scholarly journals Recent Advances in Real-Time Pluvial Flash Flood Forecasting

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 570 ◽  
Author(s):  
Andre Zanchetta ◽  
Paulin Coulibaly
Keyword(s):  
Flash Flood ◽  
Low Cost ◽  
Extreme Rainfall ◽  
Early Warning Systems ◽  
Flood Forecasting ◽  
Flash Floods ◽  
Atmospheric Conditions ◽  
Extreme Rainfall Events ◽  
Recent Advances ◽  
Flash Flood Forecasting

Recent years have witnessed considerable developments in multiple fields with the potential to enhance our capability of forecasting pluvial flash floods, one of the most costly environmental hazards in terms of both property damage and loss of life. This work provides a summary and description of recent advances related to insights on atmospheric conditions that precede extreme rainfall events, to the development of monitoring systems of relevant hydrometeorological parameters, and to the operational adoption of weather and hydrological models towards the prediction of flash floods. With the exponential increase of available data and computational power, most of the efforts are being directed towards the improvement of multi-source data blending and assimilation techniques, as well as assembling approaches for uncertainty estimation. For urban environments, in which the need for high-resolution simulations demands computationally expensive systems, query-based approaches have been explored for the timely retrieval of pre-simulated flood inundation forecasts. Within the concept of the Internet of Things, the extensive deployment of low-cost sensors opens opportunities from the perspective of denser monitoring capabilities. However, different environmental conditions and uneven distribution of data and resources usually leads to the adoption of site-specific solutions for flash flood forecasting in the context of early warning systems.

scholarly journals Flash Flood Forecasting Using Support Vector Regression Model in a Small Mountainous Catchment

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1327 ◽  
Author(s):  
Jian Wu ◽  
Haixing Liu ◽  
Guozhen Wei ◽  
Tianyu Song ◽  
Chi Zhang ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Flash Flood ◽  
Flood Forecasting ◽  
Flash Floods ◽  
Support Vector ◽  
Phase Lag ◽  
Model Input ◽  
Mountainous Catchment ◽  
Input Variables ◽  
Flash Flood Forecasting

Flash floods in mountainous catchments are often caused by the rainstorm, which may result in more severe consequences than plain area floods due to less timescale and a fast-flowing front of water and debris. Flash flood forecasting is a huge challenge for hydrologists and managers due to its instantaneity, nonlinearity, and dependency. Among different methods of flood forecasting, data-driven models have become increasingly popular in recent years due to their strong ability to simulate nonlinear hydrological processes. This study proposed a Support Vector Regression (SVR) model, which is a powerful artificial intelligence-based model originated from statistical learning theory, to forecast flash floods at different lead times in a small mountainous catchment. The lagged average rainfall and runoff are identified as model input variables, and the time lags associated with the model input variables are determined by the hydrological concept of the time of response. There are 69 flash flood events collected from 1984 to 2012 in a mountainous catchment in China and then used for the model training and testing. The contribution of the runoff variables to the predictions and the phase lag of model outputs are analyzed. The results show that: (i) the SVR model has satisfactory predictive performances for one to three-hours ahead forecasting; (ii) the lagged runoff variables have a more significant effect on the predictions than the rainfall variables; and (iii) the phase lag (time difference) of prediction results significantly exists in both two- and three-hours-ahead forecasting models, however, the input rainfall information can assist in mitigating the phase lag of peak flow.

scholarly journals A staggered approach to flash flood forecasting – case study in the Cévennes region

2011 ◽  
Vol 29 ◽  
pp. 13-20 ◽  
Author(s):  
L. Alfieri ◽  
P. J. Smith ◽  
J. Thielen-del Pozo ◽  
K. J. Beven
Keyword(s):  
Flash Flood ◽  
Early Stage ◽  
Mechanistic Model ◽  
Flood Forecasting ◽  
Flash Floods ◽  
Decision Makers ◽  
System A ◽  
Flood Alert ◽  
A Chain ◽  
Flash Flood Forecasting

Abstract. A staggered approach to flash flood forecasting is developed within the IMPRINTS project (FP7-ENV-2008-1-226555). Instead of a single solution system, a chain of different models and input data is being proposed that act in sequence and provide decision makers with information of increasing accuracy in localization and magnitude as the events approach. The first system in the chain is developed by adapting methodologies of the European Flood Alert System (EFAS) to forecast flash floods and has the potential to provide early indication for probability of flash floods at the European scale. The last system in the chain is an adaptation of the data based mechanistic model (DBM) to probabilistic numerical weather predictions (NWP) and observed rainfall, with the capability to forecast river levels up to 12 h ahead. The potential of both systems to provide complementary information is illustrated for a flash flood event occurred on 2 November 2008 in the Cévennes region in France. Results show that the uncertainty in meteorological forecasts largely affects the outcomes. However, at an early stage, uncertain results are still valuable to decision makers, as they raise preparedness towards prompt actions to be taken.

