Hasil untuk "River protective works. Regulation. Flood control"

Edward Miiro, Ismael Kato, Zuhra Nantege et al.

ABSTRACT This study aimed to develop a proof‐of‐concept prototype of a machine learning system to forecast and mitigate the effect of floods in Kasese District. The researchers used a participatory design science approach. The researchers conducted document reviews and brainstorming to obtain past climate data from the representatives of affected communities, the Makerere University Department of Meteorology, and the Uganda National Meteorological Authority. Qualitative data were transcribed from recordings of the brainstorming sessions and notes from literature. The data were then summarized in tables and analyzed using Visual Network Analysis (VNA) with Word Clouds and Gephi Open Source Software. We employed a combination of C++ programming, sensors wired to Arduino 2 and 3 Integrated Development Environment System to build the prototype. Two machine learning algorithms, including linear regression and K‐nearest neighbours (KNN) were used to learn from collected hydrological data and make necessary predictions. Using sensors, we were able to read water levels, temperature, and humidity. The prototype successfully demonstrated the ability to send early‐warning alerts to users, contributing to both theoretical advancements in disaster risk reduction and practical tools for mitigating flood‐related losses in Uganda. The researchers recommend further study to validate the use of this system and evaluate its efficacy and predictive accuracy in averting floods in affected areas.

Erin Rugland, Edmund C. Penning‐Rowsell

ABSTRACT The World Bank is a leading global institution for disaster risk management, the bulk of which is dedicated to flood risk management (FRM). Due to the Bank's power as a lending agency and the global distribution of flood risks it has addressed, the Bank's project financial agreements (FAs) are an expression of a power relationship worthy of detailed investigation. These FAs present an opportunity in which the Bank could impose its policy preferences and set the parameters for FRM in recipient countries, thus illuminating both an important driver for change and the Bank's fundamental modus vivendi. This paper uses qualitative content analysis to investigate 52 FAs from 1975 to 2023, searching for patterns in the FRM measures they emphasise. We examine how FRM measures advocated by the Bank have changed over time, finding that the Bank has used its power to promote early adoption of integrated structural and non‐structural FRM strategies in a mutually reinforcing complementary arrangement. The Bank advanced integrated FRM approaches well before other international bodies and national agencies and thus features as a world leader in this respect. We also find that common criticisms of neoliberalism and gender equality against the Bank are not entirely unfounded, but progress has occurred in these directions in recent years.

Philip Bubeck, Sara Rrokaj, Daniela Molinari

ABSTRACT In recent years, extreme rainfall events led to severe flooding in several European countries which caused extraordinary human and economic losses. Such events, which are projected to become more likely because of climate change, expose many citizens to extremely stressful situations that involve intense fear, shock, and loss. Previous studies clearly point toward a direct relation between such flood experiences and negative mental health outcomes of those affected. However, existing studies commonly focus on directly exposed populations only, preventing a direct comparison with a control group. This makes it more difficult to separate the effect of the flood event from other factors that potentially affect the mental health of the respondents. Here, we use survey data from 698 residents from the Marche region in Italy, which was affected by disastrous flooding in 2022. The survey focused not only on the directly affected population (n = 392) but also included a nonaffected control group (n = 306). We use the short version of the Kessler Distress Scale (K6) as a screener for severe mental distress. Results show that directly affected respondents exhibit a 13.1%–16.7% higher prevalence rate of indications of severe mental distress compared to the control group. The significant impact of the flood event on negative mental health outcomes is further confirmed by regression analyses, which show a direct influence of several flood stressors on severe mental distress, including physical health impacts and higher water levels on one's own building. Since mental illness is associated with high burdens for those affected and their families, as well as high socioeconomic costs, this aspect deserves more attention in postdisaster contexts. The results presented in this article can be used as a reference by responsible authorities for estimating the additional demand for psychological assistance needed in the aftermath of such severe events.

Negin Binesh, Giuseppe T. Aronica, Emina Hadzic et al.

