Search Results (12)

This study proposes a framework, the RA_WLTE_River model, for quantifying the reliability of flood-altering water-level thresholds, considering rainfall and runoff-related uncertainties. The Keelung River in northern Taiwan is selected as the study area, and associated hydrological data from 2008 to 2016 are applied in the development and application of the model. According to the results from the model development and demonstration, the average and maximum rainfall intensities, roughness coefficients, and maximum tide depths exhibit a significant contribution to the reliability quantification of the estimated water-level thresholds. In addition, empirically based water-level thresholds can achieve the goal of rainfall-induced flood early warning, with a high likelihood of nearly 0.95. Additionally, the probabilistically based water-level thresholds derived from the described reliability can efficiently ensure consistent flood early warning performance at all control points along the river. Full article

An integrated early warning system (EWS) for compound coastal and fluvial flooding is developed for Pyrgos, Western Greece, where low-lying geomorphology and past storm events highlight the need for rapid, impact-based forecasting. The methodology couples historical and climate-informed metocean and river discharge datasets within a numerical modeling framework consisting of a mild-slope wave model, the CSHORE coastal profile model, and HEC-RAS 2D inundation simulations. A weighted K-Means clustering approach is used to generate representative extreme scenarios, yielding more than 4000 coupled simulations that train and validate Artificial Neural Networks (ANNs). The optimal feed-forward ANN accurately predicts spatially distributed flood depths across the HEC-RAS grid using only offshore wave characteristics, water level, and river discharge as inputs, reducing computation time from hours to seconds. Blind tests demonstrate close agreement with full numerical simulations, with average differences typically below 5% and minor deviations confined to negligible water depths. These results confirm the ANN’s capability to emulate complex compound flooding dynamics with high computational efficiency. Deployed as a web application (EWS_CoCoFlood), the system provides actionable, near-real-time inundation forecasts to support local civil protection authorities. The framework is modular and scalable, enabling future integration of urban and rainfall-induced flooding processes and coastal morphological change. Full article

The Loess Plateau, possessing the world’s most extensive loess deposits, is highly vulnerable to accelerated soil erosion and vegetation loss triggered by extreme hourly rainfall (EHR) events due to the inherently erodible nature of its porous, weakly cemented sediment structure. EHR exacerbates soil erosion, induces flash flooding, compromises power infrastructure, and jeopardizes agricultural productivity. Through analysis of 43 years (1981–2023) of station observational data and ERA5 reanalysis, we present the first comprehensive assessment of EHR characteristics across the plateau. Results reveal pronounced spatial heterogeneity, with southeastern regions exhibiting higher EHR intensity thresholds and frequency compared to northwestern areas. EHR frequency correlates positively with elevation, while intensity decreases with altitude, demonstrating orographic modulation. Synoptic-scale background environment of EHR events is characterized by upper-level divergence, mid-tropospheric warm advection, and lower-tropospheric convergence, all of which are linked to summer monsoon systems. Temporally, EHR peaks in July during the East Asian summer monsoon and exhibits a bimodal diurnal cycle (0700/1700 LST). Long-term trends reveal a significant overall increase in the frequency of EHR events (~0.82 events a⁻¹). While an overall increase in EHR intensity is also observed, it fails to achieve statistical significance due to opposing regional signals. Collectively, these trends elevate the risks of slope failures and debris flows. Our findings highlight three priority interventions: (i) implementation of elevation-adapted early warning systems, (ii) targeted agricultural soil conservation practices, and (iii) climate-resilient infrastructure design for high-risk valleys—all essential for safeguarding this ecologically sensitive region against intensifying hydroclimatic extremes. Full article

Against the backdrop of increasingly severe global climate change, the risk of rainstorm-induced waterlogging has become the primary threat to the safety of historic and cultural districts worldwide. This paper focuses on the historic and cultural districts of Beijing, China, and explores techniques and methods for identifying extreme rainstorm warnings in cultural heritage areas. Refined warning and forecasting have become important non-engineering measures to enhance these districts’ waterlogging prevention control and emergency management capabilities. This paper constructs a rainstorm-induced waterlogging risk warning model tailored for Beijing’s historical and cultural districts. This model system encompasses three sets of models: a building waterlogging early-warning model, a road waterlogging early-warning model, and a public evacuation early-warning model. During the construction of the model, the core concepts and determination methods of “1 h rainfall intensity water logging index” and “the waterlogging risk index in historical and cultural districts” were proposed. The construction and application of the three models take into full account the correlation between rainfall intensity and rainwater accumulation, while incorporating the characteristics of flood resilience in buildings, roads, and the society in districts. This allows for a precise grading of warning levels, leading to the formulation of corresponding warning response measures. Empirical tests have shown that the construction method proposed in this paper is reliable. The innovative results not only provide a new perspective and method for the early-warning of rainstorm-induced waterlogging, but also offer scientific support for emergency planning and response in historical and cultural districts. Full article

