Articles | Volume 14, issue 12
https://doi.org/10.5194/gmd-14-7795-2021
© 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/gmd-14-7795-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
HydroPy (v1.0): a new global hydrology model written in Python
Tobias Stacke
CORRESPONDING AUTHOR
Helmholtz-Zentrum Hereon, Institute of Coastal Systems – Analysis and Modelling, Max-Planck-Strasse 1,
21502 Geesthacht, Germany
now at: Max Planck Institute for Meteorology, Bundesstraße 53, 20146 Hamburg, Germany
Stefan Hagemann
Helmholtz-Zentrum Hereon, Institute of Coastal Systems – Analysis and Modelling, Max-Planck-Strasse 1,
21502 Geesthacht, Germany
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We measured over 13,000 methane fluxes at a site in the Canadian Arctic and linked them with drone and free satellite images. We tested four machine-learning methods and two map scales. Metre-scale maps captured small wet and dry features that strongly affect methane release, while coarser maps blurred them. Different models shifted the monthly methane estimate. This helps choose the right data and tools to map methane, design monitoring networks, and check climate models.
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ICON XPP is a newly developed Earth System model configuration based on the ICON modeling framework. It merges accomplishments from the recent operational numerical weather prediction model with well-established climate components for the ocean, land and ocean-biogeochemistry. ICON XPP reaches typical targets of a coupled climate simulation, and is able to run long integrations and large-ensemble experiments, making it suitable for climate predictions and projections, and for climate research.
Hannes Müller Schmied, Simon Newland Gosling, Marlo Garnsworthy, Laura Müller, Camelia-Eliza Telteu, Atiq Kainan Ahmed, Lauren Seaby Andersen, Julien Boulange, Peter Burek, Jinfeng Chang, He Chen, Lukas Gudmundsson, Manolis Grillakis, Luca Guillaumot, Naota Hanasaki, Aristeidis Koutroulis, Rohini Kumar, Guoyong Leng, Junguo Liu, Xingcai Liu, Inga Menke, Vimal Mishra, Yadu Pokhrel, Oldrich Rakovec, Luis Samaniego, Yusuke Satoh, Harsh Lovekumar Shah, Mikhail Smilovic, Tobias Stacke, Edwin Sutanudjaja, Wim Thiery, Athanasios Tsilimigkras, Yoshihide Wada, Niko Wanders, and Tokuta Yokohata
Geosci. Model Dev., 18, 2409–2425, https://doi.org/10.5194/gmd-18-2409-2025, https://doi.org/10.5194/gmd-18-2409-2025, 2025
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Global water models contribute to the evaluation of important natural and societal issues but are – as all models – simplified representation of reality. So, there are many ways to calculate the water fluxes and storages. This paper presents a visualization of 16 global water models using a standardized visualization and the pathway towards this common understanding. Next to academic education purposes, we envisage that these diagrams will help researchers, model developers, and data users.
Alberto Elizalde, Gibran Romero-Mujalli, Tobias Stacke, and Stefan Hagemann
EGUsphere, https://doi.org/10.5194/egusphere-2024-3645, https://doi.org/10.5194/egusphere-2024-3645, 2025
Preprint archived
Short summary
Short summary
This study examines phosphorus land-to-sea transport in Europe, exploring changes over time and predicting future trends under various scenarios. It integrates human and environmental factors, offering a comprehensive analysis. Our findings show how global warming-induced rainfall patterns affect phosphorus levels. While pollution reduction policies are helpful, population growth, land-use changes, and increased rainfall could lead to higher phosphorus levels in the future.
Philipp de Vrese, Goran Georgievski, Jesus Fidel Gonzalez Rouco, Dirk Notz, Tobias Stacke, Norman Julius Steinert, Stiig Wilkenskjeld, and Victor Brovkin
The Cryosphere, 17, 2095–2118, https://doi.org/10.5194/tc-17-2095-2023, https://doi.org/10.5194/tc-17-2095-2023, 2023
Short summary
Short summary
The current generation of Earth system models exhibits large inter-model differences in the simulated climate of the Arctic and subarctic zone. We used an adapted version of the Max Planck Institute (MPI) Earth System Model to show that differences in the representation of the soil hydrology in permafrost-affected regions could help explain a large part of this inter-model spread and have pronounced impacts on important elements of Earth systems as far to the south as the tropics.
