Articles | Volume 21, issue 3
https://doi.org/10.5194/nhess-21-961-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-21-961-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Mar 2021
Research article |
| 12 Mar 2021
| 12 Mar 2021
The impact of hydrological model structure on the simulation of extreme runoff events
Gijs van Kempen
Hydrology and Quantitative Water Management, Wageningen University, Wageningen, the Netherlands
Karin van der Wiel
Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Hydrology and Quantitative Water Management, Wageningen University, Wageningen, the Netherlands
Related authors
No articles found.
René M. van Westen, Karin van der Wiel, Swinda K. J. Falkena, and Frank Selten
EGUsphere, https://doi.org/10.5194/egusphere-2025-1440, https://doi.org/10.5194/egusphere-2025-1440, 2025
Short summary
Short summary
The Atlantic Meridional Overturning Circulation (AMOC) moderates the European climate. The AMOC is a tipping element and may collapse to a substantially weaker state under climate change. Such an event induces global and regional climate shifts. The European hydroclimate becomes drier under an AMOC collapse, this response is not considered in the 'standard' hydroclimate projections. Our results indicate a considerable influence of the AMOC on the European hydroclimate.
Sarra Kchouk, Louise Cavalcante, Lieke A. Melsen, David W. Walker, Germano Ribeiro Neto, Rubens Gondim, Wouter J. Smolenaars, and Pieter R. van Oel
Nat. Hazards Earth Syst. Sci., 25, 893–912, https://doi.org/10.5194/nhess-25-893-2025, https://doi.org/10.5194/nhess-25-893-2025, 2025
Short summary
Short summary
Droughts impact water and people, yet monitoring often overlooks impacts on people. In northeastern Brazil, we compare official data to local experiences, finding data mismatches and blind spots. Mismatches occur due to the data's broad scope missing finer details. Blind spots arise from ignoring diverse community responses and vulnerabilities to droughts. We suggest enhanced monitoring by technical extension officers for both severe and mild droughts.
Janneke O. E. Remmers, Rozemarijn ter Horst, Ehsan Nabavi, Ulrike Proske, Adriaan J. Teuling, Jeroen Vos, and Lieke A. Melsen
EGUsphere, https://doi.org/10.5194/egusphere-2025-673, https://doi.org/10.5194/egusphere-2025-673, 2025
Short summary
Short summary
In hydrological modelling, a notion exists that a model is a neutral tool. However, this notion has several, possibly harmful, consequences. In critical social sciences, this non-neutrality in methods and results is an established topic of debate. We propose that in order to deal with it in hydrological modelling, the hydrological modelling network can learn from, and with, critical social sciences. The main lesson, from our perspective, is that responsible modelling is a shared responsibility.
Henrique M. D. Goulart, Irene Benito Lazaro, Linda van Garderen, Karin van der Wiel, Dewi Le Bars, Elco Koks, and Bart van den Hurk
Nat. Hazards Earth Syst. Sci., 24, 29–45, https://doi.org/10.5194/nhess-24-29-2024, https://doi.org/10.5194/nhess-24-29-2024, 2024
Short summary
Short summary
We explore how Hurricane Sandy (2012) could flood New York City under different scenarios, including climate change and internal variability. We find that sea level rise can quadruple coastal flood volumes, while changes in Sandy's landfall location can double flood volumes. Our results show the need for diverse scenarios that include climate change and internal variability and for integrating climate information into a modelling framework, offering insights for high-impact event assessments.
Germano G. Ribeiro Neto, Sarra Kchouk, Lieke A. Melsen, Louise Cavalcante, David W. Walker, Art Dewulf, Alexandre C. Costa, Eduardo S. P. R. Martins, and Pieter R. van Oel
Hydrol. Earth Syst. Sci., 27, 4217–4225, https://doi.org/10.5194/hess-27-4217-2023, https://doi.org/10.5194/hess-27-4217-2023, 2023
Short summary
Short summary
People induce and modify droughts. However, we do not know exactly how relevant human and natural processes interact nor how to evaluate the co-evolution of people and water. Prospect theory can help us to explain the emergence of drought impacts leading to failed welfare expectations (“prospects”) due to water shortage. Our approach helps to explain socio-hydrological phenomena, such as reservoir effects, and can contribute to integrated drought management considering the local context.
