Flash Flood Warning Research Articles

A low-cost post-processing technique improves weather forecasts around the world

Computer-generated weather forecasts divide the Earth’s surface into gridboxes, each currently spanning about 400 km2, and predict one value per gridbox. If weather varies markedly within a gridbox, forecasts for specific sites inevitably fail. Here we present a statistical post-processing method for ensemble forecasts that accounts for the degree of variation within each gridbox, bias on the gridbox scale, and the weather dependence of each. When applying this post-processing, skill improves substantially across the globe; for extreme rainfall, for example, useful forecasts extend 5 days ahead, compared to less than 1 day without post-processing. Skill improvements are attributed to creation of huge calibration datasets by aggregating, globally rather than locally, forecast-observation differences wherever and whenever the observed “weather type” was similar. A strong focus on meteorological understanding also contributes. We suggest that applications for our methodology include improved flash flood warnings, physics-related insights into model weaknesses and global pointwise re-analyses.

A Short-Term Quantitative Precipitation Forecasting Approach Using Radar Data and a RAP Model

Very short-term (0~3 h) radar-based quantitative precipitation forecasting (QPF), also known as nowcasting, plays an essential role in flash flood warning, water resource management, and other hydrological applications. A novel nowcasting method combining radar data and a model wind field was developed and validated with two hurricane precipitation events. Compared with several existing nowcasting approaches, this work attempts to enhance the prediction capabilities from two major aspects. First, instead of using a radar reflectivity field, this work proposes the use of the rainfall rate field estimated from polarimetric radar variables in the motion field derivation. Second, the derived motion field is further corrected by the Rapid Refresh (RAP) model field. With the corrected motion field, the future rainfall rate field is predicted through a linear extrapolation method. The proposed method was validated using two hurricanes: Harvey and Irma. The proposed work shows an enhanced performance according to statistical scores. Compared with the model only and centroid-tracking only approaches, the average probability of detection (POD) increases about 25% and 50%; the average critical success index (CSI) increases about 20% and 37%; and the average false alarm rate (FAR) decreases about 14% and 16%, respectively.

Flash flood vulnerability assessment of roads in China based on support vector machine

Flash flood is one of the most devastating types of disasters. Roads are linear strip structures exposed to the natural environment and they are vulnerable to damage when flash flood occurs. Thus, an accurate assessment of flash flood vulnerability for roads is mandatory for the improvement of transportation flash flood forecasting and warnings. In this study, we constructed an assessment model for assessing the flash flood vulnerability of roads based on their exposure and disaster reduction capability. Then, considering the mechanism of flash floods acting on the road, we preliminarily established an index system with analytic hierarchy process (AHP), and used a support vector machine (SVM) to aggregate the indices. The use of SVM avoids the subjectivity of vulnerability evaluation. The Assessment results showed that 4.29% of the land area has high or extremely high vulnerability. The moderate vulnerability areas accounted for 5.19% of the land area. The low and extremely low vulnerability areas accounted for 15.81% of the land area; and the land area with no roads accounted for 43.94%. The suburban areas with dense roads in the southeast and the areas near the main roads in the northwest, had high and extremely high vulnerability. The low and extremely low vulnerability areas are mainly distributed in the areas with sparse roads and close to cities. The results of this study satisfy the need for the high precision and large-scale evaluation of flash flood vulnerability of roads. Based on the results of the vulnerability assessment, different disaster prevention and reduction measures should be taken in the northwest and southeast. The northwestern region should attach importance to the construction of roads and improve the disaster prevention and reduction system; while the southeastern region should plan layout and construction of new roads rationally and enhance the disaster resistance of existing roads.