Abstract Hazard vulnerability assessment of critical infrastructures (CIs) is crucial for ranking infrastructures based on their level of criticality, enabling the urban managers to prioritize CIs for allocating funds in the hazard mitigation/recovery process. This study aims to provide a framework for ranking CIs based on a rapid and preliminary flood vulnerability assessment by introducing a methodology for classifying CIs according to their vulnerability to riverine flooding. An indicator‐based vulnerability curve is calculated both quantitatively (using Fuzzy Logic Toolbox in MATLAB) and qualitatively (using susceptibility–exposure matrix), based on which CIs prioritization is accomplished with a focus on functional flood vulnerability considering structural/nonstructural damages. Besides, this study addresses the consequences that a damaged infrastructure may have on the rest of CIs and estimates their vulnerability given the additive impact of the surrounding failed infrastructures considering their interdependence. The methodology was applied to Berat (Albania) and Sarajevo (Bosnia‐Herzegovina) with findings compared to those of a multi‐criteria decision‐making‐based approach commonly used in CI ranking literature. The obtained results from both methods represent that roads are the most vulnerable studied infrastructure in the case of Berat, while regarding the city of Sarajevo, road infrastructures are considered the least vulnerable to riverine floods compared to bridges and schools.

Ashton Eaves, Paul Kench, Garry McDonald et al.

Abstract We develop a methodological approach through integrated assessment using System Dynamics modelling and Scenario Planning to investigate the economic vulnerability of coastal communities to the compounding impacts of sea‐level rise (SLR) and storm flooding and inundation associated with climate change. The approach uses a coastal flood risk assessment that quantifies physical drivers alongside socio‐economic well‐being for coastal communities to provide a methodology for managing uncertain futures through causal relationships in System Dynamics. A New Zealand case study is used to illustrate the long‐term economic impacts of inaction under different SLR projections and recognise critical tolerance thresholds to help exposed property owners plan their future. Modelling scenarios using this integrated approach identified two stand‐out drivers that influence a behavioural response of communities to coastal inundation at the local scale: first, the ongoing likelihood of risk transfer to the insurance industry, and second, the decisions of households and firms to accept risk for the added value of coastal living. Model outputs suggest that the threat posed by coastal hazards drives a behavioural, socio‐economic response that exceeds the initial economic exposure of capital assets. In the economic short term (1–10 years) and medium term (10–20 years), vulnerable communities accept the risk of capital loss and loss of insurability, favouring the amenity of coastal living. However, in the long term (+20 years), economic losses from repeat flooding increase risk‐based insurance premiums, promote insurance withdrawal and drive negative corrections in property valuations. Unanticipated insights were obtained from the modelling, including the likely timing of tolerance thresholds, particularly the insurance withdrawal point, which is critical to insurer/consumer decision‐making and community planning.

Nan Sun, Wei Huang, Maggie Creed et al.

ABSTRACT In small mountain catchments, the spatial and temporal resolution of rainfall can vary significantly across the catchment. However, rainfall gauging stations can be sparse in these regions, and collected data may not reflect the real rainfall distribution across the catchment. When modelling flash floods, finding a suitable approach to estimate the actual rainfall distribution is nontrivial. In this study, the effectiveness of different methods for obtaining a spatial and temporal rainfall distribution for use in numerical modelling of flash floods was investigated using a full two‐dimensional depth‐averaged shallow‐water hydrodynamic model. It was demonstrated that the Thiessen polygon method and the inverse distance weighted interpolation method (IDW), with appropriate empirical coefficients, produce results in agreement with observed stage and discharge hydrographs. We show that the uniform distribution method cannot be used to represent realistic spatial and temporal variability of rainfall for flash flood events in small mountain catchments. By combining available data with the common IDW method, missing rainfall timeseries data in a small catchment can be estimated, even for short‐duration time scales, such as a single flash flood event.