Locust plagues inflict severe agricultural damage. Climate change-induced extreme events like rainfall and droughts have expanded locust habitats. These new areas, often beyond routine monitoring, could become potential high-risk locust areas (PHRLA). Quantitatively understanding the outbreak mechanism driving drought and flood dynamics is crucial for identifying PHRLA, but such studies are scarce. To address this gap, we conducted a case study on locust outbreaks in Xiashan Reservoir, the largest reservoir in Shandong Province, China, in 2017 and 2018. Using time series satellite imagery and meteorological products, we quantitatively analyzed how drought–flood dynamics and temperature affect locust habitats, reproduction, and aggregation. Employing an object-oriented random forest classifier, we generated locust habitat classification maps with 93.77% average overall accuracy and Kappa coefficient of 0.90. Combined with meteorological analysis, we found that three consecutive drought years from 2014 to 2016 reduced the water surface area by 75%, expanding suitable habitats (primarily reeds and weeds) to cover 60% of the reservoir. Warm winters and high temperatures during locust key growth periods, coupled with expanding suitable habitats, promoted multi-generational locust reproduction. However, substantial flooding events in 2017 and 2018, driven by plentiful rainfall during key growth periods, reduced suitable habitats by approximately 54% and 29%, respectively. This compression led to high locust density, causing the locust plague and high-density spots of locusts (HDSL). Our study elucidates locust plague outbreak mechanisms under dynamic drought and flood environments. Based on this, we propose an approach to identify PHRLA by monitoring changes in drought and flood patterns around water bodies and variations in suitable habitat size and distribution, as well as surrounding topography. These findings hold significant implications for enhancing locust monitoring and early warning capabilities, reducing pesticide usage, and ensuring food and ecological security and sustainable agriculture. Full article

This study marks a significant step toward the future development of river discharges forecasted in real time for flash flood early warning system (EWS) disaster prevention frameworks in the Chugoku region of Japan, and presumably worldwide. To reduce the disaster impacts with EWSs, accurate integrated hydrometeorological real-time models for predicting extreme river water levels and discharges are needed, but they are not satisfactorily accurate due to large uncertainties. This study evaluates two calibration methods with 7 and 5 parameters using the hydrological Cell Distributed Runoff Model version 3.1.1 (CDRM), calibrated by the University of Arizona’s Shuffled Complex Evolution optimization method (SCE-UA). We hypothesize that the proposed ensemble hydrological parameter calibration approach can forecast similar future events in real time. This approach was applied to seven major rivers in the region to obtain hindcasts of the river discharges during the Heavy Rainfall Event of July 2018 (HRE18). This study introduces a new historical extreme rainfall event classification selection methodology that enables ensemble-averaged validation results of all river discharges. The reproducibility metrics obtained for all rivers cumulatively are extremely high, with Nash–Sutcliffe efficiency values of 0.98. This shows that the proposed approach enables accurate predictions of the river discharges for the HRE18 and, similarly, real-time forecasts for future extreme rainfall-induced events in the Japanese region. Although our methodology can be directly reapplied only in regions where observed rainfall data are readily available, we suggest that our approach can analogously be applied worldwide, which indicates a broad scientific contribution and multidisciplinary applications. Full article

Climate change is linked to a higher risk of hydrometeorological disasters, which are driven by both the increased global surface temperature, which leads to more frequent droughts, and heavy rainfall events inducing floods. The Municipality of Ermionida is a dry area in Greece facing flash flood events, mainly during the autumn and early winter period. To address this, the Municipal Enterprise for Water and Wastewater of Ermionida (DEYA.ER) and the Laboratory of Climatology and Atmospheric (LACAE) of NKUA are collaborating on a project entitled “Design of a smart early warning hydrometeorological system (EASY)”. The project aims to develop an integrated local-scale forecasting system for flood awareness and, ultimately, for protection purposes. In the context of EASY, an online tool for data processing is provided, including hydrometric and meteorological monitoring, and it is a user-friendly platform that allows real-time access to data and enables the capability of designing specific diagrams according to the parameter and station. Full article

