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Author: Rakovec, Oldrich ×

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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
    (Katlenburg-Lindau : Copernicus, 2021-6-24) Telteu, Camelia-Eliza; Müller Schmied, Hannes; Thiery, Wim; Leng, Guoyong; Burek, Peter; Liu, Xingcai; Boulange, Julien Eric Stanislas; Andersen, Lauren Seaby; Grillakis, Manolis; Gosling, Simon Newland; Satoh, Yusuke; Rakovec, Oldrich; Stacke, Tobias; Chang, Jinfeng; Wanders, Niko; Shah, Harsh Lovekumar; Trautmann, Tim; Mao, Ganquan; Hanasaki, Naota; Koutroulis, Aristeidis; Pokhrel, Yadu; Samaniego, Luis; Wada, Yoshihide; Mishra, Vimal; Liu, Junguo; Döll, Petra; Zhao, Fang; Gädeke, Anne; Rabin, Sam S.; Herz, Florian
    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 modelling 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 16 state-of-the-art GWMs are designed. We analyse water storage compartments, water flows, and human water use sectors included in models 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 enhance model intercomparison, improvement, and communication. In this study, WaterGAP2 used the highest number of water storage compartments, 11, and CWatM used 10 compartments. Six models used six compartments, while four models (DBH, 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 for the irrigation sector. We conclude that, even though hydrological processes are often based on similar equations for various processes, in the end these equations have been adjusted or models have used different values for specific parameters or specific variables. The similarities and differences found among the models analysed in this study are expected to enable us to reduce the uncertainty in multi-model ensembles, improve existing hydrological processes, and integrate new processes.
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    Multimodel assessment of flood characteristics in four large river basins at global warming of 1.5, 2.0 and 3.0 K above the pre-industrial level
    (Bristol : IOP Publ., 2018) Huang, Shaochun; Kumar, Rohini; Rakovec, Oldrich; Aich, Valentin; Wang, Xiaoyan; Samaniego, Luis; Liersch, Stefan; Krysanova, Valentina
    This study assesses the flood characteristics (timing, magnitude and frequency) in the pre-industrial and historical periods, and analyzes climate change impacts on floods at the warming levels of 1.5, 2.0 and 3.0 K above the pre-industrial level in four large river basins as required by the Paris agreement. Three well-established hydrological models (HMs) were forced with bias-corrected outputs from four global climate models (GCMs) for the pre-industrial, historical and future periods until 2100. The long pre-industrial and historical periods were subdivided into multiple 31-year subperiods to investigate the natural variability. The mean flood characteristics in the pre-industrial period were derived from the large ensemble based on all GCMs, HMs and 31-year subperiods, and compared to the ensemble means in the historical and future periods. In general, the variance of simulated flood characteristics is quite large in the pre-industrial and historical periods. Mostly GCMs and HMs contribute to the variance, especially for flood timing and magnitude, while the selection of 31-year subperiods is an important source of variance for flood frequency. The comparison between the ensemble means shows that there are already some changes in flood characteristics between the pre-industrial and historical periods. There is a clear shift towards earlier flooding for the Rhine (1.5 K scenario) and Upper Mississippi (3.0 K scenario). The flood magnitudes show a substantial increase in the Rhine and Upper Yellow only under the 3.0 K scenario. The floods are projected to occur more frequently in the Rhine under the 1.5 and 2.0 K scenarios, and less frequently in the Upper Mississippi under all scenarios.