Filiz Barbaros, Türkay Baran

ABSTRACT Global climate change has significantly altered meteorological conditions, leading to substantial shifts in temperature and precipitation patterns. These changes have increased the frequency and intensity of extreme weather events, posing significant risks to ecosystems, agriculture, and human settlements. This study analyzed precipitation variability in the Eastern Black Sea Basin—one of Turkey's most precipitation‐prone regions—using an entropy‐based approach. Long‐term precipitation data (1975–2012) from 11 meteorological stations were evaluated to determine the rain pattern variability and the susceptibility to extreme events. The Intensity Disorder Index (IDI), derived from Intensity Entropy (IE), was used to quantify fluctuations in precipitation patterns. The results revealed that the regions in the western part of the basin, especially around Ünye and Ordu, showed the highest variability due to the increase in the number of extreme precipitation events and the associated increased flood risk. Seasonal assessments have shown that precipitation variability is increasing significantly in the summer months, with an increasing number of extreme events, highlighting the increasing impact of climate change on seasonal precipitation distribution. The entropy‐based index maps developed in this study provide valuable insights for regional flood risk assessments, disaster preparedness, and water resource management. These findings underscore the necessity of incorporating entropy‐based methodologies in climate resilience strategies, aiding decision‐makers in mitigating the effects of climate change on hydrological systems.

Mengxue Zhang, Jiahong Liu, Tianxu Song et al.

ABSTRACT Flood risk assessment serves as a critical tool, providing theoretical foundations for minimizing flood damage and effective flood management. In June 2023, the Kakhovka Dam failure highlighted the need for accurate flood risk assessment under different breach scenarios. This study assessed the spatiotemporal flood risk to population and built‐up areas under partial (S1) and complete (S2) dam‐breach scenarios using the HEV (hazard–exposure–vulnerability) framework. Hotspot analysis was used to identify high‐risk zones, and classification differences between HEV and DV methods were compared. The results are as follows: Flood propagation was simulated using the TELEMAC‐2D model, yielding an NSE of 0.98 based on observed water depths. Spatial validation produced a precision (P) of 70.7%, a false positive rate (FPR) of 22.9%, a false negative rate (FNR) of 3.2%, and an accuracy of 84.6%. For population flood risk, the total number of people at risk was approximately 101,000 under S1, increasing sharply within the first 41 h. Under S2, the total rose to approximately 296,000, with a rapid increase observed in the first 20 h. For built‐up area, the total flood‐affected extent was 51.6 km2 under S1, showing a sharp increase in the first 41 h. Under S2, the total affected area expanded to 157.4 km2, with a rapid rise during the first 20 h. High‐risk clusters of both population and buildings were mainly located near the dam site and in the midstream right‐bank urban area. New high‐risk zones also emerged in the southern part of the left riverbank under S2. Although the DV‐based method produced a wider spatial extent of flood risk, it underestimated risk levels in both scenarios. The results provide a practical basis for flood risk assessment and emergency management.

Bhaswatee Baishya, Arup Kumar Sarma

The reduction in water retention capacity due to sedimentation in wetlands poses a serious threat to the ecology of this imperative freshwater resource. This also increases the risk of flooding in the river catchment areas that feed these wetlands. Sediments are mostly produced by unplanned development and hillcutting in upstream catchments. Henceforth, it is crucial to understand the trend in sediment deposition to determine management measures. However, measuring sediment deposition in wetlands is challenging. This study investigates a novel and cost-effective approach to discover trends in sediment deposition within wetlands via geospatial techniques and compares their water extents in the Deepor Beel Wetland, a Ramsar site in Assam, India. The water spread areas were assessed via supervised classified Landsat images and the band rationing technique, i.e., the normalized difference pond index (NDPI). This study establishes that sedimentation causes the water level in wetlands to rise, resulting in an increase in the area of water spread. This often suggests that the lean period water volume is increasing, indicating better health of the stream-wetland ecosystem. However, this apparent increase in water volume is actually due to the displacement of water by the sediment that has been deposited on the bed over time. This approach has identified a reduction in the water holding capacity of the wetland to 9.19 million m3 in 18 years by utilizing geospatially derived water spread and elevation data from two years (2003 and 2021) with comparable rainfall. In the absence of recent and past years with comparable precipitation, the proposed method can still be applied by adjusting the apparent increase in volume by considering the difference in the net inflow volume between the two years of interest. The results of sediment transport toward the wetland, as computed by the Soil and Water Assessment Tool (SWAT) model while accounting for the sediment retention rate of the wetland, strongly agree with the proposed approach. The present approach can be extended to similar stream-wetland ecosystems to examine sediment dynamics and help create better management strategies for wetlands and associated catchment conservation and restoration.

