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En France, le Ministère de la Transition Ecologique avec Vigicrues fournis une information sur le risque de crues à venir dans les prochaines 24 h (Vigilance Crue) sur plus de 20000km de cours d’eau. En complément de cette information, des prévisions de débits ou de hauteurs d’eau sont produites sur plus de 500 stations hydrométriques. Pour les besoins de la prévision des crues, PLATHYNES, une plateforme multi-modèles a été développée. L’article permettra de revenir sur l’origine de ce projet et détaillera ses trois principaux modèles distribués. Ils permettent sur le pourtour méditerranéen français mais aussi dans d’autres contextes hydroclimatiques de fournir des prévisions de crues lors d’épisodes pluvieux très intenses.Le mode global – semi-distribué de PLATHYNES sera également présenté, il permet notamment de créer des modèles simples de propagation débit-débit.Enfin dans le cadre de son plan stratégique, le réseau Vigicrues ambitionne de fournir sur les stations présentant les enjeux les plus importants, des prévisions plus fiables et avec une échéance d’au moins 24 h. En outre, une information de vigilance crues devra être publiée à terme, sur l’ensemble des cours d’eau de métropole. Pour atteindre ces objectifs ambitieux l’outil PLATHYNES de par sa polyvalence constitue un atout important.

Copyright of Journal of Shenzhen University Science & Engineering is the property of Editorial Department of Journal of Shenzhen University Science & Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The most important information required to successfully issue a flood warning is the quantitative precipitation forecasts (QPFs). This is important to run subsequent rainfall–runoff simulations. A rainfall–runoff simulation derives its accuracy mainly from the accuracy of the input QPFs. The dynamically based global numerical weather prediction models (NWPMs) are strong candidate sources of QPFs. A main problem is the real-time selection of which NWPM should be used to provide the QPFs for flood warning simulations. This paper develops an automated technique to solve this problem. The technique performs real-time comparisons with measured rainfall fields using a novel ‘tolerant’ hydrologic approach. The ‘tolerant’ approach performs the comparison on the basin scale and allows for timing shifts in the forecasts. This is because QPFs can be good but only a few hours early or late. Two events are used for illustration, and the proposed real-time application in flood warning is presented. The developed technique, employing the tolerant approach, could eliminate the effects of the timing shifts and, accordingly, succeeded to select the QPFs to be used. A Python package was developed for automation. The developed technique is expected to also be useful for offline assessments of historical performances of NWPMs. HIGHLIGHTS An automated technique was developed for the assessment of numerical weather prediction model (NWPM) forecasts.; This technique employs a novel tolerant hydrologic approach imitating the human eye/brain judgment for evaluating the forecasting skills.; Real-time selection of the best NWPM, including real-time bias and uncertainty estimation, for flood warning applications is described.; A Python package for automation was developed.;

To calculate warning thresholds for compound-flooding events triggered by heavy rainfall coupled with storm tides in Taiwan’s coastal urban areas, we applied copula-based analysis to observation data collected from 2001 till 2022 for Taipei City and New Taipei City and developed an empirical formula that accounts for both the capacity of the drainage infrastructure, which partially depends on the coastal sea level and varies over time, and the amount of precipitation. Compared against observation data from flood detectors, our predictions exhibited an accuracy of 85.2 % and 78.8 % for Taipei City and New Taipei City, respectively, thus improving upon the 62.8 % and 68.5 % success rates for thresholds estimated using only the hourly accumulated rainfall. These promising preliminary results suggest that reliable flood warnings for tidal-basin regions can be expedited by employing our formula and inputting rainfall and sea-level values from ensemble typhoon and storm-surge forecasts.

The need for reliable energy is an ongoing challenge. Poor energy access, particularly in off-grid areas, constrains socioeconomic development and reduces resilience against natural hazards. With water-based disasters becoming more frequent and intense, it is important that holistic insights are applied to the assessment of community vulnerabilities and capabilities. Humanitarian engineering interventions that combine renewable energy and flood early warning at the local level offer comprehensive solutions, have long-term potential, and promote synergies between community and professional stakeholders. This study examines a community-centered approach to localised hydropower and flood response within a framework of sustainable development. Using a systems approach, we develop strategies that potentially address multiple needs, including the intersecting needs of key stakeholder groups.

