HS1.3.3 | Revisiting good modelling practices – where are we today and where to tomorrow?
Revisiting good modelling practices – where are we today and where to tomorrow?
Convener: Diana SpielerECSECS | Co-conveners: Janneke RemmersECSECS, Keirnan FowlerECSECS, Wouter KnobenECSECS, Lieke MelsenECSECS
| Wed, 17 Apr, 10:45–12:30 (CEST)
Room 2.31
Posters on site
| Attendance Wed, 17 Apr, 16:15–18:00 (CEST) | Display Wed, 17 Apr, 14:00–18:00
Hall A
Orals |
Wed, 10:45
Wed, 16:15
Many papers have advised on careful consideration of the approaches and methods we choose for our hydrological modelling studies as they potentially affect our modelling results and conclusions. However, there is no common and consistently updated guidance on what good modelling practice is and how it has evolved since e.g. Klemes (1986), Refsgaard & Henriksen (2004) or Jakeman et al. (2006). In recent years several papers have proposed useful practices such as benchmarking (e.g. Seibert et al., 2018), controlled model comparison (e.g. Clark et al., 2011), careful selection of calibration periods (e.g. Motavita et al., 2019) and methods (e.g. Fowler et al., 2018 ), or testing the impact of subjective modelling decisions along the modelling chain (Melsen et al., 2019). However, despite their very justified existence, none of the proposed methods have become quite as common and indispensable as the split sample test (KlemeŠ, 1986) and its generalisation to cross-validation.

This session intends to provide a platform for a visible and ongoing discussion on what ought to be the current standard(s) for an appropriate modelling protocol that considers uncertainty in all its facets and promotes transparency in the quest for robust and reliable results. We aim to bring together, highlight and foster work that develops, applies, or evaluates procedures for a trustworthy modelling workflow or that investigates good modelling practices for particular aspects of the workflow. We invite research that aims to improve the scientific basis of the entire modelling chain and puts good modelling practice in focus again. This might include (but is not limited to) contributions on:

(1) Benchmarking model results
(2) Developing robust calibration and evaluation frameworks
(3) Going beyond common metrics in assessing model performance and realism
(4) Conducting controlled model comparison studies
(5) Developing modelling protocols and/or reproducible workflows
(6) Examples of adopting the FAIR (Findable, Accessible, Interoperable and Reusable) principles in the modelling chain
(7) Investigating subjectivity and documenting choices along the modelling chain and
(8) Uncertainty propagation along the modelling chain
(9) Communicating model results and their uncertainty to end users of model results
(10) Evaluating implications of model limitations and identifying priorities for future model development and data acquisition planning

Orals: Wed, 17 Apr | Room 2.31

Chairpersons: Diana Spieler, Janneke Remmers, Wouter Knoben
Virtual presentation
Mark Thyer, David McInerney, Dmitri Kavetski, Seth Westra, Holger Maier, Margaret Shanafield, Barry Croke, Hoshin Gupta, Bree Bennett, and Michael Leonard

Risk-based decision making for water resource systems often relies on streamflow predictions from hydrological models. These predictions are integral for estimating the frequency of high consequence extreme events, such as floods and droughts. However, streamflow predictions are known to have errors due to various factors such as incomplete hydrological understanding, parameter misspecification, and uncertain data. Despite these errors being well known, they are frequently neglected when undertaking risk-based decision-making. This paper demonstrates that neglecting hydrological errors can impact on drought risk estimation for high stakes decisions with potentially severe consequences for water resource system performance. A generic framework is introduced to evaluate the impact of hydrological errors for a wide range of water resource system properties. This framework is applied in two Australian case study catchments, where we use a stochastic rainfall model, the GR4J hydrological model, a residual error model, and a simplified reservoir storage model to estimate water resource performance metrics (risk and yield). The results underscore the impact of neglecting hydrological errors on decision-making. In one case study catchment, the yield was over-estimated by ~15%-55%, resulting in the (actual) risk of running out of water being ~2-30 times larger than reservoir design. The magnitude of these errors in water resource performance metrics is striking, especially considering that the streamflow predictions appear reasonable based on typical performance metrics (e.g., NSE of ~0.7). The errors in performance metrics stem from the complex propagation of hydrological errors through the water resource system modelling chain. By accounting for critically important hydrological errors we can mitigate highly erroneous risk estimates and improve decision-making related to water resource management

How to cite: Thyer, M., McInerney, D., Kavetski, D., Westra, S., Maier, H., Shanafield, M., Croke, B., Gupta, H., Bennett, B., and Leonard, M.: Neglecting hydrological errors can severely impact predictions of water resource system performance , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7238, https://doi.org/10.5194/egusphere-egu24-7238, 2024.

On-site presentation
Rolf Hut, Niels Drost, Nick van de Giesen, Peter Kalverla, Stefan Verhoeven, Bart Schilperoort, and Jerom Aerts

Over the last decade our answer to the question: “but what is eWaterCycle?” has changed considerably. In 2014 we presented the first iteration of eWaterCycle: we showed that it was feasible to build a real time hydrological forecasting system that ran an ensemble of global models, forced with weather forecasts, assimilating satellite observations at every timestep, all from pre-existing openly available components.

