Articles | Volume 25, issue 3
https://doi.org/10.5194/hess-25-1307-2021
© 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/hess-25-1307-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Climate change impacts model parameter sensitivity – implications for calibration strategy and model diagnostic evaluation
Hydrology and Quantitative Water Management, Wageningen University, Wageningen, the Netherlands
Björn Guse
GFZ German Research Centre for Geosciences, Section Hydrology, Potsdam, Germany
Christian-Albrechts-University of Kiel, Institute of Natural Resource Conservation, Department of Hydrology and Water Resources Management, Kiel, Germany
Related authors
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.
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.
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.
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.
Gijs van Kempen, Karin van der Wiel, and Lieke Anna Melsen
Nat. Hazards Earth Syst. Sci., 21, 961–976, https://doi.org/10.5194/nhess-21-961-2021, https://doi.org/10.5194/nhess-21-961-2021, 2021
Short summary
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.
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.
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.
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).
Sergiy Vorogushyn, Li Han, Heiko Apel, Viet Dung Nguyen, Björn Guse, Xiaoxiang Guan, Oldrich Rakovec, Husain Najafi, Luis Samaniego, and Bruno Merz
Nat. Hazards Earth Syst. Sci., 25, 2007–2029, https://doi.org/10.5194/nhess-25-2007-2025, https://doi.org/10.5194/nhess-25-2007-2025, 2025
Short summary
Short summary
The July 2021 flood in central Europe was one of the deadliest floods in Europe in the recent decades and the most expensive flood in Germany. In this paper, we show that the hydrological impact of this event in the Ahr valley could have been even worse if the rainfall footprint trajectory had been only slightly different. The presented methodology of spatial counterfactuals generates plausible unprecedented events and helps to better prepare for future extreme floods.
Maria Staudinger, Anna Herzog, Ralf Loritz, Tobias Houska, Sandra Pool, Diana Spieler, Paul D. Wagner, Juliane Mai, Jens Kiesel, Stephan Thober, Björn Guse, and Uwe Ehret
EGUsphere, https://doi.org/10.5194/egusphere-2025-1076, https://doi.org/10.5194/egusphere-2025-1076, 2025
Short summary
Short summary
Four process-based and four data-driven hydrological models are compared using different training data. We found process-based models to perform better with small data sets but stop learning soon, while data-driven models learn longer. The study highlights the importance of memory in data and the impact of different data sampling methods on model performance. The direct comparison of these models is novel and provides a clear understanding of their performance under various data conditions.
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.
Ralf Loritz, Alexander Dolich, Eduardo Acuña Espinoza, Pia Ebeling, Björn Guse, Jonas Götte, Sibylle K. Hassler, Corina Hauffe, Ingo Heidbüchel, Jens Kiesel, Mirko Mälicke, Hannes Müller-Thomy, Michael Stölzle, and Larisa Tarasova
Earth Syst. Sci. Data, 16, 5625–5642, https://doi.org/10.5194/essd-16-5625-2024, https://doi.org/10.5194/essd-16-5625-2024, 2024
Short summary
Short summary
The CAMELS-DE dataset features data from 1582 streamflow gauges across Germany, with records spanning from 1951 to 2020. This comprehensive dataset, which includes time series of up to 70 years (median 46 years), enables advanced research on water flow and environmental trends and supports the development of hydrological models.
Elena Macdonald, Bruno Merz, Björn Guse, Viet Dung Nguyen, Xiaoxiang Guan, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci., 28, 833–850, https://doi.org/10.5194/hess-28-833-2024, https://doi.org/10.5194/hess-28-833-2024, 2024
Short summary
Short summary
In some rivers, the occurrence of extreme flood events is more likely than in other rivers – they have heavy-tailed distributions. We find that threshold processes in the runoff generation lead to such a relatively high occurrence probability of extremes. Further, we find that beyond a certain return period, i.e. for rare events, rainfall is often the dominant control compared to runoff generation. Our results can help to improve the estimation of the occurrence probability of extreme floods.
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.