Due to extreme weather phenomena, precipitation-induced flooding has become a frequent, widespread, and destructive natural disaster. Risk assessments of flooding have thus become a popular area of research. In this study, we studied the severe precipitation-induced flooding that occurred in Zhengzhou, Henan Province, China, in July 2021. We identified 16 basic indicators, and the random forest algorithm was used to determine the contribution of each indicator to the Zhengzhou flood. We then optimised the selected indicators and introduced the XGBoost algorithm to construct a risk index assessment model of precipitation-induced flooding. Our results identified four primary indicators for precipitation-induced flooding in the study area: total rainfall for three consecutive days, extreme daily rainfall, vegetation cover, and the river system. The Zhengzhou storm and flood risk evaluation model was constructed from 12 indicators: elevation, slope, water system index, extreme daily rainfall, total rainfall for three consecutive days, night-time light brightness, land-use type, proportion of arable land area, gross regional product, proportion of elderly population, vegetation cover, and medical rescue capacity. After streamlining the bottom four indicators in terms of contribution rate, it had the best performance, with an accuracy rate reaching 91.3%. Very high-risk and high-risk areas accounted for 11.46% and 27.50% of the total area of Zhengzhou, respectively, and their distribution was more significantly influenced by the extent of heavy rainfall, direction of river systems, and land types; the medium-risk area was the largest, accounting for 33.96% of the total area; the second-lowest-risk and low-risk areas together accounted for 27.09%. The areas with the highest risk of heavy rainfall and flooding in Zhengzhou were in the Erqi, Guanchenghui, Jinshui, Zhongyuan, and Huizi Districts and the western part of Xinmi City; these areas should be given priority attention during disaster monitoring and early warning and risk prevention and control. Full article

Central Italy is characterized by complex orography. The territorial response to heavy precipitation may activate different processes in terms of hydrogeological hazards. Floods, flash floods, and wet mass movements are the main ground effects triggered by heavy or persistent rainfall. The main aim of this work is to present a unique tool that is based on a distributed hydrological model, able to predict different rainfall-induced phenomena, and essential for the civil protection early warning activity. The Cetemps Hydrological Model is applied to the detection of hydrologically stressed areas over a spatial domain covering the central part of Italy during a weather event that occurred in 2014. The validation of three hydrological stress indices is proposed over a geographical area of approximately 64,500 km² that includes catchments of varying size and physiography. The indices were used to identify areas subject to floods, flash floods, or landslides. Main results showed very high accuracies (~90%) for all proposed indices, with flood false alarms growing downstream to larger basins, but very close to zero in most cases. The three indices can give complementary information about the predominant phenomenon and are able to distinguish fluvial floods from pluvial floods. Nevertheless, the results were influenced by the presence of artificial reservoirs that regulated flood wave propagation, therefore, indices timing slightly worsen downstream in larger basins. Full article

An in-depth analysis of historical heavy rainfall fields clearly constitutes an important aspect in many related topics: as examples, mesoscale models for early warning systems and the definition of design event scenarios can be improved, with the consequent upgrading in the prediction of induced phenomena (mainly floods and landslides) into specific areas of interest. With this goal, in this work the authors focused on Calabria region (southern Italy) and classified the main precipitation systems through the analysis of selected heavy rainfall events from high resolution rain gauge network time series. Moreover, the authors investigated the relationships among the selected events and the main synoptic atmospheric patterns derived by the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 Reanalysis dataset, in order to assess the possible large-scale scenarios which can induce heavy rainfall events in the study area. The obtained results highlighted: (i) the importance of areal reduction factors, rainfall intensities and amounts in order to discriminate the investigated precipitations systems for the study area; (ii) the crucial role played by the position of the averaged low-pressure areas over the Mediterranean for the synoptic systems, and by low-level temperature for the convective systems. Full article

In times of increasing weather extremes and expanding vulnerable cities, a significant risk to civilian security is posed by heavy rainfall induced flash floods. In contrast to river floods, pluvial flash floods can occur anytime, anywhere and vary enormously due to both terrain and climate factors. Current early warning systems (EWS) are based largely on measuring rainfall intensity or monitoring water levels, whereby the real danger due to urban torrential floods is just as insufficiently considered as the vulnerability of the physical infrastructure. For this reason, this article presents a concept for a risk-based EWS as one integral component of a multi-functional pluvial flood information system (MPFIS). Taking both the pluvial flood hazard as well as the damage potential into account, the EWS identifies the urban areas particularly affected by a forecasted heavy rainfall event and issues object-precise warnings in real-time. Further, the MPFIS performs a georeferenced documentation of occurred events as well as a systematic risk analysis, which at the same time forms the foundation of the proposed EWS. Based on a case study in the German city of Aachen and the event of 29 May 2018, the operation principle of the integrated information system is illustrated. Full article

Flash floods induced by heavy rainfall occur frequently in China, which cause severe damages or even casualties every year. Flash floods generally occur in small catchments, and therefore were poorly documented. A Database including 963 flash flood events in China is compiled and studied in this study. Analytical results (a) indicate flash flood condition in China; (b) shed light on the spatial-temporal distribution of flash flood under heavy rainfall and (c) detect the characteristics of the 2016 flash flood. In 2016, the deaths due to flash floods were severe and concentrated, accounting for about half of the elderly and children. Hebei and Fujian provinces were most affected by flash floods. The disasters mainly occurred in July and the major types were river floods. Despite the frequent torrential rains, inadequate monitoring and early warning systems made the flash flooding condition even worse in 2016. Full article