Development of GIS-based FFPI for China's flash flood forecasting

Jordan experienced flash floods that resulted in numerous fatalities and injuries. This research focuses on identifying the Wadi Mujib’s flash flood potential zones and evaluating their potential magnitude. In this work, hydrological models were developed by integrating GIS settings with HEC-HMS software (V. 4.11). The hydrological model for Wadi Mujib is simulated in this research by means of the Soil Conservation Service (curve number method) while using rainfall data from 1970 to 2022. The results show that the optimum curve number values (CN) were 78.5 at normal antecedent moisture content. Additionally, in order to aid in the decision-making process for flash flood warnings, a flash flood potential index (FFPI) was also introduced based on four main physiographic parameters (slope, land use, plant cover, and soil texture) ranging from 1 to 10. The accumulative chart’s FFPI threshold, which indicates the areas with the highest potential for flash floods, was set at 95% or above. The FFPI threshold was chosen using the accumulative chart of FFPI, which shows that the FFPM threshold value is 7 and covers 13.39% of the study area.

Quantifying uncertainty associated with flash flood warning or forecast systems is required to enable informed decision making by those responsible for operation and management of natural hazard protection systems. The current system used by the U.S. National Weather Service (NWS) to issue flash-flood warnings and watches over the Unites States is a purely deterministic system. The authors propose a simple approach to augment the Flash Flood Guidance System (FFGS) with uncertainty propagation components. The authors briefly discuss the main components of the system, propose changes to improve it, and allow accounting for several sources of uncertainty. They illustrate their discussion with examples of uncertainty quantification procedures for several small basins of the Illinois River basin in Oklahoma. As the current FFGS is tightly coupled with two technologies, that is, threshold-runoff mapping and the Sacramento Soil Moisture Accounting Hydrologic Model, the authors discuss both as sources of uncertainty. To quantify and propagate those sources of uncertainty throughout the system, they develop a simple version of the Sacramento model and use Monte Carlo simulation to study several uncertainty scenarios. The results point out the significance of the stream characteristics such as top width and the hydraulic depth on the overall uncertainty of the Flash Flood Guidance System. They also show that the overall flash flood guidance uncertainty is higher under drier initial soil moisture conditions. The results presented herein, although limited, are a necessary first step toward the development of probabilistic operational flash flood guidance forecast-response systems.

Flash floods account for a large proportion of flood-based fatalities, and they are becoming more frequent due to climate change. A global flash flood warning system therefore has the potential to be life saving.Standard approaches to flash flood forecasting – such as the Flash Flood Guidance (FFG) system in the US National Weather Service (NWS), or the European Runoff Index based on Climatology (ERIC) in the European Flood Awareness System (EFAS) – are utilizing recent weather and soil conditions, physiographic characteristics of basins, and weather forecasts, in order to produce a forecast for possible flash floods. These forecasts are not disseminated directly to the public. Instead, they are firstly refined by hydrologists that have intimate knowledge of the relevant basins and of previous flood events. This introduces a difficulty to scaling these methods worldwide, as the training of professional hydrologists in every region is costly and time consuming.Recent applications of Machine Learning (ML) to hydrology show that a learning system has the potential to train on data-rich basins and generalize to data-poor basins, with a skill that is comparable to state of the art hydrological models. In this work we attempt to build a ML model to produce daily flash flood forecasts, based on globally available weather reanalysis and physiographic characteristics from HydroATLAS. We discuss the model architecture, and evaluate it against NWS Flash Flood Warnings (FFW). While such models may not surpass the skill of a professional hydrologist, they have the potential to provide reasonable warnings in regions that do not currently have any such system in place.