Katrin M. Nissen, Stefan Rupp, Thomas M. Kreuzer, Björn Guse, Bodo Damm, and Uwe Ulbrich
Nat. Hazards Earth Syst. Sci., 22, 2117–2130, https://doi.org/10.5194/nhess-22-2117-2022, https://doi.org/10.5194/nhess-22-2117-2022, 2022
Short summary
Short summary
A statistical model is introduced which quantifies the influence of individual potential triggering factors and their interactions on rockfall probability in central Europe. The most important factor is daily precipitation, which is most effective if sub-surface moisture levels are high. Freeze–thaw cycles in the preceding days can further increase the rockfall hazard. The model can be applied to climate simulations in order to investigate the effect of climate change on rockfall probability.
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.
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.
Gijs van Kempen, Karin van der Wiel, and Lieke Anna Melsen
Nat. Hazards Earth Syst. Sci., 21, 961–976, https://doi.org/10.5194/nhess-21-961-2021, https://doi.org/10.5194/nhess-21-961-2021, 2021
Short summary
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.
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.
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.
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
Abebe, N., Ogden, F., and Pradhan, N.: Sensitivity and uncertainty analysis of the conceptual HBV rainfall-runoff model: Implications for parameter estimation, J. Hydrol., 389, 301–310, https://doi.org/10.1016/j.jhydrol.2010.06.007, 2010. a
Addor, N., Newman, A., Mizukami, N., and Clark, M.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, version 1.0., UCAR/NCAR, Boulder, CO, https://doi.org/10.5065/D6G73C3Q, 2017. a
Bergström, S.: Development and application of a conceptual runoff model for Scandinavian catchments, Tech. rep., SMHI Report RHO 7, Norrköping, 1976. a
Bergström, S.: The HBV model – its structure and applications, Tech. Rep. 4, SMHI reports hydrology, SMHI, Norrköping, Sweden, 1992. a
Beven, K.: Changing ideas in hydrology – The case of physically-based models, J. Hydrol., 105, 157–172, https://doi.org/10.1016/0022-1694(89)90101-7, 1989. a
Borgonovo, E., Lu, X., Plischke, E., Rakovec, O., and Hill, M.: Making the most out of a hydrological model data set: Sensitivity analysis to open the model black box, Water Resour. Res., 53, 7933–7950, https://doi.org/10.1002/2017WR020767, 2017. a
Brunner, M. I., Melsen, L. A., Newman, A. J., Wood, A. W., and Clark, M. P.: Future streamflow regime changes in the United States: assessment using functional classification, Hydrol. Earth Syst. Sci., 24, 3951–3966, https://doi.org/10.5194/hess-24-3951-2020, 2020. a
Burnash, R., Ferral, R., and McGuire, R.: A generalized streamflow simulation system – conceptual modeling for digital computers, Tech. rep., US Department of Commerce, National Weather Service and State of California, Department of Water Resources, Sacramento, California, USA, 1973. a
Chaney, N. W., Herman, J. D., Reed, P. M., and Wood, E. F.: Flood and drought hydrologic monitoring: the role of model parameter uncertainty, Hydrol. Earth Syst. Sci., 19, 3239–3251, https://doi.org/10.5194/hess-19-3239-2015, 2015. a
Chegwidden, O. S., Nijssen, B., Rupp, D. E., Arnold, J. R., Clark, M. P., Hamman, J. J., Kao, S., Mao, Y., Mizukami, N., Mote, P. W., Pan, M., Pytlak, E., and Xiao, M.: How do modeling decisions affect the spread among hydrologic climate change projections? Exploring a large ensemble of simulations across a diversity of hydroclimates, Earths Future, 7, 623–637, https://doi.org/10.1029/2018EF001047, 2019. a
