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

Marisa Fuchs, Jennifer Oriwol, Matthias Zimny et al.

ABSTRACT The increasing severity of flood events poses growing risks to critical infrastructures (CI), with failures often triggering cascading effects across sectors. Despite this, flood risk management (FRM) frequently overlooks service disruptions of CI and systemic interdependencies between infrastructure sectors because of its place‐based approach. This study addresses this gap by developing and applying an integrated methodological framework that combines GIS‐based flood risk analysis and qualitative criticality assessment. Focusing on Euskirchen County, Germany, the study analyses the exposure, vulnerability, and criticality of CI—specifically in the energy, (tele)communications, emergency service, and transport sectors—under extreme flood scenarios. Expert interviews and workshops informed the identification of cascading failure chains and potential intervention points. Findings reveal significant systemic risks, particularly stemming from power outages, and highlight key interdependencies between infrastructure sectors. The results provide actionable insights for spatial planners and emergency managers, including recommendations for strategic CI siting and contingency planning. By integrating scientific rigor with practical relevance, the study contributes to the advancement of risk‐based spatial planning and supports the development of resilient infrastructure systems in the face of increasing flood risks.

Leonardo Zandonadi Moura, Jean-Michel Martinez, William Santini et al.

This study aims to evaluate sediment transport processes in the Madeira River, a high-load Amazon tributary altered by the Jirau run-of-river hydropower dam. A methodology for sensitivity analysis and calibration of the HEC-RAS one-dimensional morphodynamic model is developed. It integrates multiple model to measured comparisons, including conventional monitoring and water color remote sensing data. The study underscores the value of employing products derived from satellite imagery, refining model differentiation and improving the spatial and temporal resolution of sediment transport predictions. A simple, regionally significant method of estimating depth-integrated concentrations form surface index concentrations is discussed, showing that for high concentrations a 1.10–2 multiplicative factor suffices. Sensitivity analysis highlights the dominant influence of sand content in the upstream sediment load and the necessity of using the Krone–Partheniades transport formula to simulate fine sediment retention. The calibrated model estimates a sediment retention efficiency of 21.3% in the backwater-affected reach over a five-year period, with over 90% of the sand fraction being deposited. Results suggest that the wash load threshold for this system is medium to coarse silts and clay-silt flocs larger than 0.016 mm. These are the key size classes to understand deposition of fines. Flocculation processes may play a role, requiring adjustments in the input sediment load grain size distribution. A multivariate sediment rating curve, incorporating tributary discharge dynamics, enhances model performance, particularly in reproducing seasonal concentration variations in the backwater reach. These findings provide insights into the best practices for sediment modeling in high-load rivers impacted by hydropower and highlight the importance of multi-objective calibration approaches.

Manuel Bertulessi, Francesca Ceccato, Francesco Ioli et al.

ABSTRACT Real‐time monitoring of the functionality of river embankments, especially during flood events, is an increasingly important topic in the new scenario outlined by climate change. To address this issue, the concept of the “smart levee” has recently emerged, based on the fusion of information from different types of sensors to obtain, remotely and in real time, an improved knowledge of its structural health status. The work here presented applies this concept by proposing an innovative monitoring solution consisting of an anti‐erosion reinforced geomat equipped with Fiber Optic Sensors. This “smart revetment” aims to detect any surface displacement induced by failure mechanisms such as erosion and piping phenomena, as well as any physical change in soil properties (e.g., temperature and water content). These measurements, once integrated with other environmental variables, can then be associated with the variability of the most common environmental and operational factors, such as the rise in river levels during high flow events, or used to detect anomalies in the original geometry of the embankment surface. Four prototypes of this smart revetment were installed on a real levee by the city of Modena (Italy) in March 2023. The results obtained after 1 year of operation provided positive feedback on the smart revetment concept, which proved to be an innovative technological tool to improve flood resilience, both by increasing the efficiency of maintenance planning and by supporting civil protection surveillance during flood events.

Sazzad Hossain, Hannah L. Cloke, Andrea Ficchì et al.