Awad M. Ali, Lieke A. Melsen, and Adriaan J. Teuling
Hydrol. Earth Syst. Sci., 27, 4057–4086, https://doi.org/10.5194/hess-27-4057-2023, https://doi.org/10.5194/hess-27-4057-2023, 2023
Short summary
Short summary
Using a new approach based on a combination of modeling and Earth observation, useful information about the filling of the Grand Ethiopian Renaissance Dam can be obtained with limited data and proper rainfall selection. While the monthly streamflow into Sudan has decreased significantly (1.2 × 109–5 × 109 m3) with respect to the non-dam scenario, the negative impact has been masked due to higher-than-average rainfall. We reveal that the dam will need 3–5 more years to complete filling.
Laura Muntjewerf, Richard Bintanja, Thomas Reerink, and Karin van der Wiel
Geosci. Model Dev., 16, 4581–4597, https://doi.org/10.5194/gmd-16-4581-2023, https://doi.org/10.5194/gmd-16-4581-2023, 2023
Short summary
Short summary
The KNMI Large Ensemble Time Slice (KNMI–LENTIS) is a large ensemble of global climate model simulations with EC-Earth3. It covers two climate scenarios by focusing on two time slices: the present day (2000–2009) and a future +2 K climate (2075–2084 in the SSP2-4.5 scenario). We have 1600 simulated years for the two climates with (sub-)daily output frequency. The sampled climate variability allows for robust and in-depth research into (compound) extreme events such as heat waves and droughts.
Sigrid Jørgensen Bakke, Niko Wanders, Karin van der Wiel, and Lena Merete Tallaksen
Nat. Hazards Earth Syst. Sci., 23, 65–89, https://doi.org/10.5194/nhess-23-65-2023, https://doi.org/10.5194/nhess-23-65-2023, 2023
Short summary
Short summary
In this study, we developed a machine learning model to identify dominant controls of wildfire in Fennoscandia and produce monthly fire danger probability maps. The dominant control was shallow-soil water anomaly, followed by air temperature and deep soil water. The model proved skilful with a similar performance as the existing Canadian Forest Fire Weather Index (FWI). We highlight the benefit of using data-driven models jointly with other fire models to improve fire monitoring and prediction.
Elisabeth Tschumi, Sebastian Lienert, Karin van der Wiel, Fortunat Joos, and Jakob Zscheischler
Biogeosciences, 19, 1979–1993, https://doi.org/10.5194/bg-19-1979-2022, https://doi.org/10.5194/bg-19-1979-2022, 2022
Short summary
Short summary
Droughts and heatwaves are expected to occur more often in the future, but their effects on land vegetation and the carbon cycle are poorly understood. We use six climate scenarios with differing extreme occurrences and a vegetation model to analyse these effects. Tree coverage and associated plant productivity increase under a climate with no extremes. Frequent co-occurring droughts and heatwaves decrease plant productivity more than the combined effects of single droughts or heatwaves.
Sarra Kchouk, Lieke A. Melsen, David W. Walker, and Pieter R. van Oel
Nat. Hazards Earth Syst. Sci., 22, 323–344, https://doi.org/10.5194/nhess-22-323-2022, https://doi.org/10.5194/nhess-22-323-2022, 2022
Short summary
Short summary
The aim of our study was to question the validity of the assumed direct linkage between drivers of drought and its impacts on water and food securities, mainly found in the frameworks of drought early warning systems (DEWSs). We analysed more than 5000 scientific studies leading us to the conclusion that the local context can contribute to drought drivers resulting in these drought impacts. Our research aims to increase the relevance and utility of the information provided by DEWSs.
Henrique M. D. Goulart, Karin van der Wiel, Christian Folberth, Juraj Balkovic, and Bart van den Hurk
Earth Syst. Dynam., 12, 1503–1527, https://doi.org/10.5194/esd-12-1503-2021, https://doi.org/10.5194/esd-12-1503-2021, 2021
Short summary
Short summary
Agriculture is sensitive to weather conditions and to climate change. We identify the weather conditions linked to soybean failures and explore changes related to climate change. Additionally, we build future versions of a historical extreme season under future climate scenarios. Results show that soybean failures are likely to increase with climate change. Future events with similar physical conditions to the extreme season are not expected to increase, but events with similar impacts are.
Peter T. La Follette, Adriaan J. Teuling, Nans Addor, Martyn Clark, Koen Jansen, and Lieke A. Melsen
Hydrol. Earth Syst. Sci., 25, 5425–5446, https://doi.org/10.5194/hess-25-5425-2021, https://doi.org/10.5194/hess-25-5425-2021, 2021
Short summary
Short summary
Hydrological models are useful tools that allow us to predict distributions and movement of water. A variety of numerical methods are used by these models. We demonstrate which numerical methods yield large errors when subject to extreme precipitation. As the climate is changing such that extreme precipitation is more common, we find that some numerical methods are better suited for use in hydrological models. Also, we find that many current hydrological models use relatively inaccurate methods.