Christensen, N., Wood, A., Voisin, N., Lettenmaier, D., and Palmer, R.: The effects of climate change on the hydrology and water resources of the Colorado River basin, Climatic Change, 62, 337–363, 2004. a
Demaria, E. M., Nijssen, B., and Wagener, T.: Monte Carlo sensitivity analysis of land surface parameters using the Variable Infiltration Capacity model, J. Geophys. Res., 112, D11113, https://doi.org/10.1029/2006JD007534, 2007. a, b, c
Devak, M. and Dhanya, C. T.: Sensitivity analysis of hydrological models: review and way forward, J. Water Clim. Change, 8, 557–575, https://doi.org/10.2166/wcc.2017.149, 2017. a
Duethmann, D., Blöschl, G., and Parajka, J.: Why does a conceptual hydrological model fail to correctly predict discharge changes in response to climate change?, Hydrol. Earth Syst. Sci., 24, 3493–3511, https://doi.org/10.5194/hess-24-3493-2020, 2020. a
Dufresne, J.-L., Foujols, M.-A., Denvil, S., Caubel, A., Marti, O., Aumont, O., Balkanski, Y., Bekki, S., Bellenger, H., Benshila, R., Bony, S., Bopp, L., Braconnot, P., Brockmann, P., Cadule, P., Cheruy, F., Codron, F., Cozic, A., Cugnet, D., de Noblet, N., Duvel, J.-P., Ethé, C., Fairhead, L., Fichefet, T., Flavoni, S., Friedlingstein, P., Grandpeix, J.-Y., Guez, L., Guilyardi, E., Hauglustaine, D., Hourdin, F., Idelkadi, A., Ghattas, J., Joussaume, S., Kageyama, M., Krinner, G., Labetoulle, S., Lahellec, A., Lefebvre, M.-P., Lefevre, F., Levy, C., Li, Z. X., Lloyd, J., Lott, F., Madec, G., Mancip, M., Marchand, M., Masson, S., Meurdesoif, Y., Mignot, J., Musat, I., Parouty, S., Polcher, J., Rio, C., Schulz, M., Swingedouw, D., Szopa, S., Talandier, C., Terray, P., Viovy, N., and Vuichard, N.: Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5, Clim. Dynam., 40, 2123–2165, https://doi.org/10.1007/s00382-012-1636-1, 2013. a
Fontrodona Bach, A., van der Schrier, G., Melsen, L., Klein Tank, A., and Teuling, A.: Widespread and Accelerated Decrease of Observed Mean and Extreme Snow Depth Over Europe, Geophys. Res. Lett., 45, 12312–12319, https://doi.org/10.1029/2018GL079799, 2018. a
Fowler, K., Peel, M., Western, A., Zhang, L., and Peterson, T.: Simulating runoff under changing climatic conditions: Revisiting an apparent deficiency of conceptual rainfall-runoff models, Water Resour. Res., 52, 1820–1846, https://doi.org/10.1002/2015WR018068, 2016. a
Fowler, K., Coxon, G., Freer, J., Peel, M., Wagener, T., Western, A., Woods, R., and Zhang, L.: Simulating runoff under changing climatic conditions: A framework for model improvement, Water Resour. Res., 54, 9812–9832, https://doi.org/10.1029/2018WR023989, 2018. a, b, c
Gent, P., Danabasoglu, G., Donner, L. J., Holland, M. M., Hunke, E. C., Jayne, S. R., Lawrence, D. M., Neale, R. B., Rasch, P. J., Vertenstein, M., Worley, P. H., Yang, Z., and Zhang, M.: The Community Climate System Model Version 4, J. Climate, 24, 4973–4991, https://doi.org/10.1175/2011JCLI4083.1, 2011. a
Giorgetta, M. A., Jungclaus, J., Reick, C. H., Legutke, S., Bader, J., Böttinger, M., Brovkin, V., Crueger, T., Esch, M., Fieg, K., Glushak, K., Gayler, V., Haak, H., Hollweg, H.-D., Ilyina, T., Kinne, S., Kornblueh, L., Matei, D., Mauritsen, T., Mikolajewicz, U., Mueller, W., Notz, D., Pithan, F., Raddatz, T., Rast, S., Redler, R., Roeckner, E., Schmidt, H., Schnur, R., Segschneider, J., Six, K. D., Stockhause, M., Timmreck, C., Wegner, J., Widmann, H., Wieners, K.-H., Claussen, M., Marotzke, J., and Stevens, B.: Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5, J. Adv. Model. Earth Sy., 5, 572–597, https://doi.org/10.1002/jame.20038, 2013. a
