Forecasting of flash floods by Algorithm of Storm Prediction

Floods are one of the most devastating natural hazards around the world, and flash floods are extremely dangerous to property and human lives. This study reviews the current flash flood forecasting in China and proposes a new methodology to develop a flash flood potential index (FFPI) based on GIS to further improve China's flash flood guidance system and national weather forecasting services. We collected datasets required to calculate the FFPI and tested a series of schemes to determine the method to calculate the FFPI over China. The result indicates that the calculated China's FFPI distribution can represent the risk of flash flood well, especially in Central and Southeast China. It is also implied that the southwest and western complex terrains are more susceptible to rain storms. Then we chose Yunnan Province as a test-bed and also yielded good results which can offer reference to the current China's flash flood forecasting system. Further research can be placed on incorporation of soil moisture and rainfall frequency components into the FFPI calculation method for dynamic warning and forecasting of flash floods.

<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>

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.

Risk assessment of flash flood under climate and land use and land cover change in Tianshan Mountains, China

Flash floods pose significant threats to the socio-economic development of the Jalpaiguri and Darjeeling Districts. These districts situated in the piedmont zone of Sikkim–Darjeeling and Bhutanese Himalayas suffer from the flash floods, and those cause tremendous loss of life and property more or less every year. This study assessed flash flood risk of watersheds of the Himalayan foreland of Jalpaiguri and Darjeeling Districts in support of mitigation planning. Different hydro-geomorphological setup controls the magnitude, frequency and distribution of flash flood like topography, geomorphology, geology and climatology of the area under study. Land use/land cover and soils also have an integral relationship with run-off generation in the watersheds. Thus, we adopted holistic approach considering the topographic, hydrological, climatological, geological, soil and land use/land cover factors to assess the relative susceptibility to flash floods of the watersheds of Himalayan foreland of Jalpaiguri and Darjeeling Districts. Jaxa 30m DSM, Landsat8 OLI/TIRS and Sentinel 2A satellite images, digitized drainage network, geological, rainfall, soil and geomorphological map were analysed in GIS environment to infer lithology, land use, hydrological soil type and watershed morphometrics. The morphometric parameters were used to assign the relative susceptibility of the watersheds to flash flood, applying the weighted sum average method. Soil Conservation Service rainfall–runoff model of USDA and synthetic unit hydrograph were used to infer the hydrological response of the basin including curve number, runoff depth, time of concentration, lag time, peak discharge, etc. Final flash flood risk map was achieved by the integration of both the susceptibility maps. Higher weightage was given to the susceptibility map produced from run-off modelling and synthetic hydrograph parameters. The result shows that 63% of basins are fall in the high to very high categories of flash flood risk, 28% under medium and only 9% in the low categories of flash flood risk. Accuracy of the model was assessed using the flood inventory coupled with field diagnosis of past flood damages and available records. The resulting flash flood risk map could be used by the planners to adopt mitigation strategies to reduce the severity of the flash flood hazard.

Numerical weather prediction (NWP) models produce a quantitative precipitation forecast (QPF), which is vital for a wide range of applications, especially for accurate flash flood forecasting. The under- and over-estimation of forecast uncertainty pose operational risks and often encourage overly conservative decisions to be made. Since NWP models are subject to many uncertainties, the QPFs need to be post-processed. The NWP biases should be corrected prior to their use as a reliable data source in hydrological models. In recent years, several post-processing techniques have been proposed. However, there is a lack of research on post-processing the real-time forecast of NWP models considering bias lead-time dependency for short- to medium-range forecasts. The main objective of this study is to use the total least squares (TLS) method and the lead-time dependent bias correction method—known as dynamic weighting (DW)—to post-process forecast real-time data. The findings show improved bias scores, a decrease in the normalized error and an improvement in the scatter index (SI). A comparison between the real-time precipitation and flood forecast relative bias error shows that applying the TLS and DW methods reduced the biases of real-time forecast precipitation. The results for real-time flood forecasts for the events of 2002, 2007 and 2011 show error reductions and accuracy improvements of 78.58%, 81.26% and 62.33%, respectively.