Primarily Results of a Real-Time Flash Flood Warning System in Vietnam

In recent years, losses and damages from flash floods have been steadily increasing worldwide as well as in Vietnam, due to physical factors, human activities, especially under a changing climate. This is a hotspot issue which requires immediate response from scientists and policy-makers to monitor and mitigate the negative impacts of flash floods. This study presents a way to reduce losses through increasing the accuracy of real-time flash flood warning systems in Vietnam, a case study developed for Ha Giang province where the topography is relatively complex with severe flash floods observed. The objective of this paper is to generate the real-time flash flood system based on bankfull discharge threshold. To do this, HEC-HMS model is applied to calibrate and validate observer inflow to the reservoir with nine automatic rain gauges installed. More importantly, on the basic of measured discharge at 35 locations from the fieldtrips, an empirical equation constructed is to identify the bankful discharge values. It bases on the relationship between basin characteristics of river length, basin area and bankfull discharge. The results indicate an effective approach to determine bankfull threshold with the established-empirical equation. On the scale of a small basin, it depicts the consistency of flood status and warning time with the reality. Doi: 10.28991/cej-2021-03091687 Full Text: PDF

A review of flash floods in the Dobrogea Region

The last decade is characterized by an increase in the occurrence of flash floods, fact which is confirmed by the worldwide endeavors undertaken by the WMO (World Meteorological Association) which initiated the signing of a series of memorandums for collaboration with other worldwide institutions meant to lead to the elaboration and implementing of a complex flash flood warning system. The aim of this paper is to analyze the level of implementation of said system in Europe and Romania and the analysis of the flash flood occurrence, specifically in the Dobrogea region of Romania.

Effect of Precipitation Sampling Error on Flash Flood Monitoring and Prediction: Anticipating Operational Rapid-Update Polarimetric Weather Radars

AbstractQuantitative precipitation estimates (QPE) at high spatiotemporal resolution are essential for flash flood forecasting, especially in urban environments and headwater areas. An accurate quantification of precipitation is directly related to the temporal and spatial sampling of the precipitation system. The advent of phased array radar (PAR) technology, a potential next-generation weather radar, can provide updates that are at least 4-5 times faster than the conventional WSR-88D scanning rate. In this study, data collected by the KOUN WSR-88D radar with ~1 minute temporal resolution is used as an approximation of data that a future PAR system could provide to force the Ensemble Framework for Flash Flood Forecasting (EF5) hydrologic model. To assess the effect of errors resulting from temporal and spatial sampling of precipitation on flash flood warnings, KOUN precipitation data (1-km/1-min) is used to generate precipitation products at other spatial/temporal resolutions commonly used in hydrologic models, such as those provided by conventional WSR-88D radar (1-km/5-min), spaced-based observations (10-km/30-min), and hourly rainfall products (1-km/60-min). The effect of precipitation sampling errors on flash flood warnings are then examined and quantified by using discharge simulated from KOUN (1-km/1-min) as truth to assess simulations conducted using other generated coarser spatial/temporal resolutions of other precipitation products. Our results show that: 1) observations with coarse spatial and temporal sampling can cause large errors in quantification of the amount, intensity, and distribution of precipitation, 2) time series of precipitation products show that QPE peak values decrease as the temporal resolution gets coarser, and 3) the effect of precipitation sampling error on flash flood forecasting is large in headwater areas and decrease quickly as drainage area increases.

Simultaneous and collocated tornado and flash flood warnings associated with tropical cyclones in the contiguous United States

AbstractSimultaneous and collocated tornado and flash flood (TORFF) warnings are a dangerous hazard because the recommended protective action for the two threats are opposite, leaving residents unsure if they should shelter below or seek higher ground. Tropical cyclones (TCs) cause both tornadoes and flash flooding and are thus favourable environments for TORFF warnings. In this study, we provide a unique examination of TORFF warnings in 32 TCs that made landfall in the contiguous United States between 2008 and 2018. We identify TC TORFF warning characteristics including duration, area, distance from coastline, geographic location, and location relative to TC centre, and we compare these results to established findings on TC tornadoes. We found that TORFF warnings were geographically most common in the states of Texas, Louisiana, and Mississippi, and within 200 km of the coastline. TORFF warnings occurred almost exclusively east of TC centre. When compared to TC tornadoes, TORFF warnings were relatively more frequent nearer to the coastline and in the right‐back quadrant of the TC. Over half (59%) of the 32 TCs we studied produced at least one TORFF warning. Using logistic regression, we determined that TC intensity effectively determines how likely a TC is to produce at least one TORFF warning, while TC translational velocity determines how likely a TC is to produce many TORFF warnings. Thus, intense TCs were likely to produce at least a few TORFF warnings, while intense and slow‐moving TCs were likely to produce many TORFF warnings. These findings establish a knowledge base on the climatological characteristics of this unique and dangerous hazard.