Grigg, A. and Hughes, J.: Nonstationarity driven by multidecadal change in catchment groundwater storage: A test of modifications to a common rainfall-run-off model, Hydrol. Process., 32, 3675–3688, https://doi.org/10.1002/hyp.13282, 2018. a, b
Guse, B., Reusser, D. E., and Fohrer, N.: How to improve the representation of hydrological processes in SWAT for a lowland catchment – Temporal analysis of parameter sensitivity and model performance, Hydrol. Process., 28, 2651–2670, https://doi.org/10.1002/hyp.9777, 2014. a
Guse, B., Pfannerstill, M., Gafurov, A., Fohrer, N., and Gupta, H.: Demasking the integrated information of discharge: Advancing sensitivity analyses to consider different hydrological components and their rates of change, Water Resour. Res., 52, 8724–8743, https://doi.org/10.1002/2016WR018894, 2016a. a, b, c, d
Guse, B., Pfannerstill, M., Strauch, M., Reusser, D. E., Volk, M., Gupta, H. V., and Fohrer, N.: On characterizing the temporal dominance patterns of model parameters and processes, Hydrol. Process., 30, 2255–2270, https://doi.org/10.1002/hyp.10764, 2016b. a
Haghnegahdar, A., Razavi, S., Yassin, F., and Wheater, H.: Multicriteria sensitivity analysis as a diagnostic tool for understanding model behaviour and characterizing model uncertainty, Hydrol. Process., 31, 4462–4476, https://doi.org/10.1002/hyp.11358, 2017. a
Herman, J. D., Kollat, J. B., Reed, P. M., and Wagener, T.: Technical Note: Method of Morris effectively reduces the computational demands of global sensitivity analysis for distributed watershed models, Hydrol. Earth Syst. Sci., 17, 2893–2903, https://doi.org/10.5194/hess-17-2893-2013, 2013a. a
Herman, J., Reed, P., and Wagener, T.: Time-varying sensitivity analysis clarifies the effects of watershed model formulation on model behavior, Water Resour. Res., 49, 1400–1414, https://doi.org/10.1002/wrcr.20124, 2013b. a
Jakeman, A. and Hornberger, G.: How much complexity is warranted in a rainfall-runoff model?, Water Resour. Res., 29, 2637–2649, https://doi.org/10.1029/93WR00877, 1993. a
Knutti, R., Masson, D., and Gettelman, A.: Climate model genealogy: Generation CMIP5 and how we got there, Geophys. Res. Lett., 40, 1194–1199, https://doi.org/10.1002/grl.50256, 2013. a
Koutroulis, A., Tsanis, I., Daliakopoulos, I., and Jacob, D.: Impact of climate change on water resources status: A case study for Crete Island, Greece, J. Hydrol., 479, 146–158, https://doi.org/10.1016/j.jhydrol.2012.11.055, 2013. a
Koutsoyiannis, D. and Montanari, A.: Negligent killing of scientific concepts: the stationarity case, Hydrolog. Sci. J., 60, 1174–1183, https://doi.org/10.1080/02626667.2014.959959, 2015. a
Lhomme, J.: An examination of the Priestley-Taylor equation using a convective boundary layer model, Water Resour. Res., 33, 2571–2578, https://doi.org/10.1029/97WR01897, 1997. a, b, c
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., 99, 14415–14458, https://doi.org/10.1029/94JD00483, 1994. a, b
Liang, X., Wood, E. F., and Lettenmaier, D. P.: Surface soil moisture parameterization of the VIC-2L model: Evaluation and modification, Global Planet. Change, 13, 195–206, https://doi.org/10.1016/0921-8181(95)00046-1, 1996. a
Mai, J. and Tolson, A.: Model Variable Augmentation (MVA) for diagnostic assessment of sensitivity analysis results, Water Resour. Res., 55, 2631–2651, https://doi.org/10.1029/2018WR023382, 2019. a
