Hydrological Model Structure Research Articles

A decision‐led evaluation approach for flood forecasting system developments: An application to the Global Flood Awareness System in Bangladesh

AbstractScientific and technical changes to flood forecasting models are implemented to improve forecasts. However, responses to such changes are complex, particularly in global models, and evaluation of improvements remains focussed on generalised skill assessments and not on the most relevant outcomes for those taking decisions. Recently, the Global Flood Awareness System (GloFAS) flood forecasting model has been upgraded from version 2.1 to 3.1 with a significant change to its hydrological model structure. In the updated version 3.1, a single fully configured hydrological model (LISFLOOD) has been adopted, including ground water and river routing processes, instead of two coupled models, a land surface and a simplified hydrological model, of the previous version 2.1. This study aims to evaluate changes in the simulated behaviour of floods and the forecast skill of the two GloFAS versions based on different decision criteria for early action. We evaluate GloFAS reforecasts for the Brahmaputra and the Ganges Rivers in Bangladesh for the period 1999–2018. For the Brahmaputra River, the old GloFAS 2.1 version performs better than the 3.1 version, especially in predicting low‐ (90th percentile) and medium‐level (95th percentile) floods. For the Ganges, GloFAS 3.1 shows improved probability of detection of low‐ to medium‐level floods compared to version 2.1, especially for lead times longer than 10 days. Both versions show limited skill for more extreme floods (99th percentile) but results are less robust for these less frequent floods given the lower number of events. Using lead‐time dependent thresholds improves the false alarm ratio while reducing the probability of detection. The changes in model structures influence the model performance in a complex and varied way and forecast skill needs further investigation across regions and decision‐making criteria. Understanding the skill changes between different model versions is important for decision‐makers; however, focused case studies such as this should also be used by model developers to guide future changes to the system to ensure that they lead to improvements in decision‐making ability.

A priori selection of hydrological model structures in modular modelling frameworks: application to Great Britain

ABSTRACT Multi-model studies are widespread in large-sample hydrology. However, significant challenges remain in identifying interpretable connections between high-performing model structures and catchment characteristics, and thus in developing a coherent strategy for developing tailored multi-model ensembles. Here, we assess the importance of selecting model structures that are consistent with the expected hydrological variability across the study domain. We compare results of two modular modelling frameworks across 998 catchments in Great Britain. The RRMT framework includes model structures historically evolved in the UK, while the FUSE framework employs model structures from diverse global origins. While both groups of model structures contain high-performing members, the historically evolved group members separate between catchments in line with our expectation of hydrologic differences. We find that four hydrologic signatures organize these distinctions. Our results emphasize (1) the importance of model structure selection based on explicit perceptual models, and (2) the need to look beyond statistical performance alone.

Evaluation of the Impact of Multi-Source Uncertainties on Meteorological and Hydrological Ensemble Forecasting

Evaluating the impact of multi-source uncertainties in complex forecasting systems is essential to understanding and improving the systems Previous studies have paid little attention to the influence of multi-source uncertainties in complex meteorological and hydrological forecasting systems. In this study, we developed a general ensemble framework based on Bayesian model averaging (BMA) for evaluating the impact of multi-source uncertainties in complex forecast systems. Based on this framework, we used eight numerical weather prediction products from the International Grand Global Ensemble (TIGGE) dataset, four hydrological models with different structures, and 1000 sets of parameters to comprehensively account for the input, structure, and parameter uncertainties. The framework’s application to the Chitan Basin in China revealed that the numerical weather prediction input uncertainty in the forecasting system was more significant than the hydrological model uncertainty. The hydrological model structure uncertainty was more prominent than the parameter uncertainty. The accuracy of the numerical weather prediction dominates the accuracy of the forecast of high flows. In addition, the structures and parameters of the hydrological model and their interactions contributed to the main uncertainty of the low flow forecasts. The streamflow was more realistically represented when the three uncertainty sources were considered jointly. By accounting for the significant uncertainty sources in complex forecast systems, the BMA ensemble forecasting produces more realistic and reliable predictions and reduces the influences of other incomplete considerations. The developed multi-source uncertainty assessment framework improves our understanding of the complex meteorological and hydrological forecasting system. Therefore, the framework is promising for improving the accuracy and reliability of complex forecasting systems.