Comments on “Flash Flood Verification: Pondering Precipitation Proxies”

AbstractNew operational tools for monitoring flash flooding based on radar quantitative precipitation estimates (QPEs) have become available to U.S. National Weather Service forecasters. Herman and Schumacher examined QPE exceedance thresholds for several tools and compared them to each other, to flash flood reports (FFRs), and to flash flood warnings. The Next Generation Radar network has been updated with dual-polarization capabilities since the publication of Herman and Schumacher, which has changed the characteristics of the derived QPEs. Updated thresholds on Multi-Radar Multi-Sensor version 12 products that are associated to FFRs are provided and thus can be used as guidance by the operational forecasting community and other end-users of the products.

Assessment of Arequipa's Hydrometeorological Monitoring Infrastructure to Support Water Management Decisions

AbstractHydrometeorological monitoring of weather, streamflow, and water quality is essential for understanding available water resources, protecting populations from hazard, and identifying changes in environmental conditions over time. To meet such competing goals, monitoring networks require representative parameters, uniform sampling protocols, and stable locations, selected to reliably measure the phenomenon of interest. However, budgets are always limited, and immediate operational needs and short‐term decisions often influence monitoring decisions. Here, the hydrometeorological monitoring systems in Arequipa, Peru, are examined with respect to established criteria for their ability to support these competing goals. The Arequipa Department in Peru has a well‐established, stable, weather monitoring program, although reliance on manual observers results in variable data quality. The lack of observations in high altitude areas limits estimation of water availability, and high temporal resolution, automatic stations are needed to improve flash flood warnings. The streamflow monitoring system is designed to quantify water transfers throughout this heavily managed system. Twenty‐one discharge monitoring stations were identified to serve as Historic Hydrologic Reference Stations, but many were only operational in the 1960s and 1970s and cannot be used to evaluate environmental trends. Twelve stations are identified that should be maintained for establishment of a future reference network. State sponsored water quality monitoring in the Department is fairly new, and a stratified sampling method has been used to maximize sample locations. Uniform sampling in fewer locations along intermediate sized tributaries, at least two times per year, would improve the reliability of the system and allow better detection of change over time.

Development of a new rainfall‐triggering index of flash flood warning‐case study in Yunnan province, China

AbstractFlash floods, characterized by rapid, short‐duration, and high‐velocity flows, are the major causes of property damage and casualties worldwide. Flash flood warning is one of the key measures to prevent flash floods. Relying upon rain gage data and official statistics of flash flood events with casualties, this study proposes a new rainfall triggering index, β, defined as the ratio of accumulated rainfall to intraday rainfall, which effectively divides floods into events triggered by heavy intraday rainfall (0 < β ≤ 5) and those triggered by high cumulative rainfall (β > 5). Then, historical disaster events were used to evaluate the performance of the proposed index. Results reveal that: (1) when 0 < β ≤ 5, the intensity–duration (I–D) curve method is more desirable, and the simulation result has a high correlation coefficient (r = 0.95) with the measured result; (2) when β > 5, the rainfall triggering index (RTI) method is more suitable (r = 0.8); (3) the cumulative critical rainfall using the RTI method ranges from 50 to 400 mm. This paper stretches the thought of flash flood warning method and provides the reference for flood‐prone regions.

