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.
The changing Arctic climate alters the dynamics of melting and freezing in the ground. A methodology has been developed to assess small magnitude, ice-induced earthquakes, called frost quakes, by estimating thermal stresses in the soil in Oulu, Finland. Information on temporally and spatially varying soil properties, such as soil temperature and soil ice content, is required to calculate thermal stress. Developing this methodology further on a larger scale, over the whole country, is challenging due to a lack of in-situ measurements of those parameters with high spatial and temporal coverage. However, they can be simulated using land surface models, one of which is HydroBlocks. Previously, HydroBlocks has been applied in the contiguous United States. The goal of this paper was to configure the model in subarctic and arctic Finland. However, another challenge with large scale LSMs is that they often use coarse global soil datasets, and their simplified soil presentations can introduce errors in the modelling results. For that reason, using local soil data helps to achieve a more accurate representation of soil and improves the model performance. Hence, in this paper, the HydroBlocks model was run first by using coarser SoilGrids soil data and second, by using local soil data provided by the Geological Survey of Finland (GTK). HydroBlocks' ability to produce accurate snow accumulation and melt approximations, as well as estimate soil temperature and soil water content at different depths in Finland, has not been evaluated before. The snow model (snow water equivalent) and the modeled soil temperatures and soil water contents were compared with observational data to evaluate the model performance. For six observational snow water equivalent stations, with GTK's soil data the average RMSE and KGE were 29 mm and 0.34, respectively. The worst KGE was −0.30, and the best was 0.90. For the three observational soil stations, the soil temperature had an average RMSE and KGE of 1.3 °C and 0.7, respectively. The worst KGE was 0.22, and the best was 0.86. For the soil water content, using local soil data provided by the GTK significantly improved the modelling. The average RMSE and KGE were first, 0.15 vol/vol and −3.5, and after calibration, they were reduced to 0.03 vol/vol and increased to 0.09, respectively. For the calibrated model, the worst KGE was −0.8, and the best was 0.49. The modelling results emphasize the importance of calibrating the model with local soil hydraulic parameters. The modeling results indicate that outputs from HydroBlocks can generally predict soil conditions in Finland. In addition, modelled soil ice content in Talvikangas was observed. Furthermore, the obtained soil temperature and soil ice content show potential to be used to calculate thermal stresses in soils and identify frost quake-prone areas regionally across Finland over recent decades, ultimately estimating the risk caused by frost quakes.
Ecohydrological water isotope studies often rely on destructive sampling of soils and plants. Recently, techniques for collecting equilibrated water vapor were developed as alternative. Here, we present a systematic evaluation of water vapor sampling methods with the goal of identifying how key parameters influence δ18O and δ2H values. In controlled laboratory experiments we first tested the isotopic stability of three container types: 250 mL infusion glass bottles, 1 L FlexFoil sample bags, and 500 mL Aluminum-zip bags. The most suitable container was subsequently tested for different storage times (6 h to 7 d), storage temperatures (4 to 40 °C) and sampling flow rates (35 to 125 mL min−1). The 250 mL infusion glass bottles showed the best performance, with deviations to reference standards of ±0.5 ‰ for δ18O and ±1 ‰ for δ2H followed by FlexFoil sample bags (deviations of ±1 ‰ for δ18O and −3 ‰ to +2 ‰ for δ2H). Aluminum bags showed the largest deviations (−2.5 ‰ to −3 ‰ for δ18O and −12 ‰ to −25 ‰ for δ2H). Further extensive testing of the most suitable container type – the infusion glass bottles – revealed that the Mean Absolute Error (MAE) for δ18O remained stable under all tested conditions (<0.6 ‰). For δ2H, best results were obtained using flow rates of 100–125 mL min−1, and storage times of ≤1 d under ambient conditions (20–25 °C) (MAE =±0.5 for δ18O and ±1.5 ‰ for δ2H); hence, our recommendation is using these settings. Although vapor sampling cannot match the analytical precision of conventional methods, it offers a practical, inexpensive framework. The protocol enables reliable water vapor isotope measurements and is especially advantageous in remote areas or locations with limited infrastructure. Therefore, it has great potential for investigating ecohydrological processes in a customizable spatiotemporal resolution without the need of repeated destructive sampling or full in situ setups.