Real-Time Flash Flood Forecasting

2018 ◽  
Author(s):  
Jonathan J. Gourley ◽  
Robert A. Clark
Keyword(s):  
United States ◽  
Real Time ◽  
Flash Flood ◽  
Weather Prediction ◽  
The United States ◽  
Flood Forecasting ◽  
Flash Floods ◽  
Hydrologic Model ◽  
The World ◽  
Flash Flood Forecasting

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.

scholarly journals Spatiotemporal clustering of flash floods in a changing climate (China, 1950–2015)

2020 ◽  
Author(s):  
Nan Wang ◽  
Luigi Lombardo ◽  
Marj Tonini ◽  
Weiming Cheng ◽  
Liang Guo ◽  
...  
Keyword(s):  
Flash Flood ◽  
Extreme Rainfall ◽  
Flash Floods ◽  
Scan Statistics ◽  
Spatiotemporal Pattern ◽  
Precipitation Trend ◽  
Wet Season ◽  
Extreme Rainfall Events ◽  
Space And Time ◽  
Flood Disasters

Abstract. The persistence over space and time of flash flood disasters – flash floods that have caused either economical or life losses, or both – is a diagnostic measure of areas subjected to hydrological risk. The concept of persistence can be assessed via clustering analyses, performed here to analyse the national inventory of flash flood disasters in China occurred in the period 1950–2015. Specifically, we investigated the spatiotemporal pattern distribution of the flash flood disasters and their clustering behavior by using both global and local methods: the first, based on the Ripley's K-function, and the second on Scan Statistics. As a result, we could visualize patterns of aggregated events, estimate the cluster duration and make assumptions about their evolution over time, also with respect precipitation trend. Due to the large spatial (the whole Chinese territory) and temporal (66 years) scale of the dataset, we were able to capture whether certain clusters gather in specific locations and times, but also whether their magnitude tends to increase or decrease. Overall, the eastern regions in China are much more subjected to flash flood disasters compared to the rest of the country. Detected clusters revealed that these phenomena predominantly occur between July and October, a period coinciding with the wet season in China. The number of detected clusters increases with time, but the associated duration drastically decreases in the recent period. This may indicate a change towards triggering mechanisms which are typical of short-duration extreme rainfall events. Finally, being flash flood disasters directly linked to precipitation and their extreme realization, we indirectly assessed whether the magnitude of the trigger itself has also varied through space and time, enabling considerations in the context of climatic changes.

scholarly journals Characteristics of the extreme rainfall event and consequent flash floods in W Slovenia in September 2007

2009 ◽  
Vol 9 (3) ◽  
pp. 947-956 ◽  
Author(s):  
S. Rusjan ◽  
M. Kobold ◽  
M. Mikoš
Keyword(s):  
Flash Flood ◽  
Sampling Error ◽  
Extreme Rainfall ◽  
Weather Forecast ◽  
Flash Floods ◽  
Flood Damage ◽  
Statistical Characteristics ◽  
Extreme Rainfall Event ◽  
Return Periods ◽  
Extreme Rainfall Events

Abstract. During a weather front that passed over large parts of Slovenia on 18.9.2007, extreme rainfall events were triggered causing several severe flash floods with six casualties. Out of 210 municipalities in Slovenia, 60 were reporting flood damages, and the total economic flood damage was later estimated at close to 200 million Euro; highest damage was claimed by Železniki municipality in NW Slovenia. The main purpose of the study presented in this paper was to put together available meteorological and hydrological data in order to get better insight into temporal and spatial dynamics and variability of the flash flood event along the Selška Sora River flowing through the town of Železniki. The weather forecast by the Environmental Agency of the Republic of Slovenia (ARSO) lead to early warning of floodings but has underestimated rainfall amounts by a factor of 2. Also meteorological radar underestimated ground rainfall as much as by 50%. During that day, in many rainfall gauging stations operated by ARSO in the area under investigation, extreme rainfall amounts were measured, e.g. 303 mm in 24 h or 157 mm in 2 h. Some of the measured rainfall amounts were the highest registered amounts in Slovenia so far. Statistical analysis using Gumble distribution was performed and rainfall return periods were estimated. When assessing rainfall return periods, a question of the sampling error as a consequence of short rainfall records used was raised. Furthermore, measured rainfall data were used to reconstruct hydrographs on selected water stations along the Selška Sora River. The cumulative areal precipitation for the Selška Sora River catchment upstream of Železniki amounted to 219 mm, while the modeled effective precipitation used to simulate the hydrograph peak was only 57 mm. The modeled direct runoff coefficient therefore amounts to 0.26. Surprisingly low value is mainly caused by the applied unit hydrograph method that seeks to meet the peak discharge rather than hydrograph volume. However, the spatial distribution of the rainfall in the area was highly variable and present spatial positioning of rain gauges is obviously inadequate for proper representation of the actual spatial amount of rainfall. The study confirmed that post-flood investigation should focus on discharges and hydrological response of the catchment rather than simply analyzing statistical characteristics of rainfall.