In the light of the discussion in the hydrological community on reproducible science [Hutton, 2016] we build the next iteration of eWaterCycle: a platform that allows everyone to use commonly available hydrological models. Years (and a pandemic) later this platform is now openly available [Hut, 2022]. The vision behind the platform is to take as much as possible the computer-related headaches of running other people’s models away to let hydrologists focus on the hydrology. Furthermore, eWaterCycle is ‘FAIR by Design’: it should be easy to make any analysis done by eWaterCycle adhere to the FAIR principals. Using eWaterCycle MSc students have been able to do the type of research that previously was done by a PhD and PhDs have done the type of research that previously would require a whole team of people. Large Sample hydrology studies, Model coupling and climate change impact studies have all been done using eWaterCycle.

Adding one’s own model to the platform, however, still required considerable effort which limited the uptake by the broader hydrological community. That’s why recently we released v2.0 of eWaterCycle which fixes this: it is now significantly easier to add models to eWaterCycle!

Looking forward, among other things we will be:

  • Making teaching material on hydrological modelling available as Open Educational Resources through eWaterCycle [funded project]
  • Adding data assimilation as a module to eWaterCycle [funded project]
  • Add easy access to Large Sample Hydrology datasets (camels / caravan) [looking for students]
  • Study the impact of climate change on all catchments of the world, using many different hydrological models [looking for students]
  • Connect or host eWaterCycle on the infrastructure currently being developed for Destination Earth (DestinE) [looking for funds and collaborations]

In this presentation I will reflect on the achievements of the last decade, highlight the scientific results generated with eWaterCycle and look forward to the next decade.


Hutton, C., T. Wagener, J. Freer, D. Han, C. Duffy, and B. Arheimer (2016), Most computational hydrology is not reproducible, so is it really science?, Water Resour. Res., 52, 7548–7555, doi:10.1002/2016WR019285.

Hut, R., Drost, N., van de Giesen, N., van Werkhoven, B., Abdollahi, B., Aerts, J., Albers, T., Alidoost, F., Andela, B., Camphuijsen, J., Dzigan, Y., van Haren, R., Hutton, E., Kalverla, P., van Meersbergen, M., van den Oord, G., Pelupessy, I., Smeets, S., Verhoeven, S., de Vos, M., and Weel, B.: The eWaterCycle platform for open and FAIR hydrological collaboration, Geosci. Model Dev., 15, 5371–5390, https://doi.org/10.5194/gmd-15-5371-2022, 2022.

How to cite: Hut, R., Drost, N., van de Giesen, N., Kalverla, P., Verhoeven, S., Schilperoort, B., and Aerts, J.: 10 years of eWaterCycle: from prototype-forecast to platform for Open and FAIR hydrology, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13157, https://doi.org/10.5194/egusphere-egu24-13157, 2024.

On-site presentation
Antoine Degenne, François Bourgin, Charles Perrin, and Vazken Andréassian

The use of machine learning (ML) methods in rainfall-runoff modelling has apparently led to better prediction, but there are some concerns about the interpretability of these models. The emergence of hybrid modelling, which couples the data driven approach with the classical physics-based conceptual approach, has shown promise in enhancing both interpretability and accuracy. ML models and conceptual models each come with their own modelling practices and habits. To develop a hybrid approach, it is necessary to consider them.

While some of the steps in these modelling chains are similar (for instance the selection of the right metric during the calibration or learning step), others are more specifics, such as the optimization of the hyper-parameters of ML models. Furthermore, the hybrid approach comes with specific methodological challenges that emerge when coupling the two different types of models. For instance, depending on the choice made by the modeller, the parameters of the conceptual model are either trained with the ML model parameters or calibrated separately by a non-ML method.

There is a need to better understand the variety of hybrid approaches and to estimate the impact of their methodological choices. This work is based on a literature review and on large-sample modelling experiments with hybridizations of two classical models running at different time steps: the monthly GR2M model and the daily GR4J model. 

How to cite: Degenne, A., Bourgin, F., Perrin, C., and Andréassian, V.: Towards a better understanding of the hybrid modelling methodology for streamflow prediction, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10546, https://doi.org/10.5194/egusphere-egu24-10546, 2024.

On-site presentation
Zhenyu Wang, Larisa Tarasova, and Ralf Merz

Shifts in generation processes of streamflow events driven by advancing climate change are raising concerns about the adaptability of conceptual hydrological models to changing hydrological systems. Using 30-year streamflow data across 395 German catchments, we evaluate the performance of a conceptual rainfall-runoff model for three distinct streamflow event types: events associated with snow and icy conditions (Snow-or-Ice), rainfall on dry soils (Rain-on-Dry), or wet soils (Rain-on-Wet). We focus on a two-dimensional evaluation of the timing and magnitude of streamflow events using the Series-Distance approach (Seibert et al., 2016) while also diagnosing the impact of inherent process limitations on model performance using random forest. The results reveal that the modelled streamflow consistently exhibits time delays and underestimations of magnitude for all types of events. Specifically, the Rain-on-Dry are associated with the most considerable delays, while underestimation of streamflow is the largest for Snow-or-Ice events. Given the statistically significant increasing trends in the occurrence of Rain-on-Dry events across 78.8% of catchments (Mann-Kendall test, p < 0.05), it can be assumed that the timing errors might further deteriorate in the future, compromising the reliability of the model-based early-warning systems for future flood events. Additionally, the errors vary across different hydrograph components (rising limbs, peaks, and recessions) for each type of streamflow event. Peaks are the most underestimated component in all events. Further diagnostics of the links between errors and drivers identifies the pre-event errors are the most important factors of timing and magnitude errors during the events. The process limitation in the model (e.g., groundwater recharge and fast runoff process) and properties of the events themselves (e.g., duration and peak discharge of events) cause the error heterogeneity among the events and exacerbate the errors in peaks of the events. Therefore, our study highlights the critical need for further improvement of process representation in hydrological models and more accurate simulation of pre-event conditions in order to address emerging challenges posed by changing hydrological systems.