Manuel Schlund, Birgit Hassler, Axel Lauer, Bouwe Andela, Patrick Jöckel, Rémi Kazeroni, Saskia Loosveldt Tomas, Brian Medeiros, Valeriu Predoi, Stéphane Sénési, Jérôme Servonnat, Tobias Stacke, Javier Vegas-Regidor, Klaus Zimmermann, and Veronika Eyring
Geosci. Model Dev., 16, 315–333, https://doi.org/10.5194/gmd-16-315-2023, https://doi.org/10.5194/gmd-16-315-2023, 2023
Short summary
Short summary
The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for routine evaluation of Earth system models. Originally, ESMValTool was designed to process reformatted output provided by large model intercomparison projects like the Coupled Model Intercomparison Project (CMIP). Here, we describe a new extension of ESMValTool that allows for reading and processing native climate model output, i.e., data that have not been reformatted before.
Camelia-Eliza Telteu, Hannes Müller Schmied, Wim Thiery, Guoyong Leng, Peter Burek, Xingcai Liu, Julien Eric Stanislas Boulange, Lauren Seaby Andersen, Manolis Grillakis, Simon Newland Gosling, Yusuke Satoh, Oldrich Rakovec, Tobias Stacke, Jinfeng Chang, Niko Wanders, Harsh Lovekumar Shah, Tim Trautmann, Ganquan Mao, Naota Hanasaki, Aristeidis Koutroulis, Yadu Pokhrel, Luis Samaniego, Yoshihide Wada, Vimal Mishra, Junguo Liu, Petra Döll, Fang Zhao, Anne Gädeke, Sam S. Rabin, and Florian Herz
Geosci. Model Dev., 14, 3843–3878, https://doi.org/10.5194/gmd-14-3843-2021, https://doi.org/10.5194/gmd-14-3843-2021, 2021
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We analyse water storage compartments, water flows, and human water use sectors included in 16 global water models that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b. We develop a standard writing style for the model equations. We conclude that even though hydrologic processes are often based on similar equations, in the end these equations have been adjusted, or the models have used different values for specific parameters or specific variables.
Philipp de Vrese, Tobias Stacke, Thomas Kleinen, and Victor Brovkin
The Cryosphere, 15, 1097–1130, https://doi.org/10.5194/tc-15-1097-2021, https://doi.org/10.5194/tc-15-1097-2021, 2021
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With large amounts of carbon stored in frozen soils and a highly energy-limited vegetation the Arctic is very sensitive to changes in climate. Here our simulations with the land surface model JSBACH reveal a number of offsetting factors moderating the Arctic's net response to global warming. More importantly we find that the effects of climate change may not be fully reversible on decadal timescales, leading to substantially different CH4 emissions depending on whether the Arctic warms or cools.
Kseniia Ivanova, Anna-Maria Virkkala, Victor Brovkin, Tobias Stacke, Barbara Widhalm, Annett Bartsch, Carolina Voigt, Oliver Sonnentag, and Mathias Göckede
EGUsphere, https://doi.org/10.5194/egusphere-2025-3968, https://doi.org/10.5194/egusphere-2025-3968, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
We measured over 13,000 methane fluxes at a site in the Canadian Arctic and linked them with drone and free satellite images. We tested four machine-learning methods and two map scales. Metre-scale maps captured small wet and dry features that strongly affect methane release, while coarser maps blurred them. Different models shifted the monthly methane estimate. This helps choose the right data and tools to map methane, design monitoring networks, and check climate models.
Wolfgang A. Müller, Stephan Lorenz, Trang V. Pham, Andrea Schneidereit, Renate Brokopf, Victor Brovkin, Nils Brüggemann, Fatemeh Chegini, Dietmar Dommenget, Kristina Fröhlich, Barbara Früh, Veronika Gayler, Helmuth Haak, Stefan Hagemann, Moritz Hanke, Tatiana Ilyina, Johann Jungclaus, Martin Köhler, Peter Korn, Luis Kornblüh, Clarissa Kroll, Julian Krüger, Karel Castro-Morales, Ulrike Niemeier, Holger Pohlmann, Iuliia Polkova, Roland Potthast, Thomas Riddick, Manuel Schlund, Tobias Stacke, Roland Wirth, Dakuan Yu, and Jochem Marotzke
EGUsphere, https://doi.org/10.5194/egusphere-2025-2473, https://doi.org/10.5194/egusphere-2025-2473, 2025
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ICON XPP is a newly developed Earth System model configuration based on the ICON modeling framework. It merges accomplishments from the recent operational numerical weather prediction model with well-established climate components for the ocean, land and ocean-biogeochemistry. ICON XPP reaches typical targets of a coupled climate simulation, and is able to run long integrations and large-ensemble experiments, making it suitable for climate predictions and projections, and for climate research.