Joost Buitink, Lieke A. Melsen, and Adriaan J. Teuling
Earth Syst. Dynam., 12, 387–400, https://doi.org/10.5194/esd-12-387-2021, https://doi.org/10.5194/esd-12-387-2021, 2021
Short summary
Short summary
Higher temperatures influence both evaporation and snow processes. These two processes have a large effect on discharge but have distinct roles during different seasons. In this study, we study how higher temperatures affect the discharge via changed evaporation and snow dynamics. Higher temperatures lead to enhanced evaporation but increased melt from glaciers, overall lowering the discharge. During the snowmelt season, discharge was reduced further due to the earlier depletion of snow.
Lieke Anna Melsen and Björn Guse
Hydrol. Earth Syst. Sci., 25, 1307–1332, https://doi.org/10.5194/hess-25-1307-2021, https://doi.org/10.5194/hess-25-1307-2021, 2021
Short summary
Short summary
Certain hydrological processes become more or less relevant when the climate changes. This should also be visible in the models that are used for long-term predictions of river flow as a consequence of climate change. We investigated this using three different models. The change in relevance should be reflected in how the parameters of the models are determined. In the different models, different processes become more relevant in the future: they disagree with each other.
Laurène J. E. Bouaziz, Fabrizio Fenicia, Guillaume Thirel, Tanja de Boer-Euser, Joost Buitink, Claudia C. Brauer, Jan De Niel, Benjamin J. Dewals, Gilles Drogue, Benjamin Grelier, Lieke A. Melsen, Sotirios Moustakas, Jiri Nossent, Fernando Pereira, Eric Sprokkereef, Jasper Stam, Albrecht H. Weerts, Patrick Willems, Hubert H. G. Savenije, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 25, 1069–1095, https://doi.org/10.5194/hess-25-1069-2021, https://doi.org/10.5194/hess-25-1069-2021, 2021
Short summary
Short summary
We quantify the differences in internal states and fluxes of 12 process-based models with similar streamflow performance and assess their plausibility using remotely sensed estimates of evaporation, snow cover, soil moisture and total storage anomalies. The dissimilarities in internal process representation imply that these models cannot all simultaneously be close to reality. Therefore, we invite modelers to evaluate their models using multiple variables and to rely on multi-model studies.
Johannes Vogel, Pauline Rivoire, Cristina Deidda, Leila Rahimi, Christoph A. Sauter, Elisabeth Tschumi, Karin van der Wiel, Tianyi Zhang, and Jakob Zscheischler
Earth Syst. Dynam., 12, 151–172, https://doi.org/10.5194/esd-12-151-2021, https://doi.org/10.5194/esd-12-151-2021, 2021
Short summary
Short summary
We present a statistical approach for automatically identifying multiple drivers of extreme impacts based on LASSO regression. We apply the approach to simulated crop failure in the Northern Hemisphere and identify which meteorological variables including climate extreme indices and which seasons are relevant to predict crop failure. The presented approach can help unravel compounding drivers in high-impact events and could be applied to other impacts such as wildfires or flooding.
Sarah F. Kew, Sjoukje Y. Philip, Mathias Hauser, Mike Hobbins, Niko Wanders, Geert Jan van Oldenborgh, Karin van der Wiel, Ted I. E. Veldkamp, Joyce Kimutai, Chris Funk, and Friederike E. L. Otto
Earth Syst. Dynam., 12, 17–35, https://doi.org/10.5194/esd-12-17-2021, https://doi.org/10.5194/esd-12-17-2021, 2021
Short summary
Short summary
Motivated by the possible influence of rising temperatures, this study synthesises results from observations and climate models to explore trends (1900–2018) in eastern African (EA) drought measures. However, no discernible trends are found in annual soil moisture or precipitation. Positive trends in potential evaporation indicate that for irrigated regions more water is now required to counteract increased evaporation. Precipitation deficit is, however, the most useful indicator of EA drought.
Manuela I. Brunner, Lieke A. Melsen, Andrew W. Wood, Oldrich Rakovec, Naoki Mizukami, Wouter J. M. Knoben, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 25, 105–119, https://doi.org/10.5194/hess-25-105-2021, https://doi.org/10.5194/hess-25-105-2021, 2021
Short summary
Short summary
Assessments of current, local, and regional flood hazards and their future changes often involve the use of hydrologic models. A reliable model ideally reproduces both local flood characteristics and regional aspects of flooding. In this paper we investigate how such characteristics are represented by hydrologic models. Our results show that both the modeling of local and regional flood characteristics are challenging, especially under changing climate conditions.