Drought is a dominant factor influencing terrestrial ecosystem water-use efficiency (WUE). However, the coupling relationship between WUE and drought remains insufficiently understood. Currently, the coupling relationship is primarily assessed using correlation coefficients or linear regression slopes. However, the optimal drought timescale at which WUE responds to drought has largely been overlooked. Therefore, this study investigated the spatiotemporal patterns of the WUE – meteorological drought coupling relationship across global terrestrial ecosystems from 1982 to 2018 with satellite- derived and model-simulated WUE, together with the Standardized Precipitation-Evapotranspiration Index (SPEI), and explored the potential causal mechanisms. Within the framework of WUE-SPEI coupling, the maximum correlation coefficient between WUE and SPEI represents the sensitivity of WUE to meteorological drought (Rmax), whereas the corresponding optimal drought timescale represents its resistance time (Topt). The results indicated that the sensitivity of WUE to meteorological drought decreased at a rate of −0.0003 yr−1 (p< 0.01), while the resistance time increased at a rate of 0.0155 month yr−1 (p< 0.01), indicating a weakening of the coupling between WUE and meteorological drought. Attribution analysis indicated that CO2 fertilization was the primary factor contributing to the weakening of the coupling relationship. Surface soil moisture was the most critical hydrometeorological driver, exhibiting nearly opposite effects and significant threshold effects on Rmax and Topt. Peter & Clark Momentary Conditional Independence (PCMCI+) algorithm was further employed to construct a causality diagnosis framework for identifying the relationships between WUE-drought coupling and temperature, precipitation, radiation, wind speed, vapor pressure deficit, and surface and root-zone soil moisture. The results showed that the decrease in the Rmax had direct negative causal effects on precipitation, temperature, and radiation. In contrast, the increase in the Topt was primarily driven by a negative causal effect of radiation. This study highlights the weakened coupling between WUE and meteorological drought, suggesting that vegetation's carbon-water trade-off is evolving toward drought adaptation, which is crucial for understanding the adaptive strategies of vegetation in response to climate change.
Accurate field-scale near-surface soil moisture is essential for infiltration, runoff generation, land-atmosphere exchange, and agricultural water management. Drone-based low-frequency (L-band) radiometry offers a promising intermediate scale between in situ measurements and satellite observations, but retrieval remains ill-posed because brightness temperature depends jointly on soil dielectric properties, vegetation attenuation, surface temperature, and sub-footprint heterogeneity. This study develops an uncertainty-aware Bayesian retrieval framework that integrates unmanned aerial vehicle (UAV) dual-polarized L-band brightness temperature with red-green-blue (RGB) and thermal infrared (TIR) information through footprint-consistent priors. Optical fraction vegetation cover, thermal state, and texture descriptors are used to constrain vegetation optical depth (τ) and its uncertainty at the scale of the radiometric footprint. The method was evaluated over heterogeneous cropland in Pengzhou, China, using independent calibration (4 scenes, about 1.3 ha) and validation datasets (13 scenes, about 12.6 ha). The proposed approach reduced root mean square error (RMSE) from about 0.057 to about 0.024 m3 m−3 and largely eliminated the systematic dry bias of the conventional τ–ω inversion. The uncertainty diagnostics showed that footprint-scale texture was more strongly associated with τ-related uncertainty than with posterior soil-moisture variance or absolute retrieval error. Overall, the results indicate that physically informed multi-source priors can improve both accuracy metrics and interpretability for field-scale hydrological soil-moisture observation.
