Flash Floods Research Articles

Enhanced groundwater availability through rainwater harvesting and managed aquifer recharge in arid regions

Climate change in desert areas and semi-arid watersheds may offer a promising solution for the water scarcity problem that Bedouins and local inhabitants face. This study investigated the integrated water resources management in arid and semi-arid regions using rainwater harvesting in combination with the managed aquifer recharge (RWH-MAR) technique. The study also used recharge wells and storage dams to achieve the sustainability of groundwater supplies in the context of climate change and management of the flow to the Gulf of Suez. Therefore, different return periods of 10, 25, 50, and 100 years were considered for the annual flood volume resulting from those watersheds. Moreover, hydrologic modeling was carried out for the El Qaa plain area, South Sinai, Egypt, using the Watershed Modeling System (WMS) and the groundwater modeling of SEAWAT code. Our findings show that for different scenarios of climate change based on return periods of 10, 25, 50, and 100 years, the aquifer potentiality reached 24.3 MCM (million cubic meters) per year, 28.8 MCM, 36.7 MCM, and 49.4 MCM compared to 21.7 MCM at 2014 with storage of groundwater ranges 11.8%, 32.1%, 69%, and 127.4%, respectively. These findings have significant implications for the system of RWH-MAR and groundwater sustainability in El Qaa Plain, South Sinai. The RWH-MAR proved to be an effective approach that can be applied in different water-stressed and arid regions to support freshwater resources for sustainable future development and food security, as well as protect communities from extreme flash flood events.

Designing and Psychometric Evaluation of a Questionnaire for Assessing Society's Perception of Flash Flood Risk

Background: Public perception of risks associated with natural disasters like flash floods significantly influences disaster management effectiveness. Flash floods pose a major threat to life and property, and a lack of tools to evaluate societal risk perception creates a critical gap. Objectives: This study aimed to design and evaluate the psychometric properties of a questionnaire specifically assessing society's perception of flash flood risk. Methods: A two-stage approach was employed. The first stage involved collecting questionnaire items through a systematic literature review and a qualitative study. In the second stage, a comprehensive psychometric evaluation was conducted, including assessments of face validity, content validity, and construct validity. Reliability was established using Cronbach's alpha. Confirmatory factor analysis was further conducted with data collected from 136 community members who had experienced flash floods. Data analysis was performed using SPSS version 20 and AMOS software. Results: A combination of qualitative data and systematic review findings facilitated the development of 36 initial questionnaire items. Applying quantitative and qualitative construct validity measures led to the refinement of the instrument, resulting in 29 final items categorized into three domains: Risk perception (12 items), awareness (10 items), and preparedness (7 items). The internal consistency of the instrument was confirmed by a Cronbach's alpha coefficient of 0.88. Factor analysis further supported the good fit of the hypothesized model to the data. Conclusions: This study successfully developed and rigorously evaluated a questionnaire to assess society's perception of flash flood risk. This instrument offers valuable insights for informed decision-making and identification of factors influencing risk perception, ultimately contributing to improved disaster preparedness and management.

A high-resolution perspective of extreme rainfall and river flow under extreme climate change in Southeast Asia

Abstract. This article provides high-resolution information on the projected changes in annual extreme rainfall and high- and low-streamflow events over Southeast Asia under extreme climate change. The analysis was performed using the bias-corrected result of the High-Resolution Model Intercomparison Project (HighResMIP) multi-model experiment for the period 1971–2050. Eleven rainfall indices were calculated, along with streamflow simulation using the PCR-GLOBWB hydrological model. The historical period 1981–2010 and the near-future period 2021–2050 were considered for this analysis. Results indicate that, over former mainland Southeast Asia, Myanmar will face more challenges in the near future. The east coast of Myanmar will experience more extreme high-rainfall conditions, while northern Myanmar will have longer dry spells. Over the Indonesian maritime continent, Sumatra and Java will suffer from an increase in dry-spell length of up to 40 %, while the increase in extreme high rainfall will occur over Borneo and mountainous areas in Papua. Based on the streamflow analysis, the impact of climate change is more prominent in a low-flow event than in a high-flow event. The majority of rivers in the central Mekong catchment, Sumatra, Peninsular Malaysia, Borneo, and Java will experience more extreme low-flow events. More extreme dry conditions in the near future are also seen from the increasing probability of future low-flow occurrences, which reaches 101 % and 90 %, on average, over Sumatra and Java, respectively. In addition, based on our results over Java and Sumatra, we found that the changes in extreme high- and low-streamflow events are more pronounced in rivers with steep hydrographs (rivers where flash floods are easily triggered), while rivers with flat hydrographs have a higher risk in terms of the probability of low-flow change.