Streamflow forecasting in a climate change perspective using E-FUSE

Abstract The present work aims to identify the best hydrological model structure suitable for the Lower Godavari River Basin, India, that forecasts streamflows. An extended version of the Framework for Understanding Structural Errors (FUSE), termed E-FUSE, is developed for this purpose. It consists of 1248 model structures. K means cluster analysis (KCA), and Davies Bouldin Cluster Validation Index (DBCVI) are used for identifying optimal clusters, whereas Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is employed for the best model structure. Correlation coefficient (r), normalized root mean square error (NRMSE), and mean bias error (MBE) are employed as evaluation criteria. The best model structure obtained exhibits r, NRMSE and MBE of 0.734, 0.74 and -0.09 respectively during calibration and 0.69, 0.802 and -0.28 respectively during validation. The best model structure is then used to forecast discharges for a global climate model, EC-Earth3, and four Shared Socioeconomic Pathways, SSP126, SSP245, SSP370, and SSP585 scenarios. Analysis was made for three time horizons, namely, the near-future scenario (2021–2046), mid-future scenario (2047–2072), and far future scenario (2073–2099). It is observed that the July–September months contribute greatly to total runoff for four SSPs and three time horizons.

Quantifying the sources of uncertainty for hydrological predictions with WRF-Hydro over the snow-covered region in the Upper Indus Basin, Pakistan

Conceptual snowmelt runoff hydrological models involve uncertainties originating from various sources. For example, uncertainties related to the structure of hydrological models, robustness, parameters, calibration approaches, and input and output datasets may lead to considerably uncertain hydrological predictions. Quantifying such uncertainties is an essential engineering and scientific endeavor. In this study, the newly developed Weather Research and Forecasting Model Hydrological Glacier (WRF-Hydro/Glacier) modeling framework was applied over the snow-fed Astore catchment in the Upper Indus Basin; the hydrological processes were simulated more effectively, while demonstrating their uncertainties as well as the overall model robustness. Three precipitation datasets were used for calibrating and validating the WRF-Hydro/Glacier. The model parameters with different meteorological forcings were robust for simulating the hydrological processes in Astore. Furthermore, the uncertainty contributed by the optimized parameters and precipitation inputs was segregated to simulate the daily streamflow, snow-cover area (SCA), and evapotranspiration (ET). For streamflow, the optimized parameters were the primary source of uncertainty; for SCA and ET, the optimized parameters were a major source of uncertainty in spring and summer, whereas input precipitation was the key uncertainty in fall and winter. Overall, the robustness of the WRF-Hydro/Glacier was demonstrated using multiple optimized parameters from satellite-based precipitation datasets, while also demonstrating the uncertainties in the prediction of hydrological processes in Astore.

A multiple hydrograph separation technique for identifying hydrological model structures and an interpretation of dominant process controls on flow duration curves

AbstractThere has been significant progress made towards understanding the process controls that govern the shapes of flow duration curves (FDC). However, the transferability of the knowledge gained to large spatial scales is still lacking, partly due to the fixed representation of the model structures representing the unobserved underground layer. In the context of improving large scale applicability/transferability of FDC studies: (1) we propose a novel approach to transform/restructure the representation of the subsurface component in hydrological models to be flexible and adaptable in any environment and (2) provide a thorough interpretation of the dominant processes that impact the shape of FDCs. The flexible structure is designed from a series of multiple connected linear reservoirs that are developed from a unique multiple hydrograph separation procedure, to estimate the number of distinct hydrological processes at play. The main results are: (1) wet catchments have more complex model structures (more reservoirs) than arid catchments; indicating that subsurface processes are potentially more present in wet catchments than in drier ones. Dry catchments have more near surface occurring processes, thus have less complex model structures. However, more complexity is potentially required within the internal reservoirs of dry catchments. (2) The strength of interference in the intermediate reservoirs in controlling the dominant processes towards the bottom determined the shape of FDCs, and this increased with aridity. The novel finding is that a model structure can be determined by the catchment's ability to generate flows as well as the strength of low flow persistence. Hence, we propose a conceptual framework for the development of flexible model structures based on the shapes of FDCs. The transformation of models to be flexible in this research, should contribute to studies that seek the transferability of models over large spatial scales.

