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Global hydrological models (GHMs) have been applied to assess global flood hazards, but their capacity to capture the timing and amplitude of peak river discharge-which is crucial in flood simulations-has traditionally not been the focus of examination. Here we evaluate to what degree the choice of river routing scheme affects simulations of peak discharge and may help to provide better agreement with observations. To this end we use runoff and discharge simulations of nine GHMs forced by observational climate data (1971-2010) within the ISIMIP2a project. The runoff simulations were used as input for the global river routing model CaMa-Flood. The simulated daily discharge was compared to the discharge generated by each GHM using its native river routing scheme. For each GHM both versions of simulated discharge were compared to monthly and daily discharge observations from 1701 GRDC stations as a benchmark. CaMa-Flood routing shows a general reduction of peak river discharge and a delay of about two to three weeks in its occurrence, likely induced by the buffering capacity of floodplain reservoirs. For a majority of river basins, discharge produced by CaMa-Flood resulted in a better agreement with observations. In particular, maximum daily discharge was adjusted, with a multi-model averaged reduction in bias over about 2/3 of the analysed basin area. The increase in agreement was obtained in both managed and near-natural basins. Overall, this study demonstrates the importance of routing scheme choice in peak discharge simulation, where CaMa-Flood routing accounts for floodplain storage and backwater effects that are not represented in most GHMs. Our study provides important hints that an explicit parameterisation of these processes may be essential in future impact studies.

Accurate river discharge forecasting is essential for effective water resource management, particularly in regions prone to monsoonal variability and extreme weather events. This study presents an interpretable deep learning framework for daily river discharge forecasting in the Subarnarekha river basin (SRB), integrating Kolmogorov Arnold networks (KAN) with Shapley additive exPlanations (SHAP). Leveraging hydroclimatic inputs from five coupled model intercomparison project phase 6 (CMIP6) general circulation models (GCM) under the high emissions shared socioeconomic pathway (SSP585) scenario, the model was trained and evaluated across four active gauging stations: Muri, Adityapur, Jamshedpur, and Ghatsila covering the period 1980 to 2022, with projections extending to 2100. The main findings of this study are (1) KAN demonstrated high predictive performance with root mean squared error (RMSE) values ranging from 42.7 to 58.3 m 3 /s, Nash–Sutcliffe efficiency (NSE) between 0.80 and 0.87, mean absolute error (MAE) between 28.9 to 52.7 and R 2 values between 0.84 and 0.90 across stations. (2) SHAP based feature contribution analysis identified Relative humidity (hurs) , specific humidity (huss) , and temperature (tas) as key predictors, while ( pr) showed limited contribution due to spatial inherent inconsistencies in GCM precipitation data. (3) The bootstrapped SHAP distributions highlighted substantial variability in feature importance, particularly for humidity variables, revealing station specific uncertainty patterns in model interpretation. (4) The KAN framework results indicate strong temporal alignment and physical realism, confirming KAN’s robustness in capturing seasonal discharge dynamics and extreme flow events under monsoon influence environments. (5) In this study KAN with SHAP (SHapley additive exPlanations) is implemented for hydrological modeling under monsoon-influenced and data-limited regions such as SRB, offering improved accuracy, functional precision and efficiency compared to traditional models. The explainability offered by SHAP confirms informed water resource planning. This novel framework presents a reproducible and climate-resilient decision support tool, particularly suitable for monsoon-influenced, data-limited basins susceptible to extreme hydroclimatic events.

Importance of evaluation of global hydrological models (gHMs) before doing climate impact assessment was underlined in several studies. The main objective of this study is to evaluate the performance of six gHMs in simulating observed discharge for a set of 57 large catchments applying common metrics with thresholds for the monthly and seasonal dynamics and summarize them estimating an aggregated index of model performance for each model in each basin. One model showed a good performance, and other five showed a weak or poor performance in most of the basins. In 15 catchments, evaluation results of all models were poor. The model evaluation was supplemented by climate impact assessment for a subset of 12 representative catchments using (1) usual ensemble mean approach and (2) weighted mean approach based on model performance, and the outcomes were compared. The comparison of impacts in terms of mean monthly and mean annual discharges using two approaches has shown that in four basins, differences were negligible or small, and in eight catchments, differences in mean monthly, mean annual discharge or both were moderate to large. The spreads were notably decreased in most cases when the second method was applied. It can be concluded that for improving credibility of projections, the model evaluation and application of the weighted mean approach could be recommended, especially if the mean monthly (seasonal) impacts are of interest, whereas the ensemble mean approach could be applied for projecting the mean annual changes. The calibration of gHMs could improve their performance and, consequently, the credibility of projections.

