Historical Flood Research Articles

Geospatial Assessment of Urban Flood Susceptibility Using AHP-Based Multi-Criteria Technique: Case Study of Musanze, Rwanda

Over the last decade, flood events in the northern and western urban areas of Rwanda have increased due to anthropogenic activities and the effects of climate change. This study aims to use the Analytic Hierarchy Process (AHP) geospatial multi-criteria technique to identify flood susceptibility areas and exposed assets in Musanze City. Seven factors were considered such as Topographic Wetness Index (TWI), Rainfall, Land Use Land Cover (LULC), Slope, Soil Texture, River, and Digital Elevation Model (DEM) to create a flood susceptibility map with 5 classes (from very low to very high). To validate the final map, 26 GPS points were collected from areas with a history of flooding, and the susceptibility classes were compared. Results showed that 33% of the area (~2367 ha) was classified as high to very high risk, 23% (~1645 ha) was classified as moderate, and 44% (~3113 ha) was classified as low and very low susceptibility. The study also identified physical infrastructure and land use exposed in high and very high flooding areas, including 24 schools, 2 markets, and 1 health facility, 18668 km of roads, and 3400 buildings that are highly exposed to flooding. The applied methods in this study are useful for urban planners and government officials in developing flood mitigation policies, strategies, and plans.

Assessment of Three GPM IMERG Products for GIS-Based Tropical Flood Hazard Mapping Using Analytical Hierarchy Process

The use of satellite precipitation products can overcome the limitations of rain gauges in flood hazard mapping for mitigation purposes. Hence, this study aims to evaluate the capabilities of three global precipitation measurement (GPM) integrated multisatellite retrievals for GPM (IMERG) products in tropical flood hazard mapping in the Kelantan River Basin (KRB), Malaysia, using the GIS-based analytic hierarchy process (AHP) method. In addition to the precipitation factor, another eleven factors that contribute to flooding in the KRB were included in the AHP method. The findings demonstrated that the spatial pattern and percentage area affected by floods simulated under the IMERG-Early (IMERG-E), IMERG-Late (IMERG-L), and IMERG-Final (IMERG-F) products did not differ significantly. The receiver operating characteristics curve analysis showed that all three IMERG products performed well in generating flood hazard maps, with area under the curve values greater than 0.8. Almost all the recorded historical floods were placed in the moderate-to-very-high flood hazard areas, with only 1–2% found in the low flood hazard areas. The middle and lower parts of the KRB were identified as regions of “very high” and “high” hazard levels that require particular attention from local stakeholders.

Employing the Flash Flood Potential Index (FFPI) with Physical Environmental Factors in Baling, Kedah through GIS Analysis

On July 4, 2022, Sungai Kupang, Baling, Kedah experienced a devastating flood that caused 3 fatalities, destroyed or damaged 17 houses, affected 3,546 residents, and resulted in losses estimated at RM25.91 million. The flood was triggered by heavy rainfall in the highland area, which caused multiple landslides to occur simultaneously. The landslides led to a debris flow phenomenon in four main river branches, ultimately resulting in a tragic debris and mud flood in the lowlands and downstream villages. The aim of this study is to analyze the location of flash flood occurrences in Baling and to estimate the likelihood of flash floods based on the identified land physical factors. This study also identifies the critical area of Baling basin that have high potential for a flash flood and evaluates the effectiveness and applicability of the FFPI model compared with historical flood events and remote sensing imagery which have occurred in the few watersheds area. The FFPI model, which was created for the first time in 2003, is used in this study to analyze the flash flood that occurred in Baling by considering slope, land cover, soil data, and vegetation. The FFPI technique is applied in five scenarios to determine the flash flood potential, and the value used is also based on the references. A value of 1 on the index denotes a minimal probability of flash floods, while a value of 10 indicates the highest probability. Based on the findings, the study area had a high possibility of having a flash flood at an index value of 7. The danger level of a severe flash flood is present throughout the research region in all scenarios when the value is more than 50%. The outcome is then utilized to do comparisons using historical information on flash floods and their hotspots area, as well as utilizing satellite imagery to determine the true scale of the flood. This is also important to reduce the impact of floods occurrence in the same place as well managing risk and to plan for disaster-mitigation operations.