Christine M. Albano, Christopher E. Soulard, Blake A. Minor et al.

ABSTRACT California's Central Valley is increasingly vulnerable to winter floods. A comprehensive spatial baseline of flood extents is critical for inundation analyses that can enhance future flood predictions, but cloud cover has prevented the regular observation of surface water extents with optical satellite imagery. In this study, we leveraged the daily resolution of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data to create a continuous series of monthly Dynamic Surface Water Extent (DSWEmod) images across the Central Valley from January 2003 to January 2023. We used the timeseries to assess the climatic driving forces of winter (Oct–April) surface water variability at sub‐basin and pixel scales. At the sub‐basin scale, we evaluated the influences of winter precipitation, occurrence of atmospheric rivers, and antecedent soil moisture on monthly surface water extents and found that the greatest correspondence occurs in mid‐winter (Dec–Feb); in contrast, non‐precipitation drivers such as water management play a stronger role in autumn and spring. The pixel‐level analysis identified the probabilities of precipitation‐driven surface water occurrences in the Sacramento basin are highest along rivers, conveyance channels, and floodways, with higher probabilities under wetter antecedent soil moisture conditions. Precipitation‐driven surface water occurrences are also common in leveed areas and outside flood boundaries designated by state and federal agencies where exposure of structures to inundation was larger in terms of their value. Finally, areas with more frequent precipitation‐driven flooding have poor recharge potential but are commonly within 5 km of areas classified as having good potential. This study demonstrates a novel approach for exploring the utility of MODIS for understanding surface water dynamics in mid‐winter, a period characterized by peak precipitation, flood risk, and surface water extent. This information can provide valuable insights for (1) assessing flood risks for infrastructure and populations, (2) identifying areas most suited to strategic water management investments to increase recharge, and (3) analyzing precipitation thresholds that trigger flooding to allow proactive water management strategies to minimize damage and maximize recharge.

Cristiane Bahi dos Santos, Iran Stallivière Corrêa, Jair Weschenfelder et al.

Connections between changes in sediment distribution and diatom species are sensitive to environment-related deposition patterns of fine-grained sediment. A large lagoon in the southernmost Brazilian coastal plain was selected to test the sensitivity of diatoms to sediment changes and their ability to classify depositional environments. Selected grain-size parameters, including mean grain size, skewness, kurtosis, standard deviation, and sand-silt-clay ratios, were evaluated for interpretation. The trigonal diagram shows that most of the lagoon sub-bottom deposits lie in the fine-grained tail. Silt is dominant within the Holocene fluvial-estuarine, estuarine-transitional, and shallow marine deposits. Sand fractions (> 76%) are distributed in the coastal barrier and in a few fluvial-estuarine intervals. Most deposits are polymodal, lying between poorly sorted sediment, indicating a low-energy depositional environment. The results were interpreted as indicated by the Principal Component Analysis (PCA), which revealed resulting shifts in sediment and diatom composition connected to five sedimentary facies controlled by sea-level oscillations. Diatom species recovered from sediment cores have distinctive capacities for living under high marine, freshwater, and terrestrial conditions. Each quadrant of the PCA reflects the adaptation of species to particular depositional conditions during the Late Pleistocene and Holocene. Allochthonous taxa provide valuable ecological information about adjacent environments in the coastal area, offering insight into the paleogeography of the study area. The major advantage of the analytical methods is their applicability in distinguishing different environments involving mixed deposition and transport mechanisms. The sediment deposited on the bottom of a large lagoon provide significant implications not only for sedimentological analysis by improving understanding of high deposition of mud and fine-grained sands but also for predictions of the source-to-sink routes.