Large-sample study of uncertainty of hydrological model components over North America

The impacts of hydrological model components on performance and uncertainty of streamflow simulations are investigated using a multi-model approach over 698 catchments in North America. An ensemble of 180 different hydrological model structures is obtained by combining five snow models (SM), four potential evapotranspiration formulations (PET), three vertical (VFS) and three horizontal (HFS) flow rounting schemes. This study aims to first compare the multiple component combinations of SM, PET, VFS and HFS in terms of their ability at simulating streamflows for ten Köppen climate zones. A secondary goal is to evaluate the contribution of each model component to the total variance of the streamflow response according to the model structure for three streamflow metrics representing mean, high and low flows. Results indicate that there is no clear relationship between the structural and parametric complexity of the hydrological components and model performance for all three streamflow metrics. There is no single model structure which performs best in all climate zones and for all streamflow metrics. Streamflow uncertainty generated by the 180 synthetic hydrological model structures depends primarily on the metrics under study, but also on the climate zone. The evapotranspiration formulation is only critical for the mean flow metric. For the low-flow criterion, the VFS generates the most uncertainty and is therefore the most critical, whereas for the high-flow metric, performance is mostly related to the HFS component and SM for Nordic catchments. A better understanding of how hydrological model components generate uncertainty for a given climate and streamflow metric will lead to more robust model structures and therefore reduce the uncertainty of hydrological simulations.

Impacts of land use, climate change and hydrological model structure on nitrate fluxes: Magnitudes and uncertainties

Nitrate pollution and eutrophication are of increasing concern in agriculturally dominated regions, and with projected future climate changes, these issues are expected to worsen for both surface and groundwater. Changes in land use and management have the potential to mitigate some of these concerns. However, to what extent these changes will interact is unknown, and are associated with significant uncertainty. Here, we estimate nitrate fluxes and contributions of major uncertainty sources (variance decomposition analysis) affecting nitrate leaching from the root zone and river load from groundwater sources for an agricultural catchment in Denmark under future changes (2080–2099) in climate (four climate models) and land use (four land use scenarios). To investigate the uncertainty from impact model choice, two different agro-hydrological models (SWAT and DAISY-MIKE SHE) both traditionally used for nitrate impact assessments are used for projecting these effects. On average, nitrate leaching from the root zone increased by 55%–123% due to different climate models, while the impact of land use scenarios showed changes between −9% and 88%, with similar projections for river loads, while the worst-case combination of the three factors yielded a fivefold increase in nitrate transport. Thus, in the future, major land use changes will be necessary to mitigate nitrate pollution likely in combination with other measures such as advanced management and farming technologies and differentiated regulation. The two agro-hydrological models showed substantially different reaction patterns and magnitude of nitrate fluxes, and while the largest uncertainty source was the land use scenarios for both models, DAISY-MIKE SHE was to a higher degree affected by climate model choice. The dominating uncertainty source was found to be the agro-hydrological model; however, both uncertainties related to land use scenario and climate model were important, thus highlighting the need to include all influential factors in future nitrate flux impact studies.

An Exploration of Bayesian Identification of Dominant Hydrological Mechanisms in Ungauged Catchments

AbstractHydrological modeling of ungauged catchments, which lack observed streamflow data, is an important practical goal in hydrological sciences. A major challenge is to identify a model structure that reflects the hydrological processes relevant to the catchment of interest. This study contributes a Bayesian framework for identifying individual model mechanisms (process representations) from flow indices regionalized to the catchment of interest. We extend a method previously introduced for mechanism identification in gauged basins, by formulating the inference equations in the space of (regionalized) flow indices and by accounting for posterior parameter uncertainty. A flexible hydrological model is used to generate candidate mechanisms and model structures, followed by statistical hypothesis testing to identify “dominant” (more a posterior probable) model mechanisms. The proposed method is illustrated using real data and synthetic experiments based on 92 catchments from northern Spain, from which 16 catchments are treated as ungauged. 624 hydrological model structures from the flexible framework FUSE are employed. In real data experiments, the method identifies a dominant mechanism in 27% of 112 trials (processes and catchments). The most identifiable process is routing, whereas the least identifiable processes are percolation and unsaturated zone processes. In synthetic experiments, where “true” mechanisms are known, the reliability of method varies from 60% to 95% depending on the combined regionalization and hydrological error; the probability of making an identification remains stable at around 25%. More broadly, the study contributes perspectives on hydrological mechanism identification under data‐scarce conditions; limitations and opportunities for improvement are outlined.