Discrepancies in estimated flood losses on paddy production: Application of damage models on historical flood records of the Northwest States of Peninsular Malaysia

Information of flood damage to crops production is essential for guiding risk-based decisions in reducing future adverse impacts of floods. For paddy, its susceptibility to floods is one of the factors that may influence the loss of paddy’s production. To date, there have been limited studies that have been undertaken to examine in detail how different damage functions (with and without a susceptibility index) influence flood damage estimates of paddy using past recorded flood information. This study attempts to examine flood damage functions of different forms in two conditions: as their stand-alone forms and when being applied for damage and risk estimations. Furthermore, recorded revenues from the past were used to investigate the effects of paddy’s susceptibility to floods on revenues. Historical flood evidence and revenues between 1987 and 2021 occurred on paddy granaries under the Muda Agricultural Development Authority’s (MADA) monitoring located in Perlis and Kedah states of Peninsular Malaysia and were used in this study. Application of three damage functions reveals that the exclusion of susceptibility index from the damage calculation results in significantly overestimations of losses, with a standard deviation reaching as high as 38 and overestimations up to four times that of the function with susceptibility index. The high estimates could hinder the acceptance of risk-based flood assessment (and its results) in flood alleviation decision-making. Furthermore, as the percentage of flooded area increases, the revenue loss increases, indicating the effects of floods on harvested paddy quality. This further highlights the potential role of the susceptibility index in the damage function.

Validation of Synthetic Design Hydrographs through 2D hydrodynamic modelling

The procedure for the determination of Synthetic Design Hydrographs (SDHs), proposed in previous works, is validated by comparing the peak discharges obtained by routing a long series of historical floods and the synthetic floods at different stations along a complex river system.At this aim, the 60 km long terminal stretch of the Dora Baltea river (Northern Italy) has been modelled according to fully 2D high resolution hydrodynamic approach. The fluvial branch is of considerable complexity due to a strong contraction induced by the presence of a narrow Roman bridge, which, during the most important flood events, causes the reactivation of a paleochannel and the flooding of a part of the city of Ivrea. The hydraulic model has been calibrated on the basis of the main historical floods. Then, all the historical floods over a period of more than 80 years (1939–2020) and the SDHs derived by the same series have been routed. Historical and synthetic peak discharges at two downstream stations have been then compared in probability plots. The results show that the peak discharge distributions derived by routing the historical floods and the SDHs compare well. This suggests that SDHs construction procedure is reliable and has statistical significance.

Exploring a similarity search-based data-driven framework for multi-step-ahead flood forecasting

Due to a small proportion of observations, reliable and accurate flood forecasts for large floods present a fundamental challenge to artificial neural network models, especially when the forecast horizons exceed the flood concentration time of a river basin. This study proposed for the first time a Similarity search-based data-driven framework, and takes the advanced Temporal Convolutional Network based Encoder-Decoder model (S-TCNED) as an example for multi-step-ahead flood forecasting. A total of 5232 hourly hydrological data were divided into two datasets for model training and testing. The input sequence of the model included hourly flood flows of a hydrological station and rainfall data (traced back to the previous 32 h) of 15 gauge stations, and the output sequence stepped into 1- up to 16-hour-ahead flood forecasts. A conventional TCNED model was also built for comparison purposes. The results demonstrated that both TCNED and S-TCNED could make suitable multi-step-ahead flood forecasts, while the proposed S-TCNED model not only could effectively mimic the long-term rainfall-runoff relationship but also could provide more reliable and accurate forecasts of large floods than the TCNED model even in extreme weather conditions. There is a significant positive correlation between the mean sample label density improvement and the mean Nash-Sutcliffe Efficiency (NSE) improvement of the S-TCNED over the TCNED at the long forecast horizons (13 h up to 16 h). Based on the analysis of the sample label density, it is found that the similarity search largely improves the model performance by enabling the S-TCNED model to learn the development process of similar historical floods in a targeted manner. We conclude that the proposed S-TCNED model that converts and associates the previous rainfall-runoff sequence with the forecasting runoff sequence under a similar scenario can enhance the reliability and accuracy of flood forecasts while extending the length of forecast horizons.

