J.P.M. Aerts
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1
eWaterCycle in Practice
Reproducible and Diagnostic Approaches for Large-Sample Catchment Based Hydrological Model Evaluation
This dissertation addresses multiple challenges in hydrological modeling, focusing on reproducibility, large-sample catchment diagnostics, and the role of observational uncertainty in interpreting model performance. Through a series of applied studies, it proposes practical tools and methods to improve the transparency, comparability, and interpretability of hydrological models, without overstating what current data or models can reliably deliver. Throughout, the thesis argues that apparent limitations in model evaluation, including the persistence of equifinality, can be reduced by expanding the observational basis against which models are tested, rather than accepting them as inherent properties of the system.
Central to this work are the development and application of the eWaterCycle platform: a modular, community-driven system that promotes reproducible modeling\\ through standardized interfaces and containerized environments. By building on existing models through the Basic Model Interface (BMI) and deploying experiments at scale, eWaterCycle enables model intercomparison and refinement with fewer technical barriers. Although it does not eliminate all integration complexity, the platform provides a practical foundation for reproducible, FAIR-compliant workflows and has already enabled new forms of collaborative experimentation.
Using this platform, the thesis evaluates the effects of spatial resolution on hydrological model performance. Results from 454 CAMELS-US catchments show that a finer spatial resolution (down to 200~m) does not universally improve streamflow predictions at catchment outlets. Although resolution matters in some contexts, particularly where topography or land cover is complex, the overall gains are limited and often fall within uncertainty bounds. These findings suggest that investing in higher spatial resolution without a corresponding investment in observational density is a questionable allocation of research effort. ``Hyper-resolution'' modeling, though technically feasible, must be approached with clear use cases and a realistic assessment of whether the available data can actually support it.
Expanding the scope of model diagnostics, the research applies machine learning (specifically, a random forest) to link catchment attributes to model performance across 617 CAMELS-GB catchments. The analysis shows that model suitability often varies with catchment scale and physical characteristics, with different models (wflow\_sbm and PCR-GLOBWB) excelling under different conditions. This underscores the ``reality of locality'' in hydrology: every catchment presents a unique combination of dominant processes, making the pursuit of a universal model impractical. Although the results expose structural model limitations, they also demonstrate the potential of machine learning as a diagnostic tool, an application that deserves greater emphasis alongside its more common use in prediction.
A key contribution of the dissertation is the integration of uncertainty in discharge observations into model evaluation. Using quantified uncertainty estimates from 299 CAMELS-GB catchments, the analysis shows that a significant share of perceived performance improvements, whether achieved through parameter tuning or arising from structural differences, may fall within the uncertainty bounds of the observations. Notably, no clear relationship was found between model complexity and model performance, challenging the assumption that more complex models are inherently more capable. Together, these findings call into question how model results are interpreted and underline the need to report and account for uncertainty more systematically.
The concluding chapters synthesize these results, arguing for a more critical yet constructive modeling practice: one that embraces reproducibility and large-sample evaluation while recognizing the limits of model resolution, structural complexity, and observational quality. They make the case that advancing hydrology depends as much on investing in new and better observations---from rehabilitating existing gauging networks to deploying novel sensing technologies and satellite products---as it does on improving models. By embedding uncertainty diagnostics into the evaluation process and facilitating open, repeatable workflows, this work contributes to a more robust and realistic foundation for model improvement and application.
Looking ahead, the thesis outlines clear directions for future research: expanding eWaterCycle to include more models and domains; exploiting underutilized satellite missions and opportunistic data sources; and bridging the cultural divide between modelers and field scientists so that data collection and model experimentation become two sides of the same scientific process. Although challenges remain, particularly in the community-wide adoption of reproducible practices, the tools and insights developed here offer a tangible step forward.
In summary, this dissertation contributes to hydrological science by combining reproducible modeling tools with diagnostic depth. It promotes a more transparent and critical modeling practice, one that acknowledges uncertainty, challenges complacency about data scarcity, and encourages the responsible use of models. Rather than seeking breakthroughs, the work focuses on enabling more reproducible, interpretable, and broadly applicable hydrological modeling, thereby helping build a stronger foundation for both scientific progress and practical decision-making. The path forward is not to accept the limitations we have inherited, but to systematically reduce them. ...
Central to this work are the development and application of the eWaterCycle platform: a modular, community-driven system that promotes reproducible modeling\\ through standardized interfaces and containerized environments. By building on existing models through the Basic Model Interface (BMI) and deploying experiments at scale, eWaterCycle enables model intercomparison and refinement with fewer technical barriers. Although it does not eliminate all integration complexity, the platform provides a practical foundation for reproducible, FAIR-compliant workflows and has already enabled new forms of collaborative experimentation.
Using this platform, the thesis evaluates the effects of spatial resolution on hydrological model performance. Results from 454 CAMELS-US catchments show that a finer spatial resolution (down to 200~m) does not universally improve streamflow predictions at catchment outlets. Although resolution matters in some contexts, particularly where topography or land cover is complex, the overall gains are limited and often fall within uncertainty bounds. These findings suggest that investing in higher spatial resolution without a corresponding investment in observational density is a questionable allocation of research effort. ``Hyper-resolution'' modeling, though technically feasible, must be approached with clear use cases and a realistic assessment of whether the available data can actually support it.
Expanding the scope of model diagnostics, the research applies machine learning (specifically, a random forest) to link catchment attributes to model performance across 617 CAMELS-GB catchments. The analysis shows that model suitability often varies with catchment scale and physical characteristics, with different models (wflow\_sbm and PCR-GLOBWB) excelling under different conditions. This underscores the ``reality of locality'' in hydrology: every catchment presents a unique combination of dominant processes, making the pursuit of a universal model impractical. Although the results expose structural model limitations, they also demonstrate the potential of machine learning as a diagnostic tool, an application that deserves greater emphasis alongside its more common use in prediction.