Abstract. Stable isotopes of oxygen (O) and hydrogen (H) in streams and precipitation are cardinal tools to assess water sources, flow paths, and age. However, their spatial and temporal variability in the context of climate change remain largely unknown – essentially due to the limited and often fragmented availability of precipitation O and H isotope records. To overcome this limitation in hydro-ecological studies, we aim to assess the influence of synoptic atmospheric changes on precipitation isotope signatures. In this study, we conjecture that contrasted moisture origins affect precipitation δ18O and d-excess signals in precipitation, and precipitation δ18O residuals after removing local meteorologic effects with a multiple linear regression model. To test our hypothesis, we collected high-resolution (i.e., sub-daily) δ18O and δ2H data at Belvaux (Luxembourg) from 2017 to 2022. We also used a pre-established Lagrangian model to visualise 120 h air mass trajectories and determine the moisture origins for 648 precipitation events. We then analysed how moisture origins affect precipitation isotope signatures by mapping isotope signatures based on the moisture uptake locations we determined. Our results demonstrated effects of moisture origins on precipitation isotope signals in Luxembourg (Western Europe). More specifically, we found that remote (> 1500 km) moisture sources over the Atlantic Ocean are major contributors to precipitation in autumn and winter, while replaced by mid-range (< 1500 km) and local (< 500 km) moisture sources in spring and summer, with an average δ18O value of −8.1 ‰ and d-excess value of +10.8 ‰. We also found that differences in isotope signals from contrasting moisture origins are season-dependent, which we argue is linked to changes in the balance of transpiration and evaporation in moisture stemming from land sources, or specific properties of the Western Mediterranean and the Bay of Biscay. Orographic barriers, such as the Pyrenees, Alps, or Massif Central also had an influence precipitation isotope signatures. The δ18O residuals from the multiple linear regression model, used to exclude local meteorologic effects, did not yield significant differences in moisture origin-specific isotope signatures. This was probably due to local meteorological variables already containing inherent information on remote conditions during moisture formation processes or inaccurate or unrepresentative boundaries for the classification of the moisture origins. Ultimately, with the maps of isotope signatures based on the moisture origins, this study offers a nuanced insight into atmospheric moisture origins affecting precipitation and precipitation isotope signals in Western Europe. This information improves the interpretation of precipitation isotope signals and could contribute to assessing potential changes of the moisture origins of precipitation.
Abstract. Water scarcity is a key limiting factor for sustainable socioeconomic development especially in arid and semi-arid basins, and managing water effectively there often requires coherent and holistic policies and regulations at the basin scale. This study develops an integrated basin-scale hydroeconomic optimization model. The model reasonably details the representation of the hydrologic, infrastructural, water demand and regulatory components, with an objective to maximize overall economic benefits of irrigated crop production, water supply and hydropower generation, subject to resource, infrastructural, operational and policy constraints. A baseline calibration enhances the model's reliability and empirical validity for analysing interconnected physical processes and decision-making, based on the interdependence of hydrologic and economic components. The model is applied to the Yellow River Basin (YRB), where water has been fully allocated and intense competition exists among different water users across the basin. Results show that water availability decreases by approximately 75 % from upstream to downstream, with the corresponding marginal values rising from CNY 0 to 9.14 m−3 along the river in a severe dry year, implying greater challenges for downstream water security (especially the critical ecological requirements) and a larger requirement for water saving in upstream areas. Basin-wide management strategies highlighted in this study include: reallocating water to economically high-value production sectors; coordinating the operation of cascade reservoirs and taking advantage of reservoirs with inter-year operation capacity; monitoring and balancing water availability between upstream and downstream areas. The stabilization of water supply heavily relies on coordinated operation of aquifers and reservoirs with inter-year storage capacity, which mitigates hydrologic variability and improves downstream water availability. Water valuation analysis based on marginal value underscores the potential benefits of water trading and inter-regional transfers. The results offer insights for basin-scale water management, showing potential of reallocation strategies for improving management flexibility and increasing water productivity. Insights from the YRB are meaningful as guides for managing basins worldwide that face similar challenges.
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 SRCdam increased linearly with the share of area they controlled and check dam density (R2 = 0.80 and R2=0.76, respectively; P < 0.001), whereas SARdam increased logarithmically with SY from upstream of the check dams (R2 = 0.62; P < 0.001). A trade-off between SRCdam and SARdam provides diagnostic information for identifying sub-basins with high storage pressure or underused storage capacity, supporting optimized check-dam management. This study provides a practical and data-efficient method for assessing sediment trapping and reduction by cascading check dam systems in large basins, offering valuable insights for improving soil and water conservation strategies in erosion-prone regions.