Hannes Müller Schmied, Simon Newland Gosling, Marlo Garnsworthy, Laura Müller, Camelia-Eliza Telteu, Atiq Kainan Ahmed, Lauren Seaby Andersen, Julien Boulange, Peter Burek, Jinfeng Chang, He Chen, Lukas Gudmundsson, Manolis Grillakis, Luca Guillaumot, Naota Hanasaki, Aristeidis Koutroulis, Rohini Kumar, Guoyong Leng, Junguo Liu, Xingcai Liu, Inga Menke, Vimal Mishra, Yadu Pokhrel, Oldrich Rakovec, Luis Samaniego, Yusuke Satoh, Harsh Lovekumar Shah, Mikhail Smilovic, Tobias Stacke, Edwin Sutanudjaja, Wim Thiery, Athanasios Tsilimigkras, Yoshihide Wada, Niko Wanders, and Tokuta Yokohata
Geosci. Model Dev., 18, 2409–2425, https://doi.org/10.5194/gmd-18-2409-2025, https://doi.org/10.5194/gmd-18-2409-2025, 2025
Short summary
Short summary
Global water models contribute to the evaluation of important natural and societal issues but are – as all models – simplified representation of reality. So, there are many ways to calculate the water fluxes and storages. This paper presents a visualization of 16 global water models using a standardized visualization and the pathway towards this common understanding. Next to academic education purposes, we envisage that these diagrams will help researchers, model developers, and data users.
Alberto Elizalde, Gibran Romero-Mujalli, Tobias Stacke, and Stefan Hagemann
EGUsphere, https://doi.org/10.5194/egusphere-2024-3645, https://doi.org/10.5194/egusphere-2024-3645, 2025
Preprint archived
Short summary
Short summary
This study examines phosphorus land-to-sea transport in Europe, exploring changes over time and predicting future trends under various scenarios. It integrates human and environmental factors, offering a comprehensive analysis. Our findings show how global warming-induced rainfall patterns affect phosphorus levels. While pollution reduction policies are helpful, population growth, land-use changes, and increased rainfall could lead to higher phosphorus levels in the future.
Stefan Hagemann, Thao Thi Nguyen, and Ha Thi Minh Ho-Hagemann
Ocean Sci., 20, 1457–1478, https://doi.org/10.5194/os-20-1457-2024, https://doi.org/10.5194/os-20-1457-2024, 2024
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We have developed a methodology for the bias correction of simulated river runoff to force ocean models in which low, medium, and high discharges are corrected once separated at the coast. We show that the bias correction generally leads to an improved representation of river runoff in Europe. The methodology is suitable for model regions with a sufficiently high coverage of discharge observations, and it can be applied to river runoff based on climate hindcasts or climate change simulations.
Pascal Simon, Martin Otto Paul Ramacher, Stefan Hagemann, Volker Matthias, Hanna Joerss, and Johannes Bieser
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-236, https://doi.org/10.5194/essd-2024-236, 2024
Revised manuscript accepted for ESSD
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Per- and Polyfluorinated Alkyl Substances (PFAS) constitute a group of often toxic, persistent, and bioaccumulative substances. We constructed a global Emissions model and inventory based on multiple datasets for 23 widely used PFAS. The model computes temporally and spatially resolved model ready emissions distinguishing between emissions to air and emissions to water covering the time span from 1950 up until 2020 on an annual basis to be used for chemistry transport modelling.
Félix García-Pereira, Jesús Fidel González-Rouco, Camilo Melo-Aguilar, Norman Julius Steinert, Elena García-Bustamante, Philip de Vrese, Johann Jungclaus, Stephan Lorenz, Stefan Hagemann, Francisco José Cuesta-Valero, Almudena García-García, and Hugo Beltrami
Earth Syst. Dynam., 15, 547–564, https://doi.org/10.5194/esd-15-547-2024, https://doi.org/10.5194/esd-15-547-2024, 2024
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According to climate model estimates, the land stored 2 % of the system's heat excess in the last decades, while observational studies show it was around 6 %. This difference stems from these models using land components that are too shallow to constrain land heat uptake. Deepening the land component does not affect the surface temperature. This result can be used to derive land heat uptake estimates from different sources, which are much closer to previous observational reports.
Philipp Heinrich, Stefan Hagemann, Ralf Weisse, Corinna Schrum, Ute Daewel, and Lidia Gaslikova
Nat. Hazards Earth Syst. Sci., 23, 1967–1985, https://doi.org/10.5194/nhess-23-1967-2023, https://doi.org/10.5194/nhess-23-1967-2023, 2023
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High seawater levels co-occurring with high river discharges have the potential to cause destructive flooding. For the past decades, the number of such compound events was larger than expected by pure chance for most of the west-facing coasts in Europe. Additionally rivers with smaller catchments showed higher numbers. In most cases, such events were associated with a large-scale weather pattern characterized by westerly winds and strong rainfall.
