ANN-based parameter regionalization for distributed hydrological models used for low-flow simulation over ungauged French catchments

As a result of no direct measurement data, accurate stream flow prediction in ungauged basins has remained a challenge in water resource management and flood forecasting. To meet this challenge, advanced hydrological models can be integrated with remote sensing and machine learning techniques. Hydrological models are used to understand the dynamics of streamflow patterns while remotely sensed information provides real-time information on land surface conditions as well as weather parameters thus enhancing the accuracy of these models. Using classical approaches, machine learning methods enable the analysis of complex datasets to detect interrelations or patterns that may not be obvious. Therefore, an integrated approach is crucial for improving flow predictions in ungauged basins that ultimately support effective water management and flood mitigation strategies. This can become even more complicated with climate change as shown by Morocco's high sensitivity to irregular rainfall patterns brought about by climate variations. One such place is the Ouzoud watershed, which stretches 315.6 km 2 across Central High Atlas Mountains and is characterized by frequent flooding leading to socio-economic impacts. This study aims at assessing streamflow prediction methods particularly the Quantile-Quantile Plot (QPPQ) method endorsed by USGS for its applicability in predicting streamflow at ungauged locations. The study area includes both gauged and ungauged watersheds such as the Bernat River, Lakhdar River, Ghassaf River, and Tassaouet River, with Ouzoud River being the only ungauged basin. The geological setting is characterized by carbonate rocks (limestone) throughout the catchment. Mediterranean climate mountainous conditions prevail in this region that experience significant rainfall variations. Other watersheds within the survey site are those of Tassaout and Bernat which have different geological and climatic influences on streamflow patterns. The QPPQ method entails estimating the Flow Duration Curve (FDC) for the catchment of interest, identifying matching donor sites in terms of hydrological characteristics, transferring non-excessive probabilities from these donor sites and mapping these probabilities to streamflow estimates. The performance of the QPPQ method is assessed based on several statistical criteria including Correlation Coefficient (CC), Coefficient of Determination (R 2 ), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Nash-Sutcliffe Efficiency (NSE), and Kling-Gupta Efficiency (KGE). For instance, the CC value of 0.55 at Bernat River Sgatt station means that there exists a moderate linear relationship between predicted and observed streamflows while R 2 value at 0.36 indicates that about 36 % of the explained variation in observed flow is covered by predicted ones. Meanwhile, MAE=22.50 mirrors an average absolute prediction error while RMSE equals 55.01 in this case refers to the square root of mean squared prediction errors. Furthermore, NSE gives information concerning how far off the model's results are from the actual data with NSE=0.36 denoting a moderate fit and KGE which equals 0.34 showing good agreement in overall terms. Together, these measures expose the strengths and weaknesses of QPPQ as a streamflow prediction technique. Although the method gives some useful indications, it should be improved further by integrating it with more data sources and processes to increase the accuracy of its predictions and make them more reliable for ungauged basins. Graphical Summary of the Study Using the QPPQ Method Applied to a Gauged Basin (Bernat Basin) and an Ungauged Basin (Ouzoud Basin) • Improved QPPQ method enhances streamflow prediction in ungauged basins. • Seasonal analysis shows best QPPQ performance in winter, weakest in autumn. • Hybrid QPPQ with LSTM and RF improves prediction of extreme streamflows. • QPPQ shows strong results in Ouzoud Basin; basin-specific calibration is crucial. • Integrating remote sensing and machine learning supports water management.