Joost Buitink, Lieke A. Melsen, James W. Kirchner, and Adriaan J. Teuling
Geosci. Model Dev., 13, 6093–6110, https://doi.org/10.5194/gmd-13-6093-2020, https://doi.org/10.5194/gmd-13-6093-2020, 2020
Short summary
Short summary
This paper presents a new distributed hydrological model: the distributed simple dynamical systems (dS2) model. The model is built with a focus on computational efficiency and is therefore able to simulate basins at high spatial and temporal resolution at a low computational cost. Despite the simplicity of the model concept, it is able to correctly simulate discharge in both small and mesoscale basins.
Sjoukje Philip, Sarah Kew, Geert Jan van Oldenborgh, Friederike Otto, Robert Vautard, Karin van der Wiel, Andrew King, Fraser Lott, Julie Arrighi, Roop Singh, and Maarten van Aalst
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 177–203, https://doi.org/10.5194/ascmo-6-177-2020, https://doi.org/10.5194/ascmo-6-177-2020, 2020
Short summary
Short summary
Event attribution studies can now be performed at short notice. We document a protocol developed by the World Weather Attribution group. It includes choices of which events to analyse, the event definition, observational analysis, model evaluation, multi-model multi-method attribution, hazard synthesis, vulnerability and exposure analysis, and communication procedures. The protocol will be useful for future event attribution studies and as a basis for an operational attribution service.
Caspar T. J. Roebroek, Lieke A. Melsen, Anne J. Hoek van Dijke, Ying Fan, and Adriaan J. Teuling
Hydrol. Earth Syst. Sci., 24, 4625–4639, https://doi.org/10.5194/hess-24-4625-2020, https://doi.org/10.5194/hess-24-4625-2020, 2020
Short summary
Short summary
Vegetation is a principal component in the Earth system models that are used for weather, climate and other environmental predictions. Water is one of the main drivers of vegetation; however, the global distribution of how water influences vegetation is not well understood. This study looks at spatial patterns of photosynthesis and water sources (rain and groundwater) to obtain a first understanding of water access and limitations for the growth of global forests (proxy for natural vegetation).
Cited articles
Addor, N. and Melsen, L. A.: Legacy, rather than adequacy, drives the selection of hydrological models, Water Resour. Res., 55, 378–390,
https://doi.org/10.1029/2018WR022958, 2019. a, b
Ajami, N. K., Duan, Q., and Sorooshian, S.: An integrated hydrologic Bayesian
multimodel combination framework: Confronting input, parameter, and model
structural uncertainty in hydrologic prediction, Water Resour. Res., 43, W01403, https://doi.org/10.1029/2005WR004745, 2007. a
Andersen, M. N., Jensen, C. R., and Lösch, R.: The interaction effects of potassium and drought in field-grown barley. I. Yield, water-use efficiency
and growth, Acta Agr. Scand. B-S. P., 42, 34–44,
https://doi.org/10.1080/09064719209410197, 1992. a
Atkinson, S. E., Woods, R. A., and Sivapalan, M.: Climate and landscape
controls on water balance model complexity over changing timescales, Water
Resour. Res., 38, 50-1–50-17, https://doi.org/10.1029/2002wr001487, 2002. a
Bethune, M. G., Selle, B., and Wang, Q. J.: Understanding and predicting deep
percolation under surface irrigation, Water Resour. Res., 44, W12430, https://doi.org/10.1029/2007WR006380, 2008. a
Beven, K. and Freer, J.: A dynamic topmodel, Hydrol. Process., 15, 1993–2011, https://doi.org/10.1002/hyp.252, 2001. a
Brauer, C. C., Teuling, A. J., Overeem, A., van der Velde, Y., Hazenberg, P., Warmerdam, P. M. M., and Uijlenhoet, R.: Anatomy of extraordinary rainfall and flash flood in a Dutch lowland catchment, Hydrol. Earth Syst. Sci., 15, 1991–2005, https://doi.org/10.5194/hess-15-1991-2011, 2011. a
Burnash, R. J., Ferral, R. L., and McGuire, R. A.: A generalized streamflow
simulation system: Conceptual modeling for digital computers, US Department
of Commerce, National Weather Service, and State of California, Department of