Developing flood hazard risk assessments is vital in early warning systems to mitigate damage resulting from floods. However, assessing flood risk zones is difficult because of complex physical processes. In this study, a two-step flood hazard zoning model based on the random forest (RF) and self-organizing map (SOM) is proposed to yield the flood hazard zones map. In the first step (flood susceptibility analysis), the flood conditioning factors are used to obtain the flood susceptibility values. In the second step (flood hazard zoning), the proposed model not only considers the flood susceptibility value of the self-pixel as the module input, but also simultaneously considers flood susceptibility values of surrounding pixels to yield the flood hazard zoning map. The proposed model was applied to the Lanyang Plain in Yilan County, Taiwan, to demonstrate its advantages. The results indicated that the proposed model with the flood susceptibility values of the self-pixel and surrounding pixels does improve the assessment performance, significantly improving percentage of the effective ratio (ER) from results for the very high and high-risk level zones is 6 and 46%, respectively. The ER of the proposed model also improved by 9.6 and 30% for the very high and high-risk level zones than the conventional model, and it could provide optimal flood hazard zoning maps. In conclusion, the proposed model is expected to be useful in supporting the formulation of adequate disaster mitigation strategies. [ABSTRACT FROM AUTHOR]

Floods are brutal, catastrophic natural hazards which affect most human beings in terms of economy and life loss, especially in the large river basins worldwide. The Indus River basin is considered as one of the world's large river basins, comprising several major tributaries, and has experienced severe floods in its history. There is currently no proper early flood warning system for the Indus River which can help administrative authorities cope with such natural hazards. Hence, it is necessary to develop an early flood warning system by integrating a hydrodynamic model, in situ information, and satellite imagery. This study used Hydrologic Engineering Center–River Analysis System (HEC–RAS) to predict river dynamics under extreme flow events and inundation modeling. The calibration and validation of the HEC–RAS v5 model was performed for 2010 and 2015 flood events, respectively. Manning's roughness coefficient (n) values were extracted using the land use information of the rivers and floodplains. Multiple combinations of n values were used and optimized in the simulation process for the rivers and floodplains. The Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI), Moderate Resolution Imaging Spectroradiometer (MODIS) MOD09A1, and MOD09GA products were used in the analysis. The Normalized Difference Water Index (NDWI), Modified NDWI1 (MNDWI1), and MNDWI2, were applied for the delineation of water bodies, and the output of all indices were blended to produce standard flood maps for accurate assessment of the HEC–RAS-based simulated flood extent. The optimized n values for rivers and floodplains were 0.055 and 0.06, respectively, with significant satisfaction of statistical parameters, indicating good agreement between simulated and observed flood extents. The HEC–RAS v5 model integrated with satellite imagery can be further used for early flood warnings in the central part of the Indus River basin. [ABSTRACT FROM AUTHOR]

Accurate water level forecasting is important to understand and provide an early warning of flood risk and discharge. It is also crucial for many plants and animal species that needs specific ranges of water level. This research focused on long term multi-step ahead forecasting of daily flood water level in duration of (2005–2021) at two stations (i.e., Baryulgil and Lilydale) of the Clarence River, in Australia, introducing a novel hybrid framework coupling time varying filter-based empirical mode decomposition (TVF-EMD), classification and regression trees (CART) feature selection, and four advanced machine learning (ML) models. The implemented ML approaches are including Long-Short Term Memory (LSTM), cascaded forward neural network (CFNN), gradient boosting decision tree (GBDT), and multivariate adaptive regression spline (MARS). Here, original time series of WL in each station was decomposed into the optimal intrinsic mode functions (IMFs) using the TVF-EMD technique and the significant lagged-time components for two desired horizons (t + 1 and t + 7 time ahead) in each station was extracted by using the CART-feature selection method. Then, the IMFs and corresponded residual obtained from the pre-processing procedure were separately implemented to feed the ML models and produce the CART-TVF-EMD-LSTM, CART-TVF-EMD-CFNN, CART-TVF-EMD-MARS, and CART-TVF-EMD-GBDT by assembling all the individual sub-sequences outcomes. Several goodness-of-fit metrics such as correlation coefficient (R), Mean absolute percentage error (MAPE), and Kling-Gupta efficiency (KGE) and the infographic tools and diagnostic analysis were employed to evaluate the robustness of the provided techniques. The outcomes of developed expert systems ascertained that CART-TVF-EMD-CFNN for one- and seven-day horizons in both stations outperformed the CART-TVF-EMD-MARS, CART-TVF-EMD-LSTM, CART-TVF-EMD-GBDT, and all the standalone counterpart models (i.e., CFNN, MARS, LSTM, and GBDT) respectively. As one of the most important achievements of this research, the LSTM did not lead to superior and promising results in the long-term highly nonstationary time series. [ABSTRACT FROM AUTHOR]