Small estuaries in Mediterranean climates display pronounced salinity variability at seasonal and event time scales. Here, we use a hydrodynamic model of the Coos Estuary, Oregon, to examine the seasonal variability of the salinity dynamics and estuarine exchange flow. The exchange flow is primarily driven by tidal processes, varying with the spring–neap cycle rather than discharge or the salinity gradient. The salinity distribution is rarely in equilibrium with discharge conditions because during the wet season the response time scale is longer than discharge events, while during low flow it is longer than the entire dry season. Consequently, the salt field is rarely fully adjusted to the forcing and common power-law relations between the salinity intrusion and discharge do not apply. Further complicating the salinity dynamics is the estuarine geometry that consists of multiple branching channel segments with distinct freshwater sources. These channel segments act as subestuaries that import both higher- and lower-salinity water and export intermediate salinities. Throughout the estuary, tidal dispersion scales with tidal velocity squared, and likely includes jet–sink flow at the mouth, lateral shear dispersion, and tidal trapping in branching channel segments inside the estuary. While the estuarine inflow is strongly correlated with tidal amplitude, the outflow, stratification, and total mixing in the estuary are dependent on the seasonal variation in river discharge, which is similar to estuaries that are dominated by subtidal exchange flow.

Large spatio‐temporal gradients in the Congo basin vegetation and rainfall are observed. However, its water‐balance (evapotranspiration minus precipitation, or ET − P) is typically measured at basin‐scales, limited primarily by river‐discharge data, spatial resolution of terrestrial water storage measurements, and poorly constrained ET. We use observations of the isotopic composition of water vapor to quantify the spatio‐temporal variability of net surface water fluxes across the Congo Basin between 2003 and 2018. These data are calibrated at basin scale using satellite gravity and total Congo river discharge measurements and then used to estimate time‐varying ET − P over four quadrants representing the Congo Basin, providing first estimates of this kind for the region. We find that the multi‐year record, seasonality, and interannual variability of ET − P from both the isotopes and the gravity/river discharge based estimates are consistent. Additionally, we use precipitation and gravity‐based estimates with our water vapor isotope‐based ET − P to calculate time and space averaged ET and net river discharge within the Congo Basin. These quadrant‐scale moisture flux estimates indicate (a) substantial recycling of moisture in the Congo Basin (temporally and spatially averaged ET/P > 70%), consistent with models and visible light‐based ET estimates, and (b) net river outflow is largest in the Western Congo where there are more rivers and higher flow rates. Our results confirm the importance of ET in modulating the Congo water cycle relative to other water sources. Plain Language Summary Rainfall and vegetation vary substantially across the Congo Basin. However, the spatial variations, seasonality, and interannual variability of the net water balance, (the difference between evapotranspiration and rainfall) is not well quantified. Atmospheric observations of the isotopic composition of water vapor are sensitive to the balance of evapotranspiration (ET) and precipitation (P). We calibrate new observations of the isotopic composition of water vapor to ET − P that is based on satellite gravity measurements and ground‐based river discharge measurements to quantify ET − P across four quadrants of the Congo basin. When combined with satellite measurements of rainfall, we show that ET is the largest source of Congo basin water vapor. As ET is about 70% of observed rainfall, vegetation therefore plays an outsized role on the Congo water cycle. Additionally, when combined with satellite measurements of gravity, we show that river discharge is higher in the western part of the basin, where there are more rivers and stronger flows. Key Points Water vapor isotopes provide a reliable proxy for evapotranspiration (ET) minus precipitation (P) in the Congo Basin ET contributes over 70% to P within the four quadrants of the Congo Basin River discharge is highest in the western part of the basin, where there are more rivers and higher flow

This study suggests a radical approach to hydrologic predictions in ungauged basins, addressing the long standing challenge of issuing predictions when in-situ river discharge does not exist. A simple but powerful rationale for measuring and modeling river discharge is proposed, using coupled advances in hydrologic modeling and satellite remote sensing. Our approach presents a Surrogate River discharge driven Model (SRM) that infers Surrogate River discharge (SR) from remotely sensed microwave signals with the ability to mimic river discharge in varying topographies and vegetation cover, which is then used to calibrate a hydrological model enabling physical realism in the resulting river discharge profile by adding an estimated mean of river discharge via the Budyko framework. The strength of SRM comes from the fact that it only uses remotely sensed data in prediction. The approach is demonstrated for 130 catchments in the Murray Darling Basin (MDB) in Australia, a region of high economic and environmental importance. The newly proposed SR (SR L , representing L-band microwave) boosts the Nash-Sutcliffe Efficiency (NSE) of modeled flow, showing a mean NSE of 0.54, with 70% of catchments exceeding NSE 0.4. We conclude that SRM effectively predicts high-flow and low-flow events related to flood and drought. Overall, this new approach will significantly improve catchment simulation capacity, enhancing water security and flood forecasting capability not only in the MDB but also worldwide.