Study regionWest Africa. Study focusThe objective of this study was to quantitively evaluate streamflow previously simulated by the Soil and Water Assessment Tool (SWAT) model in an ungauged basin (Ogun River) that was calibrated/validated using GLEAM (v3.0a) AET data (Odusanya et al., 2019). To evaluate this simulated streamflow in the ungauged Ogun River Basin, a gauged basin (Ouémé River) was selected based on proximity and on similar catchment attributes. Firstly, the non-unique parameter sets from the SWAT set-up in the ungauged Ogun River Basin were transferred to SWAT that was setup in the neighbouring gauged Ouémé River Basin (referred to as the regionalisation method). Secondly, the SWAT model in the Ouémé River Basin was setup by calibrating the model using the observed streamflow data (referred to as the conventional method). New hydrological insights for the regionIn the gauged Ouémé River Basin, using the conventional method, a higher streamflow performance was obtained for all non-unique SWAT simulations, compared with the regionalisation method. However, a satisfactory streamflow performance was obtained with the regionalisation method in the south of Ouémé River Basin (Save gauge: highest NSE = 0.79, R2 = 0.81, PBIAS = 2.1, KGE = 0.85) and at the Ouémé River Basin outlet (Bonou gauge: highest NSE = 0.79, R2 = 0.84, PBIAS = −4.5, KGE = 0.84) that had the most similar attributes to the Ogun River Basin. The study findings show that using satellite-based AET data to calibrate a hydrological model can lead to an acceptable model performance in ungauged basins. The regionalisation method was useful for assessing the simulated streamflow in the ungauged basin, especially when catchment similarity is ensured, and parameter non-uniqueness is applied. This research contributes to advancement in alternative means of modelling and evaluation of hydrologic model simulations in an ungauged basin.

Reduction of uncertainty in hydrologic predictions is important for water resources development and management, especially in ungauged basins. The objective of this study was to predict rainfall and runoff in an ungauged basin located in a mountainous region of northern Thailand by using a weather radar to estimate rainfall and a modeling approach to simulate rainfall-runoff processes. The calibrated Ze-R relationship (Ze=18R1.45) was used to estimate rainfall over a gauged basin and an assumed ungauged basin. The runoff simulations in the gauged and ungauged basins were conducted using the quasi-distributed hydrological model HEC-HMS. The simulation results in the gauged basin, for both calibration and verification, were in agreement with the observed data. Model parameters in the assumed ungauged basin were estimated using transposition and regionalization techniques. The predicted direct runoff hydrograph in the assumed ungauged basin using radar measured rainfall and the regionalization technique matches well with the measurements at the basin outlet. Based on the study results, the radar rainfall estimates and regionalization of the hydrologic model parameters are promising alternatives for improved hydrologic predictions in ungauged basins.

Predicting streamflow is vital for flash-flood early warning systems and managing water resources under climate change. However, limited streamflow observations restrict advanced prediction techniques to gauged basins, leaving most of the world's ungauged basins at a disadvantage. Developing reliable prediction methods for ungauged basins (PUB) is therefore essential. Over the past two decades, satellite-driven products, such as ERA5, have become crucial for enhancing precipitation and meteorological measurements, especially in complex terrains and changing climatic conditions. This study focuses on Morocco's arid and semi-arid regions, where water resource management is critical for agriculture, urbanization, and economic stability. Using the ERA5 dataset, which provides high-resolution atmospheric information, the study evaluates satellite-derived precipitation against ground measurements from the Sgatt station at Bernat River on daily timescales. Various statistical metrics assess ERA5's performance in representing daily precipitation and its integration into the GR4J-CemaNeige model for flow simulation. Results highlight the potential of ERA5 in improving rainfall representation and hydrological modelling in ungauged basins, verifying its effectiveness in simulating rainfall events compared to ground-based observations. This work underscores the importance of satellite-driven products for enhancing hydrological predictions and supporting water management in data-scarce regions.