A crucial component for the success of any early warning system for flood and extreme weather phenomena is understanding people's perceptions and views of the warning processes and approaches. This paper aims to explore public perceptions on flood and extreme weather warnings as well as factors that influence these perceptions in Greece, a characteristic example of a country that has suffered several climate-related disasters in the recent past. To this end, a survey of 427 residents of the country was conducted between April 2021 and June 2021. The collected data were analyzed by using both descriptive and inductive statistics. The results showed that certain factors affect participants' views on early warnings, including demographics, perceived knowledge on floods, flood risk perception, and perceived self-efficacy. The above factors present statistically significant correlations with the perceived reliability and effectiveness of warnings, as well the degree to which participants perceived the expected phenomena as a threat to their well-being or a signal to take preventive actions. These correlations are described in detail in the present study, together with certain exceptions that exist. The findings are a strong indication that public perception has the potential to impact early warning systems' actual effectiveness, leading to certain practical implications for their improvement, particularly in multi-hazard, climate change-sensitive areas like the Mediterranean region. [ABSTRACT FROM AUTHOR]

The need for reliable energy is an ongoing challenge. Poor energy access, particularly in off-grid areas, constrains socioeconomic development and reduces resilience against natural hazards. With water-based disasters becoming more frequent and intense, it is important that holistic insights are applied to the assessment of community vulnerabilities and capabilities. Humanitarian engineering interventions that combine renewable energy and flood early warning at the local level offer comprehensive solutions, have long-term potential, and promote synergies between community and professional stakeholders. This study examines a community-centered approach to localised hydropower and flood response within a framework of sustainable development. Using a systems approach, we develop strategies that potentially address multiple needs, including the intersecting needs of key stakeholder groups. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Floods are brutal, catastrophic natural hazards which affect most human beings in terms of economy and life loss, especially in the large river basins worldwide. The Indus River basin is considered as one of the world’s large river basins, comprising several major tributaries, and has experienced severe floods in its history. There is currently no proper early flood warning system for the Indus River which can help administrative authorities cope with such natural hazards. Hence, it is necessary to develop an early flood warning system by integrating a hydrodynamic model, in situ information, and satellite imagery. This study used Hydrologic Engineering Center–River Analysis System (HEC–RAS) to predict river dynamics under extreme flow events and inundation modeling. The calibration and validation of the HEC–RAS v5 model was performed for 2010 and 2015 flood events, respectively. Manning’s roughness coefficient (n) values were extracted using the land use information of the rivers and floodplains. Multiple combinations of n values were used and optimized in the simulation process for the rivers and floodplains. The Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI), Moderate Resolution Imaging Spectroradiometer (MODIS) MOD09A1, and MOD09GA products were used in the analysis. The Normalized Difference Water Index (NDWI), Modified NDWI1 (MNDWI1), and MNDWI2, were applied for the delineation of water bodies, and the output of all indices were blended to produce standard flood maps for accurate assessment of the HEC–RAS-based simulated flood extent. The optimized n values for rivers and floodplains were 0.055 and 0.06, respectively, with significant satisfaction of statistical parameters, indicating good agreement between simulated and observed flood extents. The HEC–RAS v5 model integrated with satellite imagery can be further used for early flood warnings in the central part of the Indus River basin.

• Compound-flooding events, triggered by heavy rainfall coupled with storm tides, are a significant concern in coastal urban areas. • A copula-based analysis is applied to develop an easy-to-use empirical formula for quickly assessing the threat of compound flooding. • The compound-flooding formula has been proved to be more reliable overall than the conventional rainfall-based thresholds for coastal urban areas. To calculate warning thresholds for compound-flooding events triggered by heavy rainfall coupled with storm tides in Taiwan's coastal urban areas, we applied copula-based analysis to observation data collected from 2001 till 2022 for Taipei City and New Taipei City and developed an empirical formula that accounts for both the capacity of the drainage infrastructure, which partially depends on the coastal sea level and varies over time, and the amount of precipitation. Compared against observation data from flood detectors, our predictions exhibited an accuracy of 85.2 % and 78.8 % for Taipei City and New Taipei City, respectively, thus improving upon the 62.8 % and 68.5 % success rates for thresholds estimated using only the hourly accumulated rainfall. These promising preliminary results suggest that reliable flood warnings for tidal-basin regions can be expedited by employing our formula and inputting rainfall and sea-level values from ensemble typhoon and storm-surge forecasts. [ABSTRACT FROM AUTHOR]