Water Resources, Sacramento, 1973. a
Butts, M. B., Payne, J. T., Kristensen, M., and Madsen, H.: An evaluation of
the impact of model structure on hydrological modelling uncertainty for
streamflow simulation, J. Hydrol., 298, 242–266,
https://doi.org/10.1016/j.jhydrol.2004.03.042, 2004. a
Cardona, O. D., Van Aalst, M. K., Birkmann, J., Fordham, M., Mc Gregor, G.,
Perez, R., Pulwarty, R. S., Schipper, E. L. S., and Sinh, B. T.: Determinants
of risk: exposure and vulnerability, in: Managing the Risks of Extreme Events
and Disasters to Advance Climate Change Adaptation: Special Report of the
Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, 2012. a
Clark, M., Nijssen, B., Lundquist, J., Kavetski, D., Rupp, D., Woods, R.,
Freer, J., Gutmann, E., Wood, A., Brekke, L. D., Arnold, J., Gochis, D., and
Rasmussen, R.: A unified approach for process-based hydrologic modeling: 1. Modeling concept, Water Resour. Res., 51, 2498–2514,
https://doi.org/10.1002/2015WR017198, 2015a. a
Clark, M., Nijssen, B., Lundquist, J., Kavetski, D., Rupp, D., Woods, R.,
Freer, J., Gutmann, E., Wood, A., Gochis, D., Rasmussen, R., Tarboton, D.,
Mahat, V., Flerchinger, G., and Marks, D.: A unified approach for process-based hydrologic modeling: 2. Model implementation and case studies,
Water Resour. Res., 51, 2515–2542, https://doi.org/10.1002/2015WR017200, 2015b. a
Clark, M. P., Slater, A. G., Rupp, D. E., Woods, R. A., Vrugt, J. A., Gupta, H. V., Wagener, T., and Hay, L. E.: Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between
hydrological models, Water Resour. Res., 44, W00B02, https://doi.org/10.1029/2007WR006735, 2008. a, b, c, d, e, f, g, h, i
Clarke, R. T.: A review of some mathematical models used in hydrology, with
observations on their calibration and use, J. Hydrol., 19, 1–20,
https://doi.org/10.1016/0022-1694(73)90089-9, 1973. a
Cooper, V. A., Nguyen, V. T. V., and Nicell, J. A.: Calibration of conceptual
rainfall–runoff models using global optimisation methods with hydrologic
process-based parameter constraints, J. Hydrol., 334, 455–466,
https://doi.org/10.1016/j.jhydrol.2006.10.036, 2007. a
Di Baldassarre, G., Montanari, A., Lins, H., Koutsoyiannis, D., Brandimarte, L., and Blöschl, G.: Flood fatalities in Africa: from diagnosis to mitigation, Geophys. Res. Lett., 37, L22402, https://doi.org/10.1029/2010GL045467, 2010. a
Di Baldassarre, G., Martinez, F., Kalantari, Z., and Viglione, A.: Drought and flood in the Anthropocene: feedback mechanisms in reservoir operation, Earth Syst. Dynam., 8, 225–233, https://doi.org/10.5194/esd-8-225-2017, 2017. a
Eagleson, P. S.: The emergence of global-scale hydrology, Water Resour. Res., 22, 6S–14S, https://doi.org/10.1029/WR022i09Sp0006S, 1986. a
Engeland, K., Hisdal, H., and Frigessi, A.: Practical extreme value modelling
of hydrological floods and droughts: a case study, Extremes, 7, 5–30,
https://doi.org/10.1007/s10687-004-4727-5, 2004. a
Goodrich, D. C., Faurés, J.-M., Woolhiser, D. A., Lane, L. J., and Sorooshian, S.: Measurement and analysis of small-scale convective storm rainfall variability, J. Hydrol., 173, 283–308, https://doi.org/10.1016/0022-1694(95)02703-R, 1995. a, b
Gumbel, E. J.: The return period of flood flows, Ann. Math. Stat., 12,
163–190, https://doi.org/10.1214/aoms/1177731747, 1941. a
Hazeleger, W., Wang, X., Severijns, C., Ştefănescu, S., Bintanja, R., Sterl, A., Wyser, K., Semmler, T., Yang, S., Van den Hurk, B., Van Noije, T., Van der Linden, E., and Van der Wiel, K.: EC-Earth V2.2: description and validation of a new seamless earth system prediction model, Clim. Dynam., 39, 2611–2629, https://doi.org/10.1007/s00382-011-1228-5, 2012. a
Helton, J. C. and Davis, F. J.: Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems, Reliab. Eng. Syst. Safe, 81,
23–69, https://doi.org/10.1016/S0951-8320(03)00058-9, 2003. a
Henderson-Sellers, A., Yang, Z. L., and Dickinson, R. E.: The project for
intercomparison of land-surface parameterization schemes, B. Am. Meteorol.