Comparison of two hydrodynamic models for their rain-on-grid technique to simulate flash floods in steep catchment

In this study, two hydrodynamic models, TELEMAC-2D and HEC-RAS 2D, were compared for their Rain-on-Grid (RoG) technique with a particular focus on runoff generation processes in a small and steep catchment. Curve number (CN) method was applied in both the models to simulate two single storm events up to 20 h of duration, whereas the Green-Ampt Redistribution (GAR) method was additionally applied in HEC-RAS 2D for a multi-peak flood event with sustained flow between the peaks. CN and GAR methods were compared for this flood event, and a sensitivity analysis of the GAR parameters was also done. Moreover, the two models were compared for their calibration process, computational time, mesh size and shape, and model availability, in general, as well as the results including inundated areas, water depth, and velocity. The results indicate that both the models are capable of reproducing short duration single storm floods. NSE and R2 for both models ranged from 0.70 to 0.90 and from 0.93 to 0.95. However, the models struggled to reproduce the long- duration multi-peak flood event. The sensitivity analysis showed that the results are not very sensitive to the two GAR parameters which are responsible to influence the flow of the second peak in the flood event. Neither the CN nor the GAR infiltration method successfully replicated such events because the hydraulic models permanently lose infiltrated water from the domain. The returned sub-surface flow significantly contributes to river flow during these flood events; however, none of the model incorporates a return flow algorithm.

STUDY OF FLOOD VULNERABILITY IN HIMALAYAN REGION: A CASE OF UTTRAKHAND

India's Himalayan area is well known for its magnificent scenery and abundant wildlife. However, because of its complicated geology, delicate ecosystems, and unpredictable climate, it is also extremely vulnerable to catastrophic calamities. The causes, effects, and mitigation techniques of the common natural catastrophes that affect India's Himalayan region—such as earthquakes, landslides, floods, and glacial lake outburst floods (GLOFs)—are briefly summarised in this summary. Because of the collision of the Indian and Eurasian tectonic plates, earthquakes are one of the most dangerous natural disasters in the Himalayan region. Major seismic events, like the one that struck Nepal in 2015, demonstrate how vulnerable the region's heavily inhabited areas are to devastating destruction and high fatality rates. Effective disaster preparedness and resilient infrastructure are crucial for minimizing casualties and damage. Another common risk in the Himalayan region is landslides, which can be brought on by unstable slopes, strong rains, and seismic activity. The region's susceptibility is exacerbated by these occurrences, which represent a major threat to natural ecosystems, transportation networks, and towns, particularly during the monsoon season. In the Himalayan region, floods are a common occurrence that are exacerbated by glacial melt, heavy rainfall, and glacier lake outbursts. Flash floods, like the ones that occurred in Uttarakhand in 2013, highlight the terrible effects on infrastructure, communities, and the environment. Reducing flood risks and increasing resilience requires community-based adaptation strategies, early warning systems, and sustainable land use planning. A unique hazard in the Himalayan region, glacial lake outburst floods (GLOFs) are caused by the rapid melting of glaciers and the creation of unstable glacial lakes. The communities, infrastructure, and ecosystems downstream are seriously threatened by these catastrophes, which calls for careful observation and risk assessment techniques. One of the worst natural catastrophes to have hit the state in recent memory is the flash floods in Uttarakhand. Because to the state's distinctive geography, which is made up of small valleys and steep hills, heavy rainfall events have a greater effect, especially during the monsoon season. 2013's devastating flash floods, brought on by abnormally high rainfall, caused extensive environmental damage, infrastructure ruin, and a great deal of fatalities. The abrupt influx of water, rubble, and silt caused great destruction to isolated communities, tourist attractions, and places of worship, underscoring the precariousness of human habitation in the foothills of the Himalayas. The incident also brought attention to the relationship between unplanned development, environmental deterioration, and the likelihood of disasters, spurring calls for more land use planning, early warning systems, and sustainable development techniques future flash floods in Uttarakhand. In conclusion, the broad range of natural hazards that the Himalayan area of India faces calls for multifaceted methods to disaster risk reduction and management. In this dynamic and vulnerable setting, scientific research, community participation, and sustainable development practices are critical components of effective resilience-building efforts. Keywords: Hilly areas natural disaster, flash flood, Glacial lake outburst, flash flood in Uttarakhand