Heavy rainfall leads to severe flooding problems with catastrophic socio-economic impacts worldwide. Hydrologic forecasting models have been applied to provide alerts of extreme flood events and reduce damage, yet they are still subject to many uncertainties due to the complexity of hydrologic processes and errors in forecasted timing and intensity of the floods. This study demonstrates the efficacy of using eXtreme Gradient Boosting (XGBoost) as a state-of-the-art machine learning (ML) model to forecast gauge stage levels at a 5-min interval with various look-out time windows. A flood alert system (FAS) built upon the XGBoost models is evaluated by two historical flooding events for a flood-prone watershed in Houston, Texas. The predicted stage values from the FAS are compared with observed values with demonstrating good performance by statistical metrics (RMSE and KGE). This study further compares the performance from two scenarios with different input data settings of the FAS: (1) using the data from the gauges within the study area only and (2) including the data from additional gauges outside of the study area. The results suggest that models that use the gauge information within the study area only (Scenario 1) are sufficient and advantageous in terms of their accuracy in predicting the arrival times of the floods. One of the benefits of the FAS outlined in this study is that the XGBoost-based FAS can run in a continuous mode to automatically detect floods without requiring an external starting trigger to switch on as usually required by the conventional event-based FAS systems. This paper illustrates a data-driven FAS framework as a prototype that stakeholders can utilize solely based on their gauging information for local flood warning and mitigation practices.

A unique empirical approach of estimating the urban flood warning threshold is presented. First, the rainfall depth-duration relationship was estimated based on radar-gauge merged rainfall data with 1 km–10 min space-time resolution for 319 highly urbanized watersheds of the study area, for the extreme rainfall event occurred on the September 11, 2017 in Busan, Korea. The rainfall depth-duration relationship at the watersheds were further categorized by the nine different domains of the rainfall temporal variability and the watershed flooded area proportion that is estimated from the Sentinel-1 Synthetic Aperture Radar data analysis. The minimum possible slope of the depth-duration relationship in each of the domain was determined as the flood warning rainfall intensity threshold for this urban area. The results revealed that 55 mm/hr of rainfall intensity was the universal threshold. When this threshold was applied to issue the flood warning, the number of missed warning was minimized at the expense of the increased frequencies of false warnings. The highest identified threshold was 73 mm/hr. It was also found that the temporally variable rainfall is more likely to cause floods than the one with less temporal variability. The identified rainfall threshold values were validated against the 2010 and 2011 extreme events occurred in Seoul, Korea. The validation revealed that the balance between the accuracy and the reliability of the flood warning system highly depends on the choice of the tolerable flooded area proportions. The study reveals the great potential of operational urban flood warning system composed of a weather radar and a network of ground rainfall gauges that can be simply established using satellite-based flooded area data, which may be especially useful for developing countries. [ABSTRACT FROM AUTHOR]

This study investigated households' perceptions of risk communication during the 2011 flood in Thailand, which was the most devastating in Thailand since 1942 and affected 12.9 million people. The study aim was to analyze the determinants of people's perceptions of early warning communication and its efficacy. It also examined key determinants in various aspects, including the accessibility and efficacy of warnings regarding the potential hazard from electrocution, household hygiene, and life and property issues. This study used the 2011 Flood Livelihood Survey of Thai households, conducted by the Thai National Statistical Office from July to December 2011. The results demonstrated that some household characteristics, head of household, and communication and transportation problems during the flood affected warnings regarding accessibility and the perception of warning efficacy during the 2011 flood in Thailand. The results also demonstrate the key factors in successful risk communication, i.e., flood experience and community interrelationship. It is also essential to provide comprehensive and useful information such as safety and health instructions, using the proper channels to disseminate information to the target audience. [ABSTRACT FROM AUTHOR]