Seibert, S. P., Ehret, U., & Zehe, E. (2016). Disentangling timing and amplitude errors in streamflow simulations. Hydrology and Earth System Sciences, 20(9), 3745-3763.

How to cite: Wang, Z., Tarasova, L., and Merz, R.: Event-type-based Multi-dimensional Diagnostics of Process Limitations in Hydrological Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3415, https://doi.org/10.5194/egusphere-egu24-3415, 2024.

On-site presentation
Shuyue Wu, Yuting Yang, and Jianshi Zhao

Understanding the structural uncertainties within current conceptual hydrological models is crucial, as an appropriate model structure is essential for achieving accurate and reliable hydrological simulations. The development and evaluation of conceptual models have primarily focused on replicating streamflow dynamics, with less attention given to other important processes, such as the conversion from potential evapotranspiration (PET) to actual evapotranspiration (AET). This study assesses the performance of 33 existing conceptual model structures in simulating 8-day-scale AET across 671 catchments in the United States. These models are calibrated using both daily streamflow data and 8-day remote-sensing AET data. While most models demonstrate comparable performance in streamflow simulations, significant differences are observed in their performance in AET simulations. None of these models can consistently performs well in AET simulations across all 671 catchments, indicating that the “one-model-fits-all” assumption is not applicable. The performance of most models is found to be related to one or more catchment attributes. The most relevant catchment features are climatic, vegetation and topographical characteristics, including climatic aridity, precipitation seasonality, fraction of precipitation falling as snow, green vegetation fraction and catchment mean slope. In contrast to the “one-model-fits-all” assumption, catchments with distinct climatic, vegetation and/or topographical conditions require different ways to represent the AET process. Specifically, most models tend to underestimate AET in humid catchments where the majority of rainfall occurs in winter, except those account for interception evaporation. Additionally, models that explicitly include a vegetation transpiration component tend to perform better in catchments with denser vegetation cover. This work highlights the structure uncertainties related to AET simulations and may help model structure selections in a way to reasonably represent AET process.

How to cite: Wu, S., Yang, Y., and Zhao, J.: Effects of Conceptual Model Structure Uncertainties on Actual Evapotranspiration Simulation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3940, https://doi.org/10.5194/egusphere-egu24-3940, 2024.

On-site presentation
Giulia Evangelista, Ross Woods, and Pierluigi Claps

Estimating design flood hydrographs in ungauged basins requires the determination of hydrological response parameters. These parameters are derived from relationships that often lack a solid foundation in the specific physical characteristics of the basin. Among the parameters subject to greater uncertainty, the characteristic time of the IUH function certainly stands out. Indirect methods for estimating this parameter involve the use of empirical or analytical formulas and, in the engineering practice, the use of one or more formulas is often justified on heuristic grounds, lacking solid scientific considerations to guide the choice towards the most appropriate formulation.

Here, we propose a methodological approach to provide support in choosing a robust formulation for estimating basin flood response time. We have selected 35 formulas from the literature, all containing parameters related to the basin's length and slope. After verifying the real meaning of the input parameters and units required by the formulations in the original articles where they were published, the structure of the formulas considered has been analyzed in dimensional terms, using a reasoning scheme consistent with the hydraulic relations of resistance formulas. In this way, 17 hydraulically consistent formulas have been identified.

At this stage, we point out the advantage of comparing the formulas in terms of equivalent average flow velocity rather than in terms of observed travel times. Starting from the celerities obtained as the ratio between the length of the basin's drainage path and the response times provided by each formula and using the morphology of the river network of 135 basins in northwestern Italy, we compared the variability of estimated mean travel velocities. In line with literature observations, which highlight a slight increase in mean velocities with basin size, some formulas are deemed physically inconsistent, while 5 of them were identified as hydraulically robust and consistent with empirical observations. These formulas are Chow (1962), NERC (1975), SCS (1954), McEnroe and Zhao (1999), and Watt and Chow (1985).

The results obtained analytically identify the relationships between the exponents of length and slope in each formula and those governing empirical relationships between lengths and slopes of main river reaches in the basins. These relationships allow us to identify the range of values for the exponents of length and slope in the formulas for the characteristic time for which velocity estimates increase with the basin area. Based on these relationships, it is also possible to provide a guideline for the calibration of new formulations.