Changes in Mesoscale Convective System Precipitation Structures in Response to a Warming Climate

AbstractMesoscale convective systems (MCSs) are crucial components of the hydrological cycle and often produce flash floods. Given their impact, it is important to understand how they will change under a warming climate. This study uses a satellite‐ and radar‐based MCS tracking algorithm on convection‐permitting climate model simulations and examines changes in MCS properties and precipitation structures between historical and future simulations. An underestimation in MCS total precipitation is evident in historical simulation compared to observations, due to model's depiction of MCS precipitation area and summertime occurrence frequency. Under pseudo‐global warming, increases in MCS frequency and total warm season precipitation are observed, most notably in the southern U.S. The precipitation intensity and precipitating area generated by future MCSs also rises and results in an increase in precipitation volume. MCS precipitation structures are further classified into convective core and stratiform regions to understand how change in these structures contributes to future rainfall changes. In a warmer climate, the stratiform region demonstrates minimal change in size, but increases in mean precipitation rate and mean maximum precipitation rate by 15% and 29% are noted, respectively. A more robust future response is observed in the convective core region, with its size, mean precipitation rate and mean maximum precipitation rate increasing significantly by 24%, 37% and 42%, respectively. Finally, by examining the environmental properties of MCS initial condition, future intensification of convective rain may be attributed to a combined effect of substantial increases in atmospheric instability and moisture availability.

Cumulative sedimentation hazard map of urban areas subject to hyperconcentrated flash flood: A case study of Suide County in the Wuding River basin, China

AbstractFlash floods can carry substantial sediment, posing significant sedimentation hazards in hilly cities. The sedimentation hazard map can reproduce the sediment thickness and extent of an extreme events scenario, playing an important role in sediment risk management. However, current research primarily focuses on modeling the inundation area and depth of floods, while studying sedimentation hazard caused by flash floods in urban areas remains insufficient. This paper aims to address this gap by utilizing a numerical model that simulates hyperconcentrated flow in hilly urban areas using the two‐dimensional hydro‐sediment‐morphological model to compile the cumulative sedimentation hazard map. The model, built upon the open‐source TELEMAC‐MASCARET framework, incorporates Zhang Hongwu's formula to simulate sediment‐carrying capacity, particularly suitable for hyper‐sediment concentration near the riverbed. This paper uses the data of extreme flash flood events in the Wuding River basin in 2017 to simulate and compile the cumulative sedimentation hazard map. The hazard map delineates the sedimentation hazard extent and level attributable to overbank floodplain sedimentation. Notably, the sediment thickness is highest in areas near the levees on both sides of the Dali River. Moreover, the map illustrates the extent of channel erosion resulting from hyperconcentrated floods, which could jeopardize bank stability.

Fluviomorphic trajectories for dryland ephemeral stream channels following extreme flash floods

AbstractEphemeral alluvial streams pose globally significant flood hazards to human habitation in drylands, but sparse data for these regions limit understanding of the character and impacts of extreme flooding. In this study, we document decadal changes in dryland ephemeral channel patterns at two sites in the lower Colorado River Basin (southwestern United States) that were ravaged by extraordinary flash floods in the 1970s: Bronco Creek, Arizona (1971), and Eldorado Canyon, Nevada (1974). We refer to these two floods as ‘fluviomorphic erasure events’, because they produced blank slates for the channels that were gradually moulded by more frequent but much smaller flood events. We studied georectified aerial photos that span ~60 years at each site to show that both study sites recovered to their pre‐flood condition after ~25 years. We employ channel network metrics: stream‐link area (SLA), geometric braiding index and junction‐node density. Each metric decreased during the short‐duration extreme flood erasure events. Subsequently, a fluviomorphic trajectory at a decadal tempo returned the channels to pre‐flood values. The SLA decreased at rates of 3.6%–4.1% per year in the decade following the floods. The extreme flood events decreased the pre‐flood geometric braiding index at the two sites by 56%–68%, and it took 15–24 years for this index to recover to pre‐flood values. In contrast, it took 30–35 years for the channels to recover to a uniform pre‐flood channel form, as indicated by the spatial distribution of bars and junction nodes. Our results document baseline examples of ephemeral stream channel evolution trajectories, as future climatic change will likely accelerate increases in the magnitudes and frequencies of extreme floods and geomorphic erasure events.