Snow droughts are increasingly affecting mountain regions, raising concerns about downstream water availability in glacierized catchments. Here, we quantified the role of glaciers in mitigating snow-drought impacts on downstream streamflow during the severe 2022–2023 event in the Italian Alps. In order to do so, we compared glacier-melt contribution to streamflow during these years with the 2011–2023 historical period in two catchments, Dora Baltea (Aosta Valley) and Adda (Lombardy). We employed spatially distributed estimates of glacier melt, snow water equivalent (SWE), air temperature and total precipitation over glaciers from an operational cryospheric model (S3M Italy), and compared these estimates with downstream observations of streamflow at the closure sections of both catchments. Results showed a severe snow water equivalent deficit over glaciers across both catchments and both years (between ∼ −45 % at 4000 m in 2022 and ∼ −75 % at 2000 m a.s.l. during both years), which was largely driven by anomalous air temperatures and seasonal-precipitation patterns (up to +2–3 °C and −73 %, respectively). Air-temperature anomalies displayed a clear signature of elevation – dependent warming, with anomalies at 4000 m a.s.l. that were 1 to 1.5 °C higher than at 2000 m a.s.l.. Glacier contribution to streamflow doubled to tripled during these snow droughts in both catchments, a process that manifested itself through four mechanisms: an earlier-than-usual onset of the glacier melt season, an intensification of glacier melt contribution to streamflow, an earlier-than-usual seasonal peak in glacier melt contribution, and an extension of the glacier melt season. Still, glacier melt contribution to streamflow remained highly sensitive to short-term meteorological events, such as a sudden drop of temperatures, as well as early/late season snowfalls. These results highlight the critical role of glacier melt in maintaining streamflow during severe droughts and emphasize the need to integrate glacier dynamics into water management strategies for alpine areas facing increasingly frequent and intense drought events.
Future floods in the eastern Mediterranean region are influenced by the dual pressures of accelerated climate change and rapid urbanization. Yet the small spatial scale and complexity of hydrometeorological processes make it difficult to project their joint effects. Intra-basin flood projections, in particular, remain absent. This study examines how these drivers affect peak discharge and flood volume in small to medium-sized eastern Mediterranean basins in Israel, using high-resolution weather and hydrological models. We analyze 32 rainstorms under historical (late 20th century) and future (late 21st century) climate scenarios, incorporating projected urban expansion. Results show that while short-duration and high rain rates increase, accumulated precipitation, rainfall area, storm duration, and soil water storage significantly decrease. The combined effect of changes in rainfall patterns, soil water storage, and urbanization produces contrasting trends: urbanization alone leads to a substantial increase in mean peak discharge (+43 %) and flood volume (+41 %), especially when soil water storage is undersaturated and the influence of impervious surfaces is greatest. Conversely, considering only projected rainfall scenario yields decreases in mean peak discharge (−21 %) and flood volume (−30 %), despite higher rain intensities. However, during extreme events, when soil moisture approaches saturation, flood intensification can occur. Combined climate and urban scenarios demonstrate that urbanization dominates, resulting in increased mean peak discharge (+13 %). The high-resolution modeling reveals substantial intra-basin variability, with peak discharge intensification concentrated in upstream and urbanized areas. These localized and contrasting effects highlight the need for integrated high-resolution modeling and future land-use planning to support effective flood mitigation and water-management strategies.
Actual evapotranspiration (AET) is a key component of the water cycle and a crucial source of uncertainty in hydrological modeling, particularly for sub-humid and AET-dominant regions such as West Africa. In this region, climate change is projected to be substantial, which will catalyze hydrological changes. In the climate-hydrological modeling chain for impact assessment, multiple sources of uncertainty are embedded. Hence, the present study investigates how different calibration strategies influence simulated future hydrological projections in West Africa. Given the key role of AET in West Africa, the study particularly evaluates how calibration shapes simulated future AET dynamics. In addition, we test whether a specific plant growth modeling, attributed as leaf area index (LAI), can be used as a proxy to predict AET. The Bétérou Catchment in Benin is selected as a demonstration case along hydrological modeling with the eco-hydrological SWAT-T model. To investigate calibration impacts, we apply three strategies, which range from simple (discharge (Q) only) to more comprehensive (Q and LAI; Q, LAI, and AET) approaches. We apply the Robust Parameter Estimation algorithm in each calibration strategy to address parameter equifinality. We use the standardized future climate data from ISIMIP3b (CMIP6) with five GCMs and three emission scenarios and evaluate changes for the near (2031–2050) and far (2070–2099) future periods. The calibration results demonstrate that the combined “Q + LAI” improves representation of the AET seasonal pattern compared with “Q only”. From “Q only” to “Q + LAI”, the minimum (median) performance increases from EKGE,AET=0.59 (EKGE,AET=0.84) to EKGE,AET=0.81 (EKGE,AET=0.86), respectively. The findings moreover show that projected changes in annual AET depend on the calibration strategy, where all strategies indicate increasing future AET. More precisely, the “Q + LAI” (886 to 918 mm yr−1) and “Q + LAI + AET” (904 to 932 mm yr−1) approaches show similar climate sensitivities and project higher future annual AET rates than the “Q only” approach (846 to 889 mm yr−1). For discharge, contrasting predictions of future changes depending on single GCMs are simulated, mostly indicating decreases across all calibration strategies. The present study provides insights into how calibration choices affect AET simulations for hydrological projections in West Africa. While this study demonstrates that detailed LAI modeling can improve the simulation of seasonal AET dynamics, further research is required to evaluate whether LAI can be used as a reliable proxy for AET in hydrological watershed models.