Soc., 74, 1335–1350, https://doi.org/10.1175/1520-0477(1993)074<1335:TPFIOL>2.0.CO;2,
1993. a
Immerzeel, W. W., Droogers, P., De Jong, S. M., and Bierkens, M. F. P.:
Large-scale monitoring of snow cover and runoff simulation in Himalayan river
basins using remote sensing, Remote Sens. Environ., 113, 40–49,
https://doi.org/10.1016/j.rse.2008.08.010, 2009. a
Imrie, C. E., Durucan, S., and Korre, A.: River flow prediction using
artificial neural networks: generalisation beyond the calibration range, J.
Hydrol., 233, 138–153, https://doi.org/10.1016/S0022-1694(00)00228-6, 2000. a
Jones, R. N., Chiew, F. H., Boughton, W. C., and Zhang, L.: Estimating the
sensitivity of mean annual runoff to climate change using selected
hydrological models, Adv. Water Resour., 29, 1419–1429,
https://doi.org/10.1016/j.advwatres.2005.11.001, 2006. a
Klemeš, V.: Tall tales about tails of hydrological distributions, J. Hydrol. Eng., 5, 227–231, 2000. a
Knoben, W. J. M., Freer, J. E., Fowler, K. J. A., Peel, M. C., and Woods, R. A.: Modular Assessment of RainfallRunoff Models Toolbox (MARRMoT) v1.2: an open-source, extendable framework providing implementations of 46 conceptual hydrologic models as continuous state-space formulations, Geosci. Model Dev., 12, 2463–2480, https://doi.org/10.5194/gmd-12-2463-2019, 2019. a
Knutsson, G.: Humid and arid zone groundwater recharge – a comparative
analysis, Estim. Nat. Groundw. Rech., 113, 40–49,
https://doi.org/10.1007/978-94-015-7780-9_32, 1988. a
Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F.: World map of the Köppen–Geiger climate classification updated, Meteorol. Z., 15, 259–263, https://doi.org/10.1127/0941-2948/2006/0130, 2006. a
Kustas, W. P., Rango, A., and Uijlenhoet, R.: A simple energy budget algorithm for the snowmelt runoff model, Water Resour. Res., 30, 1515–1527,
https://doi.org/10.1029/94wr00152, 1994. a, b
Laio, F., Di Baldassarre, G., and Montanari, A.: Model selection techniques for the frequency analysis of hydrological extremes, Water Resour. Res., 45, W07416, https://doi.org/10.1029/2007WR006666, 2009. a
Leavesley, G. H.: Precipitation-runoff modeling system: User's manual, in:
vol. 83, US Department of the Interior, Denver, 1984. a
Li, Z., Shao, Q., Xu, Z., and Cai, X.: Analysis of parameter uncertainty in
semi-distributed hydrological models using bootstrap method: A case study of
SWAT model applied to Yingluoxia watershed in northwest China, J. Hydrol.,
385, 76–83, https://doi.org/10.1016/j.jhydrol.2010.01.025, 2010. a
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple
hydrologically based model of land surface water and energy fluxes for
general circulation models, J. Geophys. Res.-Atmos., 99, 14415–14428,
https://doi.org/10.1029/94JD00483, 1994. a
Lidén, R. and Harlin, J.: Analysis of conceptual rainfall–runoff modelling performance in different climates, J. Hydrol, 238, 231–247, https://doi.org/10.1016/s0022-1694(00)00330-9, 2000. a
Liu, Y. and Gupta, H. V.: Uncertainty in hydrologic modeling: Toward an
integrated data assimilation framework, Water Resour. Res., 43, W07401, https://doi.org/10.1029/2006WR005756, 2007. a
Lobligeois, F., Andréassian, V., Perrin, C., Tabary, P., and Loumagne, C.: When does higher spatial resolution rainfall information improve streamflow simulation? An evaluation using 3620 flood events, Hydrol. Earth Syst. Sci., 18, 575–594, https://doi.org/10.5194/hess-18-575-2014, 2014. a
Marco, J.: Flood risk mapping, in: Coping with Floods, Springer Netherlands, 353–373, https://doi.org/10.1007/978-94-011-1098-3_20, 1994. a
Massey Jr., F. J.: The Kolmogorov-Smirnov test for goodness of fit, J. Am.