Machine learning-based potential loss assessment of maize and rice production due to flash flood in Himachal Pradesh, India.

Flash floods in mountainous regions like the Himalayas are considered to be common natural calamities. Their consequences often are more dangerous than any flood event in the plains. These hazards not only put human lives at threat but also cause economic deflation due to the loss of lands, properties, and agricultural production. Hence, assessing the impact of such hazards in the existing agricultural system is of utmost importance to understand the probable crop loss. In this paper, we studied the efficiency of the remotely sensed microwave data to map the croplands affected by the flash flood that occurred in July 2023 in Himachal Pradesh, a mountainous state in the Indian Himalayan Region. The Una, Hamirpur, Kangra, and Sirmaur districts were identified as the most affected areas, with about 9%, 6%, 5.74%, and 3.61% of the respective districts' total geographical area under flood. Further, four machine learning algorithms (random forest, support vector regressor, k-nearest neighbor, and extreme gradient boosting) were evaluated to forecast maize and rice crop production and potential loss during the Kharif season in 2023. A regression algorithm with ten predictor variables consisting of the cropland area, two vegetation indices, and seven climatic parameters was applied to forecast the maize and rice production in the state. Amongst the four algorithms, random forest showed outstanding performance compared to others. The random forest regressor estimated the production of maize and rice with R2 more than 0.8 in most districts. The mean absolute error and the root mean squared error obtained from the random forest regressor were also minimal compared to the others. The maximum production loss of maize is estimated for Solan (54.13%), followed by Una (11.06%), and of rice in Kangra (19.1%), Una (18.8%) and Kinnaur (18.5%) districts. This indicated the utility of the proposed approach for a quick in-season forecast on crop production loss due to climatic hazards.

Determining geo-disaster chains probabilities and disaster mitigation mode: A meta-analytical perspective

In a harsh geological environment, different geo-disasters might co-occur or be in a chain sequence. Currently, existing research in related fields documents numerous instances of disaster chains and responses, which provided this study with important materials for determining disaster chains probability and summarized the disaster prevention and mitigation (DPM) mode. In this study, through the depth of the geo-disaster chains cases and the DPM cases, enabled a mitigation mode analysis summary. We take the case of geo-disaster chains as an example for this study, and represent 20 typical geo-disaster chains by data fusion and structuring of 139 geo-disaster chains cases.The probability assessment of geo-disaster chains is crucial for risk management and regional resilience. We introduced a Bayesian network (BN) model to describe the process of geo-disaster chains formation to map the structure and assess the joint probability of geo-disaster chains. The BN results showed that the longer the disaster chains length, the smaller the probability of the chains occurring. Among these, the probability of “Collapses-landslides-debris flows–barrier lakes–flash floods” is the lowest (0.069). Through sensitivity analysis, it is found that precipitation is the most sensitive factor in the development of geo-disaster. Interpretive structural modeling (ISM) is employed to examine the DPM index system across three key aspects: disaster emergency preparation, disaster response, and disaster recovery, and the conceptualized interaction among these components. ISM results showed that government policy intervention and scientific DPM measures can effectively reduce the losses caused by the development of geo-disaster chains. This research combined the occurrence process of the disaster chains with the disaster response process, and determined the entire chains of characterization from disaster understanding to disaster response, and helped reduce the vulnerabilities caused by disasters.