The Ensemble Framework For Flash Flood Forecasting (EF5) v1.2: description and case study

Abstract. The Ensemble Framework For Flash Flood Forecasting (EF5) was developed specifically for improving hydrologic predictions to aid in the issuance of flash flood warnings by the US National Weather Service. EF5 features multiple water balance models and two routing schemes which can be used to generate ensemble forecasts of streamflow, streamflow normalized by upstream basin area (i.e., unit streamflow), and soil saturation. EF5 is designed to utilize high-resolution precipitation forcing datasets now available in real time. A study on flash-flood-scale basins was conducted over the conterminous United States using gauged basins with catchment areas less than 1000 km2. The results of the study show that the three uncalibrated water balance models linked to kinematic wave routing are skillful in simulating streamflow.

The 23 June 2016 West Virginia Flash Flood Event as Observed through Two Hydrometeorology Testbed Experiments

AbstractThe flash flood event of 23 June 2016 devastated portions of West Virginia and west-central Virginia, resulting in 23 fatalities and 5 new record river crests. The flash flooding was part of a multiday event that was classified as a billion-dollar disaster. The 23 June 2016 event occurred during real-time operations by two Hydrometeorology Testbed (HMT) experiments. The Flash Flood and Intense Rainfall (FFaIR) experiment focused on the 6–24-h forecast through the utilization of experimental high-resolution deterministic and ensemble numerical weather prediction and hydrologic model guidance. The HMT Multi-Radar Multi-Sensor Hydro (HMT-Hydro) experiment concentrated on the 0–6-h time frame for the prediction and warning of flash floods primarily through the experimental Flooded Locations and Simulated Hydrographs product suite. This study describes the various model guidance, applications, and evaluations from both testbed experiments during the 23 June 2016 flash flood event. Various model outputs provided a significant precipitation signal that increased the confidence of FFaIR experiment participants to issue a high risk for flash flooding for the region between 1800 UTC 23 June and 0000 UTC 24 June. Experimental flash flood warnings issued during the HMT-Hydro experiment for this event improved the probability of detection and resulted in a 63.8% increase in lead time to 84.2 min. Isolated flash floods in Kentucky demonstrated the potential to reduce the warned area. Participants characterized how different model guidance and analysis products influenced the decision-making process and how the experimental products can help shape future national and local flash flood operations.

Integrated Flood Forecasting and Warning System against Flash Rainfall in the Small-Scaled Urban Stream

The flood forecasting and warning system enable an advanced warning of flash floods and inundation depths for disseminating alarms in urban areas. Therefore, in this study, we developed an integrated flood forecasting and warning system combined inland-river that systematized technology to quantify flood risk and flood forecasting in urban areas. LSTM was used to predict the stream depth in the short-term inundation prediction. Moreover, rainfall prediction by radar data, a rainfall-runoff model combined inland-river by coupled SWMM and HEC-RAS, automatic simplification module of drainage networks, automatic calibration module of SWMM parameter by Dynamically Dimensioned Search (DDS) algorithm, and 2-dimension inundation database were used in very short-term inundation prediction to warn and convey the flood-related data and information to communities. The proposed system presented better forecasting results compared to the Seoul integrated disaster prevention system. It can provide an accurate water level for 30 min to 90 min lead times in the short-term inundation prediction module. And the very short-term inundation prediction module can provide water level across a stream for 10 min to 60 min lead times using forecasting rainfall by radar as well as inundation risk areas. In conclusion, the proposed modules were expected to be useful to support inundation forecasting and warning systems.

Hydrogeomorphological analysis and modelling for a comprehensive understanding of flash-flood damage processes: the 9 October 2018 event in northeastern Mallorca