How to cite: Evangelista, G., Woods, R., and Claps, P.: How to avoid unreliable formulas for time of concentration in ungauged basins, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1038, https://doi.org/10.5194/egusphere-egu24-1038, 2024.

On-site presentation
Li Han, Björn Guse, Viet Dung Nguyen, Oldrich Rakovec, Husain Najafi, Xiaoxiang Guan, Sergiy Vorogushyn, Luis Samaniego, and Bruno Merz

Extraordinary floods like the one in July 2021 have induced catastrophic consequences on both societal and economic domains. Robust model simulations are crucial for mitigating the adverse effects of such extreme events on human life. However, accurately reproducing and predicting exceptional floods remain a challenge in particular when only one such flood extreme is available in the reference record period. This single flood could be included either in calibration and evaluation period. In both cases, extreme events are missing in the other period. To analyze how to best handle a single extreme flood, we present a framework for calibrating and evaluating the mesoscale Hydrologic Model (mHM) using the July 2021 flood in western Germany as a case study. Hereby, we tested the effect of including the extreme 2021 flood in calibration or evaluation periods.

Our study shows that including the exceptional 2021 flood event in model calibration proves crucial for accurately reproducing high streamflow. Without including the 2021 flood in the calibration period, the model cannot learn how to reproduce extreme floods. Our findings reveal that employing the modified weighted Nash-Sutcliffe Efficiency (wNSE) as the objective function significantly improves mHM's performance in capturing flood peaks. This leads to a notable reduction from -35% to -7.8% in the difference between the simulated and observed/reconstructed peaks as demonstrated for the catchment outlet. The hydrological model performance was validated spatially for an independent set of gauges. Spatial validation is necessary for assessing model performance when only one exceptional historical event is available. In conclusion, our framework provides valuable insights into improving hydrologic modeling accuracy, emphasizing the importance of specific calibration strategies and spatial validation in capturing exceptional flood events.

How to cite: Han, L., Guse, B., Nguyen, V. D., Rakovec, O., Najafi, H., Guan, X., Vorogushyn, S., Samaniego, L., and Merz, B.: What are the best strategies for managing a single extreme flood event in hydrological model evaluation? – Insights from the extreme flood 2021 in Western Germany, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16172, https://doi.org/10.5194/egusphere-egu24-16172, 2024.

On-site presentation
Raphael Schneider, Lars Troldborg, Anker Lajer Højberg, Maria Ondracek, David Terpager Christiansen, and Simon Stisen

The National Hydrological Model for Denmark (DK-model) is a distributed, integrated hydrological model coupling 3D groundwater flow to descriptions of root zone processes, overland flow and river routing, including anthropogenic interference with the hydrological cycle. It covers all of Denmark (~43,000km2) at 500m and 100m grid scale. Its constant development over the last three decades has both been driven by research projects and projects for public authorities. It is being used for various tasks such as water resource assessments, climate change impact assessments, hydrological real-time monitoring and nutrient transport studies.

Recently, we endeavored novel ways to calibrate and parameterize the DK-model. The model is placed on the edge between research interest and practical applications, with a demand for adequately representing various aspects of the hydrological cycle across the entirety of the model domain. In combination with its large-scale distributed nature and high computational demand, conventional (groundwater) model optimization techniques are challenged: The complex nature and versatile applications of the DK-model require suitable parametrization schemes and inclusion of diverse calibration and evaluation data, beyond conventional groundwater head observations and streamflow. This also leads to trade-offs between the multiple objective functions. Hence, we moved beyond previously used single solution, gradient-based optimization algorithms.

The Pareto Archived Dynamically Dimensioned Search (PADDS) algorithm allows us to use a global parameter optimization, effective even at a few hundred model runs. Another major advantage of PADDS is that it does not require the a-priori weighting of objective function groups – instead, it explores the tradeoffs (pareto front) between the different objective function groups, allowing weighting after gaining knowledge about tradeoffs during the optimization process. Also, all solutions explored during the optimization are stored and remain open to analysis after finished optimization. This not only sheds light on tradeoffs between different objective functions in a unique manner, but also supports understanding of parameter sensitivity and uncertainty in a manner which otherwise is hard to achieve due to computational constraints.

Moreover, we included evapotranspiration patterns from satellite products as well as a machine learning based estimate of artificial drain flow as novel spatial data in the model evaluation. This helps us constraining some of the model processes crucial for e.g. nutrient transport, but otherwise poorly constrained by conventional data such as streamflow (305 stations) and groundwater heads (24,000 wells) covering practically the entire model domain.

We explored the benefits of this optimization setup applied to the DK-model, advancing not only the calibration process itself, but also our understanding of model process representation and performance.

How to cite: Schneider, R., Troldborg, L., Højberg, A. L., Ondracek, M., Christiansen, D. T., and Stisen, S.: Innovating the calibration of a national-scale integrated hydrological model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14555, https://doi.org/10.5194/egusphere-egu24-14555, 2024.