Application of BIM+GIS Integration Technology in the Construction of Urban Intelligent Water Informatization

Urban intelligent water informatization is the process of managing and operating urban water systems with the use of contemporary information and communication technology in order to increase productivity, security, and sustainability. Establishing the appropriate infrastructure is the initial stage in building an intelligent water system so that management can choose wisely how to manage and operate the water system. Building information modeling (BIM) is a digital depiction of a building's or infrastructure's structural and functional details. In most metropolitan regions, a fast development phase is predicted to cause several water issues, including water resource shortages, floods, poor drainage, and water resource contamination. As a result, a practical approach is required for urban intelligent water information technology with the help of artificial intelligence techniques. Intelligent water systems may be built using Building Information Modeling (BIM) software to increase the system's effectiveness, stability, and durability. By offering a potent tool for storing and analyzing geographical data connected to the water distribution system, a geographic information system (GIS) can play a crucial role in the development of urban intelligent water informatization using ArcGIS mapping Software. The Internet of Things (IoT) system is used to implement intelligent water informatization, hardware implementation using ESP32, and software development using MQTT Broker and Node-Red. As a result, we recommended utilizing BIM+GIS integration technology for building urban intelligent water informatization. The suggested system is evaluated using management efficiency, urban flood control, innovative Construction, intensive management, and computation cost. In this research, the main objective is to identify where there are water leaks broken pipes and early warning system and monitoring system for flash flood disasters in urban areas. The result findings show that the integration of BIM and GIS provides a comprehensive platform for managing complex water informatization infrastructure projects and maximizes efficiency and sustainability.

Experimental study on Local Scour around Bridge Pier Models generated by Flash Floods carrying Debris

The analysis of local scouring around bridge piers currently is based on typical river flood discharges. However, extreme events like flash floods with high velocity and discharge, carry wooden debris that accumulates upstream of the bridge pier. This results in additional forces due to flow diversion and reduction in flow area, so impacting hydraulic structures and exacerbating the scouring process. The study objective was to examine the impact of debris transported by flash floods on the depth and distribution patterns of scouring around bridge piers. This research was conducted in a 15.5-m-length, 0.5-m-width, and 1.0-m-height glass flume, layered by 25 cm thick of sand which is d50=0.52 mm and σg=2.327. Flash flood simulations were conducted by flowing the water mixed with debris content of 0, 5, 10, and 15% to hit a sharp nose pier model which is installed in the channel. Scour depth was measured using point gauges at 50 points around the pier. The findings indicated that an irregular distribution pattern of scour is formed around the pier, and scour depth decrease when increasing debris content. It is necessary to corrected in the range of 0.65-1.43 for 0-15% debris content carried by the flash flood compared to normal flood.

Anticipating the impact of glaciers, landslides and extreme weather events on vulnerable hydropower projects and the development of an integrated multi-hazard warning system (IMWS)

The Himalayan river basins offer great potential for hydropower development, but they are also vulnerable to various hazards such as debris flows, landslides, flash floods, glacial lake outburst floods (GLOFs), and landslide lake outburst floods (LLOFs). Despite the regional and global significance of these hazards, there is a lack of information and data on different aspects, including meteorology, hydrology, geology, and seismology. Many hazards often go unnoticed or receive little attention until they start affecting humans and their activities like damage to buildings, infrastructure, and other human-made structures. It is important to recognize that hazards can have severe and long-lasting impacts on society, even when they do not directly affect humans. For example, flash floods can disrupt ecosystems, destroy habitats, and threaten biodiversity and the complexity of climatic influences on both regional and local scales cannot be overlooked. Therefore, we highlight the importance of basin-wise and basin-wide continuous long-term monitoring in the Himalaya. It is also recommended that the highest hydropower projects in the basins should have their network of hydro-meteorological observatories at different altitudes with a provision of real-time data transmission and deployment of a multi-hazard warning system (IMWS) for flash floods.

FLASH FLOODS: RISK ASSESSMENT AND MITIGATION

In recent years, United Arab Emirates has experienced several flash flood events and extreme cyclonic events that resulted in flooding various parts of the country. This in return raised concerns on the preparedness of the infrastructure to such events considering the damage caused by them. Roads were blocked from the excessive rainfall water leading to heavy traffic jams, houses were drowned and cars were swept away. Therefore, an in-depth study was performed on a selected area of interest in the city of Ras Al-Khaimah (UAE) to explore the risks associated with flash flood events and to propose solutions for flood management. Hydrological and hydraulic models were used to simulate precipitation runoffs and calculate flood magnitudes in order to plan and design new flood control and hydraulic structures. Using the output of the hydrologic and hydraulic models, a dam and a protection channel were proposed as possible solutions to resolve flood issues in the selected area. It was concluded that with proper hydrologic modelling, flash floods can be managed to a great extent and damage to the infrastructure can be minimized. It is even more important in context of climate change that such modelling techniques should be part of urban planning and design of infrastructure drainage systems for areas that are prone to heavy rainfall and floods.

