Hydrological Hazards Research Articles

Methodology to determine the embedment depth of bridge piers considering the duration of rainfall events

Nowadays, there are many bridges that show damage or collapse due to scour, which produces serious consequences in direct and indirect costs. Although there are several studies that analyze scour in bridges, many of them consider that rainfall events are of unlimited duration, without evolution over time, which produces a conservative estimate. This work shows a methodology to develop fragility curves in bridges due to scour, evaluating the scour depth from its construction to the service life of the structure. The methodology used characterizes hydrological hazard, by evaluating the parameters: number of events, arrival time, and intensity and duration, as random series of Poisson processes. Additionally, the depth and average velocity of the flow are determined at the bridge site. With this information, scour depth values are obtained. Considering the overturning of the pier, the loss of support of the deck and the resistance of the pier as damage states, for each one the probability of failure during the useful life of the bridge is obtained. At the end of the work, some recommendations are included for defining the depth at which the pile should be placed to avoid scour.

Predictive Understanding of Links Between Vegetation and Soil Burn Severities Using Physics‐Informed Machine Learning

AbstractBurn severity is fundamental to post‐fire impact assessment and emergency response. Vegetation Burn Severity (VBS) can be derived from satellite observations. However, Soil Burn Severity (SBS) assessment—critical for mitigating hydrologic and geologic hazards—requires costly and laborious field recalibration of VBS maps. Here, we develop a physics‐informed Machine Learning model capable of accurately estimating SBS while revealing the intricate relationships between soil and vegetation burn severities. Our SBS classification model uses VBS, as well as climatological, meteorological, ecological, geological, and topographical wildfire covariates. This model demonstrated an overall accuracy of 89% for out‐of‐sample test data. The model exhibited scalability with additional data, and was able to extract universal functional relationships between vegetation and soil burn severities across the western US. VBS had the largest control on SBS, followed by weather (e.g., wind, fire danger, temperature), climate (e.g., annual precipitation), topography (e.g., elevation), and soil characteristics (e.g., soil organic carbon content). The relative control of processes on SBS changes across regions. Our model revealed nuanced relationships between VBS and SBS; for example, a similar VBS with lower wind speeds—that is, higher fire residence time—translates to a higher SBS. This transferrable model develops reliable and timely SBS maps using satellite and publicly accessible data, providing science‐based insights for managers and diverse stakeholders.

Hydrological hazards at the mouth of the Pechora River

Hydrological hazards at the mouth of the Pechora River

Using supervised machine learning for regional hydrological hazard estimation in metropolitan France

Study regionThis study is carried out for 1929 gauged catchments in France, ranging from 1 to 10,000 km², where quality hydrometric observations are available for flood frequency analysis. Study focusThe regional estimation of hydrological hazards is studied for flood risk management and prevention in hydrology. For gauged catchments, flow quantiles can be estimated from observations using statistical approaches based on suitable probability distributions or simulation approaches based on rainfall-runoff transformation models. For ungauged catchments, the lack of hydrological observations means that we have to extrapolate our knowledge of hazards from gauged catchments to ungauged catchments, using regionalization methods. It is therefore necessary to combine regionalization methods with the implemented hazard estimation approach. In this paper, two popular machine learning methods, Random Forest and Neural Networks, are tested and compared as regionalization methods. A classical regionalization method using multiple linear regression is also applied as a benchmark to evaluate the performance of all configurations. All these regionalization methods are applied to a simulation-based approach (the SHYREG method) and to a statistical-based approach using generalized extreme value distribution (GEV). New hydrological insights•Regionalization approaches based on multiple linear regression have limitations to explain parameters with environmental descriptors in Regional Flood Frequency Analysis (RFFA) domain.•Regionalizing RFFA parameters using Random Forest allows more explanatory variables to be considered through non-linear relationships, resulting in better parameter estimation.•Machine learning techniques can better handle environmental descriptors for regionalization, this providing a notable performance improvement, especially for the statistical approach.•The tested simulation-based approach is less sensitive to the choice of spatial interpolation method than the studied statistical approach.