Philipp de Vrese, Goran Georgievski, Jesus Fidel Gonzalez Rouco, Dirk Notz, Tobias Stacke, Norman Julius Steinert, Stiig Wilkenskjeld, and Victor Brovkin
The Cryosphere, 17, 2095–2118, https://doi.org/10.5194/tc-17-2095-2023, https://doi.org/10.5194/tc-17-2095-2023, 2023
Short summary
Short summary
The current generation of Earth system models exhibits large inter-model differences in the simulated climate of the Arctic and subarctic zone. We used an adapted version of the Max Planck Institute (MPI) Earth System Model to show that differences in the representation of the soil hydrology in permafrost-affected regions could help explain a large part of this inter-model spread and have pronounced impacts on important elements of Earth systems as far to the south as the tropics.
Manuel Schlund, Birgit Hassler, Axel Lauer, Bouwe Andela, Patrick Jöckel, Rémi Kazeroni, Saskia Loosveldt Tomas, Brian Medeiros, Valeriu Predoi, Stéphane Sénési, Jérôme Servonnat, Tobias Stacke, Javier Vegas-Regidor, Klaus Zimmermann, and Veronika Eyring
Geosci. Model Dev., 16, 315–333, https://doi.org/10.5194/gmd-16-315-2023, https://doi.org/10.5194/gmd-16-315-2023, 2023
Short summary
Short summary
The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for routine evaluation of Earth system models. Originally, ESMValTool was designed to process reformatted output provided by large model intercomparison projects like the Coupled Model Intercomparison Project (CMIP). Here, we describe a new extension of ESMValTool that allows for reading and processing native climate model output, i.e., data that have not been reformatted before.
Matthias Gröger, Christian Dieterich, Jari Haapala, Ha Thi Minh Ho-Hagemann, Stefan Hagemann, Jaromir Jakacki, Wilhelm May, H. E. Markus Meier, Paul A. Miller, Anna Rutgersson, and Lichuan Wu
Earth Syst. Dynam., 12, 939–973, https://doi.org/10.5194/esd-12-939-2021, https://doi.org/10.5194/esd-12-939-2021, 2021
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Regional climate studies are typically pursued by single Earth system component models (e.g., ocean models and atmosphere models). These models are driven by prescribed data which hamper the simulation of feedbacks between Earth system components. To overcome this, models were developed that interactively couple model components and allow an adequate simulation of Earth system interactions important for climate. This article reviews recent developments of such models for the Baltic Sea region.
Camelia-Eliza Telteu, Hannes Müller Schmied, Wim Thiery, Guoyong Leng, Peter Burek, Xingcai Liu, Julien Eric Stanislas Boulange, Lauren Seaby Andersen, Manolis Grillakis, Simon Newland Gosling, Yusuke Satoh, Oldrich Rakovec, Tobias Stacke, Jinfeng Chang, Niko Wanders, Harsh Lovekumar Shah, Tim Trautmann, Ganquan Mao, Naota Hanasaki, Aristeidis Koutroulis, Yadu Pokhrel, Luis Samaniego, Yoshihide Wada, Vimal Mishra, Junguo Liu, Petra Döll, Fang Zhao, Anne Gädeke, Sam S. Rabin, and Florian Herz
Geosci. Model Dev., 14, 3843–3878, https://doi.org/10.5194/gmd-14-3843-2021, https://doi.org/10.5194/gmd-14-3843-2021, 2021
Short summary
Short summary
We analyse water storage compartments, water flows, and human water use sectors included in 16 global water models that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b. We develop a standard writing style for the model equations. We conclude that even though hydrologic processes are often based on similar equations, in the end these equations have been adjusted, or the models have used different values for specific parameters or specific variables.
Philipp de Vrese, Tobias Stacke, Thomas Kleinen, and Victor Brovkin
The Cryosphere, 15, 1097–1130, https://doi.org/10.5194/tc-15-1097-2021, https://doi.org/10.5194/tc-15-1097-2021, 2021
Short summary
Short summary
With large amounts of carbon stored in frozen soils and a highly energy-limited vegetation the Arctic is very sensitive to changes in climate. Here our simulations with the land surface model JSBACH reveal a number of offsetting factors moderating the Arctic's net response to global warming. More importantly we find that the effects of climate change may not be fully reversible on decadal timescales, leading to substantially different CH4 emissions depending on whether the Arctic warms or cools.