The sensitivity of simulated streamflow to individual hydrologic processes across North America

Streamflow sensitivity to different hydrologic processes varies in both space and time. This sensitivity is traditionally evaluated for the parameters specific to a given hydrologic model simulating streamflow. In this study, we apply a novel analysis over more than 3000 basins across North America considering a blended hydrologic model structure, which includes not only parametric, but also structural uncertainties. This enables seamless quantification of model process sensitivities and parameter sensitivities across a continuous set of models. It also leads to high-level conclusions about the importance of water cycle components on streamflow predictions, such as quickflow being the most sensitive process for streamflow simulations across the North American continent. The results of the 3000 basins are used to derive an approximation of sensitivities based on physiographic and climatologic data without the need to perform expensive sensitivity analyses. Detailed spatio-temporal inputs and results are shared through an interactive website.

A multi‐objective approach to select hydrological models and constrain structural uncertainties for climate impact assessments

AbstractThe assessment of climate change impacts on water resources and flood risk is typically underpinned by hydrological models calibrated and selected based on observed streamflow records. Yet, changes in climate are rarely accounted for when selecting hydrological models, which compromises their ability to robustly represent future changes in catchment hydrology. In this paper, we test a simple framework for selecting an ensemble of calibrated hydrological model structures in catchments where contrasting climatic conditions have been observed. We start by considering 78 model structures produced with the FUSE modular modelling framework and rely on a Pareto scheme to select model structures maximizing model efficiency in both wet and dry periods. The application of this approach in three case study basins in Peru enables the identification of structures with good robustness, but also good performance according to hydrological signatures not used for model selection. We also highlight that some model structures that perform well according to traditional efficiency metrics have low performance in contrasting climates or suspicious internal states and fluxes. Importantly, the model selection approach followed here helps to reduce the spread in precipitation elasticities and temperature sensitivities, providing a clearer picture of future hydrological changes. Overall, this work demonstrates the potential of using contrasting climatic conditions in a multi‐objective framework to produce robust and credible simulations, and to constrain structural uncertainties in hydrological projections.

Machine Learning in Assessing the Performance of Hydrological Models

Machine learning has been employed successfully as a tool virtually in every scientific and technological field. In hydrology, machine learning models first appeared as simple feed-forward networks that were used for short-term forecasting, and have evolved into complex models that can take into account even the static features of catchments, imitating the hydrological experience. Recent studies have found machine learning models to be robust and efficient, frequently outperforming the standard hydrological models (both conceptual and physically based). However, and despite some recent efforts, the results of the machine learning models require significant effort to interpret and derive inferences. Furthermore, all successful applications of machine learning in hydrology are based on networks of fairly complex topology that require significant computational power and CPU time to train. For these reasons, the value of the standard hydrological models remains indisputable. In this study, we suggest employing machine learning models not as a substitute for hydrological models, but as an independent tool to assess their performance. We argue that this approach can help to unveil the anomalies in catchment data that do not fit in the employed hydrological model structure or configuration, and to deal with them without compromising the understanding of the underlying physical processes.

Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system

Abstract. This study employs a stochastic hydrologic modeling framework to evaluate the sensitivity of flood frequency analyses to different components of the hydrologic modeling chain. The major components of the stochastic hydrologic modeling chain, including model structure, model parameter estimation, initial conditions, and precipitation inputs were examined across return periods from 2 to 100 000 years at two watersheds representing different hydroclimates across the western USA. A total of 10 hydrologic model structures were configured, calibrated, and run within the Framework for Understanding Structural Errors (FUSE) modular modeling framework for each of the two watersheds. Model parameters and initial conditions were derived from long-term calibrated simulations using a 100 member historical meteorology ensemble. A stochastic event-based hydrologic modeling workflow was developed using the calibrated models in which millions of flood event simulations were performed for each basin. The analysis of variance method was then used to quantify the relative contributions of model structure, model parameters, initial conditions, and precipitation inputs to flood magnitudes for different return periods. Results demonstrate that different components of the modeling chain have different sensitivities for different return periods. Precipitation inputs contribute most to the variance of rare floods, while initial conditions are most influential for more frequent events. However, the hydrological model structure and structure–parameter interactions together play an equally important role in specific cases, depending on the basin characteristics and type of flood metric of interest. This study highlights the importance of critically assessing model underpinnings, understanding flood generation processes, and selecting appropriate hydrological models that are consistent with our understanding of flood generation processes.