A hydrologic signature approach to analysing wildfire impacts on overland flow

AbstractPost‐fire flooding and debris flows are often triggered by increased overland flow resulting from wildfire impacts on soil infiltration capacity and surface roughness. Increasing wildfire activity and intensification of precipitation with climate change make improving understanding of post‐fire overland flow a particularly pertinent task. Hydrologic signatures, which are metrics that summarize the hydrologic regime of watersheds using rainfall and runoff time series, can be calculated for large samples of watersheds relatively easily to understand post‐fire hydrologic processes. We demonstrate that signatures designed specifically for overland flow reflect changes to overland flow processes with wildfire that align with previous case studies on burned watersheds. For example, signatures suggest increases in infiltration‐excess overland flow and decrease in saturation‐excess overland flow in the first and second years after wildfire in the majority of watersheds examined. We show that climate, watershed and wildfire attributes can predict either post‐fire signatures of overland flow or changes in signature values with wildfire using machine learning. Normalized difference vegetation index (NDVI), air temperature, amount of developed/undeveloped land, soil thickness and clay content were the most used predictors by well‐performing machine learning models. Signatures of overland flow provide a streamlined approach for characterizing and understanding post‐fire overland flow, which is beneficial for watershed managers who must rapidly assess and mitigate the risk of post‐fire hydrologic hazards after wildfire occurs.

Features of the Geoinformation Monitoring System Using Microwave and Optical Tools

The article discusses the issues of GIMS-technology that combines the methodology of GIS-technology and simulation modeling, giving it predictive functions when solving problems of environmental monitoring of the environment. The relationships between experimental data, algorithms and models of environmental processes are analyzed to implement effective operational control and diagnostics of the environment. Particular attention is paid to remote microwave sensing sensors, which ensure the implementation of the functions of GIMS-technology when solving specific problems of monitoring natural systems. For example, the use of microwave technology makes it possible to quickly obtain data on the state of soil moisture, and salinity water bodies, assess the possibility of critical hydrological situations and monitor the condition of hydraulic structures in regions with increased hydrological hazard. It is noted that GIMS-technology ensures the restoration of the spatial distribution of geoecosystem characteristics based on the data of route and ground measurements, which are characterized by fragmentation in space and episodicity in time. GIMS-technology makes it possible to overcome situations of irreducible information uncertainty, using the evolutionary modeling technique for this. The use of optical sensors and spectroellipsometry and spectrophotometry technologies makes it possible to calculate indicators of the quality of water resources, assessing the content of chemical elements in water and the presence of pollutant stains on the water surface.

Variation and attribution of probable maximum precipitation of China using a high-resolution dataset in a changing climate

Abstract. Accurate assessment of the probable maximum precipitation (PMP) is crucial in assessing the resilience of high-risk water infrastructures, water resource management, and hydrological hazard mitigation. Conventionally, PMP is estimated based on a static climate assumption and is constrained by the insufficient spatial resolution of ground observations, thus neglecting the spatial heterogeneity and temporal variability of climate systems. Such assumptions are critical, especially for China, which is highly vulnerable to global warming in ∼ 100 000 existing reservoirs. Here, we use the finest-spatiotemporal-resolution (1 d and 1 km) precipitation dataset from an ensemble of machine learning algorithms to present the spatial distribution of 1 d PMP based on the improved Hershfield method. Current reservoir design values, a quasi-global satellite-based PMP database, and in situ precipitation are used to benchmark against our results. The 35-year running trend from 1961–1995 to 1980–2014 is quantified and partitioned, followed by future projections using the Coupled Model Inter-comparison Project Phase 6 simulations under two scenarios. We find that the national PMP generally decreases from southeast to northwest and is typically dominated by the high variability of precipitation extremes in northern China and high intensity in southern China. Though consistent with previous project design values, our PMP calculations present underestimations by comparing them with satellite and in situ results due to differences in spatial scales and computation methods. Interannual variability, instead of the intensification of precipitation extremes, dominates the PMP running trends on a national scale. Climate change, mainly attributed to land–atmosphere coupling effects, leads to a widespread increase (> 20 %) in PMP across the country under the SSP126 scenario, which is projected to be higher along with the intensification of CO2 emissions. Our observation- and modeling-based results can provide valuable implications for water managers under a changing climate.