FLASH FLOODS: RISK ASSESSMENT AND MITIGATION

In recent years, United Arab Emirates has experienced several flash flood events and extreme cyclonic events that resulted in flooding various parts of the country. This in return raised concerns on the preparedness of the infrastructure to such events considering the damage caused by them. Roads were blocked from the excessive rainfall water leading to heavy traffic jams, houses were drowned and cars were swept away. Therefore, an in-depth study was performed on a selected area of interest in the city of Ras Al-Khaimah (UAE) to explore the risks associated with flash flood events and to propose solutions for flood management. Hydrological and hydraulic models were used to simulate precipitation runoffs and calculate flood magnitudes in order to plan and design new flood control and hydraulic structures. Using the output of the hydrologic and hydraulic models, a dam and a protection channel were proposed as possible solutions to resolve flood issues in the selected area. It was concluded that with proper hydrologic modelling, flash floods can be managed to a great extent and damage to the infrastructure can be minimized. It is even more important in context of climate change that such modelling techniques should be part of urban planning and design of infrastructure drainage systems for areas that are prone to heavy rainfall and floods.

Impacts of Flash Flood on Ground Water Quality: Case Study of Central River Region, The Gambia

Floods rank as one of the most common natural calamities, impacting the lives of millions across the globe. With ongoing population growth and expanding water and land usage, the potential for contamination and human disturbances to negatively impact water bodies continues to rise globally. This paper aims to comprehensively analyze the repercussions of floods on the groundwater quality of a region called CRR in the Gambia, during the July 2022 flood event. The focus lies on exploring the change in water quality parameters of twelve water samples from wells and boreholes through laboratory analysis of eight parameters such as pH, turbidity, temperature, electrical conductivity, total dissolved solids, nitrate, iron, sulfate, and microbial proliferation. The laboratory analysis results of the physiochemical parameters such as turbidity, and iron exceeded the safe drinking water standards set by the World Health Organization (WHO) in about 60% of the sites and fecal coliform presence in 75 % of the samples. The other parameters varied differently across all sites. However, most of the values are observed to be more prominent at the open well than borehole. The Water Quality Index (WAWQI) score also shows that 1 site has excellent water quality, 25%, has good quality and 25% has fair quality. The rest of the sites which make up 42% of the samples, are all of poor quality and unsuitable for drinking purposes. The results provide a crucial groundwork for subsequent studies targeting the water quality in this area.

Virtual reality visualization of geophysical flows: A framework

This paper presents a comprehensive Virtual Reality (VR) based framework for visualizing numerical simulations of geophysical flows in a realistic and immersive manner. The framework allows integrating output data from various mesh-based Eulerian numerical models into a VR environment, enabling users to interact with and explore the terrain and geophysical flows through the VR experience. Three case studies, including a snow avalanche, quick clay landslide, and flash flood in Norway, demonstrate its versatility. The VR environment offers intuitive menus and user interactions, allowing users to read flow depth and velocity values, facilitating a direct link between numerical data and their visual representation. This framework can reshape geophysical flow hazard identification and disaster management by integrating physics-based numerical modeling results into VR Environments, thus enhancing knowledge dissemination among experts, the general public, non-expert stakeholders, and policymakers. The paper also highlights challenges and opportunities identified during the integration, guiding future developments.