On-site presentation
Philippe Ackerer, David Luttenauer, Aronne Dell'Oca, Alberto Guadagnini, and Sylvain Weill

Land Surface Models (LSM) grounded on physically-based mathematical models for energy and water balance can be characterized by various levels of complexity, especially when they integrate numerous processes. Diverse mathematical models (i.e., sub-models) can sometimes be formulated for some processes, due to different assumptions made during the system conceptualization stage. Therefore, running LSMs require (i) selection of a set of processes and related mathematical formulations that will be used and (ii) estimation of the corresponding parameters. A convenient way to guide model (and parameter) choice is to rely on global sensitivity analysis. In this work, we analyze sensitivity of 3 common hydrological outputs (evaporation, transpiration, and groundwater recharge fluxes) to models and parameters involved in typical LSMs. The global sensitivity analysis relies on random (Monte Carlo) sampling of values of parameters associated with each of the different formulations considered for the sub-models embedded in the LSM. This enables us to quantify the relative importance of process formulation and ensuing parameters. Three diverse indices based on (i) the whole (sample) probability density function (pdf) of the model output (Borgonovo et al., 2011) and (ii) the first and second moment of the pdf (corresponding to the moment-based sensitivity indices introduced by Dell’Oca et al. (2017)) are used. The joint use of these metrics is exemplified upon relying on realistic field conditions (in terms of, e.g., climate, vegetation, and soil type) associated with two watersheds in the Vosges region (France). Results show that sensitivity analysis plays a crucial role in identifying sub-models and parameters that contribute significantly to the uncertainty of model outputs. It is found that the main characteristics of the soil comprising the litter layer and root zone play an important role in the evaluation of the evaporation and groundwater recharge fluxes. As such, our results strengthen the need for targeted studies on the characterization of flow in these layers.


Borgonovo et al., https://doi.org/10.1111/j.1539-6924.2010.01519.x, 2011.

Dell’Oca et al., https://doi.org/10.5194/hess-21-6219-2017.


How to cite: Ackerer, P., Luttenauer, D., Dell'Oca, A., Guadagnini, A., and Weill, S.: Land surface hydrological modeling: do we really need complex model formulations?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14444, https://doi.org/10.5194/egusphere-egu24-14444, 2024.


Posters on site: Wed, 17 Apr, 16:15–18:00 | Hall A

Display time: Wed, 17 Apr 14:00–Wed, 17 Apr 18:00
Chairpersons: Lieke Melsen, Keirnan Fowler
Leon Frederik De Vos, Karan Mahajan, and Nils Rüther

Hydrological and hydraulic models are historically different disciplines and work on different scales.   The recent increase in computational resources allows for the two models to be combined into one model having one holistic approach. This removes the bottleneck of the data linkage between the two disciplines.

In this study, we apply the two-dimensional module of the open-source software openTELEMAC-MASCARET with the included SCS-CN method on an ungauged catchment in central Germany with an area of around 58 km². The catchment is part of the Main River tributary. We describe the excessive data preprocessing of the building and land use data, and the topography to sufficiently represent the small-scale stream geometry. This preprocessing is subjective in selecting different thresholds, such as the degree of mesh refinement in the streams and the foreland, or a minimum size for buildings to be represented in the model. Additionally, the SCS-CN method is highly sensitive to the model results, as small changes in the CN-values already significantly alter the total volume of water in the model. We collect the different sources of subjectivity and uncertainty and rank them based on the impact on the model results. The results will lead to a better view of the potential of combined hydrological-hydraulic models.

How to cite: De Vos, L. F., Mahajan, K., and Rüther, N.: Can we quantify the impact of the modeler on the model?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15853, https://doi.org/10.5194/egusphere-egu24-15853, 2024.

Marleen Lam, Liduin Bos-Burgering, Lieke Melsen, Pieter van Oel, Miriam Coenders, Ruud Bartholomeus, Petra Hellegers, and Ryan Teuling

The recent report from the Joint Research Centre (JRC) of the European Commission emphasizes a growing impact of drought on the whole of Europe, worsened by climate change. Even in temperate climates such as the Netherlands, the impact of droughts is on the rise. Drought can be divided into three stages: meteorological drought, soil moisture drought, and hydrological drought. These stages often coincide with specific policy phases that require different approaches. In the Netherlands, these policy phases are Phase 0 (focused on drought adaptation), Phase 1 (addressing impending water scarcity), Phase 2 (managing actual water shortages), and Phase 3 (dealing with an area-wide crisis). Each phase involves a shift in organizational management. Phase 0 and, to some extent, Phase 1 focus on strategic development for drought, while operational management is important from Phase 1 through Phase 3 as the drought progresses. Decision-making in these phases is often supported by specialized tools, with hydrological numerical models often playing a key role, either embedded in monitoring dashboards or directly used by water managers. This research aims to uncover the role of hydrological models as decision-support tools across different drought phases. In this way, this study wants to contribute to the development of effective decision-support tools for drought management as drought is expected to increase in frequency and intensity. The Netherlands is chosen as a case study because of the novelty of drought events, the prevalence of model-based water management systems, and regional variations in water management practices. The primary research methods include a survey and interviews. 

How to cite: Lam, M., Bos-Burgering, L., Melsen, L., van Oel, P., Coenders, M., Bartholomeus, R., Hellegers, P., and Teuling, R.: A questionnaire-based review on the role of hydrological models in operational drought management: Insights from the Netherlands, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18765, https://doi.org/10.5194/egusphere-egu24-18765, 2024.