The assessment of climate change impacts on water resources involves several methodological decisions, including choices of global climate models (GCMs), emission scenarios, downscaling techniques, and hydrologic modeling approaches. Among these, hydrologic model structure selection and parameter calibration are particularly relevant and usually have a strong subjective component. The goal of this research is to improve understanding of the role of these decisions on the assessment of the effects of climate change on hydrologic processes. The study is conducted in three basins located in the Colorado headwaters region, using four different hydrologic model structures [PRMS, VIC, Noah LSM, and Noah LSM with multiparameterization options (Noah-MP)]. To better understand the role of parameter estimation, model performance and projected hydrologic changes (i.e., changes in the hydrology obtained from hydrologic models due to climate change) are compared before and after calibration with the University of Arizona shuffled complex evolution (SCE-UA) algorithm. Hydrologic changes are examined via a climate change scenario where the Community Climate System Model (CCSM) change signal is used to perturb the boundary conditions of the Weather Research and Forecasting (WRF) Model configured at 4-km resolution. Substantial intermodel differences (i.e., discrepancies between hydrologic models) in the portrayal of climate change impacts on water resources are demonstrated. Specifically, intermodel differences are larger than the mean signal from the CCSM–WRF climate scenario examined, even after the calibration process. Importantly, traditional single-objective calibration techniques aimed to reduce errors in runoff simulations do not necessarily improve intermodel agreement (i.e., same outputs from different hydrologic models) in projected changes of some hydrological processes such as evapotranspiration or snowpack.

Abstract. The main objective of the paper is to understand the contributions to the uncertainty in low-flow projections resulting from hydrological model uncertainty and climate projection uncertainty. Model uncertainty is quantified by different parameterisations of a conceptual semi-distributed hydrologic model (TUWmodel) using 11 objective functions in three different decades (1976–1986, 1987–1997, 1998–2008), which allows for disentangling the effect of the objective function-related uncertainty and temporal stability of model parameters. Climate projection uncertainty is quantified by four future climate scenarios (ECHAM5-A1B, A2, B1 and HADCM3-A1B) using a delta change approach. The approach is tested for 262 basins in Austria. The results indicate that the seasonality of the low-flow regime is an important factor affecting the performance of model calibration in the reference period and the uncertainty of Q95 low-flow projections in the future period. In Austria, the range of simulated Q95 in the reference period is larger in basins with a summer low-flow regime than in basins with a winter low-flow regime. The accuracy of simulated Q95 may result in a range of up to 60 % depending on the decade used for calibration. The low-flow projections of Q95 show an increase of low flows in the Alps, typically in the range of 10–30 % and a decrease in the south-eastern part of Austria mostly in the range −5 to −20 % for the climate change projected for the future period 2021–2050, relative the reference period 1978–2007. The change in seasonality varies between scenarios, but there is a tendency for earlier low flows in the northern Alps and later low flows in eastern Austria. The total uncertainty of Q95 projections is the largest in basins with a winter low-flow regime and, in some basins the range of Q95 projections exceeds 60 %. In basins with summer low flows, the total uncertainty is mostly less than 20 %. The ANOVA assessment of the relative contribution of the three main variance components (i.e. climate scenario, decade used for model calibration and calibration variant representing different objective function) to the low-flow projection uncertainty shows that in basins with summer low flows climate scenarios contribute more than 75 % to the total projection uncertainty. In basins with a winter low-flow regime, the median contribution of climate scenario, decade and objective function is 29, 13 and 13 %, respectively. The implications of the uncertainties identified in this paper for water resource management are discussed.

Regionalisation of a PDM Model for Catchment Runoff in a Mountainous Region of Korea