Abstract. A flash-flood event hit the northeastern part of Mallorca on 9 October 2018, causing 13 casualties. Mallorca is prone to catastrophic flash floods acting on a scenario of deep landscape transformation caused by Mediterranean tourist resorts. As global change may exacerbate devastating flash floods, analyses of catastrophic events are crucial to support effective prevention and mitigation measures. Field-based remote-sensing and modelling techniques were used in this study to evaluate rainfall–runoff processes at the catchment scale linked to hydrological modelling. Continuous streamflow monitoring data revealed a peak discharge of 442 m3 s−1 with an unprecedented runoff response. This exceptional behaviour triggered the natural disaster as a combination of heavy rainfall (249 mm in 10 h), karstic features and land cover disturbances in the Begura de Salma River catchment (23 km2). Topography-based connectivity indices and geomorphic change detection were used as rapid post-catastrophe decision-making tools, playing a key role during the rescue search. These hydrogeomorphological precision techniques were combined with the Copernicus Emergency Management Service and “ground-based” damage assessment, which showed very accurately the damage-driving factors in the village of Sant Llorenç des Cardassar. The main challenges in the future are to readapt hydrological modelling to global change scenarios, implement an early flash-flood warning system and take adaptive and resilient measures on the catchment scale.

A Hazard Multiple: Overlapping Tornado and Flash Flood Warnings in a National Weather Service Forecast Office in the Southeastern United States

AbstractThe U.S. weather warning system is designed to help operational forecasters identify hazards and issue alerts to assist people in taking life-saving actions. Assessing risks for separate hazards, such as flash flooding, can be challenging for individuals, depending on their contexts, resources, and abilities. When two or more hazards co-occur in time and space, such as tornadoes and flash floods, which we call TORFFs, risk assessment and available actions people can take to stay safe become increasingly complex and potentially dangerous. TORFF advice can suggest contradictory action—that people get low for a tornado and seek higher ground for a flash flood. The origin of risk information about such threats is the National Weather Service (NWS) Weather Forecast Office. This article contributes to an understanding of the warning and forecast system though a naturalistic study of the NWS during a TORFF event in the southeastern United States. Drawing on literature for the Social Amplification of Risk Framework, this article argues that during TORFFs, elements of the NWS warning operations can unintentionally amplify or attenuate one threat over the other. Our results reveal three ways this amplification or attenuation might occur: 1) underlying assumptions that forecasters understandably make about the danger of different threats; 2) threat terminology and coordination with national offices that shape the communication of risks during a multihazard event; and 3) organizational arrangements of space and forecaster expertise during operations. We conclude with suggestions for rethinking sites of amplification and attenuation and additional areas of future study.

Application of the GPM-IMERG Products in Flash Flood Warning: A Case Study in Yunnan, China

NASA’s Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) is a major source of precipitation data, having a larger coverage, higher precision, and a higher spatiotemporal resolution than previous products, such as the Tropical Rainfall Measuring Mission (TRMM). However, there rarely has been an application of IMERG products in flash flood warnings. Taking Yunnan Province as the typical study area, this study first evaluated the accuracy of the near-real-time IMERG Early run product (IMERG-E) and the post-real-time IMERG Final run product (IMERG-F) with a 6-hourly temporal resolution. Then the performance of the two products was analyzed with the improved Rainfall Triggering Index (RTI) in the flash flood warning. Results show that (1) IMERG-F presents acceptable accuracy over the study area, with a relatively high hourly correlation coefficient of 0.46 and relative bias of 23.33% on the grid, which performs better than IMERG-E; and (2) when the RTI model is calibrated with the gauge data, the IMERG-F results matched well with the gauge data, indicating that it is viable to use MERG-F in flash flood warnings. However, as the flash flood occurrence increases, both gauge and IMERG-F data capture fewer flash flood events, and IMERG-F overestimates actual precipitation. Nevertheless, IMERG-F can capture more flood events than IMERG-E and can contribute to improving the accuracy of the flash flood warnings in Yunnan Province and other flood-prone areas.