Corina Hauffe, Diana Spieler, Clara Brandes, Sofie Pahner, and Niels Schütze

Using hydrological models is a common task for almost all hydrologists. Sometimes there is enough time to conduct a comparison study before selecting a model or we use the model we already know. But do we really know “our” model? Do we test all processes and approaches implemented prior to the model application? Usually we assume that the models are working correctly and by doing so we strongly rely on the developers willingness and capability to provide a mathematically and physically well tested hydrological model.

We believe that more effort is needed to ensure the quality assurance of models. This topic is yet underdeveloped in hydrology. We argue that our models should pass a standardized quality test in which they proof physical robustness and hydrologic plausibility. The commonly used split-sample test (Klemes, 1986) for an area of interest during the model validation may not be the best option to test for model quality. Attempts to increase standardization, transparency, and model quality have already been made e.g. by introducing the good modelling practice (van Waveren et al., 1999) and the FAIR principles (Wilkinson et al., 2016).

Nevertheless, there is still much potential to improve the quality assurance of models. We suggest a framework consisting of (1) the usage of synthetic input data and catchment properties, (2) a standardized test scheme, and (3) a set of diagnostics to evaluate the model results. The current study focuses on the development of the test scheme, which includes global behaviour tests, robustness tests, and additional tests.

Applying these tests serves different purposes: (1) detecting model limitations, (2) finding unintended feedback processes, (3) wrong or hydrological implausible responses, and (4) hidden or fixed parameters of a model. This kind of functional validation already proofed to be useful. A case study for the model ArcEGMO revealed several findings, e.g. fixed parameters, undocumented process implementations for lake evaporation and an unintended model response in the calculation of the groundwater recharge. Therefore, we believe that standardized tests would improve our model understanding, model usage and the trust in the model results.


Klemeš: Operational testing of hydrological simulation models, Hydrological Sciences Journal, 31, 13–24, https://doi.org/10.1080/02626668609491024, 1986.

van Waveren et al.: Good Modelling Practice Handbook, Tech. report, Dutch Dept. of Public Works, Institute for Inland Water Management and Waste Water Treatment, https://www.researchgate.net/publication/233864541_Good_Modelling_Practice_Handbook, 1999.

Wilkinson et al.: The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, https://doi.org/10.1038/sdata.2016.18, 2016.

How to cite: Hauffe, C., Spieler, D., Brandes, C., Pahner, S., and Schütze, N.: How can we improve the correctness and plausibility of our hydrological models?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17094, https://doi.org/10.5194/egusphere-egu24-17094, 2024.

Florian Bucher, Corina Hauffe, Diana Spieler, and Niels Schuetze

Currently, the quality assurance of conceptual hydrological models is primarily based on calibration and validation procedures, such as the validation tests proposed by Klemeš [1986]. These procedures provide insufficient testing of the underlying assumptions of a model structure and their correctness and credibility for specific purposes. While we assume the models we use are implemented physically correct, actual “crash tests” (Andréassian et al. [2009]) or quality assurance procedures do not exist.

This study therefore focuses on the development of a standardized quality assurance procedure for conceptual hydrologic models. A so called functional test scheme is proposed that complements existing calibration and validation procedures. Hereby, expected and unexpected model setups and parameterizations are tested and the model response is evaluated. The applied functional approach involves self-generated time series with synthetic climate data and a synthetic catchment to systematically test individual processes and procedures. We developed a line of test series for the modular modelling framework RAVEN, where several iterative test runs with changing model setups and parameterizations have been conducted in order to gain further insights into the correctness and plausibility of the implemented approaches and equations. We developed an R package that enables the almost automated execution of the repetitive processes in the test application for the RAVEN-based models.

Preliminary results revealed some minor and major problems of model functioning, sometimes related to simple reasons like unclear information in the model documentation. For example, showed the testing that the slope correction for different slope angles is not applied on manually entered PET data, while the documentation does not explicitly mention that slope angles are only affecting internally generated PET data. The conducted experiments prove the potential of readily developed functional tests and provide a basis for further developments in this regard.

How to cite: Bucher, F., Hauffe, C., Spieler, D., and Schuetze, N.: Quality Assurance in Conceptual Hydrologic Models: Developing functional validation tests for ensuring model quality and robustness, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10629, https://doi.org/10.5194/egusphere-egu24-10629, 2024.

Saskia Salwey, Francesca Pianosi, Gemma Coxon, and Hannah Bloomfield

Human activities must now be considered as an integral part of the water cycle. Consequently, the integration of human-water interactions into hydrological modelling is essential for the large-scale simulation of flow. However, whilst the last decade has seen substantial advancements in the guidance available for modelers on how best to benchmark and evaluate flow simulations in natural catchments, there is little discussion surrounding how these practices may differ in more complex, human-impacted catchments.

Here we discuss some of the key issues in benchmarking model performance in human-impacted catchments and demonstrate these using a large-sample of reservoir-impacted catchments across Great Britain. We find that evaluation metrics designed for natural systems do not always translate to those impacted by human activity, where reservoir-impacted flow timeseries can have a substantially different distribution. In light of the new parameters and model assumptions associated with representing human activities within the natural water cycle, we suggest that the integration of uncertainty quantification and sensitivity analysis (UQ and SA) for robust model evaluation is particularly important. We discuss the need for clear accessible workflows for the application of UQ and SA in the evaluation of complex and large-scale water resource system modelling.