Flash floods are a significant threat to urban development in Malaysia, often reported to be related to construction activities. The construction activity is one of the stages of any development project. This research aims to develop a comprehensive framework using the Fuzzy Analytical Hierarchy Process (FAHP) for prioritizing flash flood risk index in new development projects. The methodology involves constructing a hierarchical model of flash flood risk vulnerability factors, conducting expert assessments using fuzzy logic to handle imprecise and uncertain information, and converting qualitative data into quantitative ratings. Six experts from academia, industry, and government were involved in assessing factors influencing the flash flood vulnerability during development projects’ pre-construction and construction phases. Findings reveal that construction phases and best management practices (BMPs) are critical in mitigating flash flood risks. The FAHP framework effectively prioritizes the flash flood risk index, offering a scientifically validated and flexible approach to decision-making. This study contributes to the field by providing a robust method for integrating expert judgments and handling uncertainties in flash flood risk assessment. This research demonstrates the effectiveness of FAHP in improving flood risk management for new development projects in Malaysia. This study enhances decision-making by addressing the identified gaps, ensuring more resilient and sustainable infrastructure development. Future research should focus on refining the FAHP model and developing user-friendly tools for wider urban planning and construction adoption.

In southwest China, flash floods occur frequently and often cause severe damage to residential building areas, especially in mountainous rural settlements. Risk assessment is crucial in the hazard mitigation policies and measures. However, the study on the quantitative assessment of flash flood risk for buildings is still less explored. In this study, an indicator-based assessment approach is proposed to assess the risk of buildings threatened by flash floods. The flood hazard is first simulated with 1D/2D hydrodynamic model to determine the buildings exposed to the flood and flood inundation indicators. Then, a combination of virtual surveys and building census information is used to collect information on indicators of exposed buildings and their surroundings. The indicator scores are calculated using the building flash flood risk indicator system constructed in this study which includes the flood hazard and building vulnerability indicators. A flood risk index (FRI) combining flood hazard index (FHI) and building vulnerability index (VI) is developed by weighted aggregation of indicators using combination weights calculated by the game theory approach. Based on FRI, the flash flood risk of mountainous buildings is quantitatively assessed. Taken a key mountainous rural settlement in southwest China as an example, the proposed methodological framework enables the quantitative calculation and assessment of the risk of rural buildings to flash flood. The overall framework can provide an applicable approach for flood mitigation decisions in mountainous settlements.

Assessing the influence of human activities on flash flood susceptibility in mountainous regions of Vietnam

[Departement_IRSTEA]Eaux [TR1_IRSTEA]ARCEAU

Globally, flash floods are the most damaging natural hazards, because of its sudden nature and difficulty in forecasting that restrains emergency responses. Flash floods triggered by thunderstorms are frequent in the high mountainous area of Hindukush Himalaya in the north of Pakistan. The current study employed two widely used approaches for flash flood risk modeling, i.e., morphometric ranking approaches (MRAs) and El-Shamy’s approach to analyzing their effectiveness for flash flood susceptibility modeling in Swat river watershed, district Swat, Pakistan. The digital elevation model was utilized to delineate the watershed and drainage network using the ArcHydro tool of ArcGIS. A total of 15 morphometric parameters have been used for flash flood modeling. The analysis suggests that the Swat river watershed consists of 17 sub-basins. The MRA-based flash flood risk assessment suggests that sub-basins B1 and B2 (12% of total sub-basins) are highly susceptible to flash flooding. While sub-basins B4, B6, B11, B13, and B17 (59% of the total sub-basin) are moderately susceptible to flash flood risk. El-Shamy-based modeling suggests that sub-basins B12, B14, and B16 (18% of total sub-basins) have high flood susceptibility while sub-basin B5 has the lowest. The field observation and 2010 flood reports by WHO and NDMA suggest that the flash flood susceptibility modeling results by the MRA are more accurate as compared with El-Shamy’s approach. The results of the study perceived to help execute appropriate remedial measures to extenuate the potential flash floods in the study area.

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.

Flash floods can be characterized by several variables. Of these, soil moisture (SM) is an important environmental factor that plays a key role in hydrological and ecological processes and affects the mechanisms that cause flash floods. To more accurately determine the occurrence probability of flash floods, the combined effects of soil moisture and rainfall indexes were considered in this paper, and the copula function approach was explored for use in joint probability analyses of flash flood risks. The results showed that (1) the Clayton copula function offered the best fit for the bivariate joint distribution and captured the occurrence probability of the combination of both peak flow (PF) and SM, while the t-copula function achieved the best fit for the multivariate joint distribution, which presented different combinations of characteristic flash flood parameters. (2) The joint distribution probability of flash floods increased with increasing risk parameter thresholds. Return period analysis indicated that the return periods of the bivariate joint distribution were smaller than those of the multivariate joint distribution. (3) If PF and SM are fixed, the occurrence probability of flash floods is higher in regions where the maximum 1-h rainfall is higher. This study provides an effective and quantitative approach to improving flash flood prediction and advances the application of this approach for the management of future flash flood risks.

Assessment of flash flood risk based on improved analytic hierarchy process method and integrated maximum likelihood clustering algorithm