Opportunities and challenges for precipitation forcing data in post‐wildfire hydrologic modeling applications

AbstractThe frequency and extent of wildfires have increased in recent decades with immediate and cascading effects on water availability in many regions of the world. Precipitation is used as primary input to hydrologic models and is a critical driver of post‐wildfire hydrologic hazards including debris flows, flash floods, water‐quality effects, and reservoir sedimentation. These models are valuable tools for understanding the hydrologic response to wildfire but require accurate precipitation data at suitable spatial and temporal resolutions. Wildfires often occur in data‐sparse, headwater catchments in complex terrain, and post‐wildfire hydrologic effects are particularly sensitive to high‐intensity, short‐duration precipitation events, which are highly variable and difficult to measure or estimate. Therefore, the assessment and prediction of wildfire‐induced changes to watershed hydrology, including the associated effects on ecosystems and communities, are complicated by uncertainty in precipitation data. When direct measurements of precipitation are not available, datasets of indirect measurements or estimates are often used. Choosing the most appropriate precipitation dataset can be difficult as different datasets have unique trade‐offs in terms of spatial and temporal accuracy, resolution, and completeness. Here, we outline the challenges and opportunities associated with different precipitation datasets as they apply to post‐wildfire hydrologic models and modeling objectives. We highlight the need for expanded precipitation gage deployment in wildfire‐prone areas and discuss potential opportunities for future research and the integration of precipitation data from disparate sources into a common hydrologic modeling framework.This article is categorized under: Science of Water > Hydrological Processes Science of Water > Methods Science of Water > Water and Environmental Change

Utilizing the HEC-HMS Model for Hydrological Modelling of the Rambiara Watershed: Rainfall and Runoff Estimation

Abstract: Hydrological modelling and flood prediction are essential components of the Rambiara watershed's water resource management. To improve disaster preparedness and reduce the danger of flooding in the area, the effectiveness of the HEC HMS model in flood forecasting must be assessed. In this study, various techniques for estimating rainfall are examined, and a customized hydrological model tailored to the hydrological complexities of the Rambiara watershed is developed. The goal of this project is to provide practical insights for better flood risk reduction and water resource management through the integration of cutting-edge modelling tools and rigorous analysis. This work advances our knowledge of watershed dynamics and provides guidance for sustainable water resource planning projects in the Rambiara watershed and beyond by thoroughly examining precipitation patterns and hydrological processes. In addition, the research takes into account a number of factors related to flood behaviour, such as determining places that are vulnerable to flooding, analysing the size of floods, and estimating how frequently they occur. By taking a holistic approach, it is possible to gain a thorough understanding of the dynamics of flooding in the Rambiara watershed, which paves the way for the development of efficient flood risk management techniques. This study looks into the temporal and spatial variability of rainfall in the Rambiara watershed in addition to research on floods. Through the comparison of several methods for estimating rainfall, including deterministic and geostatistical approaches, the study offers valuable insights into the precision and dependability of rainfall data, which are essential for hydrological modelling and flood forecasting. The hydrological processes of the Rambiara watershed can be more accurately represented by creating a bespoke hydrological model with the HEC HMS program. Informed decision-making and proactive flood risk reduction strategies are made possible by the study's model calibration and validation, which guarantee the model's dependability in simulating runoff and forecasting flood events. Overall, this study advances our knowledge of flood dynamics in the Rambiara watershed and advances hydrological modelling tools. The goal of this study is to promote sustainability and resilience in the face of hydrological hazards by offering practical insights for managing water resources and reducing the danger of flooding.