Mumbai, a densely populated coastal city, experiences frequent extreme rainfall events leading to floods and waterlogging. However, the lack of real-time flood monitoring and detailed past flooding data limits the scientific analysis to extreme rainfall assessment. To address this, we explore the usability of crowdsourced data for identifying flood hotspots and extracting reliable flood information from the past. Through an automated program, we filtered and retrieved flood-related data from Twitter, using location information to generate flood maps for past heavy rainfall events. The validity of the retrieved data is confirmed by comparing it with volunteered geographic information (VGI), which is more accurate but less abundant. In the absence of direct flood information, Twitter data is cross-verified with the Height above the Nearest Drainage (HAND) map, which serves as a proxy for elevation. Interestingly, while extreme rainfall events are increasing in frequency, recent Twitter-based information shows a decrease in flood reporting attributed to effective mitigation measures implemented at various flood hotspots. Local surveys support this finding and highlight measures such as underground storage tanks and pumping stations that have reduced flood severity. Our study demonstrates the value of crowdsourced data in identifying urban flood hotspots and its potential for real-time flood monitoring and forecasting. This approach can be adapted for data-sparse urban regions to generate location-specific warnings, contributing to improved early warnings and mitigating the impact on lives and property. • The potential of crowdsourced data in monitoring urban flood. • Evaluating mitigation strategies through crowdsourced data. • Application of citizen science in urban flood analysis. • Consistency check between rainfall, elevation, and crowdsourced flood data. [ABSTRACT FROM AUTHOR]

The Hydro-Meteorological Centre (CMI) of the Environmental Protection Agency of Liguria Region, Italy, is in charge of the hydrometeorological forecast and the in-event monitoring for the region. This region counts numerous small and very small basins, known for their high sensitivity to intense storm events, characterised by low predictability. Therefore, at the CMI, a radar-based nowcasting modelling chain called the Small Basins Model Chain, tailored to such basins, is employed as a monitoring tool for civil protection purposes. The aim of this study is to evaluate the performance of this model chain, in terms of: (1) correct forecast, false alarm and missed alarm rates, based on both observed and simulated discharge threshold exceedances and observed impacts of rainfall events encountered in the region; (2) warning times respect to discharge threshold exceedances. The Small Basins Model Chain is proven to be an effective tool for flood nowcasting and helpful for civil protection operators during the monitoring phase of hydrometeorological events, detecting with good accuracy the location of intense storms, thanks to the radar technology, and the occurrence of flash floods.

Copyright of World Water Policy is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• A flood warning system (FWS) is developed based on atmospheric ensemble forecasts. • It considers the atmospheric ensemble forecasts and all effective uncertain criteria. • It considers high flood risks in deciding warning level. • It acts cautiously for equal flood risks in deciding warning level. The purpose of this study is to develop a flood warning system based on Atmospheric Ensemble Forecasts. Although ensemble forecasts are increasingly employed for flood forecasting, developing a flood warning system based on ensemble forecasts has not been adequately addressed yet. In this study, first a Weather Research and Forecasting (WRF) model was used to forecast the heavy precipitation in Kan Basin, Iran. Ensemble storms were forecasted using five cumulus schemes including Kain-Fritsch, Betts-Miller-Janjic, Grell 3D ensemble, Multi-scale Kain-Fritsch and Grell-Devenyi ensemble cumulus scheme. Then, a Bayesian Networks (BN) was developed to estimate the flood peak using the atmospheric ensemble forecasts. Finally, a Fuzzy-TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) model was prepared for making decisions for flood warning scenarios considering all effective factors in flood warning and uncertainty associated with them. Assessment of the proposed flood warning system was examined for various scenarios. It showed that when a significantly high probability was assigned to a warning level, that level had the maximum closeness coefficient and consequently chosen as a warning level. Yet, if the probability was distributed equally between some warning levels, the flood warning system acts cautiously since the decision-making model allocated the highest rank to the stronger warning level. Regarding the reasonable results of this study, applying the Fuzzy-TOPSIS model to develop a flood warning system based on atmospheric ensemble forecasts is recommended to apply in similar catchments for addressing the uncertainties. [ABSTRACT FROM AUTHOR]

Current free to use models developed by the United States Army Corps of Engineers (USACE) perform unique functions (e.g., hydrology, hydraulics, reservoir operations, and flood impact analysis) that are widely used in numerous studies and applications. These models are commonly set up in a framework that is limited to point source connections, which is problematic in regions with flat topography and complex hydrodynamics. The separate models need to be integrally linked and jointly considered for accurate risk communication and decision-making, especially during major storm events. Recently, Hurricane Harvey (2017) exposed the shortcomings of the existing framework in West Harris County, TX, where an insufficient understanding of potential flood risk and impacts contributed to the extensive flood damages sustained in the region. This work illustrates the possibility of using a single hydraulic model, HEC-RAS 2D, to perform all hydrologic, hydraulic, and reservoir operations modeling necessary for accurate flood impact assessments. Implications of this study include a simplification of the entire flood impact analysis, which could help future flood risk communication and emergency planning.