A key contribution of the dissertation is the integration of uncertainty in discharge observations into model evaluation. Using quantified uncertainty estimates from 299 CAMELS-GB catchments, the analysis shows that a significant share of perceived performance improvements, whether achieved through parameter tuning or arising from structural differences, may fall within the uncertainty bounds of the observations. Notably, no clear relationship was found between model complexity and model performance, challenging the assumption that more complex models are inherently more capable. Together, these findings call into question how model results are interpreted and underline the need to report and account for uncertainty more systematically.
The concluding chapters synthesize these results, arguing for a more critical yet constructive modeling practice: one that embraces reproducibility and large-sample evaluation while recognizing the limits of model resolution, structural complexity, and observational quality. They make the case that advancing hydrology depends as much on investing in new and better observations---from rehabilitating existing gauging networks to deploying novel sensing technologies and satellite products---as it does on improving models. By embedding uncertainty diagnostics into the evaluation process and facilitating open, repeatable workflows, this work contributes to a more robust and realistic foundation for model improvement and application.
Looking ahead, the thesis outlines clear directions for future research: expanding eWaterCycle to include more models and domains; exploiting underutilized satellite missions and opportunistic data sources; and bridging the cultural divide between modelers and field scientists so that data collection and model experimentation become two sides of the same scientific process. Although challenges remain, particularly in the community-wide adoption of reproducible practices, the tools and insights developed here offer a tangible step forward.
In summary, this dissertation contributes to hydrological science by combining reproducible modeling tools with diagnostic depth. It promotes a more transparent and critical modeling practice, one that acknowledges uncertainty, challenges complacency about data scarcity, and encourages the responsible use of models. Rather than seeking breakthroughs, the work focuses on enabling more reproducible, interpretable, and broadly applicable hydrological modeling, thereby helping build a stronger foundation for both scientific progress and practical decision-making. The path forward is not to accept the limitations we have inherited, but to systematically reduce them. ...
This dissertation addresses multiple challenges in hydrological modeling, focusing on reproducibility, large-sample catchment diagnostics, and the role of observational uncertainty in interpreting model performance. Through a series of applied studies, it proposes practical tools and methods to improve the transparency, comparability, and interpretability of hydrological models, without overstating what current data or models can reliably deliver. Throughout, the thesis argues that apparent limitations in model evaluation, including the persistence of equifinality, can be reduced by expanding the observational basis against which models are tested, rather than accepting them as inherent properties of the system.
Central to this work are the development and application of the eWaterCycle platform: a modular, community-driven system that promotes reproducible modeling\\ through standardized interfaces and containerized environments. By building on existing models through the Basic Model Interface (BMI) and deploying experiments at scale, eWaterCycle enables model intercomparison and refinement with fewer technical barriers. Although it does not eliminate all integration complexity, the platform provides a practical foundation for reproducible, FAIR-compliant workflows and has already enabled new forms of collaborative experimentation.
Using this platform, the thesis evaluates the effects of spatial resolution on hydrological model performance. Results from 454 CAMELS-US catchments show that a finer spatial resolution (down to 200~m) does not universally improve streamflow predictions at catchment outlets. Although resolution matters in some contexts, particularly where topography or land cover is complex, the overall gains are limited and often fall within uncertainty bounds. These findings suggest that investing in higher spatial resolution without a corresponding investment in observational density is a questionable allocation of research effort. ``Hyper-resolution'' modeling, though technically feasible, must be approached with clear use cases and a realistic assessment of whether the available data can actually support it.
Expanding the scope of model diagnostics, the research applies machine learning (specifically, a random forest) to link catchment attributes to model performance across 617 CAMELS-GB catchments. The analysis shows that model suitability often varies with catchment scale and physical characteristics, with different models (wflow\_sbm and PCR-GLOBWB) excelling under different conditions. This underscores the ``reality of locality'' in hydrology: every catchment presents a unique combination of dominant processes, making the pursuit of a universal model impractical. Although the results expose structural model limitations, they also demonstrate the potential of machine learning as a diagnostic tool, an application that deserves greater emphasis alongside its more common use in prediction.
A key contribution of the dissertation is the integration of uncertainty in discharge observations into model evaluation. Using quantified uncertainty estimates from 299 CAMELS-GB catchments, the analysis shows that a significant share of perceived performance improvements, whether achieved through parameter tuning or arising from structural differences, may fall within the uncertainty bounds of the observations. Notably, no clear relationship was found between model complexity and model performance, challenging the assumption that more complex models are inherently more capable. Together, these findings call into question how model results are interpreted and underline the need to report and account for uncertainty more systematically.
The concluding chapters synthesize these results, arguing for a more critical yet constructive modeling practice: one that embraces reproducibility and large-sample evaluation while recognizing the limits of model resolution, structural complexity, and observational quality. They make the case that advancing hydrology depends as much on investing in new and better observations---from rehabilitating existing gauging networks to deploying novel sensing technologies and satellite products---as it does on improving models. By embedding uncertainty diagnostics into the evaluation process and facilitating open, repeatable workflows, this work contributes to a more robust and realistic foundation for model improvement and application.
Looking ahead, the thesis outlines clear directions for future research: expanding eWaterCycle to include more models and domains; exploiting underutilized satellite missions and opportunistic data sources; and bridging the cultural divide between modelers and field scientists so that data collection and model experimentation become two sides of the same scientific process. Although challenges remain, particularly in the community-wide adoption of reproducible practices, the tools and insights developed here offer a tangible step forward.
In summary, this dissertation contributes to hydrological science by combining reproducible modeling tools with diagnostic depth. It promotes a more transparent and critical modeling practice, one that acknowledges uncertainty, challenges complacency about data scarcity, and encourages the responsible use of models. Rather than seeking breakthroughs, the work focuses on enabling more reproducible, interpretable, and broadly applicable hydrological modeling, thereby helping build a stronger foundation for both scientific progress and practical decision-making. The path forward is not to accept the limitations we have inherited, but to systematically reduce them.
Central to this work are the development and application of the eWaterCycle platform: a modular, community-driven system that promotes reproducible modeling\\ through standardized interfaces and containerized environments. By building on existing models through the Basic Model Interface (BMI) and deploying experiments at scale, eWaterCycle enables model intercomparison and refinement with fewer technical barriers. Although it does not eliminate all integration complexity, the platform provides a practical foundation for reproducible, FAIR-compliant workflows and has already enabled new forms of collaborative experimentation.