Richard Essery, Hyungjun Kim, Libo Wang, Paul Bartlett, Aaron Boone, Claire Brutel-Vuilmet, Eleanor Burke, Matthias Cuntz, Bertrand Decharme, Emanuel Dutra, Xing Fang, Yeugeniy Gusev, Stefan Hagemann, Vanessa Haverd, Anna Kontu, Gerhard Krinner, Matthieu Lafaysse, Yves Lejeune, Thomas Marke, Danny Marks, Christoph Marty, Cecile B. Menard, Olga Nasonova, Tomoko Nitta, John Pomeroy, Gerd Schädler, Vladimir Semenov, Tatiana Smirnova, Sean Swenson, Dmitry Turkov, Nander Wever, and Hua Yuan
The Cryosphere, 14, 4687–4698, https://doi.org/10.5194/tc-14-4687-2020, https://doi.org/10.5194/tc-14-4687-2020, 2020
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Climate models are uncertain in predicting how warming changes snow cover. This paper compares 22 snow models with the same meteorological inputs. Predicted trends agree with observations at four snow research sites: winter snow cover does not start later, but snow now melts earlier in spring than in the 1980s at two of the sites. Cold regions where snow can last until late summer are predicted to be particularly sensitive to warming because the snow then melts faster at warmer times of year.
Cited articles
Alcamo, J., Döll, P., Kaspar, F., and Siebert, S.: Global change and global
scenarios of water use and availability: an application of WaterGAP 1.0,
Center for environmental systems research, University of Kassel, Kassel,
Germany, 1997. a
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration:
guidelines for computing crop water requirements, FAO Irrigation and Drainage
Paper No 56, FAO, Rome, Italy, p. 300, 1998. a
Amante, C. and Eakins, B.: ETOPO1 1 Arc-Minute Global Relief Model: Procedures,
Data Sources and Analysis,
technical Memorandum NESDIS NGDC-24, National Geophysical Data Center [data set], NOAA, https://doi.org/10.7289/V5C8276M,
2009. a, b, c, d
Arnold, J. G., Srinivasan, R., Muttiah, R. S., and Williams, J. R.: Large area
hydrologic modeling and assessment Part I: Model development, J. Am. Water Resour. As., 34,
73–89, https://doi.org/10.1111/j.1752-1688.1998.tb05961.x, 1998. a
Bauer, H., Heise, E., Pfaendtner, J., Renner, V., and Schmidt, P.: Entwicklung
und Erprobung eines ökonomischen Erdbodenmodells zur Vorhersage von
Oberflächenparametern im Rahmen eines Klimamodells, Tech. rep., final report for contract CLI-001-80-D (B), DWD,
Offenbach, Germany, 1983. a
Bergström, S.: The HBV model-its structure and applications, Tech. Rep. 4,
Swedish Meteorological and Hydrological Institute, Norrköping, Sweden,
1992. a
Bieger, K., Arnold, J. G., Rathjens, H., White, M. J., Bosch, D. D., Allen,
P. M., Volk, M., and Srinivasan, R.: Introduction to SWAT+, A Completely
Restructured Version of the Soil and Water Assessment Tool, J. Am. Water Resour. As., 53,
115–130, https://doi.org/10.1111/1752-1688.12482, 2016. a
Bierkens, M. F. P.: Global hydrology 2015: State, trends, and directions, Water
Resour. Res., 51, 4923–4947, https://doi.org/10.1002/2015wr017173, 2015. a, b
Dirmeyer, P. A., Gao, X., Zhao, M., Guo, Z., Oki, T., and Hanasaki, N.:
GSWP-2: Multimodel Analysis and Implications for Our Perception of the Land
Surface, B. Am. Meteorol. Soc., 87, 1381–1398,
https://doi.org/10.1175/bams-87-10-1381, 2006. a
Do, H. X., Zhao, F., Westra, S., Leonard, M., Gudmundsson, L., Boulange, J. E. S., Chang, J., Ciais, P., Gerten, D., Gosling, S. N., Müller Schmied, H., Stacke, T., Telteu, C.-E., and Wada, Y.: Historical and future changes in global flood magnitude – evidence from a model–observation investigation, Hydrol. Earth Syst. Sci., 24, 1543–1564, https://doi.org/10.5194/hess-24-1543-2020, 2020. a
Dümenil, L. and Todini, E.: Chapter 9 – A rainfall-runoff scheme for use in
the Hamburg climate model, in: Advances in Theoretical Hydrology, edited by:
O'Kane, J. P., European Geophysical Society Series on Hydrological Sciences,
Elsevier, Amsterdam, 129–157, https://doi.org/10.1016/b978-0-444-89831-9.50016-8,
1992. a
Forsythe, W. C., Rykiel, E. J., Stahl, R. S., Wu, H., and Schoolfield, R. M.:
A model comparison for daylength as a function of latitude and day of year,
Ecol. Modell., 80, 87–95, https://doi.org/10.1016/0304-3800(94)00034-f, 1995. a
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of
the mean squared error and NSE performance criteria: Implications for
improving hydrological modelling, J. Hydrol., 377, 80–91,
https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009. a