Stat. Assoc., 46, 68–78, https://doi.org/10.1080/01621459.1951.10500769, 1951. a
McMichael, A. J. and, C.-L. D., Kovats, S., Edwards, S., Wilkinson, P., Wilson, T., R., N., Hales, S., Tanser, F., Le Sueur, D., Schlesinger, M., and
Andronova, N.: Comparative Quantification of Health Risks: Chapter 20 Global
climate change, World Health Organization, Geneva, 2004. a
McMillan, H., Jackson, B., Clark, M., Kavetski, D., and Woods, R.: Input
uncertainty in hydrological models: an evaluation of error models for
rainfall, J. Hydrol., 1–2, 83–94, 2011a. a
McMillan, H. K., Clark, M. P., Bowden, W. B., Duncan, M., and Woods, R. A.:
Hydrological field data from a modeller's perspective: Part 1. Diagnostic
tests for model structure, Hydrol. Process., 25, 511–522,
https://doi.org/10.1002/hyp.7841, 2011b. a
Meigh, J. R., Farquharson, F. A. K., and Sutcliffe, J. V.: A worldwide
comparison of regional flood estimation methods and climate, Hydrolog. Sci. J., 42, 225–244, https://doi.org/10.1080/02626669709492022, 1997. a
Melsen, L. A., Teuling, A. J., Torfs, P. J. J. F., Uijlenhoet, R., Mizukami, N., and Clark, M. P.: HESS Opinions: The need for process-based evaluation of large-domain hyper-resolution models, Hydrol. Earth Syst. Sci., 20, 1069–1079, https://doi.org/10.5194/hess-20-1069-2016, 2016. a
Melsen, L. A., Addor, N., Mizukami, N., Newman, A. J., Torfs, P. J. J. F., Clark, M. P., Uijlenhoet, R., and Teuling, A. J.: Mapping (dis)agreement in hydrologic projections, Hydrol. Earth Syst. Sci., 22, 1775–1791, https://doi.org/10.5194/hess-22-1775-2018, 2018. a
Michele, C. D. and Rosso, R.: Uncertainty assessment of regionalized flood
frequency estimates, J. Hydrol. Eng., 6, 453–459,
https://doi.org/10.1061/(ASCE)1084-0699(2001)6:6(453), 2001. a
Morice, C. P., Kennedy, J. J., Rayner, N. A., and Jones, P. D.: Quantifying
uncertainties in global and regional temperature change using an ensemble of
observational estimates: The HadCRUT4 data set, J. Geophys. Res.-Atmos, 117, D08101 , https://doi.org/10.1029/2011JD017187, 2012. a
Morin, E., Enzel, Y., Shamir, U., and Garti, R.: The characteristic time scale for basin hydrological response using radar data, J. Hydrol., 252, 85–99, https://doi.org/10.1016/S0022-1694(01)00451-6, 2001. a
Oreskes, N., Shrader-Frechette, K., and Belitz, K.: Verification, Validation,
and Confirmation of Numerical Models in the Earth Sciences, Science, 263,
641–646, https://doi.org/10.1126/science.263.5147.641, 1994. a
Peel, M. C., Finlayson, B. L., and McMahon, T. A.: Updated world map of the Köppen–Geiger climate classification, Hydrol. Earth Syst. Sci., 11, 1633–1644, https://doi.org/10.5194/hess-11-1633-2007, 2007. a, b
Pilgrim, D. H.: Some problems in transferring hydrological relationships
between small and large drainage basins and between regions, J. Hydrol., 65,
49–72, https://doi.org/10.1016/0022-1694(83)90210-X, 1983. a
Read, L. K. and Vogel, R. M.: Reliability, return periods, and risk under
nonstationarity, Water Resour. Res., 51, 6381–6398, https://doi.org/10.1002/2015wr017089, 2015. a
Reaney, S. M., Bracken, L. J., and Kirkby, M. J.: The importance of surface
controls on overland flow connectivity in semi-arid environments: Results
from a numerical experimental approach, Hydrol. Process., 28, 2116–2128,
https://doi.org/10.1002/hyp.9769, 2014. a
Reed, S., Koren, V., Smith, M., Zhang, Z., Moreda, F., Seo, D. J., and
Participants, D.: Overall distributed model intercomparison project results, J. Hydrol., 298, 27–60, https://doi.org/10.1016/j.jhydrol.2004.03.031, 2004. a
Salas, J. D. and Obeysekera, J.: Revisiting the concepts of return period and
risk for nonstationary hydrologic extreme events, J. Hydrol. Eng., 19,
554–568, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000820, 2014. a
Savabi, M. and Williams, J.: Chapter 7. Water balance and percolation,
USDA – Water Erosion Prediction Project: Hillslope profile model
documentation, National Soil Erosion Research Laboratory, Urbana–Champaign, 1989. a
Scherrer, S. and Naef, F.: A decision scheme to indicate dominant hydrological flow processes on temperate grassland, Hydrol. Process., 17, 391–401, https://doi.org/10.1002/hyp.1131, 2003. a
Segond, M. L., Wheater, H. S., and Onof, C.: The significance of spatial
rainfall representation for flood runoff estimation: A numerical evaluation
based on the Lee catchment, J. Hydrol., 347, 116–131,
https://doi.org/10.1016/j.jhydrol.2007.09.040, 2007. a
Smith, A., Sampson, C., and Bates, P.: Regional flood frequency analysis at the global scale, Water Resour. Res., 51, 539–553, https://doi.org/10.1002/2014WR015814, 2015. a, b
Sousa, P. M., Trigo, R. M., Aizpurua, P., Nieto, R., Gimeno, L., and Garcia-Herrera, R.: Trends and extremes of drought indices throughout the 20th century in the Mediterranean, Nat. Hazards Earth Syst. Sci., 11, 33–51, https://doi.org/10.5194/nhess-11-33-2011, 2011. a
Staudinger, M., Stahl, K., Seibert, J., Clark, M. P., and Tallaksen, L. M.: Comparison of hydrological model structures based on recession and low flow simulations, Hydrol. Earth Syst. Sci., 15, 3447–3459, https://doi.org/10.5194/hess-15-3447-2011, 2011.