Using this platform, the thesis evaluates the effects of spatial resolution on hydrological model performance. Results from 454 CAMELS-US catchments show that a finer spatial resolution (down to 200~m) does not universally improve streamflow predictions at catchment outlets. Although resolution matters in some contexts, particularly where topography or land cover is complex, the overall gains are limited and often fall within uncertainty bounds. These findings suggest that investing in higher spatial resolution without a corresponding investment in observational density is a questionable allocation of research effort. ``Hyper-resolution'' modeling, though technically feasible, must be approached with clear use cases and a realistic assessment of whether the available data can actually support it.
Expanding the scope of model diagnostics, the research applies machine learning (specifically, a random forest) to link catchment attributes to model performance across 617 CAMELS-GB catchments. The analysis shows that model suitability often varies with catchment scale and physical characteristics, with different models (wflow\_sbm and PCR-GLOBWB) excelling under different conditions. This underscores the ``reality of locality'' in hydrology: every catchment presents a unique combination of dominant processes, making the pursuit of a universal model impractical. Although the results expose structural model limitations, they also demonstrate the potential of machine learning as a diagnostic tool, an application that deserves greater emphasis alongside its more common use in prediction.
A key contribution of the dissertation is the integration of uncertainty in discharge observations into model evaluation. Using quantified uncertainty estimates from 299 CAMELS-GB catchments, the analysis shows that a significant share of perceived performance improvements, whether achieved through parameter tuning or arising from structural differences, may fall within the uncertainty bounds of the observations. Notably, no clear relationship was found between model complexity and model performance, challenging the assumption that more complex models are inherently more capable. Together, these findings call into question how model results are interpreted and underline the need to report and account for uncertainty more systematically.
The concluding chapters synthesize these results, arguing for a more critical yet constructive modeling practice: one that embraces reproducibility and large-sample evaluation while recognizing the limits of model resolution, structural complexity, and observational quality. They make the case that advancing hydrology depends as much on investing in new and better observations---from rehabilitating existing gauging networks to deploying novel sensing technologies and satellite products---as it does on improving models. By embedding uncertainty diagnostics into the evaluation process and facilitating open, repeatable workflows, this work contributes to a more robust and realistic foundation for model improvement and application.
Looking ahead, the thesis outlines clear directions for future research: expanding eWaterCycle to include more models and domains; exploiting underutilized satellite missions and opportunistic data sources; and bridging the cultural divide between modelers and field scientists so that data collection and model experimentation become two sides of the same scientific process. Although challenges remain, particularly in the community-wide adoption of reproducible practices, the tools and insights developed here offer a tangible step forward.
In summary, this dissertation contributes to hydrological science by combining reproducible modeling tools with diagnostic depth. It promotes a more transparent and critical modeling practice, one that acknowledges uncertainty, challenges complacency about data scarcity, and encourages the responsible use of models. Rather than seeking breakthroughs, the work focuses on enabling more reproducible, interpretable, and broadly applicable hydrological modeling, thereby helping build a stronger foundation for both scientific progress and practical decision-making. The path forward is not to accept the limitations we have inherited, but to systematically reduce them.
Journal article
(2025)
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Sneha Chevuru, Rens L.P.H. Van Beek, Michelle T.H. Van Vliet, Jerom P.M. Aerts, Marc F.P. Bierkens
Individual hydrological and crop growth models often oversimplify underlying processes, reducing the accuracy of both simulated hydrology and crop growth dynamics. While crop models tend to generalize soil moisture processes, most hydrological models commonly use constant vegetation parameters and prescribed phenologies, neglecting the dynamic nature of crop growth. Despite some studies that have coupled hydrological and crop models, a limited understanding exists regarding the feedbacks between hydrology and crop growth. Our objective is to quantify the feedback between crop systems and hydrology on a fine-grained spatiotemporal level. To this end, the PCR-GLOBWB 2 hydrological model was coupled with the WOFOST crop growth model to quantify both the one-way and two-way interactions between hydrology and crop growth on a daily time step and at 5 arcmin (g1/4 10 km) resolution. Our study spans the contiguous United States (CONUS) region and covers the period from 1979 to 2019, allowing a comprehensive evaluation of the feedback between hydrology and crop growth dynamics. We compare individual (stand-alone) as well as one-way and two-way coupled WOFOST and PCR-GLOBWB 2 model runs and evaluate the average crop yield and its interannual variability for rainfed and irrigated crops as well as simulated irrigation water withdrawal for maize, wheat, and soybean. Our results reveal distinct patterns in the temporal and spatial variation of crop yield depending on the included interactions between hydrology and crop systems. Evaluating the model results against reported yield and water use data demonstrates the efficacy of the coupled framework in replicating observed irrigated and rainfed crop yields. Our results show that two-way coupling, with its dynamic feedback mechanisms, outperforms one-way coupling for rainfed crops. This improved performance stems from the feedback of WOFOST crop phenology to the crop parameters in the hydrological model. Our results suggest that when crop models are combined with hydrological models, a two-way coupling is needed to capture the impact of interannual climate variability on food production.
...
Individual hydrological and crop growth models often oversimplify underlying processes, reducing the accuracy of both simulated hydrology and crop growth dynamics. While crop models tend to generalize soil moisture processes, most hydrological models commonly use constant vegetation parameters and prescribed phenologies, neglecting the dynamic nature of crop growth. Despite some studies that have coupled hydrological and crop models, a limited understanding exists regarding the feedbacks between hydrology and crop growth. Our objective is to quantify the feedback between crop systems and hydrology on a fine-grained spatiotemporal level. To this end, the PCR-GLOBWB 2 hydrological model was coupled with the WOFOST crop growth model to quantify both the one-way and two-way interactions between hydrology and crop growth on a daily time step and at 5 arcmin (g1/4 10 km) resolution. Our study spans the contiguous United States (CONUS) region and covers the period from 1979 to 2019, allowing a comprehensive evaluation of the feedback between hydrology and crop growth dynamics. We compare individual (stand-alone) as well as one-way and two-way coupled WOFOST and PCR-GLOBWB 2 model runs and evaluate the average crop yield and its interannual variability for rainfed and irrigated crops as well as simulated irrigation water withdrawal for maize, wheat, and soybean. Our results reveal distinct patterns in the temporal and spatial variation of crop yield depending on the included interactions between hydrology and crop systems. Evaluating the model results against reported yield and water use data demonstrates the efficacy of the coupled framework in replicating observed irrigated and rainfed crop yields. Our results show that two-way coupling, with its dynamic feedback mechanisms, outperforms one-way coupling for rainfed crops. This improved performance stems from the feedback of WOFOST crop phenology to the crop parameters in the hydrological model. Our results suggest that when crop models are combined with hydrological models, a two-way coupling is needed to capture the impact of interannual climate variability on food production.
