GloFAS – global ensemble streamflow forecasting and flood early warning

Alfieri, Lorenzo; Burek, Peter; Dutra, Emanuel; Krzeminski, Blazej; Muraro, Davide; Thielen, Joanna; Pappenberger, Florian

doi:10.5194/hess-17-1161-2013

Cited by 460 publications

(322 citation statements)

References 49 publications

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“…Rainfall estimates from ERA-Interim Land (Balsamo et al, 2015) are used to force the Global Flood Awareness System, a global hydrological model (Alfieri et al, 2013). We calculate anomaly correlations between rainfall input and the predicted flooding, which is defined as the proportion of river cells that has extreme discharge in a region in a given time period (Stephens et al, 2015).…”

Section: Methodsmentioning

confidence: 99%

“…The system used here is the Global Flood Awareness System (GloFAS), which is comprised of a HTESSEL land surface model to generate surface and subsurface runoff and a Lisflood model to complete the routing and groundwater flows at a 0.1 • resolution for the entire global land surface (Alfieri et al, 2013). In this study we focus on river flooding only; therefore, we only consider GloFAS river grid points which have a greater than 1000 km 2 upstream basin area.…”

Section: Floodingmentioning

confidence: 99%

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Should seasonal rainfall forecasts be used for flood preparedness?

Pérez

Stephens

Bischiniotis

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. In light of strong encouragement for disaster managers to use climate services for flood preparation, we question whether seasonal rainfall forecasts should indeed be used as indicators of the likelihood of flooding. Here, we investigate the primary indicators of flooding at the seasonal timescale across sub-Saharan Africa. Given the sparsity of hydrological observations, we input bias-corrected reanalysis rainfall into the Global Flood Awareness System to identify seasonal indicators of floodiness. Results demonstrate that in some regions of western, central, and eastern Africa with typically wet climates, even a perfect tercile forecast of seasonal total rainfall would provide little to no indication of the seasonal likelihood of flooding. The number of extreme events within a season shows the highest correlations with floodiness consistently across regions. Otherwise, results vary across climate regimes: floodiness in arid regions in southern and eastern Africa shows the strongest correlations with seasonal average soil moisture and seasonal total rainfall. Floodiness in wetter climates of western and central Africa and Madagascar shows the strongest relationship with measures of the intensity of seasonal rainfall. Measures of rainfall patterns, such as the length of dry spells, are least related to seasonal floodiness across the continent. Ultimately, identifying the drivers of seasonal flooding can be used to improve forecast information for flood preparedness and to avoid misleading decision-makers.

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Section: Methodsmentioning

confidence: 99%

Section: Floodingmentioning

confidence: 99%

Should seasonal rainfall forecasts be used for flood preparedness?

Pérez

Stephens

Bischiniotis

et al. 2017

Hydrol. Earth Syst. Sci.

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“…The Global Flood Awareness System (GloFAS) uses the ERA-Interim (EI) reanalysis (Dee et al, 2011), with precipitation adjusted using data from the Global Precipitation Climatology Project (GPCP; Huffman et al, 2009a) on a monthly timescale (Alfieri et al, 2013;Hirpa et al, 2016). Another global-scale model system is the Global Flood Forecasting Information System (GLOFFIS), where the meteorological forcing data are derived from several sources, such as gauge measurements, CPC unified gridded precipitation (Chen et al, 2008) and the ECMWF control forecast (Emerton et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Near-real-time adjusted reanalysis forcing data for hydrology

Berg

Donnelly

Gustafsson

2018

Hydrol. Earth Syst. Sci.

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Abstract. Extending climatological forcing data to current and real-time forcing is a necessary task for hydrological forecasting. While such data are often readily available nationally, it is harder to find fit-for-purpose global data sets that span long climatological periods through to near-real time. Hydrological simulations are generally sensitive to bias in the meteorological forcing data, especially relative to the data used for the calibration of the model. The lack of highquality daily resolution data on a global scale has previously been solved by adjusting reanalysis data with global gridded observations. However, existing data sets of this type have been produced for a fixed past time period determined by the main global observational data sets. Long delays between updates of these data sets leaves a data gap between the present day and the end of the data set. Further, hydrological forecasts require initializations of the current state of the snow, soil and lake (and sometimes river) storage. This is normally conceived by forcing the model with observed meteorological conditions for an extended spin-up period, typically at a daily time step, to calculate the initial state. Here, we present and evaluate a method named HydroGFD (Hydrological Global Forcing Data) to combine different data sets in order to produce near-real-time updated hydrological forcing data of temperature and precipitation that are compatible with the products covering the climatological period. HydroGFD resembles the already established WFDEI (WATCH Forcing Data-ERA-Interim) method (Weedon et al., 2014) closely but uses updated climatological observations, and for the near-real time it uses interim products that apply similar methods. This allows HydroGFD to produce updated forcing data including the previous calendar month around the 10th of each month. We present the HydroGFD method and therewith produced data sets, which are evaluated against global data sets, as well as with hydrological simulations with the HYPE (Hydrological Predictions for the Environment) model over Europe and the Arctic regions. We show that HydroGFD performs similarly to WFDEI and that the updated period significantly reduces the bias of the reanalysis data. For real-time updates until the current day, extending HydroGFD with operational meteorological forecasts, a large drift is present in the hydrological simulations due to the bias of the meteorological forecasting model.

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“…In recent years, such deterministic model-based flood forecasting systems have been established all over the world for different spatial scales. Examples are: the Global Flood Awareness System (GloFAS) [4] and Global Flood Forecasting and Information System (GLOFFIS) [5] on a global scale; the European Flood Alert System [6] on a continental scale; the flood forecasting system of England, Scotland and Wales [7] on a national scale; and the forecasting system for the Salado Creek in Texas [8] on a basin scale. Further examples are provided in [9,10].…”

Section: Current Practices In Flood Forecastingmentioning

confidence: 99%

Methods and tools to support real time risk-based flood forecasting - a UK pilot application

et al. 2016

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Abstract. Flood managers have traditionally used probabilistic models to assess potential flood risk for strategic planning and non-operational applications. Computational restrictions on data volumes and simulation times have meant that information on the risk of flooding has not been available for operational flood forecasting purposes. In practice, however, the operational flood manager has probabilistic questions to answer, which are not completely supported by the outputs of traditional, deterministic flood forecasting systems. In a collaborative approach, HR Wallingford and Deltares have developed methods, tools and techniques to extend existing flood forecasting systems with elements of strategic flood risk analysis, including probabilistic failure analysis, two dimensional flood spreading simulation and the analysis of flood impacts and consequences. This paper presents the results of the application of these new operational flood risk management tools to a pilot catchment in the UK. It discusses the problems of performing probabilistic flood risk assessment in real time and how these have been addressed in this study. It also describes the challenges of the communication of risk to operational flood managers and to the general public, and how these new methods and tools can provide risk-based supporting evidence to assist with this process.

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GloFAS – global ensemble streamflow forecasting and flood early warning

Cited by 460 publications

References 49 publications

Should seasonal rainfall forecasts be used for flood preparedness?

Should seasonal rainfall forecasts be used for flood preparedness?

Near-real-time adjusted reanalysis forcing data for hydrology

Methods and tools to support real time risk-based flood forecasting - a UK pilot application

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