How to cite: Salwey, S., Pianosi, F., Coxon, G., and Bloomfield, H.: Benchmarking model performance in complex human-water systems., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3454, https://doi.org/10.5194/egusphere-egu24-3454, 2024.

Björn Guse, Anna Herzog, Tobias Houska, Diana Spieler, Stephan Thober, Maria Staudinger, Paul Wagner, Doris Düthmann, Ralf Loritz, Uwe Ehret, Jens Kiesel, Sebastian Müller, Lieke Melsen, Sandra Pool, Larisa Tarasova, Juliane Mai, Thorsten Wagener, Doerthe Tetzlaff, and Nicola Fohrer and the other members of the DFG scientific network IMPRO

Hydrological models differ in the way how hydrological processes are implemented. A rigorous comparison of different hydrological model structures is needed to disentangle the link between similarities and differences in process representations and simulated hydrological processes, states and fluxes. A major challenge in model comparison is to identify effects of individual processes. To move a step in this direction, we developed controlled experiments and compared three hydrological models (HBV, mHM, SWAT+) in nine German catchments (400-3000 km²) along an elevation gradient. We aim at presenting a framework for a consistent comparison of process representations in model structures consisting of three steps:

(1) A model comparison protocol was developed for a detailed comparison of process representations in model structures. Consistency was achieved by using the same input data for all models. By grouping the processes in a standardized way, differences and similarities between the models were identified.

(2) To investigate the dominant model components, a daily parameter sensitivity analysis was carried out for the three models with different hydrological variables as target variables (e.g. actual evapotranspiration, soil moisture, snow and discharge). The dominant model parameters and associated processes vary more between the models than between the catchments. This also applies to the temporal variability of the parameter sensitivity.

(3) The model performance was analysed for a set of different performance criteria. The optimal parameter values differ greatly depending on which performance criteria were selected. This is in particular true for soil and evapotranspiration parameters. Typical patterns can be derived between catchments of different landscapes.

The joint analysis of these three methodological steps demonstrates the benefit of a detailed process analysis in model structures for a better understanding of suitable process representations. Therefore, it shows the potentials for improving model structures.

How to cite: Guse, B., Herzog, A., Houska, T., Spieler, D., Thober, S., Staudinger, M., Wagner, P., Düthmann, D., Loritz, R., Ehret, U., Kiesel, J., Müller, S., Melsen, L., Pool, S., Tarasova, L., Mai, J., Wagener, T., Tetzlaff, D., and Fohrer, N. and the other members of the DFG scientific network IMPRO: Improving process understanding using multi-criteria model comparison for different catchments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16855, https://doi.org/10.5194/egusphere-egu24-16855, 2024.

Revisiting hydrological modelling strategies for low flow simulations in the French Moselle catchment in response to recent hydrological droughts
Hajar El Khalfi, Claire Delus, Gilles Drogue, Benjamin Grelier, Sébastien Lebaut, Luc Manceau, and Didier François
Jingyi Niu, Marc Vis, and Jan Seibert

For hydrological modeling in snow-free catchments, precipitation (P) and potential evapotranspiration (Epot) are the two key input time series. There are different methods to observe, calculate and interpolate these time series. In the Australian large sample data set for hydrological modeling (CAMELS-AUS, Catchment Attributes and Meteorology for Large-sample Studies)  with data for 222 catchments, two different time series for P and seven different time series for Epot are provided. Here, we address the open question of which data should be used as input to an hydrological model.

Our basic assumption is that the most suitable combination of P and Epot is the one that results in the best model performances in terms of runoff simulations. For this we first tested the differences between the different input time series. Secondly, we conducted a thorough comparison of the 14 possible combinations of P and Epot time series. First analyses show that the differences between the two P time series are relatively minor, whereas the seven Epot time series differ more substantially from each other, especially in terms of seasonality and magnitude. Despite these differences, preliminary modeling results show that for the majority of the catchments there is no significant difference in model performance between the model calibrations carried out for each of the 14 different P/Epot combinations, suggesting that the model has a certain capability to compensate for differences in the input data by adapting its (soil) parameters. However, for some of the catchments there is a clear trend between the mean Epot and the corresponding model performance. Characterizing and further investigating these catchments can help to gain insight in the impact of different input data on the model performance, as well as to provide general recommendations that can help the user of a hydrological model to make an informed choice when it comes to the selection of the input data.

How to cite: Niu, J., Vis, M., and Seibert, J.: Evaluation of different precipitation and potential evapotranspiration time series for hydrological modeling in Australian catchments , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1022, https://doi.org/10.5194/egusphere-egu24-1022, 2024.