Late Holocene paleoclimatic reconstruction inferred from El Bibane lagoon in southeast Tunisia

The reconstruction of paleofloods in the late Holocene has become an important research highlight in the context of the present climate change. The Tunisian Mediterranean coast is a region that is prone to experiencing severe weather. The flood events have a significant impact on the infrastructure and populations residing in coastal lowlands. Studies on lagoon sediments have recently revealed important information about the past extreme hydrological events as well as their relationship with past climate changes. This study aims to reconstruct the historical extreme flood-event record using sediment cores taken from the El Bibane coastal lagoon, southeast of Tunisia. Three sediment cores BL12–5, BL12–7 and BL12–10 were studied. Through a combination of analytical techniques such as the chronological, geochemical and sedimentological analyses, past flash floods were identified. Results display that the sedimentological signature of the paleoflood levels recognized within the lagoonal sequence is characterized by high values of the elemental ratios (Ti/Ca and Fe/Ca) and high content of fine material such as clay and silt. These floods are thought to have occurred as a result of intense precipitation that increased the terrigenous input to the El Bibane lagoon by the Fessi River. Chronologies of 210Pb, 137Cs, and 14C have been used to calculate the age of these floods. The records offer a good temporal correlation with historical floods recorded in the central Mediterranean region. In the southeast of Tunisia, periods with more frequent floods coincide with periods generally under alternation of wetter and drier conditions and minima of solar activity. The paleoflood record of the El Bibane lagoon can help researchers better understand the physical mechanisms responsible for changes in the intensity and/or frequency of extreme events in the south of Tunisia. Our research proves that the proxies utilized here have a lot of potential for reconstructing past floods, which is important knowledge for future flood prediction and mitigation in southeast Tunisia.

Flood Vulnerability Assessment of an Urban Area: A Case Study in Seoul, South Korea

Climate change has led to frequent and extreme flooding events in urban areas such as Seoul, a city that is particularly vulnerable due to drainage systems that were not originally designed to handle such conditions. This study aims to develop region-specific storm risk matrices for the 25 districts in Seoul and predict storm risks. By accounting for local meteorological and geographic characteristics, these matrices will enable a more targeted approach to issuing heavy rainfall warnings, as opposed to the current nationwide system. The methodology involves calculating entropy weight based on various factors, assessing flood vulnerability, and estimating region-specific rainfall associated with warning levels. These warning levels are then used to create storm risk matrices, which are tested for conformity against historical flood events. Finally, a storm risk prediction technique is developed using rainfall forecasting data. Results demonstrate the feasibility of using the newly developed storm risk matrices to predict flood damage up to 72 h in advance. This greatly contributes to the development of effective mitigation plans for addressing climate change-driven urban flood damage. The study’s findings offer valuable insights for enhancing local-specific heavy rainfall warning systems and ensuring better preparation in the face of increasing urban flood risks due to climate change.

Geospatial-Based Analytical Hierarchy Process (AHP) and Weighted Product Model (WPM) Techniques for Mapping and Assessing Flood Susceptibility in the Wadi Hanifah Drainage Basin, Riyadh Region, Saudi Arabia

This paper aimed to map areas prone to flooding in the Wadi Hanifah drainage basin located in the Riyadh region, and identify the most important factors that contribute to flooding through examining the influence of ten topographical, hydrological, and environmental variables affecting flood occurrence. Remote sensing data from Landsat-8, Shuttle Radar Topography Mission (SRTM), and other ancillary datasets were used to map relevant variables. Two weighted overlay techniques were used, including: analytical hierarchy process (AHP) and weighted product model (WPM). A correlation matrix and optimum index factor (OIF) were employed to identify the relative importance of each factor. The two derived flood susceptibility maps were assessed through validation by comparing the locations of historical flood events to susceptibility zones. The results confirmed the validity of the WPM map. The results also showed that nearly 50% of the study area was dominated by the “moderate” flood susceptibility zone, while about 33% of the total land area was classified as a “high” flood susceptibility zone. The “slope” factor was found to be the most effective variable for flood occurrence, followed by the “geology” variable, while the “distance to the drainage network” was the least important variable. The results of the OIF indicated that the best combination of factors dictating the variability of all flood susceptibility areas were “geology”, “land use/cover (LULC)”, and “soil type”. The study findings are expected to be useful in understanding the effects of each factor on the spatial variation in flood occurrence and in improving flood control, and can be reapplied to other regions with similar climatic and environmental conditions worldwide.