Seasonal hydrological forecasts are of critical importance for the effective management of water resources, particularly in complex and vulnerable basins such as the Mediterranean area which have both a deficitary water regimen and a high anthropogenic pressure. Nevertheless, inaccuracies in meteorological inputs can propagate through hydrological models, amplifying uncertainties in flow predictions. It is imperative to rectify these forecasts, whether meteorological or hydrological, to enhance prediction reliability and provide robust data for informed decision-making. This study proposes an advanced framework for seasonal hydrological forecasting that integrates raw and corrected weather forecasts with distributed hydrological modeling and sophisticated post-processing techniques to enhance flow prediction accuracy in the Júcar River in Spain as a representative case study of the Mediterranean area.The catchment hydrological model was implemented using the model TETIS v9.1 wich was calibrated and validated using observed records from 1981 to 2019 at seven control points. To do this, a split sample test was conducted using the period 2009–2019 for calibration, and the rest of the time series for validation (1981–2008). The process involved refining parameter maps to ensure good or acceptable performance at all control points. Meteorological data were sourced from the W5E5 dataset, downscaled to a 0.09° resolution using ERA5-Land. This improved the spatial and temporal resolution of the hydrological model. Once we have established an acceptable hydrological model,, the seasonal forecast hindcasts were evaluated using meteorological inputs from global forecasting systems, including ECMWF-SEAS5, CMCC_SPSv35, DWD_GCFS21, and MeteoFrance System8. To address uncertainties in the meteorological forecasts and their propagation to hydrological outputs, two complementary correction strategies were implemented. First, artificial intelligence(fuzzy logic) was applied to correct meteorological inputs before integration into the hydrological model, assuming errors originate solely from meteorological data and treating the hydrological model as a “perfect” simulator. Second, a hydrological error model was developed to identify and adjust discrepancies between simulated and observed flows, addressing systematic biases and errors in the hydrological simulation.The results demonstrated that forecasts based on corrected meteorological inputs exhibited significant accuracy improvements compared to those using unprocessed inputs. The hydrological error model further enhanced prediction reliability by mitigating systematic biases. These findings underscore the effectiveness of combining meteorological forecasts with AI-driven corrections to address uncertainties, thereby improving the robustness of seasonal hydrological predictions. This study highlights the potential for integrating advanced correction techniques into seasonal hydrological forecasting frameworks, offering a replicable methodology for other basins with similar complexities. The proposed framework enhances both the reliability and applicability of forecasts, ensuring their relevance for effective decision-making in complex hydrological systems as the Mediterranean area. The improved accuracy of these forecasts provides a sound scientific support for adaptive water resource management, particularly in the face of increasing climatic variability and environmental changes.Acknowledgments: This study was funded by the Colombian Ministry of Science, Technology, and Innovation (MINCIENCIAS) through the Call for Doctorates Abroad 885-2; by the Valencian Regional Government through the WATER4CAST 2.0 (CIPROM/2023/5) research project; and Spanish Ministry of Science and Innovation through the research project TETISPREDICT (PID2022-141631OB-I00). 

Abstract. The present study explores the application of a data assimilation (DA) procedure to correct the radar rainfall inputs of an event-based, distributed, parsimonious hydrological model. An extended Kalman filter algorithm was built on top of a rainfall-runoff model in order to assimilate discharge observations at the catchment outlet. This work focuses primarily on the uncertainty in the rainfall data and considers this as the principal source of error in the simulated discharges, neglecting simplifications in the hydrological model structure and poor knowledge of catchment physics. The study site is the 114 km2 Lez catchment near Montpellier, France. This catchment is subject to heavy orographic rainfall and characterised by a karstic geology, leading to flash flooding events. The hydrological model uses a derived version of the SCS method, combined with a Lag and Route transfer function. Because the radar rainfall input to the model depends on geographical features and cloud structures, it is particularly uncertain and results in significant errors in the simulated discharges. This study seeks to demonstrate that a simple DA algorithm is capable of rendering radar rainfall suitable for hydrological forecasting. To test this hypothesis, the DA analysis was applied to estimate a constant hyetograph correction to each of 19 flood events. The analysis was carried in two different modes: by assimilating observations at all available time steps, referred to here as reanalysis mode, and by using only observations up to 3 h before the flood peak to mimic an operational environment, referred to as pseudo-forecast mode. In reanalysis mode, the resulting correction of the radar rainfall data was then compared to the mean field bias (MFB), a corrective coefficient determined using rain gauge measurements. It was shown that the radar rainfall corrected using DA leads to improved discharge simulations and Nash-Sutcliffe efficiency criteria compared to the MFB correction. In pseudo-forecast mode, the reduction of the uncertainty in the rainfall data leads to a reduction of the error in the simulated discharge, but uncertainty from the model parameterisation diminishes data assimilation efficiency. While the DA algorithm used is this study is effective in correcting uncertain radar rainfall, model uncertainty remains an important challenge for flood forecasting within the Lez catchment.