Climate change has a direct impact on flash floods, and indirectly on the environment, society, and economy, due to the rapid development and difficulty of predicting this hydrological phenomenon. The main objective of this study is to assess the potential flash flood hazard areas in the Likodra watershed (218.62 km2), one of the most vulnerable parts to flash floods in Serbia, using the flash flood potential index (FFPI) and analytic hierarchy process (AHP) method. Recurring events from 1995 to this day and the devastating impact on settlements of the analyzed area show that this territory is extremely vulnerable. The data used include hydrological statistics (maximum daily rainfall) and spatial data on watershed geographical characteristics (slope, soils, land use, vegetation, drainage density) obtained or derived from various sources (maps, satellite images, digital databases) which were integrated into the GIS environment. The results indicate a severe flash flood hazard level, with high flash flood susceptibility classes occupying 76.20%, 87.78%, and 91.73% of the area, depending on the considered criteria and weights assigned to them.

Flash floods have the potential to damage infrastructure and buildings, and pose a considerable threat to human life. The short lag times associated with flash floods (typically a few hours) present a significant challenge to existing flood warning systems. These systems currently rely on rainfall and runoff measurements, as well as hydrological models. Consequently, they are of limited applicability in ungauged catchments. This is a critical issue given that climate change is intensifying extreme rainfall and thereby increasing the potential for flash flooding.This study investigates the detection of flash floods based solely on rainfall characteristics, thus eliminating dependency on runoff measurements and hydrological infrastructure. Using high-resolution radar rainfall data and 15-minute runoff observations, 1,330 extreme rainfall-runoff events are selected across 147 German catchments with an area of up to 100 km². These events are subsequently classified as either flash or non-flash floods using a rainfall-runoff-based classification scheme as a reference, with 103 of the selected events identified as flash floods. For each event, various space-time rainfall characteristics are quantified. A random forest model for flash flood detection is then trained using only the rainfall metrics and static catchment attributes. The main aim is to assess the potential for rainfall-driven flash flood detection without relying on runoff. In addition, the most relevant rainfall characteristics associated with flash floods are identified, thereby improving our understanding of the underlying drivers.In future work, the developed detection approach will be combined with real-time rainfall nowcasting to enable earlier prediction and warning of flash floods.

The intensive agricultural activities in the Cilongkrang Sub-Watershed are causing environmental changes that can lead to various disasters. This study aims to map the spatial distribution of multiple hazards, including flash floods, erosion, and landslides. Three hazards selected because the critical condition of Cilongkrang Sub-Watershed as an upstream area caused the area to be prone to those hazards. The flash flood hazard model was created using the Flash Flood Potential Index (FFPI) method, which uses land use, slope, vegetation cover, and soil texture. The erosion hazard model was created using the Universal Soil Loss Equation (USLE) method with parameters such as erosivity, erodibility, slope length factor, crop management index, and soil conservation index. The landslide hazard model was created using the Spatial Multi-Criteria Evaluation (SMCE) method, which uses slope, landform, land use, and soil texture. The results showed that the dominant flash flood-prone areas were in the medium class of 1.556 hectares, the erosion tended to be very low, which was 2.699,5 tons/ha/year, and The Argapura Sub-District encompasses multiple regions that are highly susceptible to landslides.

Given that floods continue to cause yearly significant worldwide human and material damages, flood risk mitigation is a key issue and a permanent challenge in developing policies and strategies at various spatial scales. Therefore, a basic phase is elaborating hazard and flood risk maps, documents which are an essential support for flood risk management. The aim of this paper is to develop an approach that allows for the identification of flash-flood and flood-prone susceptible areas based on computing and mapping of two indices: FFPI (Flash-Flood Potential Index) and FPI (Flooding Potential Index). These indices are obtained by integrating in a GIS environment several geographical variables which control runoff (in the case of the FFPI) and favour flooding (in the case of the FPI). The methodology was applied in the upper (mountainous) and middle (hilly) catchment of the Prahova River, a densely populated and socioeconomically well-developed area which has been affected repeatedly by water-related hazards over the past decades. The resulting maps showing the spatialization of the FFPI and FPI allow for the identification of areas with high susceptibility to flash-floods and flooding. This approach can provide useful mapped information, especially for areas (generally large) where there are no flood/hazard risk maps. Moreover, the FFPI and FPI maps can constitute a preliminary step for flood risk and vulnerability assessment.