Gädeke, A., Krysanova, V., Aryal, A., Chang, J., Grillakis, M., Hanasaki, N.,
Koutroulis, A., Pokhrel, Y., Satoh, Y., Schaphoff, S., Schmied, H. M.,
Stacke, T., Tang, Q., Wada, Y., and Thonicke, K.: Performance evaluation of
global hydrological models in six large Pan-Arctic watersheds, Climatic Change,
163, 1329–1351, https://doi.org/10.1007/s10584-020-02892-2, 2020. a
Haddeland, I., Clark, D. B., Franssen, W., Ludwig, F., Voß, F., Arnell,
N. W., Bertrand, N., Best, M., Folwell, S., Gerten, D., Gomes, S., Gosling,
S. N., Hagemann, S., Hanasaki, N., Harding, R., Heinke, J., Kabat, P.,
Koirala, S., Oki, T., Polcher, J., Stacke, T., Viterbo, P., Weedon, G. P.,
and Yeh, P.: Multimodel Estimate of the Global Terrestrial Water Balance:
Setup and First Results, J. Hydrometeorol., 12, 869–884,
https://doi.org/10.1175/2011jhm1324.1, 2011. a, b
Haddeland, I., Heinke, J., Biemans, H., Eisner, S., Flörke, M., Hanasaki, N.,
Konzmann, M., Ludwig, F., Masaki, Y., Schewe, J., Stacke, T., Tessler, Z. D.,
Wada, Y., and Wisser, D.: Global water resources affected by human
interventions and climate change, P. Natl. Acad. Sci. USA, 111, 3251–3256,
https://doi.org/10.1073/pnas.1222475110, 2014. a
Hagemann, S. and Dümenil, L.: Documentation for the Hydrological Discharge
Model, Tech. Rep. 17, Deutsches Klimarechenzentrum, Hamburg, Germany, 1998. a
Hagemann, S. and Stacke, T.: Impact of the soil hydrology scheme on simulated
soil moisture memory, Clim. Dynam., 44, 1731–1750,
https://doi.org/10.1007/s00382-014-2221-6, 2015. a, b, c, d
Hagemann, S., Botzet, M., Dümenil, L., and Machenhauer, B.: Derivation of
global GCM boundary conditions from 1 km land use satellite data, Tech. Rep.
289, Max Planck Institute for Meteorology, Hamburg, Germany, 1999. a
Hagemann, S., Stacke, T., and Ho-Hagemann, H. T. M.: High Resolution Discharge
Simulations Over Europe and the Baltic Sea Catchment, Front. Earth Sci., 8, 12,
https://doi.org/10.3389/feart.2020.00012, 2020. a, b
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P.,
Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R.,
Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del
Río, J. F., Wiebe, M., Peterson, P., Gérard-Marchant, P.,
Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant,
T. E.: Array programming with NumPy, Nature, 585, 357–362,
https://doi.org/10.1038/s41586-020-2649-2, 2020. a
Hoyer, S. and Hamman, J. J.: xarray: N-D labeled Arrays and Datasets in Python,
J. Open Res. Softw., 5, 11, https://doi.org/10.5334/jors.148, 2017. a
Kleidon, A.: Global Datasets of Rooting Zone Depth Inferred from Inverse
Methods, J. Climate, 17, 2714–2722,
https://doi.org/10.1175/1520-0442(2004)017<2714:gdorzd>2.0.co;2, 2004. a
Knoben, W. J. M., Freer, J. E., and Woods, R. A.: Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores, Hydrol. Earth Syst. Sci., 23, 4323–4331, https://doi.org/10.5194/hess-23-4323-2019, 2019. a, b
Lehner, B. and Döll, P.: Development and validation of a global database of
lakes, reservoirs and wetlands, J. Hydrol., 296, 1–22,
https://doi.org/10.1016/j.jhydrol.2004.03.028, 2004. a, b, c, d
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,
https://doi.org/10.1029/94jd00483, 1994. a
Loveland, T. R., Reed, B. C., Brown, J. F., Ohlen, D. O., Zhu, Z., Yang, L.,
and Merchant, J. W.: Development of a global land cover characteristics
database and IGBP DISCover from 1 km AVHRR data, Int. J. Remote Sens.,
21, 1303–1330, https://doi.org/10.1080/014311600210191, 2000. a
Manabe, S.: Climate and the ocean circulation: I. The atmospheric circulation
and the hydrology of the earth's surface, Mon. Weather Rev., 97, 739–774,
https://doi.org/10.1175/1520-0493(1969)097<0739:catoc>2.3.co;2, 1969. a
Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017. a, b
Matthews, E. and Fung, I.: Methane emission from natural wetlands: Global
distribution, area, and environmental characteristics of sources, Global
Biogeochem. Cy., 1, 61–86, https://doi.org/10.1029/gb001i001p00061, 1987. a, b
Mbaye, M. L., Hagemann, S., Haensler, A., Stacke, T., Gaye, A. T., and Afouda,
A.: Assessment of Climate Change Impact on Water Resources in the Upper
Senegal Basin (West Africa), Am. J. Clim. Change, 4, 77–93,
https://doi.org/10.4236/ajcc.2015.41008, 2015. a
Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., and Dolman, A. J.: Global land-surface evaporation estimated from satellite-based observations, Hydrol. Earth Syst. Sci., 15, 453–469, https://doi.org/10.5194/hess-15-453-2011, 2011. a, b
