mechanisms are associated with soil-depth, texture, permeability, lithology, and hydrogeological descriptors, which we interpret as structural contexts that condition state-dependent functional connectivity. Together, the synthesis supports a connectivity-based framework in which rainfall forcing interacts with catchment state and structural constraints to activate or connect runoff pathways.
As the atmosphere warms, atmospheric evaporative demand is expected to increase across many high-latitude ecosystems, while the duration of seasonal snow cover is projected to change. In Norway, a typically moisture-rich region, improved understanding of the controls on evaporation is needed to assess how these changes may affect ecosystem hydrology. In this study, we used year-round evaporation estimates from four eddy-covariance wetland sites in Norway to quantify evaporation and identify its main controls. To estimate monthly, seasonal, and annual evaporation, eddy-covariance data were gap-filled using a random forest model. The sites cover a latitudinal gradient from 60 to 78° N, a precipitation gradient from 218 to 968 mm−3.9kPamm, equivalent to 9 % to 30 % of the total precipitation. In the warm season, evaporation was typically around 50 % of the seasonal precipitation, reaching a maximum of 72 %. Sites and years with long-duration snow-cover had lower annual evaporation. Compared to other northern latitude sites in the FLUXNET2015 data set, evaporation was lower than expected from the mean temperature of the warm season. Our results show that evaporation is an important part of the northern latitude water balance, especially during the warm season and in regions with low precipitation. Furthermore, our results indicate that earlier snow-cover melt-out and increased vapour pressure deficit have the potential to increase annual evaporation.