Simbi: historical hydro-meteorological time series and signatures for 24 catchments in Haiti

Abstract. Haiti, a Caribbean country, is highly vulnerable to hydroclimatic hazards due to heavy rainfall, which is partly linked to tropical cyclones. Additionally, its steep slopes generate flash floods, particularly in small catchments. Moreover, the hydrology of this region remains poorly understood and understudied. Unfortunately, there is no accessible database for the scientific community to use in this country. To fill this gap, hydroclimatic data were collected to create the first historical database in Haiti. This database, called Simbi (guardian of rivers, freshwater, and rain in Haitian mythology), includes 156 monthly rainfall series over the period 1905–2005, 59 daily rainfall series over the period 1920–1940, 70 daily streamflow series, and 23 monthly temperature series, not necessarily continuous, over the period 1920–1940. It also provides simulated streamflow series over the period 1920–1940 using the GR2M and GR4J rainfall–runoff models for 24 catchments and 49 attributes covering a wide range of topographic, climatic, geological, land use, hydrogeological, and hydrological signature indices. Simbi is the first open-access hydro-meteorological dataset for Haiti and will contribute to a better knowledge of hydrological risk in Haiti. Several sources of uncertainty associated with Simbi are acknowledged, including data quality (historical data), digitisation of paper archives, identification of relevant rain gauges, and rainfall–runoff models. It is important to consider these uncertainties when using Simbi. The database will be regularly updated to include additional historical data that will be digitised in the future. It will thus contribute toward better knowledge of the hydrology of Haitian catchments and will enable the implementation of various hydrological calculations useful for designing structures or flow forecasting. Simbi is an open-access database and is available for download at https://doi.org/10.23708/02POK6 (Bathelemy et al., 2023).

"FLOODGUARD: An IoT-Powered Real-Time Flash Flood Monitoring and Forecasting System"

Abstract: In recent times, the surge in flooding incidents has become a pressing global issue, inflicting extensive damage on both lives and livelihoods. While floods remain an inevitable force of nature, their devastating impact can be alleviated. With the aid of cutting-edge technologies like the Internet of Things (IoT), proactive flood forecasting has become increasingly feasible. Through the seamless integration of IoT data, populations can receive early warnings and evacuate to safety, safeguarding both lives and valuables. A real-time solution is imperative, leveraging IoT data streams to deliver timely flood alerts. Our study presents an IoT-driven prototype designed to collect hydrological data from rivers, encompassing metrics such as water flow, level, and discharge, alongside meteorological parameters like temperature, humidity, wind speed, and direction. Employing a Long Short-Term Memory (LSTM) model, we analyze and classify collected data based on water discharge, level, rainfall, and temperature, predicting flood events as "no alert," "yellow alert," "orange alert," or "red alert." Given the dynamic nature of rivers, accurately measuring total water discharge poses challenges. To address this, we propose a novel methodology utilizing water flow, sectional area width, and average depth. Furthermore, we tackle the complexities associated with accurately measuring rainfall due to erratic weather conditions. Our system demonstrates robust performance in predicting flood event states, achieving high F1-scores of 97% for "no alert," 97% for "yellow alert," 96% for "orange alert," and 98% for "red alert.

Predicting post-fire hydrological and erosive catchment response during rainfall events. A comparison of OpenLISEM and MOHID Land models

Wildfires are a source of instability for the natural water cycle in forested watersheds, endangering the water quantity and quality reaching downstream water bodies. The faster hydrological response of a burned area leads to increased runoff and transport of sediment and ash particles during and after rainfall events. Therefore, the use of an adequate spatiotemporal resolution in hydrological models is necessary to properly estimate post-fire impacts. Especially when addressing hydrological events such as flash floods and debris flows, which are highly unpredictable and are characterized by short duration and high impact outside the burned area.This study aims to compare the ability of two hydrological models to simulate the hydrological response and sediment transport during the first year after a fire to ultimately understand which one would best serve as a post-fire hydrological predicting tool at event scale. To achieve this goal, OpenLISEM, an event-based hydrological model, and MOHID Land, a continuous model with variable timestep, were compared. Driven by several limitations identified in previous modeling exercises at this scale during the calibration phase, this work performed a parametrization through the variation in boundary conditions characterizing each event.OpenLISEM and MOHID Land models exhibited similar capabilities in simulating runoff during the first post-fire year. However, the larger erosion input parameters required by MOHID Land increase the complexity of erosion prediction and increase equifinality. In addition, MOHID Land limited capacity to perform sensitivity and uncertainty analyses emerged as a major disadvantage, hindering the assessment of the reliability of the model's predictions. Despite its limitations for not integrating subsurface flow and base flow, OpenLISEM is the most suitable model for assessing post-fire impacts on runoff and sediment production at the event scale, because of its ease of implementation and its reduced computational requirements.