The complex composition of hydrological systems, climates and landscapes makes it challenging to explain and predict hydrological streamflow response. Many previous large-sample studies, mostly focused on the United States, identified climate as the primary control, with landscape exerting only a minor role in shaping hydrological behaviour. Yet, a few other studies report contradictory results with landscape being a more dominant driver. In this study, we use an unprecedented sample of more than 7000 catchments in Europe from the EStreams dataset to identify and map functionally similar catchments, together with their spatially variable climate and landscape controls. The wide spatial and temporal gradient of the study catchments was used to identify hydrological response types (HRTs) based on 40 hydrological streamflow signatures related to long-term averages and inter-annual variability of magnitude, timing, duration, frequency, and seasonality. Overall, 10 HRTs could be identified. Several HRTs are well defined and well distinguishable, largely due to catchments with strongly seasonal or more extreme behaviour. Other HRTs remain difficult to distinguish, as these catchments represent more transitional conditions with increasingly overlapping characteristics between HRTs. The underlying drivers of the HRTs were identified by using 84 climate- and landscape attributes to predict catchment membership to their respective HRT with a Random Forest classification model. Climate emerges as the dominant driver of hydrological behaviour at the continental scale. However, landscape was found, in 4 out of 10 HRTs, to be at least as strong or even stronger a control on the hydrological streamflow response. These results highlight that the complex, integrated nature of hydrological response remains challenging to disentangle, even with extensive datasets and advanced modelling approaches, and therefore, climate and landscape need to be understood as joint drivers in a co-evolutionary perspective.
The distribution of rainfall intensity through time at a fixed spatial location, referred to here as event temporal loading, can significantly influence hydrological and geomorphological responses, including run-off generation, urban flood risk, and soil erosion. Numerous approaches have been developed to analyse rainfall event temporal loading, but these differ in how they characterise rainfall behaviour and in the aspects of storm structure they emphasise. Emerging research further suggests that climate change may alter rainfall temporal loading in complex and regionally dependent ways, underlining the importance of clear and consistent approaches to its quantification. In this study, we identify 48 metrics which have been previously applied to describe event temporal loading, and define a further five metrics representing aspects not fully captured in existing metrics. We calculate these metrics for 233 128 rainfall events recorded at Danish rain gauges. We use data-driven cluster analysis to reveal how the metrics relate, highlighting groups of metrics that describe similar properties, and others that are more distinct. Based on this, we conceptualise five aspects of temporal loading: mass timing, peak timing, magnitude concentration, temporal concentration, and intermittency. We demonstrate that some metrics are robust to changes in rainfall event temporal resolution and pre-processing, while others are highly sensitive. Drawing on these findings, we recommend one representative metric per aspect: the 4th quartile mass fraction (or D50 if a continuous measure is preferred) for mass timing; peak position ratio for peak timing; the Gini coefficient for magnitude concentration; temporal standard deviation for temporal concentration; and the wet-dry transition rate for intermittency. Together, these recommendations provide a practical framework for deliberate metric selection and more consistent cross-study comparison of rainfall temporal loading.