This article focuses on the forecasting of flash floods using the Algorithm of Storm Prediction as a new tool to predict convective precipitation, severe phenomena and the risk of flash floods. The first part of the article contains information on methods for predicting dangerous severe phenomena. This algorithm uses mainly data from numerical weather prediction models (NWP models), database of historic weather events and relief characteristics describing the influence of orography on the initiation of atmospheric convection. The result section includes verification of predicted algorithm outputs, selected NWP models and warnings of CHMI and ESTOFEX on three events related to the floods that hit the Zlín Region between years of 2015 - 2017. The main result is a report with prediction outputs of the algorithm visualized in maps for the territory of municipalities with extended competence and their regions. The outputs of the algorithm will be used primarily to increase the effectiveness of preventive measures against flash floods not only by the Fire Rescue Service of Czech Republic but also by the flood and crisis management authorities.

Abstract. Beijiang catchment is a small catchment in southern China locating in the centre of the storm areas of the Pearl River Basin. Flash flooding in Beijiang catchment is a frequently observed disaster that caused direct damages to human beings and their properties. Flood forecasting is the most effective method for mitigating flash floods, the goal of this paper is to develop the flash flood forecasting model for Beijiang catchment. The catchment property data, including DEM, land cover types and soil types, which will be used for model construction and parameter determination, are downloaded from the website freely. Based on the Liuxihe Model, a physically based distributed hydrological model, a model for flash flood forecasting of Beijiang catchment is set up. The model derives the model parameters from the terrain properties, and further optimized with the observed flooding process, which improves the model performance. The model is validated with a few observed floods occurred in recent years, and the results show that the model is reliable and is promising for flash flood forecasting.

Flash floods occur frequently and distribute widely in mountainous areas because of complex geographic and geomorphic conditions and various climate types. Effective flash flood forecasting with useful lead times remains a challenge due to its high burstiness and short response time. Recently, machine learning has led to substantial changes across many areas of study. In hydrology, the advent of novel machine learning methods has started to encourage novel applications or substantially improve old ones. This study aims to establish a discharge forecasting model based on Long Short-Term Memory (LSTM) networks for flash flood forecasting in mountainous catchments. The proposed LSTM flood forecasting (LSTM-FF) model is composed of T multivariate single-step LSTM networks and takes spatial and temporal dynamics information of observed and forecast rainfall and early discharge as inputs. The case study in Anhe revealed that the proposed models can effectively predict flash floods, especially the qualified rates (the ratio of the number of qualified events to the total number of flood events) of large flood events are above 94.7% at 1–5 h lead time and range from 84.2% to 89.5% at 6–10 h lead-time. For the large flood simulation, the small flood events can help the LSTM-FF model to explore a better rainfall-runoff relationship. The impact analysis of weights in the LSTM network structures shows that the discharge input plays a more obvious role in the 1-h LSTM network and the effect decreases with the lead-time. Meanwhile, in the adjacent lead-time, the LSTM networks explored a similar relationship between input and output. The study provides a new approach for flash flood forecasting and the highly accurate forecast contributes to prepare for and mitigate disasters.

A procedure for the objective reduction of a rain gauge network is developed and applied for the Susquehanna River Basin (SRB) in the United States. The procedure utilizes evaluation of the theoretical error variance associated with precipitation analysis using a variant of the National Weather Service’s (NWS) Multisensor Precipitation Estimator (MPE). The uncertainty analysis is carried out for 16 different combinations of the precipitation accumulation period, season, magnitude, and areal extent, and use or nonuse of the Flash Flood Potential Index (FFPI), which is used as a proxy for the spatially varying runoff ratio. To estimate the statistical parameters of the procedure, the historical archive of the MPE products operationally produced by the Middle Atlantic River Forecast Center (MARFC) was used. The marginal value of each rain gauge in the Susquehanna Flood Forecasting and Warning System (SFFWS) network to the parent network is assessed by calculating the increase in the theoretical error...