Performance dependence of multi-model combination methods on hydrological model calibration strategy and ensemble size

The multi-model combination is a technique to improve the performances of hydrological streamflow simulations. An area that has not been investigated much is the performance dependence of combination techniques on the hydrological model calibration strategy and ensemble size. This study aims at investigating the joint effect of the hydrological models, calibration strategies and ensemble sizes on combination abilities for selecting the most appropriate multi-model combination method. The ensemble members were constructed by applying four hydrological models and four objective functions over 383 catchments in China. The ensemble members were combined by using nine commonly used methods, which are Equal Weights (EWA), Akaike Information Criterion (AICA), Bayes Information Criterion (BICA), Bates and Granger (BGA), Granger Ramanathan A, B, and C (GRA, GRB, and GRC), Bayesian Model Averaging (BMA) and Multi-model Super Ensemble (MMSE). The GRC is found as the best multi-model combination method for hydrological simulations. Adding ensemble members by either multiple hydrological models or calibration strategies could help to improve the simulation abilities. Specifically, the increase of ensemble members can obviously enhance the performance of multi-model combinations when the ensemble size is less than six, while only limited improvement is achieved when the ensemble size is more than nine. The combination of ensemble members with various calibration strategies is hard to compensate for the weakness of hydrological model structures. As well, the application of a single calibration strategy in ensemble members only emphasizes single discharge periods and neglects other important discharge periods. This study found that various models with different objective functions are more robust and efficient. The combination performs better than any individual model in terms of Nash–Sutcliffe efficiency (NSE) for approximately 70% catchments, but the multi-model combination is less efficient in terms of low-flow simulations.

Impact of climate change on monthly streamflow in the Verde River Basin using two hydrological models

This study assessed the impact of climate change on monthly streamflow in the Verde River Basin, located in the Grande River Basin headwater. For this purpose, the SWAT and VIC hydrological models were used to simulate the monthly streamflow under RCP4.5 and RCP8.5 scenarios, obtained by Regional Climate Models Eta-HadGEM2-ES, Eta-CanESM2 and Eta-MIROC5 in the baseline period (1961-2005) and three time-slice (2011-2040, 2041-2070, and 2071-2099) inputs. At the end of the century, the Eta-HadGEM2-ES showed larger decrease of precipitation in both radiative scenarios, with an annual reduction of 17.4 (RCP4.5) and 32.3% (RCP8.5), while the Eta-CanESM2 indicated major warming, with an annual increase of 4.7 and 10.2°C under RCP4.5 and RCP8.5, respectively. As well as precipitation changes, the Eta-HadGEM2-ES also showed greater impacts on streamflow under RCP4.5 for the first time-slice (2011-2040), with an annual decrease of 58.0% for both hydrological models, and for the RCP8.5 scenario by the end the century (2071-2099), with an annual reduction of 54.0 (VIC model) and 56.8% (SWAT model). Regarding monthly streamflow, the Eta-HadGEM2-ES and Eta-CanESM2 inputs indicated decrease under the RCP8.5 scenario by the end the century, varying from 7.2 to 66.3 % (VIC model) and 37.0 to 64.7% (SWAT model). In general, Eta-MIROC5 presented the opposite in terms of direction in the simulations with both hydrological models at the end of the century. Combined effects of climate models, hydrological model structures and scenarios of climate change should be considered in assessments of uncertainties of climate change impacts.

Future projection of low flows in the Chungju basin, Korea and their uncertainty decomposition

AbstractLow flow projections in response to global warming and the uncertainty decomposition into different components are performed with the focus on the Chungju basin, South Korea. Four downscaled climate simulations, two bias correction methods, and two hydrological models with two different potential evapotranspiration computation methods are applied to constitute the 32 ensemble members (4 × 2 × 4) of low flows projections. An analysis of the variance is used to decompose the total variance in the low flow projections into the contribution from three different sources. The result shows that hydrological models, particularly their structures, are the key factor that affects the low flow projections. They contribute more than 85% of the total uncertainty, and the projected change of low flow using different hydrological models can be of opposite signs, even under the same climate input data. On the other hand, the uncertainties related to climate simulation and bias correction are much smaller, comprising around 10 and 2%, respectively. The possible mechanisms behind the large uncertainty of projected low flow are investigated by comparing the relevant hydrological variables obtained from different hydrological simulations. The different representations in the hydrological models for the freezing of the soil layer during winter, which will undergo large changes as temperature increases, are likely to affect the projected change in low flow greatly. The difference in potential evapotranspiration methods also affects the low flow projections, but its contribution is relatively weaker than the effects of the hydrological model structure.

