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The floods are extreme hydrological events that require rigorous analysis to achieve structural and non – structural measures to mitigate their risk. This paper aims to analyze the floods’ potential in the Jiu River Catchment (the catchment area has 10131 km²). The main geographical factors which are favoring the floods production are the climatic conditions and the morphometric factors. The monthly and annual maximum discharge recorded at 18 hydrometric stations, have been analyzed for the interval 1982 – 2012. In this paper the frequency of the annual floods was determined and the features of 180 floods have been estimated. The used methods are statistical analysis and spatial analysis using GIS. The peak discharges recorded at the studied hydrometric station ranged from 34.2 m³/s to 1220 m³/s. These exceeded the estimated values of the maximum discharge with the probabilities of 1% (Broşteni, Teleşti and Corcova hydrometric stations), 3% (Sadu, Runcu, Stolojani, Târmigani, Strehaia and Breasta hydrometric stations), 5% (Iscroni, Rovinari, Podari, Zăval, Turburea and Turceni hydrometric stations) and 10% (Filiaşi, Răcari and Celei hydrometric stations). The annual floods occurred predominantly in the spring and are the result of the abundant rainfall associated with snowmelt.

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. [ABSTRACT FROM AUTHOR]

WaterGAP is a global hydrological model that quantifies human use of groundwater and surface water as well as water flows and water storage and thus water resources on all land areas of the Earth. Since 1996, it has served to assess water resources and water stress both historically and in the future, in particular under climate change. It has improved our understanding of continental water storage variations, with a focus on overexploitation and depletion of water resources. In this paper, we describe the most recent model version WaterGAP 2.2d, including the water use models, the linking model that computes net abstractions from groundwater and surface water and the WaterGAP Global Hydrology Model (WGHM). Standard model output variables that are freely available at a data repository are explained. In addition, the most requested model outputs, total water storage anomalies, streamflow and water use, are evaluated against observation data. Finally, we show examples of assessments of the global freshwater system that can be achieved with WaterGAP 2.2d model output. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

WaterGAP is a global hydrological model that quantifies human use of groundwater and surface water as well as water flows and water storage and thus water resources on all land areas of the Earth. Since 1996, it has served to assess water resources and water stress both historically and in the future, in particular under climate change. It has improved our understanding of continental water storage variations, with a focus on overexploitation and depletion of water resources. In this paper, we describe the most recent model version WaterGAP 2.2d, including the water use models, the linking model that computes net abstractions from groundwater and surface water and the WaterGAP Global Hydrology Model WGHM. Standard model output variables that are freely available at a data repository are explained. In addition, the most requested model outputs, total water storage anomalies, streamflow and water use, are evaluated against observation data. Finally, we show examples of assessments of the global freshwater system that can be done with WaterGAP2.2d model output. [ABSTRACT FROM AUTHOR]

To improve the understanding of trends in extreme flows related to flood events at the global scale, historical and future changes of annual maxima of 7 d streamflow are investigated, using a comprehensive streamflow archive and six global hydrological models. The models' capacity to characterise trends in annual maxima of 7 d streamflow at the continental and global scale is evaluated across 3666 river gauge locations over the period from 1971 to 2005, focusing on four aspects of trends: (i) mean, (ii) standard deviation, (iii) percentage of locations showing significant trends and (iv) spatial pattern. Compared to observed trends, simulated trends driven by observed climate forcing generally have a higher mean, lower spread and a similar percentage of locations showing significant trends. Models show a low to moderate capacity to simulate spatial patterns of historical trends, with approximately only from 12 % to 25 % of the spatial variance of observed trends across all gauge stations accounted for by the simulations. Interestingly, there are statistically significant differences between trends simulated by global hydrological models (GHMs) forced with observational climate and by those forced by bias-corrected climate model output during the historical period, suggesting the important role of the stochastic natural (decadal, inter-annual) climate variability. Significant differences were found in simulated flood trends when averaged only at gauged locations compared to those averaged across all simulated grid cells, highlighting the potential for bias toward well-observed regions in our understanding of changes in floods. Future climate projections (simulated under the RCP2.6 and RCP6.0 greenhouse gas concentration scenarios) suggest a potentially high level of change in individual regions, with up to 35 % of cells showing a statistically significant trend (increase or decrease; at 10 % significance level) and greater changes indicated for the higher concentration pathway. Importantly, the observed streamflow database under-samples the percentage of locations consistently projected with increased flood hazards under the RCP6.0 greenhouse gas concentration scenario by more than an order of magnitude (0.9 % compared to 11.7 %). This finding indicates a highly uncertain future for both flood-prone communities and decision makers in the context of climate change. [ABSTRACT FROM AUTHOR]

Multi-model ensembles have become a standard tool for assessing global climate change impacts. Interpretation of such ensembles is complicated because each model group has a different modeling concept and framework. For example, global scale land surface, water and vegetation models have been widely applied to understand the complex hydrological cycle of the Earth and to assess associated past and future changes. Additionally to this purpose, land surface models assess energy and biogeochemical cycles while vegetation models assess vegetation and carbon cycles. Therefore, all these models differ with respect to the specific processes of the hydrological cycle included in their structure. In this study, we demonstrate how the similarities and differences between models can be better understood and illustrated by using a standard representation of the main model features. We analyze twelve models from the global water sector of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) phase 2b: six land surface models (LSMs), five global hydrological models (GHMs) and one dynamic global vegetation model (DGVM). The majority of the models are run with a daily temporal resolution and with a spatial resolution of 0.5°. Part of these models include a reservoir scheme and water use sectors. The heuristic mappings of the models are designed to ensure the opportunity to choose a model at the initial stage of the analysis, based on the most important qualities, relationships and characteristics, which provide users with significant time saving. Therefore, the review study will provide the basis for: (i) achieving further model (inter)comparison; (ii) selecting the right model(s) output(s) for specific applications; and (iii) assessing the similarities and differences among the models. The models characteristics will be presented in three levels of complexity allowing to reach a large audience. The target audience includes the modeling community, the stakeholder community, and the general public interested in understanding large-scale models, simulating climate change and its impacts. Additionally, stakeholder insights, gathered mostly in Eastern Europe and West Africa, have been considered in the study design. Stakeholders were identified according to their need for climate-impact information provided within the ISIMIP framework and included academics, government officials, employees working in international organizations, NGOs, consultancies, and private companies. In conclusion, the presentation describes the study approach and preliminary results, with particular emphasis on the standard model diagram, differences between the models, and the stakeholder engagement. [ABSTRACT FROM AUTHOR]