Journal article
(2024)
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W. Hazeleger, J.P.M. Aerts, P. Bauer, M.F.P. Bierkens, G. Camps-Valls, M.M. Dekker, F. J. Doblas-Reyes, S. Lhermitte, F.C. Vossepoel, More Authors...
Digital twins of the Earth are digital representations of the Earth system, spanning scales and domains. Their purpose is to monitor, forecast and assess the Earth system and the consequences of human interventions on the Earth system. Providing users with the capability to interact with and interrogate the system, digital twins of the Earth are decision support systems for addressing environmental challenges. By informing humans of their impact on the Earth system, digital twins aspire to promote new pathways moving forward. By answering causal queries through intervention analysis, they can enhance evidence-based policy making. Existing digital twins of the Earth are primarily technological information systems that represent the physical world. However, as the social and physical worlds are intrinsically interconnected, we argue that humans must be accounted for both within and outside digital twins of the Earth: Within twins to represent human impacts and responses that are integral to the Earth system; and outside twins to govern access and development and to guide responsible use of information acquired from twins. Incorporating human interactions in digital twins of the Earth represents a transformative frontier, promising unparalleled insights into Earth system dynamics and empower humans for action.
...
Digital twins of the Earth are digital representations of the Earth system, spanning scales and domains. Their purpose is to monitor, forecast and assess the Earth system and the consequences of human interventions on the Earth system. Providing users with the capability to interact with and interrogate the system, digital twins of the Earth are decision support systems for addressing environmental challenges. By informing humans of their impact on the Earth system, digital twins aspire to promote new pathways moving forward. By answering causal queries through intervention analysis, they can enhance evidence-based policy making. Existing digital twins of the Earth are primarily technological information systems that represent the physical world. However, as the social and physical worlds are intrinsically interconnected, we argue that humans must be accounted for both within and outside digital twins of the Earth: Within twins to represent human impacts and responses that are integral to the Earth system; and outside twins to govern access and development and to guide responsible use of information acquired from twins. Incorporating human interactions in digital twins of the Earth represents a transformative frontier, promising unparalleled insights into Earth system dynamics and empower humans for action.
Journal article
(2024)
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Jerom P. M. Aerts, Jannis M. Hoch, Gemma Coxon, Nick C. van de Giesen, Rolf W. Hut
For users of hydrological models, the suitability of models can depend on how well their simulated outputs align with observed discharge. This study emphasizes the crucial role of factoring in discharge observation uncertainty when assessing the performance of hydrological models. We introduce an ad hoc approach, implemented through the eWaterCycle platform, to evaluate the significance of differences in model performance while considering the uncertainty associated with discharge observations. The analysis of the results encompasses 299 catchments from the Catchment Attributes and MEteorology for Large-sample Studies Great Britain (CAMELS-GB) large-sample catchment dataset, addressing three practical use cases for model users. These use cases involve assessing the impact of additional calibration on model performance using discharge observations, conducting conventional model comparisons, and examining how the variations in discharge simulations resulting from model structural differences compare with the uncertainties inherent in discharge observations.
Based on the 5th to 95th percentile range of observed flow, our results highlight the substantial influence of discharge observation uncertainty on interpreting model performance differences. Specifically, when comparing model performance before and after additional calibration, we find that, in 98 out of 299 instances, the simulation differences fall within the bounds of discharge observation uncertainty. This underscores the inadequacy of neglecting discharge observation uncertainty during calibration and subsequent evaluation processes. Furthermore, in the model comparison use case, we identify numerous instances where observation uncertainty masks discernible differences in model performance, underscoring the necessity of accounting for this uncertainty in model selection procedures. While our assessment of model structural uncertainty generally indicates that structural differences often exceed observation uncertainty estimates, a few exceptions exist. The comparison of individual conceptual hydrological models suggests no clear trends between model complexity and subsequent model simulations falling within the uncertainty bounds of discharge observations.
Based on these findings, we advocate integrating discharge observation uncertainty into the calibration process and the reporting of hydrological model performance, as has been done in this study. This integration ensures more accurate, robust, and insightful assessments of model performance, thereby improving the reliability and applicability of hydrological modelling outcomes for model users. ...
Based on the 5th to 95th percentile range of observed flow, our results highlight the substantial influence of discharge observation uncertainty on interpreting model performance differences. Specifically, when comparing model performance before and after additional calibration, we find that, in 98 out of 299 instances, the simulation differences fall within the bounds of discharge observation uncertainty. This underscores the inadequacy of neglecting discharge observation uncertainty during calibration and subsequent evaluation processes. Furthermore, in the model comparison use case, we identify numerous instances where observation uncertainty masks discernible differences in model performance, underscoring the necessity of accounting for this uncertainty in model selection procedures. While our assessment of model structural uncertainty generally indicates that structural differences often exceed observation uncertainty estimates, a few exceptions exist. The comparison of individual conceptual hydrological models suggests no clear trends between model complexity and subsequent model simulations falling within the uncertainty bounds of discharge observations.
Based on these findings, we advocate integrating discharge observation uncertainty into the calibration process and the reporting of hydrological model performance, as has been done in this study. This integration ensures more accurate, robust, and insightful assessments of model performance, thereby improving the reliability and applicability of hydrological modelling outcomes for model users. ...