Flushing and dilution are major phenomena of solute export dynamics during precipitation events in headwater catchments but are hard to predict, even if catchment properties are well known. Normalized cumulative load (NCL) functions have been used to visualize and classify event-based discharge-load relationships, distinguishing between dilution, flushing, and constant export behavior. This study presents an enhanced version of the classical NCL function approach by combining it with hydrograph separation. Over an 18 month period, discharge and solute concentrations were monitored in an agriculturally influenced headwater catchment in the German low mountain ranges, with a focus on nitrate (NO3-) and total phosphorus, and a complementary dataset of major ions. Discharge was separated using stable water isotope signals into event water and total discharge. Both discharge components were then analyzed for solute loads (NO3-, total phosphorus, and major ions). The results reveal significant differences in solute export dynamics between event water and total discharge, including unexpected similarities in the export patterns of nitrate and total phosphorus. The proposed method also highlights a shift from predominantly constant export behavior in the total discharge (coefficient of variation: 0.13) to more pronounced flushing or dilution patterns in the event water (coefficient of variation: 0.36). These findings indicate a fundamental difference between the hydrological processes governing the solute export dynamics of the catchment. While the signal of total event discharge indicates constant behavior, the separated event water exhibits strong flushing or dilution tendencies. The observed shifts in the export patterns, which are likely linked to the activation of drainage systems and depletion of NO3- legacy storages, raise the question if the event water fraction should be monitored more closely in terms of its potential for dynamic pollutant transport. The proposed method is straightforward to implement, yields statistically robust results for the dataset and provides new insights into solute input pathways in headwater catchments.
Floods are among the most frequent and damaging natural hazards worldwide, and reliable observations of water surface elevation (WSE) are essential for improving flood modelling and risk management. The Surface Water and Ocean Topography (SWOT) satellite, launched in 2022, offers new opportunities to monitor river hydrodynamics from space, but its performance in relatively narrow rivers (< 50 m width) remains poorly documented. This study evaluates the potential of SWOT WSEs for flood monitoring through a site-specific hydraulic application by comparing them with in situ observations as well as simulations from an existing large-scale hydraulic model (LISFLOOD-FP) on the Du Gouffre River (width approximate to 40 m), located in Quebec, Canada. The L2_HR_RiverSP (RiverSP) SWOT product Version D, derived from a priori database (SWORD-version 17b), was first compared with one-minute WSE measurements from a tidal gauge located downstream the Du Gouffre River in the St. Lawrence River. This comparison, based on in situ reference measurements, confirmed the overall quality of the SWOT data in this area, with a Root Mean Square Error (RMSE) of 0.25 m. Then, a major flood event (with a return period of about 60 years) which occurred on 1 May 2023, during the SWOT's calibration orbit, was used to conduct a daily analysis of the entire flood event. Eleven observation cycles, covering the period from 25 April to 7 May 2023, were analysed. Limited ground-based observations were available along the studied reach during the flood, highlighting the added value of SWOT observations in this data-scarce context. The 1D/2D hydraulic model LISFLOOD-FP was run for the discharges corresponding to eleven SWOT cycles. Comparisons between SWOT-derived WSEs and model-simulated WSEs yielded biases ranging from -0.30 to 0.43 m and RMSE values between 0.22 and 0.54 m, indicating generall consistent behavior between observed and simulated WSEs across the analyzed cycles. During the flood peak on 1 May, larger discrepancies were observed, reflecting uncertainties in upstream discharge estimates under extreme flow conditions. In this context, SWOT-derived WSEs provided complementary information that helped diagnose discharge underestimation during the peak event. These results are not intended as an independent validation of SWOT measurement accuracy but are specific to the river and flood event under study and demonstrate the practical usefulness of SWOT observations for hydraulic studies in narrow rivers. They highlight the potential contribution of SWOT data for flood monitoring and hydraulic modelling in similar data-scarce contexts, particularly under extreme hydrological conditions.
Hydrologic models are often calibrated using streamflow alone, but increasing availability of in situ and satellite-based observations provide numerous opportunities to constrain model outputs and improve process representation. However, as new observation data emerges, it is often unclear whether calibration with additional data would inform or misinform streamflow prediction. Here, we carry out a multi-observational sensitivity and uncertainty analysis using the U.S. Geological Survey's National Hydrologic Model (NHM) in four headwater catchments in the Upper Colorado River Basin. We use seven different observational data products that pertain to discharge, snow water equivalent, snow-covered area, soil moisture, and evapotranspiration. Informative model parameters are identified using the Morris screening method across all data sets, followed by parameter estimation and streamflow performance assessment using a Latin Hypercube Sample Monte-Carlo filtering approach. Results show that an increased number of informative parameters are determined through the screening process with the use of observation data representing terms beyond streamflow, and that forcing corrections and rain-snow partitioning parameters are particularly impactful to the model fit to observations. Multi-objective Monte Carlo filtering reduces the number of behavioral parameter sets, and estimated parameter values can depend strongly on the observation data criteria. Evapotranspiration is informative for streamflow prediction across all catchments included in this study, but snow and soil moisture datasets are only informative in some. These results provide new insight into the variable value of alternative observation data for streamflow prediction and highlight challenges related to model/observation scale mismatches, compensating errors, and misinformative data.
