Development of the Deltares global fluvial flood forecast system

Author(s):  
Matthijs den Toom ◽  
Jan Verkade ◽  
Albrecht Weerts ◽  
Gert-Jan Schotmeijer

<p>Deltares operates the Global Fluvial Flood Forecasting System (GLOFFIS) is a real-time fluvial forecasting system with global coverage. At any location in the world, for both the recent past and the near future, the system produces estimates of various hydrological parameters. </p><p>The continued investment in GLOFFIS is justified by various reasons. Primarily, there is an R&D rationale. Any operational system that runs in near real-time poses high requirements to the availability of input data and the runtime of the models used. This problem is augmented when applying the system to the global scale: both model domains and data volumes become significantly larger. Also, data originates from a wide variety of sources. Runtimes, however, cannot be significantly larger hence this poses additional requirements to the efficiency of models used. Solving these issues requires a considerable R&D effort. The resulting developments tend to be useful for the ‘local’ systems we develop and maintain for our clients. An additional rationale is found in the increased demand for global forecasts – notably from a client base that is not able or willing to operate forecasting systems themselves. </p><p>At its core, GLOFFIS operates a set of hydrological models that, jointly, cover the entire earth’s land. The models are forced by meteorological data – pertaining to both the recent past and the near future. The models produce estimates of various hydrological parameters such as soil moisture content, surface water runoff and streamflow rates. Future versions of GLOFFIS will include hydrodynamic models, allowing to produce estimates of water level in addition to streamflow rates. Also, future versions will include seasonal forecasts, i.e. forecasts going out several weeks if not months. In addition to real-time data, the system enables the production of long-term timeseries. </p><p>In terms of the infrastructure of the system, GLOFFIS is based on the wflow framework for hydrological modelling which is embedded within a the Delft-FEWS forecast production system. Neither of these require any licensing fees and the wflow framework is available through an open source license. The Delft-FEWS system is used for many operational flood forecasting systems including those of the US National Weather Service, the English Environment Agency, the Bureau of Meteorology and many other national forecasting agencies. Wflow is a distributed modelling framework specifically designed to accommodate multiple model schematization types and data assimilation techniques. For GLOFFIS, we opted for the physically based wflow_sbm that uses kinematic wave routing for surface and subsurface flow. So-called pedotransfer functions that translate input base maps to model parameter values ensure that the models require little calibration.</p>

2001 ◽  
Author(s):  
Joo Heon Lee ◽  
Do Hun Lee ◽  
Sang Man Jeong ◽  
Eun Tae Lee

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Chao Zhao ◽  
Jinyan Yang

The standard boxplot is one of the most popular nonparametric tools for detecting outliers in univariate datasets. For Gaussian or symmetric distributions, the chance of data occurring outside of the standard boxplot fence is only 0.7%. However, for skewed data, such as telemetric rain observations in a real-time flood forecasting system, the probability is significantly higher. To overcome this problem, a medcouple (MC) that is robust to resisting outliers and sensitive to detecting skewness was introduced to construct a new robust skewed boxplot fence. Three types of boxplot fences related to MC were analyzed and compared, and the exponential function boxplot fence was selected. Operating on uncontaminated as well as simulated contaminated data, the results showed that the proposed method could produce a lower swamping rate and higher accuracy than the standard boxplot and semi-interquartile range boxplot. The outcomes of this study demonstrated that it is reasonable to use the new robust skewed boxplot method to detect outliers in skewed rain distributions.


