scholarly journals The Present State of Flash Flood Forecasting Utilizing Numerical Weather Prediction in Europe

2017 ◽  
Vol 30 (2) ◽  
pp. 112-125 ◽  
Author(s):  
Tomoki USHIYAMA ◽  
Takahiro SAYAMA ◽  
Yoichi IWAMI
2011 ◽  
Vol 15 (7) ◽  
pp. 2391-2400 ◽  
Author(s):  
F. Pappenberger ◽  
H. L. Cloke ◽  
A. Persson ◽  
D. Demeritt

Abstract. Flood forecasting increasingly relies on numerical weather prediction forecasts to achieve longer lead times. One of the key difficulties that is emerging in constructing a decision framework for these flood forecasts is what to dowhen consecutive forecasts are so different that they lead to different conclusions regarding the issuing of warnings or triggering other action. In this opinion paper we explore some of the issues surrounding such forecast inconsistency (also known as "Jumpiness", "Turning points", "Continuity" or number of "Swings"). In thsi opinion paper we define forecast inconsistency; discuss the reasons why forecasts might be inconsistent; how we should analyse inconsistency; and what we should do about it; how we should communicate it and whether it is a totally undesirable property. The property of consistency is increasingly emerging as a hot topic in many forecasting environments.


Author(s):  
Jonathan J. Gourley ◽  
Robert A. Clark

Flash floods are one of the world’s deadliest and costliest weather-related natural hazards. In the United States alone, they account for an average of approximately 80 fatalities per year. Damages to crops and infrastructure are particularly costly. In 2015 alone, flash floods accounted for over $2 billion of losses; this was nearly half the total cost of damage caused by all weather hazards. Flash floods can be either pluvial or fluvial, but their occurrence is primarily driven by intense rainfall. Predicting the specific locations and times of flash floods requires a multidisciplinary approach because the severity of the impact depends on meteorological factors, surface hydrologic preconditions and controls, spatial patterns of sensitive infrastructure, and the dynamics describing how society is using or occupying the infrastructure. Real-time flash flood forecasting systems rely on the observations and/or forecasts of rainfall, preexisting soil moisture and river-stage states, and geomorphological characteristics of the land surface and subsurface. The design of the forecast systems varies across the world in terms of their forcing, methodology, forecast horizon, and temporal and spatial scales. Their diversity can be attributed at least partially to the availability of observing systems and numerical weather prediction models that provide information at relevant scales regarding the location, timing, and severity of impending flash floods. In the United States, the National Weather Service (NWS) has relied upon the flash flood guidance (FFG) approach for decades. This is an inverse method in which a hydrologic model is run under differing rainfall scenarios until flooding conditions are reached. Forecasters then monitor observations and forecasts of rainfall and issue warnings to the public and local emergency management communities when the rainfall amounts approach or exceed FFG thresholds. This technique has been expanded to other countries throughout the world. Another approach, used in Europe, relies on model forecasts of heavy rainfall, where anomalous conditions are identified through comparison of the forecast cumulative rainfall (in space and time) with a 20-year archive of prior forecasts. Finally, explicit forecasts of flash flooding are generated in real time across the United States based on estimates of rainfall from a national network of weather radar systems.


2011 ◽  
Vol 8 (1) ◽  
pp. 1225-1245 ◽  
Author(s):  
F. Pappenberger ◽  
H. L. Cloke ◽  
A. Persson ◽  
D. Demeritt

Abstract. Flood forecasting increasingly relies on Numerical Weather Prediction (NWP) forecasts to achieve longer lead times (see Cloke et al., 2009; Cloke and Pappenberger, 2009). One of the key difficulties that is emerging in constructing a decision framework for these flood forecasts is when consecutive forecasts are different, leading to different conclusions regarding the issuing of forecasts, and hence inconsistent. In this opinion paper we explore some of the issues surrounding such forecast inconsistency (also known as "jumpiness", "turning points", "continuity" or number of "swings"; Zoster et al., 2009; Mills and Pepper, 1999; Lashley et al., 2008). We begin by defining what forecast inconsistency is; why forecasts might be inconsistent; how we should analyse it; what we should do about it; how we should communicate it and whether it is a totally undesirable property. The property of consistency is increasingly emerging as a hot topic in many forecasting environments (for a limited discussion on NWP inconsistency see Persson, 2011). However, in this opinion paper we restrict the discussion to a hydro-meteorological forecasting chain in which river discharge forecasts are produced using inputs from NWP. In this area of research (in)consistency is receiving recent interest and application (see e.g., Bartholmes et al., 2008; Pappenberger et al., 2011).


2019 ◽  
Vol 11 (6) ◽  
pp. 642 ◽  
Author(s):  
Seong-Sim Yoon

Preparing proper disaster prevention measures is important for decreasing the casualties and property losses resulting from floods. One of the most efficient measures in this regard is real-time flood forecasting using quantitative precipitation forecasts (QPFs) based on either short-term radar-based extrapolation or longer-term numerical weather prediction. As both methods have individual advantages and limitations, in this study we developed a new real-time blending technique to improve the accuracy of rainfall forecasts for hydrological applications. We tested the hydrological applicability of six QPFs used for urban flood forecasting in Seoul, South Korea: the McGill Algorithm for Prediction Nowcasting by Lagrangian Extrapolation (MAPLE), KOrea NOwcasting System (KONOS), Spatial-scale Decomposition method (SCDM), Unified Model Local Data Assimilation and Prediction System (UM LDAPS), and Advanced Storm-scale Analysis and Prediction System (ASAPS), as well as our proposed blended approach based on the assumption that the error of the previously predicted rainfall is similar to that of current predicted rainfall. We used the harmony search algorithm to optimize real-time weights that would minimize the root mean square error between predicted and observed rainfall for a 1 h lead time at 10 min intervals. We tested these models using the Storm Water Management Model (SWMM) and Grid-based Inundation Analysis Model (GIAM) to estimate urban flood discharge and inundation using rainfall from the QPFs as input. Although the blended QPF did not always have the highest correlation coefficient, its accuracy varied less than that of the other QPFs. In addition, its simulated water depth in pipe and spatial extent were most similar to observed inundated areas, demonstrating the value of this new approach for short-term flood forecasting.


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