An All-Season Flash Flood Forecasting System for Real-Time Operations

2014 ◽  
Vol 95 (3) ◽  
pp. 399-407 ◽  
Author(s):  
Patrick Broxton ◽  
Peter A. Troch ◽  
Mike Schaffner ◽  
Carl Unkrich ◽  
David Goodrich
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Overland Flow ◽  
Flash Flood ◽  
Flood Forecasting ◽  
Flash Floods ◽  
Hydrological Models ◽  
Flood Prediction ◽  
Infiltration Excess ◽  
Flash Flood Forecasting

Flash floods can cause extensive damage to both life and property, especially because they are difficult to predict. Flash flood prediction requires high-resolution meteorological observations and predictions, as well as calibrated hydrological models, which should effectively simulate how a catchment filters rainfall inputs into streamflow. Furthermore, because of the requirement of both hydrological and meteorological components in flash flood forecasting systems, there must be extensive data handling capabilities built in to force the hydrological model with a variety of available hydrometeorological data and predictions, as well as to test the model with hydrological observations. The authors have developed a working prototype of such a system, called KINEROS/hsB-SM, after the hydrological models that are used: the Kinematic Erosion and Runoff (KINEROS) and hillslope-storage Boussinesq Soil Moisture (hsB-SM) models. KINEROS is an event-based overland flow and channel routing model that is designed to simulate flash floods in semiarid regions where infiltration excess overland flow dominates, while hsB-SM is a continuous subsurface flow model, whose model physics are applicable in humid regions where saturation excess overland flow is most important. In addition, KINEROS/hsB-SM includes an energy balance snowmelt model, which gives it the ability to simulate flash floods that involve rain on snow. There are also extensive algorithms to incorporate high-resolution hydrometeorological data, including stage III radar data (5 min, 1° by 1 km), to assist in the calibration of the models, and to run the model in real time. The model is currently being used in an experimental fashion at the National Weather Service Binghamton, New York, Weather Forecast Office.

scholarly journals Spatiotemporal clustering of flash floods in a changing climate (China, 1950–2015)

2021 ◽  
Vol 21 (7) ◽  
pp. 2109-2124
Author(s):  
Nan Wang ◽  
Luigi Lombardo ◽  
Marj Tonini ◽  
Weiming Cheng ◽  
Liang Guo ◽  
...  
Keyword(s):  
Flash Flood ◽  
Extreme Rainfall ◽  
Flash Floods ◽  
Scan Statistics ◽  
Spatiotemporal Pattern ◽  
Precipitation Trend ◽  
Wet Season ◽  
Extreme Rainfall Events ◽  
Space And Time ◽  
Flood Disasters

Abstract. The persistence over space and time of flash flood disasters – flash floods that have caused either economical losses or loss of life or both – is a diagnostic measure of areas subjected to hydrological risk. The concept of persistence can be assessed via clustering analyses, performed here to analyze the national inventory of flash flood disasters in China that occurred in the period 1950–2015. Specifically, we investigated the spatiotemporal pattern distribution of the flash flood disasters and their clustering behavior by using both global and local methods: the first based on Ripley's K function, and the second on scan statistics. As a result, we could visualize patterns of aggregated events, estimate the cluster duration and make assumptions about their evolution over time, also with respect to the precipitation trend. Due to the large spatial (the whole Chinese territory) and temporal (66 years) scale of the dataset, we were able to capture whether certain clusters gather in specific locations and times but also whether their magnitude tends to increase or decrease. Overall, the eastern regions in China are much more subjected to flash flood disasters compared to the rest of the country. Detected clusters revealed that these phenomena predominantly occur between July and October, a period coinciding with the wet season in China. The number of detected clusters increases with time, but the associated duration drastically decreases in the recent period. This may indicate a change towards triggering mechanisms which are typical of short-duration extreme rainfall events. Finally, being flash flood disasters directly linked to precipitation and their extreme realization, we indirectly assessed whether the magnitude of the trigger itself has also varied through space and time, enabling considerations in the context of climatic change.

scholarly journals Neural networks-based operational prototype for flash flood forecasting: application to Liane flash floods (France)

2016 ◽  
Vol 7 ◽  
pp. 18025
Author(s):  
Dominique Bertin ◽  
Anne Johannet ◽  
Nathalie Gaffet ◽  
Frédéric Lenne
Keyword(s):  
Neural Networks ◽  
Flash Flood ◽  
Flood Forecasting ◽  
Flash Floods ◽  
Flash Flood Forecasting
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