For users of hydrological models, the suitability of models can depend on how well their simulated outputs align with observed discharge. This study emphasizes the crucial role of factoring in discharge observation uncertainty when assessing the performance of hydrological models. We introduce an ad hoc approach, implemented through the eWaterCycle platform, to evaluate the significance of differences in model performance while considering the uncertainty associated with discharge observations. The analysis of the results encompasses 299 catchments from the Catchment Attributes and MEteorology for Large-sample Studies Great Britain (CAMELS-GB) large-sample catchment dataset, addressing three practical use cases for model users. These use cases involve assessing the impact of additional calibration on model performance using discharge observations, conducting conventional model comparisons, and examining how the variations in discharge simulations resulting from model structural differences compare with the uncertainties inherent in discharge observations.
Based on the 5th to 95th percentile range of observed flow, our results highlight the substantial influence of discharge observation uncertainty on interpreting model performance differences. Specifically, when comparing model performance before and after additional calibration, we find that, in 98 out of 299 instances, the simulation differences fall within the bounds of discharge observation uncertainty. This underscores the inadequacy of neglecting discharge observation uncertainty during calibration and subsequent evaluation processes. Furthermore, in the model comparison use case, we identify numerous instances where observation uncertainty masks discernible differences in model performance, underscoring the necessity of accounting for this uncertainty in model selection procedures. While our assessment of model structural uncertainty generally indicates that structural differences often exceed observation uncertainty estimates, a few exceptions exist. The comparison of individual conceptual hydrological models suggests no clear trends between model complexity and subsequent model simulations falling within the uncertainty bounds of discharge observations.
Based on these findings, we advocate integrating discharge observation uncertainty into the calibration process and the reporting of hydrological model performance, as has been done in this study. This integration ensures more accurate, robust, and insightful assessments of model performance, thereby improving the reliability and applicability of hydrological modelling outcomes for model users.
Based on the 5th to 95th percentile range of observed flow, our results highlight the substantial influence of discharge observation uncertainty on interpreting model performance differences. Specifically, when comparing model performance before and after additional calibration, we find that, in 98 out of 299 instances, the simulation differences fall within the bounds of discharge observation uncertainty. This underscores the inadequacy of neglecting discharge observation uncertainty during calibration and subsequent evaluation processes. Furthermore, in the model comparison use case, we identify numerous instances where observation uncertainty masks discernible differences in model performance, underscoring the necessity of accounting for this uncertainty in model selection procedures. While our assessment of model structural uncertainty generally indicates that structural differences often exceed observation uncertainty estimates, a few exceptions exist. The comparison of individual conceptual hydrological models suggests no clear trends between model complexity and subsequent model simulations falling within the uncertainty bounds of discharge observations.
Based on these findings, we advocate integrating discharge observation uncertainty into the calibration process and the reporting of hydrological model performance, as has been done in this study. This integration ensures more accurate, robust, and insightful assessments of model performance, thereby improving the reliability and applicability of hydrological modelling outcomes for model users.
Journal article
(2023)
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Theresa C. van Hateren, Harro J. Jongen, More Authors..., Hadeel Al-Zawaidah, Joris G.W. Beemster, Judith Boekee, Jerom P.M. Aerts, Sehouevi M.D. Agoungbome, Sumit M. Meshram, Bas Walraven, L. Bogerd
This paper shares an early-career perspective on potential themes for the upcoming International Association of Hydrological Sciences (IAHS) Scientific Decade (SD). This opinion paper synthesizes six discussion sessions in western Europe identifying three themes that all offer a different perspective on the hydrological threats the world faces and could serve to direct the broader hydrological community: “Tipping points and thresholds in hydrology,” “Intensification of the water cycle,” and “Water services under pressure.” Additionally, four trends were distinguished concerning the way in which hydrological research is conducted: big data, bridging science and practice, open science, and inter- and multidisciplinarity. These themes and trends will provide valuable input for future discussions on the theme for the next IAHS SD. We encourage other early-career scientists to voice their opinion by organizing their own discussion sessions and commenting on this paper to make this initiative grow from a regional initiative to a global movement.
...
This paper shares an early-career perspective on potential themes for the upcoming International Association of Hydrological Sciences (IAHS) Scientific Decade (SD). This opinion paper synthesizes six discussion sessions in western Europe identifying three themes that all offer a different perspective on the hydrological threats the world faces and could serve to direct the broader hydrological community: “Tipping points and thresholds in hydrology,” “Intensification of the water cycle,” and “Water services under pressure.” Additionally, four trends were distinguished concerning the way in which hydrological research is conducted: big data, bridging science and practice, open science, and inter- and multidisciplinarity. These themes and trends will provide valuable input for future discussions on the theme for the next IAHS SD. We encourage other early-career scientists to voice their opinion by organizing their own discussion sessions and commenting on this paper to make this initiative grow from a regional initiative to a global movement.
Journal article
(2022)
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Rolf Hut, Niels Drost, Nick van de Giesen, Ben van Werkhoven, Banafsheh Abdollahi, Jerom Aerts, Thomas Albers, Fakhereh Alidoost, Bouwe Andela, More Authors...
Hutton et al. (2016) argued that computational hydrology can only be a proper science if the hydrological community makes sure that hydrological model studies are executed and presented in a reproducible manner. Hut, Drost and van de Giesen replied that to achieve this hydrologists should not “re-invent the water wheel” but rather use existing technology from other fields (such as containers and ESMValTool) and open interfaces (such as the Basic Model Interface, BMI) to do their computational science (Hut et al., 2017). With this paper and the associated release of the eWaterCycle platform and software package (available on Zenodo: https://doi.org/10.5281/zenodo.5119389, Verhoeven et al., 2022), we are putting our money where our mouth is and providing the hydrological community with a “FAIR by design” (FAIR meaning findable, accessible, interoperable, and reproducible) platform to do science.