Moriasi, D. N., Arnold, J. G., Liew, M. W. V., Bingner, R. L., Harmel, R. D.,
and Veith, T. L.: Model Evaluation Guidelines for Systematic Quantification
of Accuracy in Watershed Simulations, T. ASABE, 50, 885–900,
https://doi.org/10.13031/2013.23153, 2007. a
Müller, C., Schaphoff, S., von Bloh, W., Thonicke, K., and Gerten, D.:
Going open-source with a model dinosaur and establishing model evaluation
standards, EGU General Assembly Conference Abstracts, EGU2018-16172, EGU General Assembly, Vienna, 2018. a
Nossent, J. and Bauwens, W.: Optimising the convergence of a Sobol' sensitivity
analysis for an environmental model: application of an appropriate estimate
for the square of the expectation value and the total variance, in: 6th
International Congress on Environmental Modelling and Software, Conference
proceedings, Leipzig, Germany, 2012. a
Pokhrel, Y., Felfelani, F., Satoh, Y., Boulange, J., Burek, P., Gädeke, A.,
Gerten, D., Gosling, S. N., Grillakis, M., Gudmundsson, L., Hanasaki, N.,
Kim, H., Koutroulis, A., Liu, J., Papadimitriou, L., Schewe, J., Schmied,
H. M., Stacke, T., Telteu, C.-E., Thiery, W., Veldkamp, T., Zhao, F., and
Wada, Y.: Global terrestrial water storage and drought severity under climate
change, Nat. Clim. Change, 11, 226–233, https://doi.org/10.1038/s41558-020-00972-w, 2021. a
Prudhomme, C., Giuntoli, I., Robinson, E. L., Clark, D. B., Arnell, N. W.,
Dankers, R., Fekete, B. M., Franssen, W., Gerten, D., Gosling, S. N.,
Hagemann, S., Hannah, D. M., Kim, H., Masaki, Y., Satoh, Y., Stacke, T.,
Wada, Y., and Wisser, D.: Hydrological droughts in the 21st century, hotspots
and uncertainties from a global multimodel ensemble experiment, P. Natl.
Acad. Sci. USA, 111, 3262–3267, https://doi.org/10.1073/pnas.1222473110, 2013. a
Rasche, L., Schneider, U. A., Lobato, M. B., Diego, R. S. D., and Stacke, T.:
Benefits of Coordinated Water Resource System Planning in the Cauca-Magdalena
River Basin, Water Econ. Policy, 4, 1, https://doi.org/10.1142/s2382624x1650034x, 2018. a
Roeckner, E., Arpe, K., Bengtsson, L., Brinkop, S., Dümenil, L., Esch, M.,
Kirk, E., Lunkeit, F., Ponater, M., Rockel, B., Sausen, R., Schlese, U.,
Schubert, S., and Windelband, M.: Simulation of the present-day climate with
the ECHAM model: impact of model physics and resolution, Tech. Rep. 93,
Max-Planck-Institute for Meteorology, Hamburg, Germany, 1992. a
Roeckner, E., Arpe, K., Bengtsson, L., Christoph, M., Claussen, M.,
Dümenil, L., Esch, M., Giorgetta, M. A., Schlese, U., and Schulzweida,
U.: The atmospheric general circulation model ECHAM-4: Model description and
simulation of present-day climate, Tech. Rep. 218, Max-Planck-Institute for
Meteorology, Hamburg, Germany, 1996. a
Schewe, J., Heinke, J., Gerten, D., Haddeland, I., Arnell, N. W., Clark, D. B.,
Dankers, R., Eisner, S., Fekete, B. M., Colón-González, F. J.,
Gosling, S. N., Kim, H., Liu, X., Masaki, Y., Portmann, F. T., Satoh, Y.,
Stacke, T., Tang, Q., Wada, Y., Wisser, D., Albrecht, T., Frieler, K.,
Piontek, F., Warszawski, L., and Kabat, P.: Multimodel assessment of water
scarcity under climate change, P. Natl. Acad. Sci. USA, 111, 3245–3250,
https://doi.org/10.1073/pnas.1222460110, 2013. a
Stacke, T. and Hagemann, S.: Development and evaluation of a global dynamical wetlands extent scheme, Hydrol. Earth Syst. Sci., 16, 2915–2933, https://doi.org/10.5194/hess-16-2915-2012, 2012. a, b
Stacke, T. and Hagemann, S.: Land surface parameter fields at 0.5deg
resolution for use with the HydroPy model, Zenodo, https://doi.org/10.5281/zenodo.4541239 [data set],
2021a. a, b
Stacke, T. and Hagemann, S.: Source code for the global hydrological model
HydroPy, Zenodo [code], https://doi.org/10.5281/zenodo.4541380, 2021b. a, b, c
Stacke, T. and Hagemann, S.: HydroPy and MPI-HM simulation data driven with GSWP3 meteorological forcing, World Data Center for Climate (WDCC) at DKRZ [data set], https://doi.org/10.26050/WDCC/HydroPy_MPI-HM_hist_sim, 2021c. a
Sutanudjaja, E. H., van Beek, R., Wanders, N., Wada, Y., Bosmans, J. H. C., Drost, N., van der Ent, R. J., de Graaf, I. E. M., Hoch, J. M., de Jong, K., Karssenberg, D., López López, P., Peßenteiner, S., Schmitz, O., Straatsma, M. W., Vannametee, E., Wisser, D., and Bierkens, M. F. P.: PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model, Geosci. Model Dev., 11, 2429–2453, https://doi.org/10.5194/gmd-11-2429-2018, 2018.
