応用水文 = Applied hydrology

. Framed within the Copernicus Climate Change Service (C3S) of the European Commission,
the European Centre for Medium-Range Weather Forecasts (ECMWF) is producing an enhanced global dataset for the land component of the fifth generation of European ReAnalysis (ERA5), hereafter referred to as ERA5-Land. Once completed, the period covered will span from 1950 to the present, with continuous updates to support land monitoring applications. ERA5-Land describes the evolution of the water and energy cycles over land in a consistent manner over the production period, which, among others, could be used to analyse trends and anomalies.
This is achieved through global high-resolution numerical integrations of the ECMWF land surface model driven by the downscaled meteorological forcing from the ERA5 climate reanalysis, including an elevation correction for the thermodynamic near-surface state. ERA5-Land shares with ERA5
most of the parameterizations that guarantees the use of the state-of-the-art land surface modelling applied to numerical weather prediction (NWP) models.
A main advantage of ERA5-Land compared to ERA5 and the older ERA-Interim is the horizontal resolution, which is enhanced globally to 9 km compared to 31 km (ERA5) or 80 km (ERA-Interim), whereas the temporal resolution
is hourly as in ERA5. Evaluation against independent in situ observations
and global model or satellite-based reference datasets shows the added value
of ERA5-Land in the description of the hydrological cycle, in particular
with enhanced soil moisture and lake description, and an overall better agreement of
river discharge estimations with available observations. However, ERA5-Land snow depth fields present a mixed performance when compared to those of ERA5, depending on geographical location and altitude.
The description of the
energy cycle shows comparable results with ERA5. Nevertheless, ERA5-Land reduces the global averaged root mean square error of the skin temperature, taking as
reference MODIS data, mainly due to the contribution of
coastal points where spatial resolution is important.
Since January 2020, the ERA5-Land period available has extended from January 1981 to the near present, with a
2- to 3-month delay with respect to real time. The segment prior to 1981 is in production, aiming for a release of the whole dataset in summer/autumn 2021.
The high spatial and temporal resolution of ERA5-Land, its extended period, and the consistency of the fields produced makes it a valuable dataset to support hydrological studies,
to initialize NWP and climate models,
and to support diverse applications dealing with water resource, land, and environmental management. The full ERA5-Land hourly ( Munoz-Sabater ,  2019 a ) and monthly ( Munoz-Sabater ,  2019 b ) averaged datasets presented in this paper are available through the C3S Climate Data Store at https://doi.org/10.24381/cds.e2161bac and https://doi.org/10.24381/cds.68d2bb30 , respectively.

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ERA5-Land: a state-of-the-art global reanalysis dataset for land applications

Rising global temperature has put increasing pressure on understanding the linkage between atmospheric warming and the occurrence of natural hazards. While the Paris Agreement has set the ambitious target to limiting global warming to 1.5°C compared to preindustrial levels, scientists are urged to explore scenarios for different warming thresholds and quantify ranges of socioeconomic impact. In this work, we present a framework to estimate the economic damage and population affected by river floods at global scale. It is based on a modeling cascade involving hydrological, hydraulic and socioeconomic impact simulations, and makes use of state-of-the-art global layers of hazard, exposure and vulnerability at 1-km grid resolution. An ensemble of seven high-resolution global climate projections based on Representative Concentration Pathways 8.5 is used to derive streamflow simulations in the present and in the future climate. Those were analyzed to assess the frequency and magnitude of river floods and their impacts under scenarios corresponding to 1.5°C, 2°C, and 4°C global warming. Results indicate a clear positive correlation between atmospheric warming and future flood risk at global scale. At 4°C global warming, countries representing more than 70% of the global population and global gross domestic product will face increases in flood risk in excess of 500%. Changes in flood risk are unevenly distributed, with the largest increases in Asia, U.S., and Europe. In contrast, changes are statistically not significant in most countries in Africa and Oceania for all considered warming levels.

Global projections of river flood risk in a warmer world

An assessment of economic flood risk trends across Europe reveals high current and future stress on risk financing schemes. The magnitude and distribution of losses can be contained by investing in flood protection, increasing insurance coverage or by expanding public compensation funds. However, these climate change adaptation instruments have vastly different efficiency, equity and acceptability implications. Moreover, the spatial variation in disaster risk can necessitate cross-subsidies between individual countries in the European Union.

Increasing stress on disaster-risk finance due to large floods

. This paper presents the development of the European Flood Alert System (EFAS), which aims at increasing preparedness for floods in trans-national European river basins by providing local water authorities with medium-range and probabilistic flood forecasting information 3 to 10 days in advance. The EFAS research project started in 2003 with the development of a prototype at the European Commission Joint Research Centre (JRC), in close collaboration with the national hydrological and meteorological services. The prototype covers the whole of Europe on a 5 km grid. In parallel, different high-resolution data sets have been collected for the Elbe and Danube river basins, allowing the potential of the system under optimum conditions and on a higher resolution to be assessed. Flood warning lead-times of 3–10 days are achieved through the incorporation of medium-range weather forecasts from the German Weather Service (DWD) and the European Centre for Medium-Range Weather Forecasts (ECMWF), comprising a full set of 51 probabilistic forecasts from the Ensemble Prediction System (EPS) provided by ECMWF. The ensemble of different hydrographs is analysed and combined to produce early flood warning information, which is disseminated to the hydrological services that have agreed to participate in the development of the system. In Part 1 of this paper, the scientific approach adopted in the development of the system is presented. The rational of the project, the system�s set-up, its underlying components, basic principles and products are described. In Part 2, results of a detailed statistical analysis of the performance of the system are shown, with regard to both probabilistic and deterministic forecasts.

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The European Flood Alert System – Part 1: Concept and development

In this paper we describe the spatially distributed LISFLOOD model, which is a hydrological model specifically developed for the simulation of hydrological processes in large European river basins. The model was designed to make the best possible use of existing data sets on soils, land cover, topography and meteorology. We give a detailed description of the simulation of hydrological processes in LISFLOOD, and discuss how the model is parameterized. We also describe how the model was implemented technically using a combination of the PCRaster GIS system and the Python programming language, and discuss the management of in-and output data. Finally, we review some recent applications of LISFLOOD, and we present a case study for the Elbe river.

J. van der Knijff

Papers

LISFLOOD: a GIS-based distributed model for river basin scale water balance and flood simulation

Assessing flood damages of the 1997 Oder flood and the 1995 Meuse flood

Using the LISFLOOD model to simulate floods in the Oder and the Meuse catchments

Parameter identification and uncertainty estimation for a spatially distributed rainfall-runoff model using global optimization