High tide, low price? Flooding alerts and hotel prices in Venice

This research explores the effects of High Tide alerts on hotel prices in Venice, a city that is vulnerable to the impacts of extreme climate events due to its fragile ecosystem and a long history of floods in the city center. By analyzing and combining price data from Booking.com with publicly available information on tides and weather, this study uses regression discontinuity design to test for changes in hotel prices when tide levels reach a critical threshold. The results offer insights into the sensitivity of hotel prices to weather alerts and provide valuable information on the potential impact of climate change on Venice’s tourism-driven economy, with implications for the cost–benefit analysis of activating protective barriers for lagoon protection.

What distinguishes 100-year precipitation extremes over central European river catchments from more moderate extreme events?

Abstract. Historical extreme flooding events in central European river catchments caused high socioeconomic impacts. Previous studies have analysed single events in detail but have not focused on a robust analysis of the underlying extreme precipitation events in general, as historical events are too rare for a robust assessment of their generic dynamical causes. This study tries to fill this gap by analysing a set of realistic daily 100-year large-scale precipitation events over five major European river catchments with the help of operational ensemble prediction data from the ECMWF. The dynamical conditions during such extreme events are investigated and compared to those of more moderate extreme events (20 to 50 year); 100-year precipitation events are generally associated with an upper-level cutoff low over central Europe in combination with a surface cyclone southeast of the specific river catchment. The 24 h before the event is decisive for the exact location of this surface cyclone, depending on the location and velocity of the upper-level low over western Europe. The difference between 100-year and more moderate extreme events varies from catchment to catchment. Dynamical mechanisms such as an intensified upper-level cutoff low and surface cyclone are the main drivers distinguishing 100-year events in the Oder and Danube catchments, whereas thermodynamic mechanisms such as a higher moisture supply in the lower troposphere east of the specific river catchment are more relevant in the Elbe and Rhine catchments. For the Weser and Ems catchment, differences appear in both dynamical and thermodynamic mechanisms.

Study on the time conversion method of early warning indicators of flash flood disaster: A case of Qufu City, Shandong Province.

To solve the problem of imperfect flash flood warning indicators in mountainous watersheds, this study proposes a conversion method of critical rainfall for different warning periods on the basis of the existing stormwater calculation formulae and applies it to typical mountainous watersheds. The specific method is to use the multiplicative power function method to interpolate and extend the early warning indicators of other time periods through the known early warning indicators of some time periods, apply them to Hengmiao Village and Gaolou Village, typical disaster prevention objects in Qufu City, Shandong Province, China, and verify the rationality of the results. The results show that the multiplicative power function method interpolates and extends the early warning indexes of 0.5, 1, 3, 6, 12, and 24 h under the same soil moisture condition. Based on the historical actual mountain flood disaster rainfall, the correlation coefficient R2 and Nash coefficient Ens are calculated to be 0.85 and 0.86, respectively, which verifies the applicability of this method. This study provides a convenient and quick way to unify the time series of regional early warning indicators and a feasible way for other regions to study regional overall early warning.

Multiobjective programming model for a class of flood disaster emergency material allocation

AbstractFlood disasters are a hot topic in the field of disaster prevention and control. To reduce the high‐density economic losses caused by floods, it is necessary to effectively and reasonably distribute emergency supplies to disaster sites during the emergency cycle. This article describes setting up a comprehensive multirescue site, multidisaster site, and multiobjective programming model to measure the total amount of time taken to transport supplies and economic losses endured in the response and recovery. Moreover, we use the evolutionary algorithm based on the Pareto concept and simulate the calculation using computer simulation. Finally, a case study is carried out based on various supply data of the historical flood disaster and the flood control supply reserves in the Jiangsu Province. The optimization results are discussed using the sorting method that approaches the ideal solution, and three feasible emergency plans are given, which can provide a reference for emergency supply transportation for urban flood control.

Holocene climatic controls on flooding regime along the Ussuri River in Northeast Asia

Knowledge of the long-term flooding response to climatic changes is critical for probing the flooding future in an oncoming warmer world. In this paper, three well-dated wetland sedimentary cores with high-resolution grain-size records were employed to reconstruct the historical flooding regime along the Ussuri River during the past 7000 years. The results show that five flooding-prone intervals marked by increased mean rates of sand-fraction accumulation occurred at 6.4–5.9 ka BP, 5.5–5.1 ka BP, 4.6–3.1 ka BP, 2.3–1.8 ka BP, and 0.5–0 ka BP, respectively. These intervals are generally consistent with the higher mean annual precipitation controlled by the strengthened East Asian summer monsoon which has been widely documented in geological records across the monsoonal regions of East Asia. Considering the prevalent monsoonal climate along the modern Ussuri River, we suggest that the regional flooding evolution during the Holocene Epoch should be generally controlled by the East Asian summer monsoon circulation which was initially linked to the ENSO activities in the tropical Pacific Ocean. While for the last interval spanning 0.5–0 ka BP, human influence, compared with the long-serving climatic controls, has played a more critical role in driving the regional flooding regime.