This study presents an off-line global evaluation of the ISBA-TRIP hydrological model including a two-way flood scheme. The flood dynamics is indeed described through the daily coupling between the ISBA land surface model and the TRIP river routing model including a prognostic flood reservoir. This reservoir fills when the river height exceeds the critical river bankfull height and vice versa. The flood interacts with the soil hydrology through infiltration and with the overlying atmosphere through precipitation interception and free water surface evaporation. The model is evaluated over a relatively long period (1986–2006) at 1° resolution using the Princeton University 3-hourly atmospheric forcing. Four simulations are performed in order to assess the model sensitivity to the river bankfull height. The evaluation is made against satellite-derived global inundation estimates as well as in situ river discharge observations at 122 gauging stations. First, the results show a reasonable simulation of the global distribution of simulated floodplains when compared to satellite-derived estimates. At basin scale, the comparison reveals some discrepancies, both in terms of climatology and interannual variability, but the results remain acceptable for a simple large-scale model. In addition, the simulated river discharges are improved in term of efficiency scores for more than 50% of the 122 stations and deteriorated for 4% only. Two mechanisms mainly explain this positive impact: an increase in evapotranspiration that limits the annual discharge overestimation found when flooding is not taking into account and a smoothed river peak flow when the floodplain storage is significant. Finally, the sensitivity experiments suggest that the river bankfull depth is potentially tunable according to the river discharge scores to control the accuracy of the simulated flooded areas and its related increase in land surface evaporation. Such a tuning could be relevant at least for climate studies in which the spatio-temporal variations in precipitation are generally poorly represented.

River discharge measurements have proven invaluable to monitor the global water cycle, assess flood risk, and guide water resource management. However, there is a delay, and ongoing decline, in the availability of gauging data and stations are highly unevenly distributed globally. While not a substitute for river discharge measurement, remote sensing is a cost-effective technology to acquire information on river dynamics in situations where ground-based measurements are unavailable. The general approach has been to relate satellite observation to discharge measured in situ, which prevents its use for ungauged rivers. Alternatively, hydrological models are now available that can be used to estimate river discharge globally. While subject to greater errors and biases than measurements, model estimates of river discharge do expand the options for applying satellite-based discharge monitoring in ungauged rivers. Our aim was to test whether satellite gauging reaches (SGRs), similar to virtual stations in satellite altimetry, can be constructed based on Moderate Resolution Imaging Spectroradiometer (MODIS) optical or Global Flood Detection System (GFDS) passive microwave-derived surface water extent fraction and simulated discharge from the World-Wide Water (W3) model version 2. We designed and tested two methods to develop SGRs across the Amazon Basin and found that the optimal grid cell selection method performed best for relating MODIS and GFDS water extent to simulated discharge. The number of potential river reaches to develop SGRs increases from upstream to downstream reaches as rivers widen. MODIS SGRs are feasible for more river reaches than GFDS SGRs due to its higher spatial resolution. However, where they could be constructed, GFDS SGRs predicted discharge more accurately as observations were less affected by cloud and vegetation. We conclude that SGRs are suitable for automated large-scale application and offer a possibility to predict river discharge variations from satellite observations alone, for both gauged and ungauged rivers.

Global hydrological forecasts are now produced operationally on a daily basis. However, the lack of global river discharge observations precludes routine flood forecast evaluation, an essential step in providing more skilful and reliable forecasts. A vision is expounded for greater and more timely exchange of global river discharge observations, which would result in improved flood awareness and socioeconomic benefits in some of the World's most vulnerable countries.

River discharge is a hydrological variable resulting from integrated processes at the basin scale, which proves challenging to be modeled due to the partial knowledge of forcings and basin characteristics. In this context, the Traditional Markov chain Model (hereafter TMM) has been extensively used in discharge modeling over the past few decades. This study addresses the key weaknesses of the TMM and enhances it using fuzzy logic, introducing the novel Fuzzy Markov Chain Model (FMM). This model integrates traditional formulas with concepts related to fuzzy membership functions (MFs). The effectiveness of FMM is investigated through two case studies: an example taken from the Literature, featuring eight cases, highlighting differences between TMM and FMM, and the case study of the monthly inflows to Zayandehrud Dam (IZD) in Isfahan, Iran. The results from the Literature example show clear distinctions between TMM and FMM. In the IZD case, the future condition, as predicted by the FMM method with a maximum probability of 0.53, indicates a moderate wet condition. In contrast, the TMM assigns an equal probability (0.33) to all three conditions (low, moderate, and high), highlighting the TMM’s inefficiency in modeling probabilities across different conditions.