a, b
Tallaksen, L., Madsen, H., and Clausen, B.: On the definition and modelling of streamflow drought duration and deficit volume, Hydrolog. Sci. J., 42, 15–33, https://doi.org/10.1080/02626669709492003, 1997. a
Van der Wiel, K., Wanders, N., Selten, F. M., and Bierkens, M. F. P.: Added
Value of Large Ensemble Simulations for Assessing Extreme River Discharge in
a 2 ∘C Warmer World, Geophys. Res. Lett., 46, 2093–2102,
https://doi.org/10.1029/2019GL081967, 2019. a, b, c
Van der Wiel, K., Selten, F. M., Bintanja, R., Blackport, R., and Screen, J.:
Ensemble climate-impact modelling: extreme impacts from moderate
meteorological conditions, Environ. Res., 15, 034050, https://doi.org/10.1088/1748-9326/ab7668, 2020. a, b, c
van Kempen, G., Melsen, L. A. (L.), and van der Wiel, K.: Meteorological forcing, and corresponding hydrological model input and output used in the paper: The impact of hydrological model structure on the simulation of extreme runoff events, https://doi.org/10.4121/13562270.v2, 2021. a
Van Loon, A. F.: Hydrological drought explained, WIREs Water, 2, 359–392, https://doi.org/10.1002/wat2.1085, 2015. a
Wagener, T., Sivapalan, M., Troch, P. A., McGlynn, B. L., Harman, C. J., Gupta, H. V., Kumar, P., Suresh, C. R., Basu, N. B., and Wilson, J. S.: The future of hydrology: An evolving science for a changing world, Water Resour. Res., 46, W05301, https://doi.org/10.1029/2009WR008906, 2010. a
Winsemius, H. C., Aerts, J. C., Van Beek, L. P., Bierkens, M. F., Bouwman, A., Jongman, B., Kwadijk, J., Ligtvoet, W., Lucas, P. L., van Vuuren, D. P., and Ward, P. J.: Global drivers of future river flood risk, Nat. Clim. Change, 6, 381–385, https://doi.org/10.1038/nclimate2893, 2016. a
Zhang, X., Hörmann, G., Gao, J., and Fohrer, N.: Structural uncertainty
assessment in a discharge simulation model, Hydrolog. Sci. J., 56, 854–869,
https://doi.org/10.1080/02626667.2011.587426, 2011. a
Zotarelli, L., Dukes, M. D., Romero, C. C., Migliaccio, K. W., and Morgan, K. T.: Step by step calculation of the Penman–Monteith Evapotranspiration (FAO-56 Method), Environm. Sci., AE459, 1–10, 2015. a
Zscheischler, J., Westra, S., Van Den Hurk, B. J., Seneviratne, S. I., Ward, P. J., Pitman, A., AghaKouchak, A., Bresch, D. N., Leonard, M., Wahl, T., and Zhang, X.: Future climate risk from compound events, Nat. Clim. Change, 8, 469–477, https://doi.org/10.1038/s41558-018-0156-3, 2018. a
Short summary
In this study, we combine climate model results with a hydrological model to investigate uncertainties in flood and drought risk. With the climate model, 2000 years of
current climatewas created. The hydrological model consisted of several building blocks that we could adapt. In this way, we could investigate the effect of these hydrological building blocks on high- and low-flow risk in four different climate zones with return periods of up to 500 years.
In this study, we combine climate model results with a hydrological model to investigate...
Altmetrics
Final-revised paper
Preprint