The Innovation Development of Early Flash Flood Warning System Based on Digital Image Processing through Android Smartphone

Flooding is a phenomenon that almost always occurs every year in various regions throughout Indonesia with a variety of cycle variations. In the rainy season, high rainfall and narrowed watersheds and silting up are no longer able to accommodate the flow of water, causing flash floods. Flood Warning Systems operate with traditional local wisdom that is highly dependent on the availability and responsiveness of the personnel assigned to oversee the river. Therefore, the development innovation of the existing system is expected to be able to provide information and data regularly and be an early warning to flash floods in order to minimize the impact. This research was conducted to design, create and implement an image-based flash flood early warning system prototype based on an Android cellular phone so that the public is expected to be able to access information on possible flash floods through their respective cell phones. Image processing is the basis of this development innovation through the Background Substraction method which is carried out to automatically detect High Levels of Water (TMA) on the control board (Peil) via an IP Camera which is an input parameter for the system and is processed into information for interested parties. The results of analysis of the detection of water level (TMA) on the peil board carried out on the server computer, become input parameters for determining the status of the dam water level. Flood early warning application that is installed on Android smartphone devices receives data from the server computer via the internet network.

A Data-Driven Probabilistic Rainfall-Inundation Model for Flash-Flood Warnings

Owing to their short duration and high intensity, flash floods are among the most devastating natural disasters in metropolises. The existing warning tools—flood potential maps and two-dimensional numerical models—are disadvantaged by time-consuming computation and complex model calibration. This study develops a data-driven, probabilistic rainfall-inundation model for flash-flood warnings. Applying a modified support vector machine (SVM) to limited flood information, the model provides probabilistic outputs, which are superior to the Boolean functions of the traditional rainfall-flood threshold method. The probabilistic SVM-based model is based on a data preprocessing framework that identifies the expected durations of hazardous rainfalls via rainfall pattern analysis, ensuring satisfactory training data, and optimal rainfall thresholds for validating the input/output data. The proposed model was implemented in 12 flash-flooded districts of the Xindian River. It was found that (1) hydrological rainfall pattern analysis improves the hazardous event identification (used for configuring the input layer of the SVM); (2) brief hazardous events are more critical than longer-lasting events; and (3) the SVM model exports the probability of flash flooding 1 to 3 h in advance.

An Operational Forecasting System for Flash Floods in Mountainous Areas in Taiwan

Flash floods are different from common floods because they occur rapidly over short time scales, and they are considered to be one of the most devastating natural hazards worldwide. Mountainous areas with high population densities are particularly threatened by flash floods because steep slopes generate high flow velocities. Therefore, there is a great need to develop an operational forecasting system (OFS) for better flash flood prediction and warning in mountainous regions. This study developed an OFS through the integration of meteorological, hydrological, and hydrodynamic models. Airborne light detection and ranging (LiDAR) data were used to generate a digital elevation model (DEM). The OFS employs high-density and high-accuracy airborne LiDAR DEM data to simulate rapid water level rises and flooding as the result of intense rainfall within relatively small watersheds. The water levels and flood extent derived from the OFS are in agreement with the measured and surveyed data. The OFS has been adopted by the National Science and Technology Center for Disaster Reduction (NCDR) for forecasting flash floods every six hours in a mountainous floodplain in Taiwan. The 1D and 2D visualization of the OFS is performed via the National Center for Atmospheric Research Command Language (NCL).

Optimize Short-Term Rainfall Forecast with Combination of Ensemble Precipitation Nowcasts by Lagrangian Extrapolation

The rainfall forecasts currently available in Korea are not sufficiently accurate to be directly applied to the flash flood warning system or urban flood warning system. As the lead time increases, the quality becomes even lower. In order to overcome this problem, this study proposes an ensemble forecasting method. The proposed method considers all available rainfall forecasts as ensemble members at the target time. The ensemble members are combined based on the weighted average method, where the weights are determined by applying the two conditions of the unbiasedness and minimum error variance. The proposed method is tested with McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE) rainfall forecasts for four storm events that occurred during the summers of 2016 and 2017 in Korea. In Korea, rainfall forecasts are generated every 10 min up to six hours, i.e., there are always a total of 36 sets of rainfall forecasts. As a result, it is found that just six ensemble members is sufficient to make the ensemble forecast. Considering additional ensemble members beyond six does not significantly improve the quality of the ensemble forecast. The quality of the ensemble forecast is also found to be better than that of the single forecast, and the weighted average method is found to be better than the simple arithmetic average method.