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Understanding each other's models: an introduction and a standard representation of 16 global water models to support intercomparison, improvement, and communication

Urheber*innen

Telteu,  Camelia-Eliza
External Organizations;

Müller Schmied,  Hannes
External Organizations;

Thiery,  Wim
External Organizations;

Leng,  Guoyong
External Organizations;

Burek,  Peter
External Organizations;

Liu,  Xingcai
External Organizations;

Boulange,  Julien Eric Stanislas
External Organizations;

/persons/resource/andersen

Andersen,  Lauren
Potsdam Institute for Climate Impact Research;

Grillakis,  Manolis
External Organizations;

Gosling,  Simon Newland
External Organizations;

Satoh,  Yusuke
External Organizations;

Rakovec,  Oldrich
External Organizations;

Stacke,  Tobias
External Organizations;

Chang,  Jinfeng
External Organizations;

Wanders,  Niko
External Organizations;

Shah,  Harsh Lovekumar
External Organizations;

Trautmann,  Tim
External Organizations;

Mao,  Ganquan
External Organizations;

Hanasaki,  Naota
External Organizations;

Koutroulis,  Aristeidis
External Organizations;

Pokhrel,  Yadu
External Organizations;

Samaniego,  Luis
External Organizations;

Wada,  Yoshihide
External Organizations;

Mishra,  Vimal
External Organizations;

Liu,  Junguo
External Organizations;

Döll,  Petra
External Organizations;

Zhao,  Fang
External Organizations;

/persons/resource/Anne.Gaedeke

Gädeke,  Anne
Potsdam Institute for Climate Impact Research;

Rabin,  Sam S.
External Organizations;

Herz,  Florian
External Organizations;

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25619oa.pdf
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Zitation

Telteu, C.-E., Müller Schmied, H., Thiery, W., Leng, G., Burek, P., Liu, X., Boulange, J. E. S., Andersen, L., 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., Herz, F. (2021): Understanding each other's models: an introduction and a standard representation of 16 global water models to support intercomparison, improvement, and communication. - Geoscientific Model Development, 14, 6, 3843-3878.
https://doi.org/10.5194/gmd-14-3843-2021


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_25619
Zusammenfassung
Global water models (GWMs) simulate the terrestrial water cycle, on the global scale, and are used to assess the impacts of climate change on freshwater systems. GWMs are developed within different modeling frameworks and consider different underlying hydrological processes, leading to varied model structures. Furthermore, the equations used to describe various processes take different forms and are generally accessible only from within the individual model codes. These factors have hindered a holistic and detailed understanding of how different models operate, yet such an understanding is crucial for explaining the results of model evaluation studies, understanding inter-model differences in their simulations, and identifying areas for future model development. This study provides a comprehensive overview of how state-of-the-art GWMs are designed. We analyze water storage compartments, water flows, and human water use sectors included in 16 GWMs that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b). We develop a standard writing style for the model equations to further enhance model improvement, intercomparison, and communication. In this study, WaterGAP2 used the highest number of water storage compartments, 11, and CWatM used 10 compartments. Seven models used six compartments, while three models (JULES-W1, Mac-PDM.20, and VIC) used the lowest number, three compartments. WaterGAP2 simulates five human water use sectors, while four models (CLM4.5, CLM5.0, LPJmL, and MPI-HM) simulate only water used by humans for the irrigation sector. We conclude that even though hydrologic processes are often based on similar equations, in the end, these equations have been adjusted or have used different values for specific parameters or specific variables. Our results highlight that the predictive uncertainty of GWMs can be reduced through improvements of the existing hydrologic processes, implementation of new processes in the models, and high-quality input data.