Flood forecasting based on machine learning pattern recognition and dynamic migration of parameters

Study regionTypical basin in semi-arid and semi humid areas in the middle reaches of the Yellow River Study focusFloods are among the most devastating natural disasters. Timely and accurate forecasting of runoff is crucial to safeguard human lives, minimize property damage, enhance the efficiency of reservoir power generation, and ensure the safety of water supply. In this study, the rainfall and flow data of 98 floods occurring between 1971 and 2014 in the Jingle sub-basin, a tributary of the Yellow River basin, China, were analyzed using dynamic clustering and random forest techniques to identify flood types and select appropriate model parameters. The Xin’anjiang model was then used for real-time flood forecasting. The results indicate that the rainfall characteristic indicators developed by the model can effectively identify potential flood types, and the model parameters determined by historical flood rates can be adapted and utilized for new forecasting tasks based on similarities. Ensemble forecasting results, which consider the probability of flood types, are superior to single fractal forecasting outcomes and diminish uncertainty. The proposed method can identify extreme flood events, facilitate flood classification and prediction, promote basin disaster mitigation, and enable the efficient use of water resources. New hydrological insights for the regionThe proposed flood classification and identification method effectively analyzes flood events in the basin. The floods that may occur are characterized by relevant statistical characteristics of rainfall, allowing for selection of corresponding flood forecasting model parameters for improved accuracy in real-time flood forecasting and extended prediction period.

Changes in Rainfall and Flood Characteristics under Nonstationarity in a Mountain Basin of Northwest China

Nonstationarity in hydrology has aroused great concern among people, and various influence factors have been proved to challenge the assumptions of the traditional hydrological analysis. For the selection of a reasonable flood frequency model and rationality of hydraulic engineering design, it is of great significance to study the variation characteristics of rainfall and floods and their relationship under the nonstationary condition. Taking the Yue River Basin, a mountain basin in northwest China, as an example, the nonparametric Mann-Kendall test and moving t-test methods were used to test the temporal trends and mutation of hydrological variables during the recent 53 years. The recession curve was derived from the historical flood series by the genetic algorithm, and the flood hydrographs were separated by the constant-slope base flow separation method. The results showed that the annual rainfall and runoff in the Yue River Basin during the recent 53 years exhibited insignificant increasing and decreasing trends, respectively. The abrupt change point of annual runoff series occurred in 1985. The average proportion of underground runoff changed from 21% before 1985 to 17% after 1985, and the relationship between rainfall and floods became weakened after 1985, indicating that the stationarity of flood was challenged.

The Trends and Effects of Flood Occurrences in the Shire River Basin in Chikwawa District of Malawi: A Historical Perspective (1980 - 2019)

Purpose: The purpose of this study was to examine historical trends in flooding in Malawi’s Shire River Basin (SRB). The basin is prone to severe flooding and most affected in the country. For many years, flooding has posed a significant threat to communities in and around the basin. Therefore, the main objective of the study was to document the historical perspective of flood occurrences in terms of frequency and impact in the Shire River Basin of Chikwawa District, Malawi, from 1980 to 2019. 
 Methodology: A mixed survey design was used for the study. Both random and purposive sampling were used to identify participants. A total of 384 respondents took part in the study. The study collected both primary and secondary data. Primary sources of data were collected using household surveys, interviews with key informants, and focus group discussions, while secondary sources of data were collected using documents from both non-governmental and Malawian governmental departments and agencies (MDAs) as well as existing literature. Data from focus group discussions (FGDs), interviews with community elders (CEIs), and key informant interviews (KIIs) were recorded in Chichewa with a voice recorder, transcribed in Chichewa, and then translated into English. Household survey data were processed using Statistical Package for Social Scientists (SPSS) version 25 and secondary data were converted to time series data and then analysed using STATA software to produce charts and graphs. 
 Findings: The results showed that all three hydrometeorological extremes (mean annual temperature, average rainfall and baseflow index) increased, suggesting that both the intensity and frequency of flooding in the Shire River Basin of Chikwawa District were also increasing in absolute terms. People’s perceptions of flooding showed that the Shire River Basin in Chikwawa District experienced excessive flooding every 5 years. This 5-year cycle was, among other factors the result of climate change and forest loss in the study area, leading to an increase in river sedimentation.
 Recommendations: This article found that the local people in the Shire River Basin of Chikwawa District will always live with increasing levels of flooding. Therefore, they should be proactive in flood management and understand the process as an ongoing activity for future risk assessment. Analysing the history of river basin flooding is important as it helps the Malawi's Department of Disaster Management Affairs (DoDMA) officials to be alert and estimate the time frame for the next flood in the Shire River Basin of Chikwawa District to strengthen the flood forecasting accuracy.