The eWaterCycle platform separates the experiments done on the model from the model code. In eWaterCycle, hydrological models are accessed through a common interface (BMI) in Python and run inside of software containers. In this way all models are accessed in a similar manner facilitating easy switching of models, model comparison and model coupling. Currently the following models and model suites are available through eWaterCycle: PCR-GLOBWB 2.0, wflow, Hype, LISFLOOD, MARRMoT, and WALRUS While these models are written in different programming languages they can all be run and interacted with from the Jupyter notebook environment within eWaterCycle. Furthermore, the pre-processing of input data for these models has been streamlined by making use of ESMValTool. Forcing for the models available in eWaterCycle from well-known datasets such as ERA5 can be generated with a single line of code. To illustrate the type of research that eWaterCycle facilitates, this paper includes five case studies: from a simple “hello world” where only a hydrograph is generated to a complex coupling of models in different languages.
In this paper we stipulate the design choices made in building eWaterCycle and provide all the technical details to understand and work with the platform. For system administrators who want to install eWaterCycle on their infrastructure we offer a separate installation guide. For computational hydrologists that want to work with eWaterCycle we also provide a video explaining the platform from a user point of view (https://youtu.be/eE75dtIJ1lk, last access: 28 June 2022).
With the eWaterCycle platform we are providing the hydrological community with a platform to conduct their research that is fully compatible with the principles of both Open Science and FAIR science. ...
The eWaterCycle platform separates the experiments done on the model from the model code. In eWaterCycle, hydrological models are accessed through a common interface (BMI) in Python and run inside of software containers. In this way all models are accessed in a similar manner facilitating easy switching of models, model comparison and model coupling. Currently the following models and model suites are available through eWaterCycle: PCR-GLOBWB 2.0, wflow, Hype, LISFLOOD, MARRMoT, and WALRUS While these models are written in different programming languages they can all be run and interacted with from the Jupyter notebook environment within eWaterCycle. Furthermore, the pre-processing of input data for these models has been streamlined by making use of ESMValTool. Forcing for the models available in eWaterCycle from well-known datasets such as ERA5 can be generated with a single line of code. To illustrate the type of research that eWaterCycle facilitates, this paper includes five case studies: from a simple “hello world” where only a hydrograph is generated to a complex coupling of models in different languages.
In this paper we stipulate the design choices made in building eWaterCycle and provide all the technical details to understand and work with the platform. For system administrators who want to install eWaterCycle on their infrastructure we offer a separate installation guide. For computational hydrologists that want to work with eWaterCycle we also provide a video explaining the platform from a user point of view (https://youtu.be/eE75dtIJ1lk, last access: 28 June 2022).
With the eWaterCycle platform we are providing the hydrological community with a platform to conduct their research that is fully compatible with the principles of both Open Science and FAIR science. ...
Hutton et al. (2016) argued that computational hydrology can only be a proper science if the hydrological community makes sure that hydrological model studies are executed and presented in a reproducible manner. Hut, Drost and van de Giesen replied that to achieve this hydrologists should not “re-invent the water wheel” but rather use existing technology from other fields (such as containers and ESMValTool) and open interfaces (such as the Basic Model Interface, BMI) to do their computational science (Hut et al., 2017). With this paper and the associated release of the eWaterCycle platform and software package (available on Zenodo: https://doi.org/10.5281/zenodo.5119389, Verhoeven et al., 2022), we are putting our money where our mouth is and providing the hydrological community with a “FAIR by design” (FAIR meaning findable, accessible, interoperable, and reproducible) platform to do science.
The eWaterCycle platform separates the experiments done on the model from the model code. In eWaterCycle, hydrological models are accessed through a common interface (BMI) in Python and run inside of software containers. In this way all models are accessed in a similar manner facilitating easy switching of models, model comparison and model coupling. Currently the following models and model suites are available through eWaterCycle: PCR-GLOBWB 2.0, wflow, Hype, LISFLOOD, MARRMoT, and WALRUS While these models are written in different programming languages they can all be run and interacted with from the Jupyter notebook environment within eWaterCycle. Furthermore, the pre-processing of input data for these models has been streamlined by making use of ESMValTool. Forcing for the models available in eWaterCycle from well-known datasets such as ERA5 can be generated with a single line of code. To illustrate the type of research that eWaterCycle facilitates, this paper includes five case studies: from a simple “hello world” where only a hydrograph is generated to a complex coupling of models in different languages.
In this paper we stipulate the design choices made in building eWaterCycle and provide all the technical details to understand and work with the platform. For system administrators who want to install eWaterCycle on their infrastructure we offer a separate installation guide. For computational hydrologists that want to work with eWaterCycle we also provide a video explaining the platform from a user point of view (https://youtu.be/eE75dtIJ1lk, last access: 28 June 2022).
With the eWaterCycle platform we are providing the hydrological community with a platform to conduct their research that is fully compatible with the principles of both Open Science and FAIR science.
The eWaterCycle platform separates the experiments done on the model from the model code. In eWaterCycle, hydrological models are accessed through a common interface (BMI) in Python and run inside of software containers. In this way all models are accessed in a similar manner facilitating easy switching of models, model comparison and model coupling. Currently the following models and model suites are available through eWaterCycle: PCR-GLOBWB 2.0, wflow, Hype, LISFLOOD, MARRMoT, and WALRUS While these models are written in different programming languages they can all be run and interacted with from the Jupyter notebook environment within eWaterCycle. Furthermore, the pre-processing of input data for these models has been streamlined by making use of ESMValTool. Forcing for the models available in eWaterCycle from well-known datasets such as ERA5 can be generated with a single line of code. To illustrate the type of research that eWaterCycle facilitates, this paper includes five case studies: from a simple “hello world” where only a hydrograph is generated to a complex coupling of models in different languages.
In this paper we stipulate the design choices made in building eWaterCycle and provide all the technical details to understand and work with the platform. For system administrators who want to install eWaterCycle on their infrastructure we offer a separate installation guide. For computational hydrologists that want to work with eWaterCycle we also provide a video explaining the platform from a user point of view (https://youtu.be/eE75dtIJ1lk, last access: 28 June 2022).
With the eWaterCycle platform we are providing the hydrological community with a platform to conduct their research that is fully compatible with the principles of both Open Science and FAIR science.