The response of global terrestrial water storage to drought based on multiple climate scenarios

Drought is a hydrological hazard phenomenon that spreads across the world and will become more intense and frequent with climate change in the future. How to scientifically and effectively monitor and evaluate drought events has become a significant issue. At present, various types of drought monitoring indices have been proposed. However, the criteria for drought that occurs in each month is different and the interaction between drought and the environmental factors of earth is also difficult to understand. To solve this problem, evapotranspiration (ET), leaf area index (LAI), soil moisture (SM), precipitation (P) and surface temperature(ST) data from five different scenarios (Hist-1950, SSP126, SSP245, SSP370 and SSP585) of coupled Model Intercomparison Project Phase6 (CMIP6) were obtained to construct a standardized integrated drought index (SIDI) which was used to monitor drought at global level. Then the cumulative effects of drought on terrestrial water storage in different scenarios were explored. The results showed that the global land surface was a trend of wetting from 1950 to 2014, while the trend of dryness-wetness was different under four future scenarios. Besides, the global drought frequency will first increase and then decrease in the future, and drought frequency in three periods was ranked as follows: Historical period (6.03 months) < Pre-Century (6.15 months) > Post-Century (6.11 months). Moreover, it was found that the cumulative effects of drought on terrestrial water storage diminished gradually from 6.7 months during the historical period (1950–2014) to 5.36 months in the Pre-Century (2015–2057), and further reduced to 4.87 months in the Post-Century (2058–2100), indicating a continuous decline in the stability of global terrestrial water storage in response to drought. This study has a certain reference value for the comprehensive assessment and rational allocation of global terrestrial water storage

A Risk-Based strategy for the Scour Analysis around bridge piers

Local scour around bridge piers, especially during high floods, has been a critical issue for both the safety and serviceability of bridge foundations for decades. The objective of this study is to implement a risk-based strategy to account for local scour near the bridge piers. The methodology comprises of four fundamental steps wherein the first step involves evaluating hydrological hazards of an extreme nature. To precisely estimate the behavior of the river, the second phase uses several flow properties and depth of scour. In the third phase, the risk of scour connected with the bridge is evaluated by comparing the scour depth (DT) with foundation depth (DF) with a prioritizing factor (PF) which aids in determining the qualitative assessment of the scour risk. Lastly, an assessment of the risk of scour rating is assigned to find out the severity of the scour risk for the bridge piers. The present research discusses the incorporation of uncertainty in hydrological modeling through a predictive approach in the definition of the return period. To simulate flow parameters and assess bridge scour, the HEC-RAS program is utilized which incorporates a scour module. The study concludes with a qualitative evaluation of the impact of scouring on bridge susceptibility and safety. A case study on the Netaji Subhas Bridge across the Gomati (Gumti) river, Tripura served to validate the risk-based technique and it has been suggested that it can be effectively integrated into routine schedules for bridge examinations as a reliable risk management tool to avoid disastrous incidents.

From EU-SoilHydroGrids to HU-SoilHydroGrids: A leap forward in soil hydraulic mapping

Spatially explicit, quantitative information on soil hydraulic properties is required in various modelling schemes. At European scale, EU-SoilHydroGrids proved its applicability in a number of studies, in ecological predictions, geological and hydrological hazard assessment, agri-environmental models, among others. Inspired by its continental antecedent, an analogous, but larger scale, national, 3D soil hydraulic database was elaborated for the territory of Hungary (HU-SoilHydroGrids) supported by various improvements (i–iv) in the computation process. Pedotransfer functions (PTFs) were built in the form of i) advanced machine learning methods and ensemble models, and trained on the ii) national soil hydrophysical dataset. The set of predictors used in PTFs was supplemented by iii) additional environmental auxiliary variables. Spatial layers of the soil hydraulic parameters were generated using iv) 100 m resolution information on primary soil properties, namely DOSoReMI.hu. HU-SoilHydroGrids provides information on the most frequently required soil hydraulic properties (water content at saturation, field capacity and wilting point, saturated hydraulic conductivity and van Genuchten parameters for the description of the moisture retention curve) with national coverage at 100 m spatial resolution down to 2 m depth for six GSM standard depth layers. The HU-SoilHydroGrids has significantly lower squared error in the case of describing the moisture retention curve and hydraulic conductivity than the EU-SoilHydroGrids. The derived 3D soil hydraulic database (ver1.0) is presently available in National Laboratory for Water Science and Water Safety for project partners in order to test its functional performance in describing hydrological and ecological processes.