Research on Water Level Anomaly Data Alarm Based on CNN-BiLSTM-DA Model

With frequent extreme rainfall events caused by rapid changes in the global climate, many cities are threatened by urban flooding. Timely issuance of flood warnings can help prepare for disasters and minimize losses caused by floods. In this study, we propose a method based on a convolutional neural network-bidirectional long short-term memory-difference analysis (CNN-BiLSTM-DA) model for water level prediction analysis and flood warning. The method calculates and analyzes the difference sequence between water level monitoring values and water level prediction values, compares historical flood data to determine the alarm threshold for abnormal water level data, and achieves real-time flood warnings to provide technical references for flood prevention and mitigation. Taking Yancheng city, a low-lying city located in the plain area of Jiangsu Province in China, as an example, this study verifies the accuracy of the CNN-BiLSTM model in water level prediction, which can achieve an accuracy rate above 95%. This provides a reliable data basis for further determination of warning thresholds using the DA model. The CNN-BiLSTM-DA model achieves an accuracy rate of 85.71% in flood warnings without any missed reports, demonstrating that this method has scientific, practical, and accurate features in addressing flood warning issues.

Using Sentinel-1 and Google Earth Engine cloud computing for detecting historical flood hazards in tropical urban regions: a case of Dar es Salaam

This study investigates the potential of freely available Sentinel-1 imagery coupled with Google Earth Engine (GEE) for mapping and monitoring flooding in Dar es Salaam. Sentinel-1 images (n = 55) available during the rainy season (March–May) since 2016 were used and processed in GEE. For separating water and land surfaces, we used a histogram-based automatic thresholding method. The binarization accuracy was assessed using confusion matrix based on 1064 randomly generated points in GEE. Overall accuracy of 95% (Kappa = 0.90) were achieved. Dar es Salaam has experienced flood inundation per flood event on average over an area of 50 km2 in March 2019 and 2021. Territories located along the Ocean and inland water shores, built and bare ground were subject to flooding compared to other land cover types. Flooding inundations have been difficult to detect in the city center. With the current temporal and spatial resolution of Sentinel-1, flood detection in city centers remains a challenge yet. However, Sentinel-1 images, coupled with GEE cloud computing simplified flood mapping and monitoring in a large urban region and this approach can be applied in other large cities and their surroundings for countries where data gap and lack of processing tools are critical challenges.

Snow-detonated floods: Assessment of the U.S. midwest march 2019 event

Study regionIowa and the Nishnabotna watershed (Iowa), Midwest U.S. Study focusHistorically, Iowa and the Midwest have faced floods during the summer season. Some historical floods on record are the 2008 and 2013 floods. In March 2019, a meteorological bomb cyclone set the conditions for an unexpected major snow-related flood. This study (1) presents a comprehensive analysis of the March 2019 flood and asses the early-spring peak flows trends, (2) explores the use of a parsimonious hydrological model with a snow component, and (3) validates the model performance for the last 20 years. New Hydrological insightsThe March 2019 event was an extreme flood event that set records on at least 10% of the USGS gauges in Iowa. Moreover, the early spring peak flow analysis showed a significant increasing trend between February and April. In this period, the trend is positive for most gauges, with more than a 30% increase at an annual rate of 4% of the mean yearly peak flow. These findings showed the relevance of snow-detonated floods and their regional understanding. Considering the results' significance, we provided evidence that HLM and a conceptual snow component can represent, forecast, and provide insights regarding snow-detonated events.