Journal article
(2022)
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P.G. Wiersma, J.P.M. Aerts, H. Zekollari, undefined Rhythima, M. Hrachowitz, Niels Drost, Matthias Huss, Edwin H. Sutanudjaja, R.W. Hut
Global hydrological models have become a valuable tool for a range of global impact studies related to water resources. However, glacier parameterization is often simplistic or non-existent in global hydrological models. By contrast, global glacier models do represent complex glacier dynamics and glacier evolution, and as such, they hold the promise of better resolving glacier runoff estimates. In this study, we test the hypothesis that coupling a global glacier model with a global hydrological model leads to a more realistic glacier representation and, consequently, to improved runoff predictions in the global hydrological model. To this end, the Global Glacier Evolution Model (GloGEM) is coupled with the PCRaster GLOBal Water Balance model, version 2.0 (PCR-GLOBWB 2), using the eWaterCycle platform. For the period 2001–2012, the coupled model is evaluated against the uncoupled PCR-GLOBWB 2 in 25 large-scale (>50 000 km2), glacierized basins. The coupled model produces higher runoff estimates across all basins and throughout the melt season. In summer, the runoff differences range from 0.07 % for weakly glacier-influenced basins to 252 % for strongly glacier-influenced basins. The difference can primarily be explained by PCR-GLOBWB 2 not accounting for glacier flow and glacier mass loss, thereby causing an underestimation of glacier runoff. The coupled model performs better in reproducing basin runoff observations mostly in strongly glacier-influenced basins, which is where the coupling has the most impact. This study underlines the importance of glacier representation in global hydrological models and demonstrates the potential of coupling a global hydrological model with a global glacier model for better glacier representation and runoff predictions in glacierized basins.
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Global hydrological models have become a valuable tool for a range of global impact studies related to water resources. However, glacier parameterization is often simplistic or non-existent in global hydrological models. By contrast, global glacier models do represent complex glacier dynamics and glacier evolution, and as such, they hold the promise of better resolving glacier runoff estimates. In this study, we test the hypothesis that coupling a global glacier model with a global hydrological model leads to a more realistic glacier representation and, consequently, to improved runoff predictions in the global hydrological model. To this end, the Global Glacier Evolution Model (GloGEM) is coupled with the PCRaster GLOBal Water Balance model, version 2.0 (PCR-GLOBWB 2), using the eWaterCycle platform. For the period 2001–2012, the coupled model is evaluated against the uncoupled PCR-GLOBWB 2 in 25 large-scale (>50 000 km2), glacierized basins. The coupled model produces higher runoff estimates across all basins and throughout the melt season. In summer, the runoff differences range from 0.07 % for weakly glacier-influenced basins to 252 % for strongly glacier-influenced basins. The difference can primarily be explained by PCR-GLOBWB 2 not accounting for glacier flow and glacier mass loss, thereby causing an underestimation of glacier runoff. The coupled model performs better in reproducing basin runoff observations mostly in strongly glacier-influenced basins, which is where the coupling has the most impact. This study underlines the importance of glacier representation in global hydrological models and demonstrates the potential of coupling a global hydrological model with a global glacier model for better glacier representation and runoff predictions in glacierized basins.
Journal article
(2022)
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Jerom P. M. Aerts, Rolf Hut, Nick van de Giesen, Niels Drost, Willem J. van Verseveld, Albrecht H. Weerts, Pieter Hazenberg
Distributed hydrological modelling moves into the realm of hyper-resolution modelling. This results in a plethora of scaling-related challenges that remain unsolved. To the user, in light of model result interpretation, finer-resolution output might imply an increase in understanding of the complex interplay of heterogeneity within the hydrological system. Here we investigate spatial scaling in the form of varying spatial resolution by evaluating the streamflow estimates of the distributed wflow_sbm hydrological model based on 454 basins from the large-sample CAMELS data set. Model instances are derived at three spatial resolutions, namely 3 km, 1 km, and 200 m. The results show that a finer spatial resolution does not necessarily lead to better streamflow estimates at the basin outlet. Statistical testing of the objective function distributions (Kling–Gupta efficiency (KGE) score) of the three model instances resulted in only a statistical difference between the 3 km and 200 m streamflow estimates. However, an assessment of sampling uncertainty shows high uncertainties surrounding the KGE score throughout the domain. This makes the conclusion based on the statistical testing inconclusive. The results do indicate strong locality in the differences between model instances expressed by differences in KGE scores of on average 0.22 with values larger than 0.5. The results of this study open up research paths that can investigate the changes in flux and state partitioning due to spatial scaling. This will help to further understand the challenges that need to be resolved for hyper-resolution hydrological modelling.
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Distributed hydrological modelling moves into the realm of hyper-resolution modelling. This results in a plethora of scaling-related challenges that remain unsolved. To the user, in light of model result interpretation, finer-resolution output might imply an increase in understanding of the complex interplay of heterogeneity within the hydrological system. Here we investigate spatial scaling in the form of varying spatial resolution by evaluating the streamflow estimates of the distributed wflow_sbm hydrological model based on 454 basins from the large-sample CAMELS data set. Model instances are derived at three spatial resolutions, namely 3 km, 1 km, and 200 m. The results show that a finer spatial resolution does not necessarily lead to better streamflow estimates at the basin outlet. Statistical testing of the objective function distributions (Kling–Gupta efficiency (KGE) score) of the three model instances resulted in only a statistical difference between the 3 km and 200 m streamflow estimates. However, an assessment of sampling uncertainty shows high uncertainties surrounding the KGE score throughout the domain. This makes the conclusion based on the statistical testing inconclusive. The results do indicate strong locality in the differences between model instances expressed by differences in KGE scores of on average 0.22 with values larger than 0.5. The results of this study open up research paths that can investigate the changes in flux and state partitioning due to spatial scaling. This will help to further understand the challenges that need to be resolved for hyper-resolution hydrological modelling.