Wouter J. A. van Weerelt, Nicola Bastianello

In this paper we design a novel class of online distributed optimization algorithms leveraging control theoretical techniques. We start by focusing on quadratic costs, and assuming to know an internal model of their variation. In this set-up, we formulate the algorithm design as a robust control problem, showing that it yields a fully distributed algorithm. We also provide a distributed routine to acquire the internal model. We show that the algorithm converges exactly to the sequence of optimal solutions. We empirically evaluate the performance of the algorithm for different choices of parameters. Additionally, we evaluate the performance of the algorithm for quadratic problems with inexact internal model and non-quadratic problems, and show that it outperforms alternative algorithms in both scenarios.

Luca Ballotta, Juncal Arbelaiz, Vijay Gupta et al.

We study optimal proportional feedback controllers for spatially invariant systems when the controller has access to delayed state measurements received from different spatial locations. We analyze how delays affect the spatial locality of the optimal feedback gain leveraging the problem decoupling in the spatial frequency domain. For the cases of expensive control and small delay, we provide exact expressions of the optimal controllers in the limit for infinite control weight and vanishing delay, respectively. In the expensive control regime, the optimal feedback control law decomposes into a delay-aware filtering of the delayed state and the optimal controller in the delay-free setting. Under small delays, the optimal controller is a perturbation of the delay-free one which depends linearly on the delay. We illustrate our analytical findings with a reaction-diffusion process over the real line and a multi-agent system coupled through circulant matrices, showing that delays reduce the effectiveness of optimal feedback control and may require each subsystem within a distributed implementation to communicate with farther-away locations.

Shuhao Yan, Carsten W. Scherer

This paper studies distributionally robust regret-optimal (DRRO) control with purified output feedback for linear systems subject to additive disturbances and measurement noise. These uncertainties (including the initial system state) are assumed to be stochastic and distributed according to an unknown joint probability distribution within a Wasserstein ambiguity set. We design affine controllers to minimise the worst-case expected regret over all distributions in this set. The expected regret is defined as the difference between an expected cost incurred by an affine causal controller and the expected cost incurred by the optimal noncausal controller with perfect knowledge of the disturbance trajectory at the outset. Leveraging the duality theory in distributionally robust optimisation, we derive strong duality results for worst-case expectation problems involving general quadratic objective functions, enabling exact reformulations of the DRRO control problem as semidefinite programs (SDPs). Focusing on one such reformulation, we eliminate certain decision variables. This technique also permits a further equivalent reformulation of the SDP as a distributed optimisation problem, with potential to enhance scalability.

Md Shaheer Ali

River training is crucial for safeguarding river banks against erosion and preventing damage during floods. Various established methods like spurs, dykes, and revetments have been employed for many years. Researchers are currently investigating a novel river training approach known as bandal-like structures, aiming to demonstrate their cost-effectiveness as a potential alternative to existing structures. The bamboo bandalling technique effectively controls erosion and promotes sedimentation in rivers, reducing sediment loads. Biodegradable bamboo, readily available and cost-effective, ensures minimal harm to aquatic life. However, these structures require replacement after a single monsoon season, making them suitable for short-term river training in low-discharge, unsubmerged conditions. The current study analyzes scour around bandalling structures, emphasizing the need for further research to address discrepancies in velocity distribution and optimize scour control. Numerical simulation strengths and weaknesses highlight the suitability of Artificial Neural Networks, Genetic Algorithms, and Computational Fluid Dynamics for different aspects of the investigation.