a
Telteu, C.-E., Müller Schmied, H., Thiery, W., Leng, G., Burek, P., Liu, X., Boulange, J. E. S., Andersen, L. S., Grillakis, M., Gosling, S. N., Satoh, Y., Rakovec, O., Stacke, T., Chang, J., Wanders, N., Shah, H. L., Trautmann, T., Mao, G., Hanasaki, N., Koutroulis, A., Pokhrel, Y., Samaniego, L., Wada, Y., Mishra, V., Liu, J., Döll, P., Zhao, F., Gädeke, A., Rabin, S. S., and Herz, F.: Understanding each other's models: an introduction and a standard representation of 16 global water models to support intercomparison, improvement, and communication, Geosci. Model Dev., 14, 3843–3878, https://doi.org/10.5194/gmd-14-3843-2021, 2021. a
Wada, Y., Wisser, D., Eisner, S., Flörke, M., Gerten, D., Haddeland, I.,
Hanasaki, N., Masaki, Y., Portmann, F. T., Stacke, T., Tessler, Z., and
Schewe, J.: Multimodel projections and uncertainties of irrigation water
demand under climate change, Geophys. Res. Lett., 40, 4626–4632,
https://doi.org/10.1002/grl.50686, 2013. a
Warrilow, D. A., Sangster, A. B., and Slingo, A.: Modelling of landsurface
processes and their influence on European climate, Met O 20 Tech Note
DCTN 38, Meteorological Office, Bracknell, UK, 1986. a
Weiland, F. S., Lopez, P., Van Dijk, A., and Schellekens, J.: Global
high-resolution reference potential evaporation, in: 21st International
Congress on Modelling and Simulation, Conference Proceedings, Broadbeach,
Queensland, Australia, 2015. a
Wigmosta, M. S., Vail, L. W., and Lettenmaier, D. P.: A distributed
hydrology-vegetation model for complex terrain, Water Resour. Res., 30,
1665–1679, https://doi.org/10.1029/94wr00436, 1994. a, b
Zhang, L., Dobslaw, H., Stacke, T., Güntner, A., Dill, R., and Thomas, M.: Validation of terrestrial water storage variations as simulated by different global numerical models with GRACE satellite observations, Hydrol. Earth Syst. Sci., 21, 821–837, https://doi.org/10.5194/hess-21-821-2017, 2017. a
Zhao, F., Veldkamp, T. I. E., Frieler, K., Schewe, J., Ostberg, S., Willner,
S., Schauberger, B., Gosling, S. N., Schmied, H. M., Portmann, F. T., Leng,
G., Huang, M., Liu, X., Tang, Q., Hanasaki, N., Biemans, H., Gerten, D.,
Satoh, Y., Pokhrel, Y., Stacke, T., Ciais, P., Chang, J., Ducharne, A.,
Guimberteau, M., Wada, Y., Kim, H., and Yamazaki, D.: The critical role of
the routing scheme in simulating peak river discharge in global hydrological
models, Environ. Res. Lett., 12, 7, https://doi.org/10.1088/1748-9326/aa7250, 2017. a
Short summary
HydroPy is a new version of an established global hydrology model. It was rewritten from scratch and adapted to a modern object-oriented infrastructure to facilitate its future development and application. With this study, we provide a thorough documentation and evaluation of our new model. At the same time, we open our code base and publish the model's source code in a public software repository. In this way, we aim to contribute to increasing transparency and reproducibility in science.
HydroPy is a new version of an established global hydrology model. It was rewritten from scratch...