Over the past decade global flood hazard models have been developed and continuously improved. There is now a significant demand for testing global hazard maps generated by these models in order to understand their applicability for international risk reduction strategies and for reinsurance portfolio risk assessments using catastrophe models. We expand on existing methods for comparing global hazard maps and analyse eight global flood models (GFMs) that represent the current state of the global flood modelling community. We apply our comparison to China as a case study and, for the first time, include industry models, pluvial flooding, and flood protection standards in the analysis. In doing so, we provide new insights into how these components change the results of this comparison. We find substantial variability, up to a factor of 4, between the flood hazard maps in the modelled inundated area and exposed gross domestic product (GDP) across multiple return periods (ranging from 5 to 1500 years) and in expected annual exposed GDP. The inclusion of industry models, which currently model flooding at a higher spatial resolution and which additionally include pluvial flooding, strongly improves the comparison and provides important new benchmarks. We find that the addition of pluvial flooding can increase the expected annual exposed GDP by as much as 1.3 percentage points. Our findings strongly highlight the importance of flood defences for a realistic risk assessment in countries like China that are characterized by high concentrations of exposure. Even an incomplete (1.74 % of the area of China) but locally detailed layer of structural defences in high-exposure areas reduces the expected annual exposed GDP to fluvial and pluvial flooding from 4.1 % to 2.8%.
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Over the past decade global flood hazard models have been developed and continuously improved. There is now a significant demand for testing global hazard maps generated by these models in order to understand their applicability for international risk reduction strategies and for reinsurance portfolio risk assessments using catastrophe models. We expand on existing methods for comparing global hazard maps and analyse eight global flood models (GFMs) that represent the current state of the global flood modelling community. We apply our comparison to China as a case study and, for the first time, include industry models, pluvial flooding, and flood protection standards in the analysis. In doing so, we provide new insights into how these components change the results of this comparison. We find substantial variability, up to a factor of 4, between the flood hazard maps in the modelled inundated area and exposed gross domestic product (GDP) across multiple return periods (ranging from 5 to 1500 years) and in expected annual exposed GDP. The inclusion of industry models, which currently model flooding at a higher spatial resolution and which additionally include pluvial flooding, strongly improves the comparison and provides important new benchmarks. We find that the addition of pluvial flooding can increase the expected annual exposed GDP by as much as 1.3 percentage points. Our findings strongly highlight the importance of flood defences for a realistic risk assessment in countries like China that are characterized by high concentrations of exposure. Even an incomplete (1.74 % of the area of China) but locally detailed layer of structural defences in high-exposure areas reduces the expected annual exposed GDP to fluvial and pluvial flooding from 4.1 % to 2.8%.
Abstract
(2019)
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Jerom Aerts, Rolf Hut, Niels Drost, Ben Van Werkhoven, Ronald van Haren, Yifat Dzigan, Jaro Camphuijsen, Laurene Bouaziz, Nick van de Giesen, More authors...
Poster
(2019)
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Rolf Hut, Niels Drost, Nick van de Giesen, Ben Van Werkhoven, Jerom Aerts, Inti Pelupessy, Berend Weel, Yifat Dzigan, Martine de Vos
In 2016, Hutton et al. provided a commentary on the irreproducibility of computational hydrology due to complex methods and a lack of transparency of code and data used in many studies. Irreproducibility inhibits our growth as a community and our work quality because our ability to learn from one another is limited and errors can go unrealized. They provided several suggestions for improving research and reporting practices to adhere to FAIR - Findability, Accessibility, Interoperability, and Reusability - principles. The piece sparked discussion in the hydrology community and further suggestions and ideas to facilitate FAIRness (Hut et al., 2017; Melsen et al., 2017). Particularly, the community has highlighted the importance of communication, collaboration, and adhering to best practices in research software.
Whether you study sustainability questions that require models from different fields of geosciences, economics, social sciences, or want to compare the river discharge predictions from your model to the predictions from another research groups model: running each other (hydrological) models is often a painstaking process.
Recognising the need for hydrologist to not only have access to the software code of each others models, but also to be able to run these models without the tech-support of the researcher that made the model, we have built the eWaterCycle II platform.
The goals for the eWaterCycle II project is to provide the hydrological community with tools that:
Allow the use of a wide variety of models, written in different programming languages, without having to learn those languages.
Have access to all the relevant datasets from the community (forcing, observations)
Allow advanced use cases such as data assimilation and model coupling studies. Allow the sharing of models with the entire community, both for citing (DOIs) and re-use. Ultimately providing hydrologists with a toolset that allows them to run each other models, but also adept, couple, and in general tinker with models without the headache of having to delve into each other’s code. This fits nicely with water and climate change-related challenges: it allows for quicker solutions by combining research efforts from different fields. We will demonstrate (and make available to the community) the system we have built during the presentation.
...
In 2016, Hutton et al. provided a commentary on the irreproducibility of computational hydrology due to complex methods and a lack of transparency of code and data used in many studies. Irreproducibility inhibits our growth as a community and our work quality because our ability to learn from one another is limited and errors can go unrealized. They provided several suggestions for improving research and reporting practices to adhere to FAIR - Findability, Accessibility, Interoperability, and Reusability - principles. The piece sparked discussion in the hydrology community and further suggestions and ideas to facilitate FAIRness (Hut et al., 2017; Melsen et al., 2017). Particularly, the community has highlighted the importance of communication, collaboration, and adhering to best practices in research software.
Whether you study sustainability questions that require models from different fields of geosciences, economics, social sciences, or want to compare the river discharge predictions from your model to the predictions from another research groups model: running each other (hydrological) models is often a painstaking process.
Recognising the need for hydrologist to not only have access to the software code of each others models, but also to be able to run these models without the tech-support of the researcher that made the model, we have built the eWaterCycle II platform.
The goals for the eWaterCycle II project is to provide the hydrological community with tools that:
Allow the use of a wide variety of models, written in different programming languages, without having to learn those languages.
Have access to all the relevant datasets from the community (forcing, observations)
Allow advanced use cases such as data assimilation and model coupling studies. Allow the sharing of models with the entire community, both for citing (DOIs) and re-use. Ultimately providing hydrologists with a toolset that allows them to run each other models, but also adept, couple, and in general tinker with models without the headache of having to delve into each other’s code. This fits nicely with water and climate change-related challenges: it allows for quicker solutions by combining research efforts from different fields. We will demonstrate (and make available to the community) the system we have built during the presentation.