Oneof the natural disasters that annually causes a lot of damage to people andsociety, is flood.Timely flood warning system to ensure that finalbeneficiaries forecasting system, an appropriate warning is received at theappropriate time and the appropriate response to protect and minimize thedamage done to their finances.In this regard, Gharanghoochay river catchment,causing massive floods in Mianeh city has been selected to implement a floodwarning system. In this paper, hydrological modeling for the basin usingHEC-HMS software was obtained and for the entire sub- basin, Muskingum dynamicsequations have been used. The flood located in east Azerbaijan province, floods2004.06.03 calibrate the model rainfall - runoff flood warning was used.Continuity check with showers and amount of time before the alarm warningsignal for each station was calculated. After pre-determined according toabundance of caution showers Hashtrood station, flood warning systems forflooding areas within the mat-o-flood plan has been designed to suit thethreshold. Under-taking damage riverbank villages and history of naturaldisasters in the region and assess the damages incurred to the Mianeh city,emergency action guidelines was prepared.

Flood is a severe natural disaster which causes major damages in most regions including in Iran. Loss of human lives as a consequence of flash flood has not been sufficiently studied despite its high annual rate. Review of related literature indicates relatively low accuracy of available global relationships for estimating loss of life due to flash floods. As a result, regional equations dealing with loss of life estimation relying on all effective hydraulic and evacuation variables is a way forward. In this study, hydraulic variables, such as depth, velocity and rise rate, and evacuation parameters, including available time for evacuation and fraction of people evacuated, were adopted to develop a regional loss of life equation in residential areas of Kan watershed case study, Tehran, Iran, using a calibrated 2DHEC-RAS model. Different number of fatalities in downstream and upstream villages revealed the importance of evacuation time when an early flood warning system is operational. Comparison of the proposed regional equation with available global equations showed that the proposed equation provides more accurate estimation of the number of fatalities in the study area. Regarding the estimated mortality, a local sensitivity analysis performed on the developed equation showed the importance of flood depth to evacuation time ratio, water rising rate and flow velocity, respectively. [ABSTRACT FROM AUTHOR]

Heavy rainfall analysis is an important data for disaster management of flash floods and debris flows in mountainous areas. Those disasters may cause casualties and property damages. It is an urgent consideration to analyze the heavy rainfall characteristics in the area in order to gain well-planned disaster mitigation. Hourly rainfall data were analyzed over Mt. Merapi area especially those which are located in the Yogyakarta and Central Java Province. The rainfall data were collected from a number of automatic rainfall stations with 11 to 28 years of data length. Heavy rainfall is defined when the rainfall depth exceeds 50 mm per event. Heavy rainfall analysis at the study area indicates that heavy rainfall varies among the stations and it is likely occurred more often at the south-west of Mt. Merapi. Statistical analysis gives that maximum rainfall depth for an event varies from 99 mm at the south-east area to 282 mm at the south-west area of Mt. Merapi. Maximum rainfall intensity values show that at the south-west of Mt. Merapi have higher rainfall intensity than at the south-east area. The results indicate that orographic effects and west monsoon are important in determining the spatial distribution of heavy rainfall occurrences in Mt. Merapi area. Besides, heavy rainfall more frequent occurred from noon until late afternoon. The annual maximum heavy rainfall data at the southwest of Mt. Merapi was best fitted with the LN3 distribution. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) plays an important role in determining the antecedent condition of a watershed, while topographic attributes define how and where SM and rainfall interact to create floods. Based on this principle, we present a method to identify flood risk at a location in a watershed by using remotely sensed SM and open-access information on rainfall, soil properties, and topography. The method consists of three hydrologic modules that represent the generation, transfer, and accumulation of direct runoff. To simplify the modeling and provide timely warnings, the flood risk is ascertained based on frequency of exceedance, with warnings issued if above a specified threshold. The simplicity of the method is highlighted by the use of only three parameters for each watershed of interest, with effective regionalization allowing use in ungauged watersheds. For this proof-of-concept study, the proposed model was calibrated and tested for 65 hydrologic reference stations in the Murray–Darling Basin in Australia over a 35-year study period by using satellite-derived surface SM. The three model parameters were first estimated using the first ten-year data and then the model performance was evaluated through flood threshold exceedance analyses over the remaining 25-year study period. The results for estimated parameters and skill scores showed promise. The three model parameters can be regionalized as a function of watershed characteristics, and/or representative values estimated from neighboring watersheds, allowing use in ungauged basins everywhere. [ABSTRACT FROM PUBLISHER]