Christos Iliadis, Vassilis Glenis, Chris Kilsby

Abstract Flood risk in cities and built‐up areas is a major threat which is likely to grow due to increased urbanisation and climate change. It is a priority for urban planning, civil defence and insurance to accurately represent buildings and urban features in hydrodynamic models to assess flood risk to people, properties, assets and infrastructure in an uncertain future. The correct representation of urban features in models is currently blocked by the lack of detailed and accurate techniques and has become a priority for the improvement of urban flood modelling now that better data and computational resources are available. This study has reviewed the available approaches for the representation of buildings and urban features and implemented the widely used ‘stubby building’ approximation as well as a more realistic and innovative ‘building hole’ approach using the hydrodynamic model CityCAT. The city centre of Newcastle upon Tyne, UK, was used as a case study, allowing independent validation of the methods and direct, systematic comparison of performance. Shortcomings of the approximate method are described, and guidance given on limits to its reliable application and scope for improvement.

Siriporn Darnkachatarn, Yoshio Kajitani

Abstract The Bangkok Metropolitan Region (BMR), located in the Chao Phraya River basin delta, is particularly vulnerable to floods, with susceptibility heightened by geographical aspects and rapid urbanization. This study aimed to assess spatiotemporal flood exposure and allow proper flood‐risk recognition among all stakeholders through a three‐phase flood exposure assessment. First, land use and land cover (LULC) changes were identified based on a 30‐year Landsat time series. Second, built‐up areas that overlapped with past flood inundation maps were designated as flood exposure areas. Third, a rainfall‐runoff inundation (RRI) model simulated the 2011 Thailand Flood, the largest on record, by analyzing inundation depth implications across three decades. The findings revealed a dramatic increase in the use of built‐up areas and the associated flood exposure. In 1992, built‐up areas accounted for approximately 20% of the total area, sharply increasing to nearly 45% by 2022, according to the LULC classification. The flood exposure increased from 648.83 km2 in 1992 to 1681.26 km2 by 2022, demonstrating a linear trend. Notably, the catastrophic 2011 flood did not inhibit urbanization in flood‐prone areas, highlighting the need for robust policies, such as the segmentation of flood‐risk zones, to mitigate future exposure in the region.

Alejandro Quintela-del-Río, Mario Francisco-Fernández

Time series and extreme value analyses are two statistical approaches usually applied to study hydrological data. Classical techniques, such as ARIMA models (in the case of mean flow predictions), and parametric generalised extreme value (GEV) fits and nonparametric extreme value methods (in the case of extreme value theory) have been usually employed in this context. In this paper, nonparametric functional data methods are used to perform mean monthly flow predictions and extreme value analysis, which are important for flood risk management. These are powerful tools that take advantage of both, the functional nature of the data under consideration and the flexibility of nonparametric methods, providing more reliable results. Therefore, they can be useful to prevent damage caused by floods and to reduce the likelihood and/or the impact of floods in a specific location. The nonparametric functional approaches are applied to flow samples of two rivers in the U.S. In this way, monthly mean flow is predicted and flow quantiles in the extreme value framework are estimated using the proposed methods. Results show that the nonparametric functional techniques work satisfactorily, generally outperforming the behaviour of classical parametric and nonparametric estimators in both settings.

Amir Mohammad Arash, Mehdi Yasi

Abstract Digital Elevation Models (DEMs) act as an essential input to hydraulic models for the simulation of flood hazard maps. Coarser resolution DEMs are subject to higher levels of uncertainty, particularly in capturing river‐bed bathymetry. The principal aim of the present study was to assess the potential use of freely available DEMs and the 1D and 2D HEC‐RAS modeling approaches for flood mapping in four different types of river morphology. The DEM created from direct river survey (Surveyed DEM) was used as a benchmark to assess three remote sensing‐based DEMs (i.e., ALOS, SRTM, and ASTER). The results indicated that ALOS is more accurate. However, the regeneration of main‐channel bathymetry was poor for floodplain‐meandering and braided river reaches. The DEMs were then corrected by optimizing the number and layout of representative sections. A total of 144 tests were carried out. The results indicated that the flood‐mapping index F of ALOS increased from 86% to 91%. The flood mapping with a 1D model was sufficient in straight reaches (F‐index 84%). In floodplain‐meandering rivers, a 2D model had to be used to reach an F‐index up to 81%. The responses of both 1D and 2D models were almost identical in the case of wide‐braided rivers.

Paul Samuels