<p>Radar rainfall nowcasting promises to result in more accurate and timely rainfall forecasts up to several hours in advance, compared to numerical weather prediction model forecasts. As flood early warning systems can benefit from this, we assessed the potential of radar rainfall nowcasting for (flash) flood forecasting with a large sample of 659 individual rainfall events for 12 catchments in the Netherlands. In this assessment, we tested four open-source nowcasting algorithms: Rainymotion Sparse (RM-S), Rainymotion DenseRotation (RM-DR), Pysteps deterministic (PS-D) and Pysteps probabilistic (PS-P) with 20 ensemble members. Eulerian Persistence (EP) forecasts and zero precipitation input (ZP) were the benchmark. The discharge forecasts had a 12-h forecast horizon and were issued for every 5-min step in the available nowcasts for the set of events. We regarded the simulations using the observed radar rainfall as reference.</p><p>We found that rainfall and discharge forecast errors increase with both increasing rainfall intensity and spatial variability. For the discharge forecasts, this relationship also depends on the initial conditions, as the forecast error increases more quickly with rainfall intensity, when the initial conditions are wet and groundwater tables are shallow. Overall, discharge forecasts using RM-DR, PS-D and PS-P outperform the other nowcasting methods.</p><p>Furthermore, we tested the potential for forecasting threshold exceedances by setting the highest discharge, per event, as threshold. Compared to benchmark ZP, a threshold exceedance is, on average, forecast 223 (EP), 196 (RM-S), 213 (RM-DR), 119 (PS-D) and 143 min (PS-P) earlier. The relatively high time profits with EP are counterbalanced by both a high false alarm ratio (FAR) and inconsistent forecasts. Contrarily, PS-D and PS-P show both lower FAR and inconsistency index values, which can lead to more trust in the simulations.</p><p>Based on this large-sample analysis, we conclude that all nowcasting methods have shown a benefit for short-term discharge forecasting compared to issuing no rainfall forecasts at all, though all have shortcomings. As forecast rainfall volumes are a crucial factor in (flash) flood forecasting, we recommend a future focus on improving this aspect in nowcasting.</p>

PREVIEW is an European Commission FP6 Integrated Project which aims at developing, on an European level, new geo-information services for natural and industrial risk management. The work package WP4340 focuses on forecast of Mediterranean flash floods. Phase 1 was devoted to the assessment of the usefulness of kilometric scale atmospheric model forecast for hydrological applications and to the development of hydro-meteorological coupled systems based on high-resolution atmospheric models and hydrological models able to reproduce the hydrological behaviour of Mediterranean catchments. Four high-resolution models at 2–3 km resolution have been run on five flash-flood cases over the French Cevennes-Vivarais and Italian Piedmont regions; models are MM5 (by NOA), COSMO-2 (by MeteoSwiss), MESO-NH (by Meteo-France) and COSMO-LAMI (by Arpa Piemonte). To investigate the benefit of coupling atmospheric and hydrological models, the quantitative precipitation forecasts (QPF) have been verified against observations using both classical and categorical statistical scores, while the sensitivity of the QPF to the model initial conditions has been also examined. In addition, the various hourly precipitation forecasts were supplied as input to hydrological models to evaluate through the simulated discharges the value of high resolution forecasts for hydrological forecast purposes. Clearly the hydrological verification conclusions differ from the QPF verification ones and show the usefulness of developing such hydrological verification as the one performed here.

Flood risk and its reduction in China

Flash floods have become the nation's number one stormy weather killer in the 1970s. Deaths from flash floods are now approaching 200 each year compared to an average of less than 70 per year during the preceding 30-year period. Losses from flash floods are now nearly 10 times what they were in the 1940s. To combat these escalating losses the National Weather Service (NWS) is expanding its efforts to improve forecasting and warning of flash floods and to improve the public's response to flash flood threats. This paper summarizes the present and planned NWS operational flash flood warning and disaster preparedness programs.