Abstract. Over the past decade global flood hazard models have been developed and continuously improved. There is now a significant demand for testing global hazard maps generated by these models in order to understand their applicability for international risk reduction strategies and for reinsurance portfolio risk assessments using catastrophe models. We expand on existing methods for comparing global hazard maps and analyse eight global flood models (GFMs) that represent the current state of the global flood modelling community. We apply our comparison to China as a case study and, for the first time, include industry models, pluvial flooding, and flood protection standards in the analysis. In doing so, we provide new insights into how these components change the results of this comparison. We find substantial variability, up to a factor of 4, between the flood hazard maps in the modelled inundated area and exposed gross domestic product (GDP) across multiple return periods (ranging from 5 to 1500 years) and in expected annual exposed GDP. The inclusion of industry models, which currently model flooding at a higher spatial resolution and which additionally include pluvial flooding, strongly improves the comparison and provides important new benchmarks. We find that the addition of pluvial flooding can increase the expected annual exposed GDP by as much as 1.3 percentage points. Our findings strongly highlight the importance of flood defences for a realistic risk assessment in countries like China that are characterized by high concentrations of exposure. Even an incomplete (1.74 % of the area of China) but locally detailed layer of structural defences in high-exposure areas reduces the expected annual exposed GDP to fluvial and pluvial flooding from 4.1 % to 2.8 %.
Abstract. Distributed hydrological modelling moves into the realm of hyper-resolution modelling. This results in a plethora of scaling-related challenges that remain unsolved. To the user, in light of model result interpretation, finer-resolution output might imply an increase in understanding of the complex interplay of heterogeneity within the hydrological system. Here we investigate spatial scaling in the form of varying spatial resolution by evaluating the streamflow estimates of the distributed wflow_sbm hydrological model based on 454 basins from the large-sample CAMELS data set. Model instances are derived at three spatial resolutions, namely 3 km, 1 km, and 200 m. The results show that a finer spatial resolution does not necessarily lead to better streamflow estimates at the basin outlet. Statistical testing of the objective function distributions (Kling–Gupta efficiency (KGE) score) of the three model instances resulted in only a statistical difference between the 3 km and 200 m streamflow estimates. However, an assessment of sampling uncertainty shows high uncertainties surrounding the KGE score throughout the domain. This makes the conclusion based on the statistical testing inconclusive. The results do indicate strong locality in the differences between model instances expressed by differences in KGE scores of on average 0.22 with values larger than 0.5. The results of this study open up research paths that can investigate the changes in flux and state partitioning due to spatial scaling. This will help to further understand the challenges that need to be resolved for hyper-resolution hydrological modelling.
Abstract. Hutton (2016) argued that computational hydrology can only be a proper science if the hydrological community makes sure that hydrological model studies are executed and presented in a reproducible manner. We replied that to achieve this, hydrologists shouldn't ‘re-invent the water wheel’ but rather use existing technology from other fields (such as containers and ESMValTool) and open interfaces (such as BMI) to do their computational science (Hut, 2017). With this paper and the associated release of the eWaterCycle platform and software package1 we are putting our money where our mouth is and provide the hydrological community with a ‘FAIR by design’ platform to do our science. eWaterCycle is a platform that separates the experiment done on the model from the model code. In eWaterCycle hydrological models are accessed through a common interface (BMI) in Python and run inside of software containers. In this way all models are accessed in a similar manner facilitating easy switching of models, model comparison and model coupling. Currently the following models are available through eWaterCycle: PCR-GLOBWB 2.0, wflow, Hype, LISFLOOD, TopoFlex HBV, MARRMoT and WALRUS. While these models are written in different programming languages they can all be run and interacted with from the Jupyter notebook environment within eWaterCycle. Furthermore, the pre-processing of input data for these models has been streamlined by making use of ESMValTool. Forcing for the models available in eWaterCycle from well known datasets such as ERA5 can be generated with a single line of code. To illustrate the type of research that eWaterCycle facilitates this manuscript includes five case studies: from a simple ‘Hello World’ where only a hydrograph is generated to a complex coupling of models in different languages. In this manuscript we stipulate the design choices made in building eWaterCycle and provide all the technical details to understand and work with the platform. For system administrators who want to install eWaterCycle on their infrastructure we offer a separate installation guide. For computational hydologist who want to work with eWaterCycle we also provide a video explaining the platform from a users point of view. With the eWaterCycle platform we are providing the hydrological community with a platform to conduct their research fully compatible with the principles of Open Science as well as FAIR science.1available on Zenodo: doi.org/10.5281/zenodo.5119389
<p>The release of the European Centre for Medium-Range Weather Forecasts (ECMWF)&#8217;s Re-Analysis 5 (ERA-5) global climate forcing dataset is expected to greatly improve the quality of hydrological modeling. Following this release there is great interest in assessing the improvements of ERA-5 relative to its predecessor ERA-Interim for hydrological modeling and predictions.</p><p>In this study we compare streamflow predictions when using ERA-interim vs ERA-5 as forcing data for a suite of hydrological models from different research groups that capture the variation in modelling strategies within the hydrological modelling community. We check whether physically based models, defined as those that do not require additional parameter calibration, would lead to different conclusions in comparison to conceptual models, defined as those that require calibration. Based on the hydrological model structure we expect that conceptual models that need calibration show less difference in predicting discharge (skill) between ERA-5 and ERA-Interim, where-as the physical based (non-calibrated) models most likely will benefit from the improved accuracy of the ERA-5 input. This assessment will provide the HEPEX community with answers on how the ERA-5 dataset will improve hydrological predictions based on different hydrological modelling concepts.</p><p>An additional key objective while conducting this study is compliance to the FAIR principles of data science. To achieve this we held a workshop in Leiden, the Netherlands, where multiple hydrological models were integrated into the eWatercycle II system. eWatercycle II is a hydrological model platform containing a growing number of hydrological models. The platform facilitates research and cohesivity within the hydrological community by providing an Open-Source platform built specifically to advance the state of FAIR and Open Science in Hydrological Modeling. We also use this study to demonstrate the feasibility of eWatercycle II as a platform for FAIR hydrological models.</p><p>Preliminairy results from this comparison study were presented at the AGU Fall Meeting 2019. Here we will present the full results of the comparison study.</p>
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