GloFAS – global ensemble streamflow forecasting and flood early warning

Anticipation and preparedness for large-scale flood events have a key role in mitigating their impact and optimizing the strategic planning of water resources. Although several developed countries have well-established systems for river monitoring and flood early warning, figures of population affected every year by floods in developing countries are unsettling. This paper presents the Global Flood Awareness System, which has been set up to provide an overview on upcoming floods in large world river basins. The Global Flood Awareness System is based on distributed hydrological simulation of numerical ensemble weather predictions with global coverage. Streamflow forecasts are compared statistically to climatological simulations to detect probabilistic exceedance of warning thresholds. In this article, the system setup is described, together with an evaluation of its performance over a two-year test period and a qualitative analysis of a case study for the Pakistan flood, in summer 2010. It is shown that hazardous events in large river basins can be skilfully detected with a forecast horizon of up to 1 month. In addition, results suggest that an accurate simulation of initial model conditions and an improved parameterization of the hydrological model are key components to reproduce accurately the streamflow variability in the many different runoff regimes of the Earth.JRC.H.7-Climate Risk Managemen

Skilful seasonal forecasts of streamflow over Europe?

This paper considers whether there is any added value in using seasonal climate forecasts instead of historical meteorological observations for forecasting streamflow on seasonal timescales over Europe. A Europe-wide analysis of the skill of the newly operational EFAS (European Flood Awareness System) seasonal streamflow forecasts (produced by forcing the Lisflood model with the ECMWF System 4 seasonal climate forecasts), benchmarked against the Ensemble Streamflow Prediction (ESP) forecasting approach (produced by forcing the Lisflood model with historical meteorological observations), is undertaken. The results suggest that, on average, the System 4 seasonal climate forecasts improve the streamflow predictability over historical meteorological observations for the first month of lead time only (in terms of hindcast accuracy, sharpness and overall performance). However, the predictability varies in space and time and is greater in winter and autumn. Parts of Europe additionally exhibit a longer predictability, up to seven months of lead time, for certain months within a season. In terms of hindcast reliability, the EFAS seasonal streamflow hindcasts are on average less skilful than the ESP for all lead times. The results also highlight the potential usefulness of the EFAS seasonal streamflow forecasts for decision-making (measured in terms of the hindcast discrimination for the lower and upper terciles of the simulated streamflow). Although the ESP is the most potentially useful forecasting approach in Europe, the EFAS seasonal streamflow forecasts appear more potentially useful than the ESP in some regions and for certain seasons, especially in winter for almost 40% of Europe. Patterns in the EFAS seasonal streamflow hindcasts skill are however not mirrored in the System 4 seasonal climate hindcasts, hinting the need for a better understanding of the link between hydrological and meteorological variables on seasonal timescales, with the aim to improve climate-model based seasonal streamflow forecasting

EFAS upgrade for the extended model domain

This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. It aims to provide evidence-based scientific support to the European policymaking process. The scientific output expressed does not imply a policy position of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use that might be made of this publication.JRC.E.1-Disaster Risk Managemen

EFAS upgrade for the extended model domain

This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. It aims to provide evidence-based scientific support to the European policymaking process. The scientific output expressed does not imply a policy position of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use that might be made of this publication.JRC.E.1-Disaster Risk Managemen

A 1980-2018 global fire danger re-analysis dataset for the Canadian Fire Weather Indices

This data descriptor documents a dataset containing over 38 years of global reanalysis of wildfire danger. It consists of seven fields to assess fuel moisture as well as fire behavior. The methodology employed to generate these data is based on the Canadian Forest Fire Weather Danger Rating and utilizes weather forcing from ERA-Interim, a global reanalysis dataset produced by the European Centre for Medium-range Weather Forecasts. Global fire danger reanalysis data are used to quantify the climatological expectation of fire danger at a certain time of the year and for any location on the globe. It can be regarded as a complementary product to the fire danger forecasts issued daily by the Global Wildfire Information System (GWIS) under the umbrella of the European Copernicus program.JRC.E.1-Disaster Risk Managemen

Initial Spread Index - ERA-Interim

The Initial Spread Index (ISI) is a numeric rating of the expected rate of fire spread. It combines the effects of wind and the FFMC on rate of spread without the influence of variable quantities of fuel. This is part of a larger dataset providing gridded field calculations from the Canadian Fire Weather Index System using weather forcings from the European Centre for Medium-range Weather Forecast (ECMWF) ERA-Interim reanalysis dataset (Vitolo et al., 2019; Di Giuseppe et al., 2016). The dataset has been developed through a collaboration between the Joint Research Centre and ECMWF under the umbrella of the Global Wildfires Information System (GWIS), a joint initiative of the GEO and the Copernicus Work Programs. The whole dataset consists of seven indices, each of which describes a different aspect of the effect that fuel moisture and wind have on fire ignition probability and its behavior, if started. The indices are called: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build Up Index (BUI), Fire Weather Index (FWI) and Daily Severity Rating (DSR). For convenience, each index is archived separately. Data are generated using the open source software GEFF v3.0 (https://git.ecmwf.int/projects/CEMSF/repos/geff), which now uses settings and parameters provided by the JRC (more info here https://git.ecmwf.int/projects/CEMSF/repos/geff/browse/NEWS.md). This dataset can be manipulated using the caliver R package (Vitolo et al. 2017, 2018). Details: File format: netcdf4 Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326). Longitude range: [-180, +180] Latitude range: [-90, +90] Temporal resolution: 1 day Spatial resolution: 0.7 degrees (~80 Km) Spatial coverage: Global Time span: from 1980-01-01 to 2018-12-31Contract 933710 between JRC and ECMWF (Copernicus - Fire Danger Forecast Computation

Build Up Index - ERA-Interim

The Build Up Index (BUI) is a numeric rating of the total amount of fuel available for combustion. It combines the DMC and the DC. This is part of a larger dataset providing gridded field calculations from the Canadian Fire Weather Index System using weather forcings from the European Centre for Medium-range Weather Forecast (ECMWF) ERA-Interim reanalysis dataset (Vitolo et al., 2019; Di Giuseppe et al., 2016). The dataset has been developed through a collaboration between the Joint Research Centre and ECMWF under the umbrella of the Global Wildfires Information System (GWIS), a joint initiative of the GEO and the Copernicus Work Programs. The whole dataset consists of seven indices, each of which describes a different aspect of the effect that fuel moisture and wind have on fire ignition probability and its behavior, if started. The indices are called: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build Up Index (BUI), Fire Weather Index (FWI) and Daily Severity Rating (DSR). For convenience, each index is archived separately. Data are generated using the open source software GEFF v3.0 (https://git.ecmwf.int/projects/CEMSF/repos/geff), which now uses settings and parameters provided by the JRC (more info here https://git.ecmwf.int/projects/CEMSF/repos/geff/browse/NEWS.md). This dataset can be manipulated using the caliver R package (Vitolo et al. 2017, 2018). Details: File format: netcdf4 Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326). Longitude range: [-180, +180] Latitude range: [-90, +90] Temporal resolution: 1 day Spatial resolution: 0.7 degrees (~80 Km) Spatial coverage: Global Time span: from 1980-01-01 to 2018-12-31Contract 933710 between JRC and ECMWF (Copernicus - Fire Danger Forecast Computation