Spatial and Temporal Water Pattern Change Detection through the Normalized Difference Water Index (NDWI) for Initial Flood Assessment: A Case Study of Kuala Lumpur 1990 and 2021

Surface water pattern changes in terms of quantity and directions is caused by many hydrological hazards such as flooding. Rapid urbanization in Kuala Lumpur has destroyed urban forest trees as the most valuable urban green infrastructure (UGI). Then surface, vegetation changes and land transformation, concrete roads, covered roofs, and climate changes impacts changed the water pattern and hydrological system in Kuala Lumpur. Satellite data used to collect data from 1990 and 2021 to show how significant has been these changes. To demonstrate the spatial and temporal changes of hydrological changes in Kuala Lumpur The Normalized Difference Water Index (NDWI) applied for a 30 years assessment. Remote sensing (RS) and Geographical Information Systems (GIS) have helped us detect dynamic changes on the Earth's surface. The mean NDWI of one year was calculated in this study using Kuala Lumpur NDWI from 1990/01/01 to 1990/12/30. The extracted image is larger than the study area and extends beyond Kuala Lumpur's city limits. This image only detected water bodies on the surface of Kuala Lumpur, which is represented in grey or white, such as lakes, rivers, ponds, or any degree of wetness. Because urban forest covered more than 15% of Kuala Lumpur in 1990, surface water was naturally controlled and saved by forest tree and forest soil. Image data for 2021 was taken from Landsat 8, which uses different bands and the same algorithm to visualize NDWI based on the availability of satellite data. The data was averaged over a one-year period between 2021/01/01 to 2021/12/30; as a result, the NDWI in Kuala Lumpur was depicted using the same algorithm from Landsat 8 with different bands. The researchers discovered that urban surface water bodies have increased over the last 30 years, rising from 0.383635 and -.0593184 in 1990 to 0.593288 and -0.44379 in 2021. This indicates that the NDWI index has increased the most in Kuala Lumpur. As previously stated, rapid changes in land use and land cover in the last 30 years have had a negative impact on Kuala Lumpur's NDWI.

Anthropogenic climate change accelerating monsoon hydrological hazards in Northeastern Himalayan region of India: geospatial approach

Anthropogenic climate change accelerating monsoon hydrological hazards in Northeastern Himalayan region of India: geospatial approach

Temporal compounding increases economic impacts of atmospheric rivers in California.

Temporally compounding atmospheric river (AR) events cause severe flooding and damage in California. However, the contribution of temporal compounding to AR-induced loss has yet to be systematically quantified. We show that the strongest ARs are more likely to be part of sequences, which are periods of elevated hydrologic hazard associated with temporally clustered ARs. Sequences increase the likelihood of flood-related impacts by 8.3% on AR days and 5.4% on non-AR days, and across two independent loss datasets, we find that ARs within sequences have over three times higher expected losses compared to ARs outside of sequences. Expected losses also increase when the preceding AR is higher intensity, when time since the preceding AR is shorter, and when an AR is the second or later event within a sequence. We conclude that temporal compounding is a critical source of information for predicting an AR's potential consequences.

Predicting the peak flow and assessing the hydrologic hazard of the Kessem Dam, Ethiopia using machine learning and risk management centre-reservoir frequency analysis software