Land cover and extreme weather events are closely connected to ecosystem services like water yield and carbon sequestration. Understanding how carbon and water respond to human disturbances is critical for managing these resources and realize desired ecosystem services at the national llevel. The monthly scale ecosystem model, Water Supply Stress Index (WaSSI), was tested and applied across Germany for mapping carbon and water balances from 2001 to 2019. We estimated that ecosystems in Germany generate 84.86 billion m3 of water yield and sequester 106.03 Tg of carbon annually on average. Most of the precipitation was lost as evapotranspiration in eastern states that were comparatively drier in river flows than the rest of the country. Croplands, urban areas and Evergreen Needle Forests (ENF) provide 82.5 % of the national water yield, while the forest lands share the majority (56.3 %) of land carbon sequestration altogether. Our simulation results highlight the importance of sparse land covers (e.g. wetlands) in carbon sequestration. Findings also suggest that national water yield and carbon balances are sensitive to extreme events such as the floods in 2002 and 2013 and the extreme drought in 2003 and 2018. We found that hydrologic buffers from the previous year played an important role in mitigating negative impacts on both carbon and water availability. This study highlights that, when integrated with local data, a relatively simple modelling approach is adequate to quantify the coupled water and carbon responses to climatic and land cover variability at a large scale. We conclude that land management of both forests and croplands is vital to sustain ecosystem services under a changing climate at regional to national levels.
Seasonal snow strongly influences groundwater recharge in mountain aquifers, yet its role in mid-altitude karst systems under climate warming remains poorly quantified. We investigated the Dévoluy karst aquifer (Southern French Alps) to assess how snow controls recharge and how spring discharge may respond to rising temperatures. Using the KarstMod platform, we developed a rainfall-snow-discharge model incorporating a degree-day snow routine to partition precipitation between rainfall and snow, and simulate the snowmelt. The model was calibrated and validated over four contrasting years (two low-snow, one high-snow, and one very high-snow year). Results show that accounting for snow processes is essential to reproduce the observed discharge dynamics, highlighting the dominant role of snow accumulation and melting in controlling both flow timing and magnitude in karst environments. We then tested the sensitivity of the karst spring discharge to temperature perturbations. Under +2 and +4 °C warming scenarios, simulated winter flows increase while snowmelt peaks occur earlier, resulting in earlier and more severe summer low-flow periods. August discharge decreases by 26 % to 42 %, respectively, compared to present conditions. These findings demonstrate the critical role of seasonal snow in regulating recharge in mid-altitude karst aquifers and highlight that ongoing warming will substantially reduce summer water availability in mountain regions.
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.
Flowing stream networks expand and contract in response to dynamic groundwater levels. Field studies generally associate greater flowing network length (L) with higher streamflow (Q), but this neglects potential hysteresis caused by nonequilibrium groundwater flow after rain and snowmelt. Using a new version of the Distributed Hydrology Soil Vegetation Model (DHSVM), we predict that groundwater hysteresis may decouple L from Q across large (> 100 %) variations in Q. Groundwater hysteresis contributes to the spatial reconfiguration of active flowpaths and changes to hillslope-riparian hydrological connectivity, which can manifest as a network length scaling anomaly relative to the best-fit power law. In a 27 km2 snowy volcanic watershed, seasonal anomalies in measured stream ionic concentration indicate an outsized contribution from longer subsurface flowpaths during recession, supporting our L-Q hysteresis hypothesis and refining our model calibration. The model can reproduce observed stream network elasticity (from field surveys), and the predicted network length anomaly mirrors seasonal anomalies in measured stream ionic concentration (r=-0.92), suggesting that the model can capture seasonal changes in the spatial configuration of groundwater convergence and streamflow generation. A warmer climate is expected to cause a partial transition from snow to rain resulting in flashier streamflow, but our simulations predict that seasonal groundwater hysteresis would dampen storm-scale stream network elasticity, thereby significantly increasing L-Q hysteresis on daily to monthly timescales (p < 0.01). Conceptual models of stream networks should consider the potential effects of groundwater hysteresis, especially in a changing environment. More broadly, our investigation highlights how spatially distributed process-based hydrological modeling can reveal emergent hydrological behaviors that are not necessarily apparent from sparse field data.