ABSTRACTThe aim of this study is to assess the influence of sensor locations and varying observation accuracy on the assimilation of distributed streamflow observations, also taking into account different structures of semi-distributed hydrological models. An ensemble Kalman filter is used to update a semi-distributed hydrological model as a response to measured streamflow. Various scenarios of sensor locations and observation accuracy are introduced. The methodology is tested on the Brue basin during five flood events. The results of this work demonstrate that the assimilation of streamflow observations at interior points of the basin can improve the hydrological models according to the particular location of the sensors and hydrological model structure. It is also found that appropriate definition of the observation accuracy can affect model performance and consequent flood forecasting. These findings can be used as criteria to develop methods for streamflow monitoring network design.

Abstract. Benchmarking model performance across large samples of catchments is useful to guide model selection and future model development. Given uncertainties in the observational data we use to drive and evaluate hydrological models, and uncertainties in the structure and parameterisation of models we use to produce hydrological simulations and predictions, it is essential that model evaluation is undertaken within an uncertainty analysis framework. Here, we benchmark the capability of several lumped hydrological models across Great Britain by focusing on daily flow and peak flow simulation. Four hydrological model structures from the Framework for Understanding Structural Errors (FUSE) were applied to over 1000 catchments in England, Wales and Scotland. Model performance was then evaluated using standard performance metrics for daily flows and novel performance metrics for peak flows considering parameter uncertainty. Our results show that lumped hydrological models were able to produce adequate simulations across most of Great Britain, with each model producing simulations exceeding a 0.5 Nash–Sutcliffe efficiency for at least 80 % of catchments. All four models showed a similar spatial pattern of performance, producing better simulations in the wetter catchments to the west and poor model performance in central Scotland and south-eastern England. Poor model performance was often linked to the catchment water balance, with models unable to capture the catchment hydrology where the water balance did not close. Overall, performance was similar between model structures, but different models performed better for different catchment characteristics and metrics, as well as for assessing daily or peak flows, leading to the ensemble of model structures outperforming any single structure, thus demonstrating the value of using multi-model structures across a large sample of different catchment behaviours. This research evaluates what conceptual lumped models can achieve as a performance benchmark and provides interesting insights into where and why these simple models may fail. The large number of river catchments included in this study makes it an appropriate benchmark for any future developments of a national model of Great Britain.

Improving the accuracy of runoff simulations is a significant focus of hydrological science for multiple purposes such as water resources management, flood and drought prediction, and water environment protection. However, the simulated runoff has limitations that cannot be eliminated. This paper proposes a method that integrates the hydrological and time series models to improve the reliability and accuracy of simulated runoffs. Specifically, the block-wise use of TOPMODEL (BTOP) is integrated with three time series models to improve the simulated runoff from a hydrological model of the Zhou River Basin, China. Unlike most previous research that has not addressed the influence of runoff patterns while correcting the runoff, this study manually adds the hydrologic cycle to the machine learning-based time series model. This also incorporates scenario-specific knowledge from the researcher’s area of expertise into the prediction model. The results show that the improved Prophet model proposed in this study, that is, by adjusting its holiday function to a flow function, significantly improved the Nash–Sutcliffe efficiency (NSE) of the simulated runoff by 53.47% (highest) and 23.93% (average). The autoregressive integrated moving average (ARIMA) model and long short-term memory (LSTM) improved the runoff but performed less well than the improved Prophet model. This paper presents an effective method to improve the runoff simulation by integrating the hydrological and time series models.