Role of hydrological model structure in the assimilation of soil moisture for streamflow prediction

Soil moisture data assimilation (SMDA) has found growing application in the hydrological research community because of readily available satellite-based soil moisture (SM) data. Several past studies have explored the capability to assimilate observed SM in hydrological model to enhance streamflow prediction. However, the impact of the conceptual hydrological model (CHM) structure on the SMDA in streamflow prediction has not been investigated yet. In this study, to understand the CHM structure's role, we used three different CHMs for the SMDA: dynamic Budyko (DB), Génie Rural à 4 paramètres Journalier (GR4J), and Probability Distributed Model (PDM) model. The SM obtained from Global Land Data Assimilation System (GLDAS) was assimilated using Ensemble Kalman Filter (EnKF) for 43 Model Parameter Estimation Experiment (MOPEX) basins. The GR4J model was found to be performed best from calibration and validation results, and the DB model was found to be performed least. The results show that the performance of the DB model improved during assimilation compared to its open-loop version for all the 43 basins. However, deterioration in model performance was observed for the GR4J and PDM model during assimilation for all basins except a few. The assimilated model performance was evaluated in respect of Assimilation Efficiency (AE) and ranged from 2.56 to 81.24%, −70.9 to 17.65%, and −71.38 to 24.86%, respectively, for the DB, GR4J, and PDM model. Further, we hypothesized that if the GLDAS SM is better than the model-simulated SM, then improvement in the model performance is observed due to the SMDA. The coefficient of determination (r2) between the SM simulated by all the three models without assimilation and the GLDAS SM was found for all the basins. Results indicated that the GR4J and PDM model captured SM better than the DB model. Moreover, to strengthen this hypothesis, we run the GR4J and PDM model deterministically, considering the daily GLDAS SM as the initial condition for streamflow simulation instead of the SM simulated by these models for the basins where deterioration in model performance was observed. For all the basins, it is found that the GR4J and PDM model performance deteriorated. Even though the GLDAS SM is used as every day's initial condition to simulate the streamflow, no improvement of model performance is observed. In general, our study underlines the significance of the CHM structure in the SMDA exercise.

Simultaneous Calibration of Hydrologic Model Structure and Parameters Using a Blended Model

AbstractThe advent of hydrological modeling frameworks that support multiple model structures using the same software enables both model structure and model parameters to be calibrated and assessed. To date, the identification of optimal model structure has typically been performed manually. Here, a continuous (rather than discrete) treatment of model structure is used, which enables simultaneous automatic calibration of model structure and parameters using a conventional real‐valued decision variable optimization algorithm (the dynamically dimensioned search algorithm, DDS). The method, referred to herein as blended model structure calibration (BMSC), relies upon the calculation of each hydrologic flux (e.g., for infiltration) as a weighted average of fluxes generated from multiple process algorithm options. This method is applied to 12 lumped MOPEX catchment models and compared to the calibration of 108 fixed model structures, representing all possible permutations of fixed model structures with the given process options in this study. The BMSC method consistently identified near‐optimal model structure (as evaluated using average model rank performance) at significantly lower computational cost than calibrating the collective of fixed structure models. The BMSC method also provides a useful tool in identifying dominant processes and model structures in catchments.

The impact of hydrological model structure on the simulation of extreme runoff events

Abstract. Hydrological extremes affect societies and ecosystems around the world in many ways, stressing the need to make reliable predictions using hydrological models. However, several different hydrological models can be selected to simulate extreme events. A difference in hydrological model structure results in a spread in the simulation of extreme runoff events. We investigated the impact of different model structures on the magnitude and timing of simulated extreme high- and low-flow events by combining two state-of-the-art approaches: a modular modelling framework (FUSE) and large ensemble meteorological simulations. This combination of methods created the opportunity to isolate the impact of specific hydrological process formulations at long return periods without relying on statistical models. We showed that the impact of hydrological model structure was larger for the simulation of low-flow compared to high-flow events and varied between the four evaluated climate zones. In cold and temperate climate zones, the magnitude and timing of extreme runoff events were significantly affected by different parameter sets and hydrological process formulations, such as evaporation. In the arid and tropical climate zones, the impact of hydrological model structures on extreme runoff events was smaller. This novel combination of approaches provided insights into the importance of specific hydrological process formulations in different climate zones, which can support adequate model selection for the simulation of extreme runoff events.

Impacts of Land Use, Climate Change and Hydrological Model Structure on Nitrate Fluxes: Magnitudes and Uncertainties

Impacts of Land Use, Climate Change and Hydrological Model Structure on Nitrate Fluxes: Magnitudes and Uncertainties