2013 ◽  
Vol 17 (6) ◽  
pp. 2359-2373 ◽  
Author(s):  
E. Dutra ◽  
F. Di Giuseppe ◽  
F. Wetterhall ◽  
F. Pappenberger

Abstract. Vast parts of Africa rely on the rainy season for livestock and agriculture. Droughts can have a severe impact in these areas, which often have a very low resilience and limited capabilities to mitigate drought impacts. This paper assesses the predictive capabilities of an integrated drought monitoring and seasonal forecasting system (up to 5 months lead time) based on the Standardized Precipitation Index (SPI). The system is constructed by extending near-real-time monthly precipitation fields (ECMWF ERA-Interim reanalysis and the Climate Anomaly Monitoring System–Outgoing Longwave Radiation Precipitation Index, CAMS-OPI) with monthly forecasted fields as provided by the ECMWF seasonal forecasting system. The forecasts were then evaluated over four basins in Africa: the Blue Nile, Limpopo, Upper Niger, and Upper Zambezi. There are significant differences in the quality of the precipitation between the datasets depending on the catchments, and a general statement regarding the best product is difficult to make. The generally low number of rain gauges and their decrease in the recent years limits the verification and monitoring of droughts in the different basins, reinforcing the need for a strong investment on climate monitoring. All the datasets show similar spatial and temporal patterns in southern and north-western Africa, while there is a low correlation in the equatorial area, which makes it difficult to define ground truth and choose an adequate product for monitoring. The seasonal forecasts have a higher reliability and skill in the Blue Nile, Limpopo and Upper Niger in comparison with the Zambezi. This skill and reliability depend strongly on the SPI timescale, and longer timescales have more skill. The ECMWF seasonal forecasts have predictive skill which is higher than using climatology for most regions. In regions where no reliable near-real-time data is available, the seasonal forecast can be used for monitoring (first month of forecast). Furthermore, poor-quality precipitation monitoring products can reduce the potential skill of SPI seasonal forecasts in 2 to 4 months lead time.


2016 ◽  
Vol 21 (4) ◽  
pp. 05015031 ◽  
Author(s):  
Jen-Kuo Huang ◽  
Ya-Hsin Chan ◽  
Kwan Tun Lee

1991 ◽  
Vol 5 (3-4) ◽  
pp. 209-216 ◽  
Author(s):  
M. Borga ◽  
A. Capovilla ◽  
F. Cazorzi ◽  
S. Fattorelli

2012 ◽  
Vol 9 (9) ◽  
pp. 11093-11129 ◽  
Author(s):  
E. Dutra ◽  
F. Di Giuseppe ◽  
F. Wetterhall ◽  
F. Pappenberger

Abstract. Vast parts of Africa rely on the rainy season for livestock and agriculture. Droughts can have a severe impact in these areas which often have a very low resilience and limited capabilities to mitigate their effects. This paper tries to assess the predictive capabilities of an integrated drought monitoring and forecasting system based on the Standard precipitation index (SPI). The system is firstly constructed by temporally extending near real-time precipitation fields (ECMWF ERA-Interim reanalysis and the Climate Anomaly Monitoring System-Outgoing Longwave Radiation Precipitation Index, CAMS-OPI) with forecasted fields as provided by the ECMWF seasonal forecasting system and then is evaluated over four basins in Africa: the Blue Nile, Limpopo, Upper Niger, and Upper Zambezi. There are significant differences in the quality of the precipitation between the datasets depending on the catchments, and a general statement regarding the best product is difficult to make. All the datasets show similar patterns in the South and North West Africa, while there is a low correlation in the tropical region which makes it difficult to define ground truth and choose an adequate product for monitoring. The Seasonal forecasts have a higher reliability and skill in the Blue Nile, Limpopo and Upper Niger in comparison with the Zambezi. This skill and reliability depends strongly on the SPI time-scale, and more skill is observed at larger time-scales. The ECMWF seasonal forecasts have predictive skill which is higher than using climatology for most regions. In regions where no reliable near real-time data is available, the seasonal forecast can be used for monitoring (first month of forecast). Furthermore, poor quality precipitation monitoring products can reduce the potential skill of SPI seasonal forecasts in two to four months lead time.


Water ◽  
2016 ◽  
Vol 8 (10) ◽  
pp. 463 ◽  
Author(s):  
Silvia Barbetta ◽  
Gabriele Coccia ◽  
Tommaso Moramarco ◽  
Ezio Todini

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