Massmann, C. and Holzmann, H.: Analysing the Sub-processes of a Conceptual Rainfall-Runoff Model Using Information about the Parameter Sensitivity and Variance, Environ. Model. Assess., 20, 41–53, https://doi.org/10.1007/s10666-014-9414-6, 2015. a
Maurer, E., Wood, A., Adam, J., Lettenmaier, D., and Nijssen, B.: A Long-Term Hydrologically Based Dataset of Land Surface Fluxes and States for the Conterminous United States, J. Climate, 15, 3237–3251, https://doi.org/10.1175/JCLI-D-12-00508.1, 2002. a
McMillan, H., Seibert, J., Petersen-Overleir, A., Lang, M., White, P., Snelder, T., Rutherford, K., Krueger, T., Mason, R., and Kiang, J.: How uncertainty analysis of streamflow data can reduce costs and promote robust decisions in water management applications, Water Resour. Res., 53, 5220–5228, https://doi.org/10.1002/2016WR020328, 2017. a
Melsen, L. and Guse, B.: Hydrological drought simulations: How climate and model structure control parameter sensitivity, Water Resour. Res., 55, 10527–10547, https://doi.org/10.1029/2019WR025230, 2019. a, b, c
Melsen, L., Teuling, A., van Berkum, S., Torfs, P., and Uijlenhoet, R.: Catchments as simple dynamical systems: A case study on methods and data requirements for parameter identification, Water Resour. Res., 50, 5577–5596, https://doi.org/10.1002/2013WR014720, 2014. a
Melsen, L., Teuling, A., Torfs, P., Zappa, M., Mizukami, N., Clark, M., and Uijlenhoet, R.: Representation of spatial and temporal variability in large-domain hydrological models: case study for a mesoscale pre-Alpine basin, Hydrol. Earth Syst. Sci., 20, 2207–2226, https://doi.org/10.5194/hess-20-2207-2016, 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, b, c, d
Mendoza, P. A., Clark, M., Barlage, M., Rajagopalan, B., Samaniego, L., Abramowitz, G., and Gupta, H.: Are we unnecessarily constraining the agility of complex process-based models?, Water Resour. Res., 51, 716–728, https://doi.org/10.1002/2014WR015820, 2015. a
Merz, R., Parajka, J., and Blöschl, G.: Time stability of catchment model parameters: Implications for climate impact analyses, Water Resour. Res., 47, W02531, https://doi.org/10.1029/2010WR009505, 2011. a, b
Metin, A. D., Dung, N. V., Schröter, K., Guse, B., Apel, H., Kreibich, H., Vorogushyn, S., and Merz, B.: How do changes along the risk chain affect flood risk?, Nat. Hazards Earth Syst. Sci., 18, 3089–3108, https://doi.org/10.5194/nhess-18-3089-2018, 2018. a
Milly, P., Betancourt, J., Falkenmark, M., Hirsch, R., Kundzewicz, Z., Lettenmaier, D., and Stouffer, R.: Stationarity is dead: Whither water management?, Science, 319, 573–574, https://doi.org/10.1126/science.1151915, 2008. a
Mizukami, N., Clark, M. P., Gutmann, E. D., Mendoza, P. A., Newman, A. J., Nijssen, B., Livneh, B., Hay, L. E., Arnold, J. R., and Brekke, L. D.: Implications of the Methodological Choices for Hydrologic Portrayals of Climate Change over the Contiguous United States: Statistically Downscaled Forcing Data and Hydrologic Model, J. Hydrometeorol., 17, 73–98, https://doi.org/10.1175/JHM-D-14-0187.1, 2016. a
National Weather Service: I I.3-SAC-SMA Conceptualization of the Sacramento soil moisture accounting model, Tech. rep., National Oceanic and Atmospheric Administration (NOAA), 2002. a
Newman, A., Sampson, K., Clark, M., Bock, A., Viger, R., and Blodgett, D.: A large sample watershed-scale hydrometeorological dataset for the contiguous USA, UCAR/NCAR, Boulder, CO, https://doi.org/10.5065/D6MW2F4D, 2014. a