Abstract Flooding due to overtopping during peak flow in embankment dams primarily causes dam failure. The Kessem River watershed of the Awash basin in the Rift Valley of the Afar region in Ethiopia was studied intricately to predict the causes of the Kessem Dam safety using machine learning predictive models and risk management centre-reservoir frequency analysis. Recently developed recurrent neural network predictive models with hybrid Soil Conservation Service Curve Number (SCS-CN) were used for simulation of river flow. Peak daily inflow to the reservoir is predicted to be 467.72, 435.88, and 513.55 m3/s in 2035, 2061, and 2090, respectively. The hydrologic hazard analysis results show 2,823.57 m3/s and 935.21 m; 2,126.3 m3/s and 934.18 m; and 11,491.1 m3/s and 942.11 m peak discharge and maximum reservoir water level during the periods of 2022–2050, 2051–2075, and 2076–2100, respectively, for 0.0001 annual exceedance probability. The Kessem Dam may potentially be overtopped by a flood with a return period of about 10,000 years during the period of 2076–2100. Quantitative hydrologic risk assessment of the dam is used for dam safety evaluation to decide whether the existing structure provides an adequate level of safety, and if not, what modifications are necessary to improve the dam's safety.

Impacts of climate change on hydrological hazards: mechanisms, predictions and coping strategies

Abstract Climate change is triggering more frequent and intense hydrological disasters, which significantly impact society and economy. This paper discusses the specific impacts of these changes on hydrological hazards. It analyzes the early warning mechanism, risk assessment, and coping strategies to provide a scientific basis for effective response. This paper aims to explore the impact of climate change on hydrological disasters and develop effective prediction and response strategies. The effects of climate change on the mechanisms of hydrological disasters, risk assessment theory, influencing factors, and early warning mechanisms is studied through a comprehensive analysis method. According to the study, climate change significantly impacts the frequency and intensity of hydrological disasters. In a particular region, there has been a 30% increase in the frequency of floods caused by heavy rainfall over the past 20 years, leading to a 40% increase in economic losses. In addition, climate model-based risk assessment methods effectively predicted the potential impacts of these disasters. The study revealed that enhancing early warning systems and raising public awareness can reduce catastrophe risk. Climate change significantly impacts hydrological hazards, and more systematic and integrated management strategies are needed to reduce their impacts.

Состояние водозащитных сооружений Ханты-Мансийского автономного округа - Югры для защиты населения и территорий от гидрологических опасностей

The Khanty-Mansiysk autonomous okrug – Ugra is characterized by dangerous hydrological phenomena accompanied by impact on the territory of settlements. Moreover, the creation of effective engineering protection of territories prone to flooding and destruction of the coastline becomes an urgent issue. The purpose of the study: to analyze the state of water protection structures in the settlements of the Khanty-Mansiysk autonomous okrug – Yugra. The result of the study was an update of information on the availability and state of the system for protecting settlements from hydrological hazards. The need to protect the population and territories from the negative impact of water for 150 settlements was revealed. At the same time, a significant part of settlements is recorded that are not properly provided with water protection facilities: in 44 settlements there are no flood control dams, and in 32 there are no engineering structures for the protection of the coastline. The need for the construction and reconstruction of flood control dams on the total length of 57328 linear meters and bank protection structures (36978 linear meters) was identified.

Potential Flood Hazard Mapping Based on GIS and Analytical Hierarchy Process

Flooding is one of the most common natural dangers occurring almost everywhere. Remote sensing and geographic information systems (GIS) are common and effective tools for hydrological analysis assessment and hazard management. Using GIS and remote sensing techniques, this study aimed to identify flood hazard maps in the Diyala governorate with a higher vulnerability to floods. Nine influencing parameters were collected, including elevation, slope, distance from the road, distance from the river, rainfall, drainage density, land use and land cover, normalized vegetation index, and topographic wetness index. The collected data were processed using GIS software and then relative weights were estimated using the analytical hierarchy process (AHP) approach to produce a flood map. According to the findings of this study, the largest zone, about 64% of the study area, faces moderate potential flood hazard, a very small area of less than 1% faces very high and very low potential flood dangers, and approximately 35% of the study area is subjected to high and low flood hazard.

Integration of Adaptation Measures Responding to Hydrological Hazards for Chao Phraya River Basin, Thailand, in Comparison with Cases from Jiulong River Basin, China

Integration of Adaptation Measures Responding to Hydrological Hazards for Chao Phraya River Basin, Thailand, in Comparison with Cases from Jiulong River Basin, China