The Ganges–Brahmaputra–Meghna (GBM) river basins exhibit extremes in surface water availability at seasonal to annual time scales. However, because of a lack of basinwide hydrological data from in situ platforms, whether they are real time or historical, water management has been quite challenging for the 630 million inhabitants. Under such circumstances, a large-scale and spatially distributed hydrological model, forced with more widely available satellite meteorological data, can be useful for generating high resolution basinwide hydrological state variable data [streamflow, runoff, and evapotranspiration (ET)] and for decision making on water management. The Variable Infiltration Capacity (VIC) hydrological model was therefore set up for the entire GBM basin at spatial scales ranging from 12.5 to 25 km to generate daily fluxes of surface water availability (runoff and streamflow). Results indicate that, with the selection of representative gridcell size and application of correction factors to evapotranspiration calculation, it is possible to significantly improve streamflow simulation and overcome some of the insufficient sampling and data quality issues in the ungauged basins. Assessment of skill of satellite precipitation forcing datasets revealed that the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) product of 3B42RT fared comparatively better than the Climate Prediction Center (CPC) morphing technique (CMORPH) product for simulation of streamflow. The general conclusion that emerges from this study is that spatially distributed hydrologic modeling for water management is feasible for the GBM basins under the scenario of inadequate in situ data availability. Satellite precipitation forcing datasets provide the necessary skill for water balance studies at interannual and interseasonal scales. However, further improvement in skill may be required if these datasets are to be used for flood management at daily to weekly time scales and within a data assimilation framework.

This study presents a method to improve the reconstruction of historical flows on gauged and ungauged basins. To do so, a multi-model weighted averaging of hydrological model simulations for a large spatial domain in the province of Quebec, Canada, is used. The distributed hydrological model HYDROTEL was implemented over the region and covered 95 gauged basins. An optimal interpolation (OI) assimilation method was first implemented as a baseline to improve the HYDROTEL flow simulations over the 95 basins. Then, a series of multi-model averaging techniques were applied to an ensemble of 144 HYDROTEL simulations that were generated by modifying parameter sets, driving weather datasets and evapotranspiration modules. The averaging methods were applied in a leave-one-out cross-validation scheme, where all 94 gauged basins were pooled together to compute the weights, and those weights were applied to the 95th basin. All basins were evaluated in such a manner and compared to the OI method. Implementing a year-by-year (or shorter period) weighting scheme instead of computing weights over all available data significantly improved the results. This allowed the weights to better reflect each year’s hydrological characteristics rather than compromising to improve the overall average. The Kling-Gupta Efficiency (KGE) and peak flow metrics showed that the Granger-Ramanathan variant “A” (GRA) was similar in performance to the OI method but did not have the drawbacks that OI can typically introduce. The multi-model application can also be further improved by adding more simulations from other hydrological models, whereas the OI method cannot make use of such additional information, thus hitting a performance plateau. This study shows that it is possible to improve regional hydrological model simulations, both for overall flows and peak flows, and on the historical period for both gauged and ungauged basins. This can then be used to better estimate risk in flood frequency analysis and other statistical analyses.

This study presents a basin-scale integrative hydrological, ecological, and economic (HEE) modeling system, aimed at evaluating the impact of resources management, especially agricultural water resources management, on the sustainability of regional water resources. The hydrological model in the modeling system was adapted from SWAT, the Soil and Water Assessment Tool, to simulate the water balance in terms of soil moisture, evapotranspiration, and streamflow. An ecological model was integrated into the hydrological model to compute the ecosystem production of biomass production and yield for different land use types. The economic model estimated the monetary values of crop production and water productivity over irrigated areas. The modeling system was primarily integrated and run on a Windows platform and was able to produce simulation results at daily time steps with a spatial resolution of hydrological response unit (HRU). The modeling system was then calibrated over the period from 1983 to 1991 for the upper and middle parts of the Yellow River basin, China. Calibration results showed that the efficiencies of the modeling system in simulating monthly streamflow over 5 hydrological stations were from 0.54 to 0.68 with an average of 0.64, indicating an acceptable calibration. Preliminary simulation results from 1986 to 1995 revealed that water use in the study region has largely reduced the streamflow in many parts of the area except for that in the riverhead. Spatial distribution of biomass production, and crop yield showed a strong impact of irrigation on agricultural production. Water productivity over irrigated cropland ranged from 1 to 1640 USD/(ha·mm−1), indicating a wide variation of the production conditions within the study region and a great potential in promoting water use efficiency in low water productivity areas. Generally, simulation results from this study indicated that the modeling system was capable of tracking the temporal and spatial variability of pertinent water balance variables, ecosystem dynamics, and regional economy, and provided a useful simulation tool in evaluating long-term water resources management strategies in a basin scale.