Newman, A. J., Clark, M. P., Sampson, K., Wood, A., Hay, L. E., Bock, A., Viger, R. J., Blodgett, D., Brekke, L., Arnold, J. R., Hopson, T., and Duan, Q.: Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance, Hydrol. Earth Syst. Sci., 19, 209–223, https://doi.org/10.5194/hess-19-209-2015, 2015. a, b, c
Parajka, J., Merz, R., and Blöschl, G.: Uncertainty and multiple objective calibration in regional water balance modelling: case study in 320 Austrian catchments, Hydrol. Process., 21, 435–446, https://doi.org/10.1002/hyp.6253, 2007. a, b
Peleg, N., Shamir, E., Georgakakos, K. P., and Morin, E.: A framework for assessing hydrological regime sensitivity to climate change in a convective rainfall environment: a case study of two medium-sized eastern Mediterranean catchments, Israel, Hydrol. Earth Syst. Sci., 19, 567–581, https://doi.org/10.5194/hess-19-567-2015, 2015. a
Pfannerstill, M., Guse, B., Reusser, D., and Fohrer, N.: Process verification of a hydrological model using a temporal parameter sensitivity analysis, Hydrol. Earth Syst. Sci., 19, 4365–4376, https://doi.org/10.5194/hess-19-4365-2015, 2015. a, b
Pianosi, F., Beven, K., Freer, J., Hall, J., Rougier, J., Stephanson, D., and Wagener, T.: Sensitivity analysis of environmental models: A systematic review with practical workflow, Environ. Modell. Softw., 79, 214–232, https://doi.org/10.1016/j.envsoft.2016.02.008, 2016. a, b, c
Rakovec, O., Hill, M. C., Clark, M. P., Weerts, A. H., Teuling, A. J., and Uijlenhoet, R.: Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models, Water Resour. Res., 50, 409–426, https://doi.org/10.1002/2013WR014063, 2014. a
Razavi, S. and Gupta, H.: What do we mean by sensitivity analysis? The need for comprehensive characterization of global sensitivity in earth and environmental systems models, Water Resour. Res., 51, 3070–3092, https://doi.org/10.1002/2014WR016527, 2015. a, b, c
Reusser, D. E., Buytaert, W., and Zehe, E.: Temporal dynamics of model parameter sensitivity for computationally expensive models with FAST (Fourier Amplitude Sensitivity Test), Water Resour. Res., 47, W07551, https://doi.org/10.1029/2010WR009947, 2011. a, b
Sagarika, S., Kalra, A., and Ahmad, S.: Evaluating the effect of persistence on long-term trends and analyzing step changes in streamflows of the continental United States, J. Hydrol., 517, 36–53, https://doi.org/10.1016/j.jhydrol.2014.05.002, 2014. a
Saltelli, A., Ratto, M., Tarantola, S., and Campolongo, F.: Sensitivity analysis practices: Strategies for model-based inference, Reliab. Eng. Syst. Safe., 91, 1109–1125, 2006. a
Sheffield, J., Barrett, A., Colle, B., Fernando, D., Fu, R., Geil, K., Hu, Q., Kinter, J., Kumar, S., Langenbrunner, B., Lombardo, K., Long, L., Maloney, E., Mariotti, A., Meyerson, J., Mo, K., Neelin, J., Nigam, S., Pan, Z., Ren, T., Ruiz-Barradas, A., Serra, Y., Seth, A., Thibeault, J., Stroeve, J., Yang, Z., and Yin, L.: North American Climate in CMIP5 Experiments. Part I: Evaluation of Historical Simulations of Continental and Regional Climatology, J. Climate, 26, 9209–9244, https://doi.org/10.1175/JCLI-D-12-00592.1, 2013. a
Shin, M., Guillaume, J. H. A., Croke, B. F. W., and Jakeman, A. J.: Addressing ten questions about conceptual rainfall-runoff models with global sensitivity analyses in R, J. Hydrol., 503, 135–152, https://doi.org/10.1016/j.jhydrol.2013.08.047, 2013. a
Stewart, I. T., Cayan, D. R., and Dettinger, M. D.: Changes toward earlier streamflow timing across western North America, J. Climate, 18, 1136–1155, https://doi.org/10.1175/JCLI3321.1, 2005. a