Wouter Knoben, Martyn Clark, Louise Arnal, Shervan Gharari, Kasra Keshavarz, Hongli Liu, Alain Pietroniro, Kevin Shook, Ray Spiteri, Tricia Stadnyk, and Andy Wood

Configuring process-based hydrologic models can be a cumbersome task, especially for larger domains. In the past model inputs (data), configuration and analysis code, as well as the source code of the models themselves were only rarely openly available. More recently, the hydrology community is moving toward a more open culture, focused on shareable data, tools and code. Here we present various recent open-source advances along the entire modeling chain. These include:

  • Workflows for model configuration of large-domain hydrologic models, data-driven seasonal streamflow forecasting and forcing data processing;
  • Tools for the remapping of forcing variables from one set of spatial elements to another;
  • Tools for adjusting and correcting baseline geofabrics for internal consistency and efficient routing;
  • Computationally frugal sensitivity analysis methods;
  • Independent hydrologic process modules for specific geographic landscape features and routing through reservoirs;
  • Improved numerical methods for model solving and parallelization.

These tools are publicly available with the specific aim to make them useful to others. During this PICO, we welcome discussion about the tools, as well as general discussion about the opportunities and pitfalls surrounding open-source science.

How to cite: Knoben, W., Clark, M., Arnal, L., Gharari, S., Keshavarz, K., Liu, H., Pietroniro, A., Shook, K., Spiteri, R., Stadnyk, T., and Wood, A.: Promoting Open and Transparent Hydrologic Modeling: Workflows, Tools and Self-Contained Modules, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16542, https://doi.org/10.5194/egusphere-egu24-16542, 2024.

Robin Maes-Prior, Barnaby Dobson, and Ana Mijic

Perceptual and conceptual modelling has been used historically by hydrologists to develop models rooted in a physical reality. As human activity increases and intertwines with the natural world, hydrological systems cannot be treated in isolation, particularly in urbanised areas. We argue that expanding our models and modelling approaches to consider interactions with water infrastructure can help us to identify the dominant processes and interactions within coupled human-water systems (CHWS) and guide our modelling processes towards models that produce results for the right reasons. We develop a three-level perceptual modelling approach that maps CHWS complexity in a systemic way. Perceptual models are representations of a system of interest based on stakeholder’s understandings and rooted in reality (e.g. visualised as a cross-section of the system with processes mapped on). Conceptual models are representations that break down the perceptual model to a component and state level (e.g. visualised as buckets and flows). From these definitions the framework was created to construct a computational model from an initial understanding of the region of interest. This framework prioritises engagement of different stakeholders at key junctions in the model making process, as well as providing a clear roadmap of modelling decisions. We applied this modelling approach for the Mogden Wastewater Catchment in North West London. The Mogden case study captures the interaction between surface water, groundwater and the sewer network, giving insight into the understudied field of sewer infiltration/exfiltration, highlighting the framework’s ability to better understand impact and behaviour of complicated flow paths. The case study highlights how this framework allows for the identification of interactions between human activity and the urban water system, producing models which are rooted in reality. The case study further revealed the benefit of flexible models, such as the implemented WSIMOD, for this framework, capturing diverse system perceptions and adaptability to include dominant processes.

How to cite: Maes-Prior, R., Dobson, B., and Mijic, A.: A flexible modelling framework for model creation based on perceptual understanding in integrated human-water systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19142, https://doi.org/10.5194/egusphere-egu24-19142, 2024.

Peter Kalverla, Bart Schilperoort, Stefan Verhoeven, Niels Drost, and Rolf Hut

2024 marks the 10th anniversary of presenting eWaterCycle at EGU [i]. Over the past decade, we've been building a platform capable of running global hydrological simulations, that democratizes research, and fosters reproducibility [ii]. We've built various libraries, added models, and glued them together. Our efforts culminated in the release of eWaterCycle V1 in 2021[iii].  

For eWaterCycle V1 we initially targeted users of hydrological models, enabling researchers and students to do experiments that they would not have been able to do before. While this narrow focus was great for designing the core functionality of the platform, the process for adding or upgrading supported models was still tedious. Model developers had to make changes to the core of eWaterCycle whenever they updated their model.  

To address this, we have recently released a new version of the eWaterCycle Python package that connects all components of the platform. In V2, compatibility with existing models is facilitated through a plugin structure. In contrast to eWaterCycle V1, plugins are small, simple, and self-contained, and can easily be maintained by the model owners. This structure also facilitates gradual adoption of standards until the compatibility layer becomes obsolete.

Another improvement in eWaterCycle V2 is that it is now possible to run certain BMI models without containers. The use without containers, on the other hand, enables new use cases for purposes like education. While we recognize that this facilitates the development process, we still emphasize the use of containers for sharing and reproducibility.

The changes in V2 make eWaterCycle simpler and more robust and facilitate a better governance structure for developing and maintaining the platform and contributed models, enabling what we envision for “Hydrology as a Service”: infrastructure providers host instances of the eWaterCycle platform, model developers can register their model to make it available on these platforms, and researchers can access and use them. 

[i] https://ui.adsabs.harvard.edu/abs/2014EGUGA..16.6291V
[ii] https://doi.org/10.1002/2017WR020665

[iii] https://doi.org/10.5194/gmd-15-5371-2022

How to cite: Kalverla, P., Schilperoort, B., Verhoeven, S., Drost, N., and Hut, R.: eWaterCycle V2: enabling Hydrology as a Service (HaaS) , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15352, https://doi.org/10.5194/egusphere-egu24-15352, 2024.