Teutschbein, C., Wetterhall, F., and Seibert, J.: Evaluation of different downscaling techniques for hydrological climate-change impact studies at the catchment scale, Clim. Dynam., 37, 2087–2105, https://doi.org/10.1007/s00382-010-0979-8, 2011. a
Thirel, G., Andréassian, V., and Perrin, C.: On the need to test hydrological models under changing conditions, Hydrolog. Sci. J., 60, 1165–1173, https://doi.org/10.1080/02626667.2015.1050027, 2015. a
Uhlenbrook, S., Seibert, J., Leibundgut, C., and Rodhe, A.: Prediction uncertainty of conceptual rainfall-runoff models caused by problems in identifying model parameters and structure, Hydrolog. Sci. J., 44, 779–797, 1999. a
van Werkhoven, K., Wagener, T., Reed, P., and Tang, Y.: Characterization of watershed model behavior across a hydroclimatic gradient, Water Resour. Res., 44, W01429, https://doi.org/10.1029/2007WR006271, 2008. a, b
van Werkhoven, K., Wagener, T., Reed, P., and Tang, Y.: Rainfall characteristics define the value of streamflow observations for distributed watershed model identification, Geophys. Res. Lett., 35, L11403, https://doi.org/10.1029/2008GL034162, 2008. a, b
van Werkhoven, K., Wagener, T., Reed, P., and Tang, Y.: Sensitivity-guided reduction of parameteric dimensionality for multi-objective calibration of watershed models, Adv. Water Resour., 32, 1154–1169, https://doi.org/10.1016/j.advwatres.2009.03.002, 2009. a, b
Vaze, J., Post, D., Chiew, F., Perraud, J., Viney, N., and Teng, J.: Climate non-stationarity – Validity of calibrated rainfall-runoff models for use in climate change studies, J. Hydrol., 394, 447–457, https://doi.org/10.1016/j.jhydrol.2010.09.018, 2010. a, b
Westra, S., Thyer, M., Leonard, M., Kavetski, D., and Lambert, M.: A strategy for diagnosing and interpreting hydrological modelnonstationarity, Water Resour. Res., 50, 5090–5113, https://doi.org/10.1002/2013WR014719, 2014. a, b
Wetterhall, F., Graham, L. P., Andréasson, J., Rosberg, J., and Yang, W.: Using ensemble climate projections to assess probabilistic hydrological change in the Nordic region, Nat. Hazards Earth Syst. Sci., 11, 2295–2306, https://doi.org/10.5194/nhess-11-2295-2011, 2011. a
Willmott, C. and Fedema, J.: A more rational climatic moisture index, Prof. Geogr., 44, 84–88, https://doi.org/10.1111/j.0033-0124.1992.00084.x, 1992.
a
Wood, A., Leung, L., Sridhar, V., and Lettenmaier, D.: Hydrologic implications of dynamical and statistical approaches to downscaling climate model output, Climatic Change, 15, 189–216, https://doi.org/10.1023/B:CLIM.0000013685.99609.9e, 2004. a
Wu, H., Kimball, J. S., Li, H., Huang, M., Ruby Leung, L., and Adler, R. F.: A new global river network database for macroscale hydrologic modeling, Water Resour. Res., 48, W09701, https://doi.org/10.1029/2012WR012313, 2012. a
Yapo, P. O., Gupta, H. V., and Sorooshian, S.: Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data, J. Hydrol., 181, 23–48, https://doi.org/10.1016/0022-1694(95)02918-4, 1996. a
Zadeh, F. K., Nossent, J., Sarrazin, F., Pianosi, F., van Griensven, A., Wagener, T., and Bauwens, W.: Comparison of variance-based and moment-independent global sensitivity analysis approaches by application to the SWAT model, Environ. Modell. Softw., 91, 210–222, https://doi.org/10.1016/j.envsoft.2017.02.001, 2017. a, b
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.
Certain hydrological processes become more or less relevant when the climate changes. This...