Abstract Scientific and technical changes to flood forecasting models are implemented to improve forecasts. However, responses to such changes are complex, particularly in global models, and evaluation of improvements remains focussed on generalised skill assessments and not on the most relevant outcomes for those taking decisions. Recently, the Global Flood Awareness System (GloFAS) flood forecasting model has been upgraded from version 2.1 to 3.1 with a significant change to its hydrological model structure. In the updated version 3.1, a single fully configured hydrological model (LISFLOOD) has been adopted, including ground water and river routing processes, instead of two coupled models, a land surface and a simplified hydrological model, of the previous version 2.1. This study aims to evaluate changes in the simulated behaviour of floods and the forecast skill of the two GloFAS versions based on different decision criteria for early action. We evaluate GloFAS reforecasts for the Brahmaputra and the Ganges Rivers in Bangladesh for the period 1999–2018. For the Brahmaputra River, the old GloFAS 2.1 version performs better than the 3.1 version, especially in predicting low‐ (90th percentile) and medium‐level (95th percentile) floods. For the Ganges, GloFAS 3.1 shows improved probability of detection of low‐ to medium‐level floods compared to version 2.1, especially for lead times longer than 10 days. Both versions show limited skill for more extreme floods (99th percentile) but results are less robust for these less frequent floods given the lower number of events. Using lead‐time dependent thresholds improves the false alarm ratio while reducing the probability of detection. The changes in model structures influence the model performance in a complex and varied way and forecast skill needs further investigation across regions and decision‐making criteria. Understanding the skill changes between different model versions is important for decision‐makers; however, focused case studies such as this should also be used by model developers to guide future changes to the system to ensure that they lead to improvements in decision‐making ability.

Baljeet Kaur, Andrew Binns, Edward McBean et al.

ABSTRACT Floods are one of the most devastating natural hazards, causing adverse effects on human life, well‐being, property, and the environment. The application of five machine‐learning techniques in pluvial flood susceptibility mapping was investigated using the case study of two severe storms (2005 and 2013) in Toronto, Canada. Sixteen flood conditioning factors, including elevation, slope, topographic wetness index, stream power index, amount of permeable and impermeable surfaces, and more, were used to evaluate their importance in terms of flooding impacts for the 2005 and 2013 severe storms. Extreme gradient boosting (XGBoost) and an ensemble method are identified as the best models for the tracks of severe storms in 2005 and 2013. The AUROC (Area under the Receiver's Operating Characteristic Curve) analysis shows that precipitation was the most critical variable, followed by groundwater level and distance from sewers, during the two major storm events investigated. However, the flood susceptibility maps are specific and depend on the storm track and intensity‐duration characteristics for each significant storm event. Depending on the seasonal groundwater levels and the storm sewer drainage capacity of an area, the system may be overwhelmed, and houses may be flooded if the rainfall intensity and duration exceeds the urban stormwater drainage system capacity. This research provides a foundational understanding of the factors influencing urban flood risk and the statistical models that result from pluvial rainfall events. However, there is a need for more research on rainfall events with different tracks, intensities, and durations to provide reliable ensemble flood susceptibility mapping that could be used to calculate the flood risk for a given area.

Iftekharul Islam, Md. Abdur Rahman, Md. Ibrahim Adham et al.

ABSTRACT Flooding poses a persistent challenge in Bangladesh, where complete prevention remains difficult due to its geographical and climatic conditions. This study integrates the Analytical Hierarchy Process (AHP) with Geographic Information System (GIS) techniques to create a detailed flood susceptibility map for the Sylhet division in northern Bangladesh. The primary goal is to classify the region into distinct flood susceptibility zones, providing valuable insights for improving flood risk management, mitigation, and preparedness strategies. The study evaluates 12 critical flood‐influencing parameters, including elevation, slope, topographic wetness index (TWI), precipitation, drainage density, proximity to roads and rivers, vegetation, land use and land cover (LULC), and soil type. These factors were chosen based on their established relevance to flood dynamics, with data sourced from reliable spatial databases to ensure accuracy. Using AHP, weights were assigned to each parameter based on expert input, reflecting their relative importance in flood risk. These weighted factors were then integrated using GIS overlay analysis and weighted linear combination techniques to generate a flood susceptibility map. The results show that approximately 35.27% of the Sylhet division, particularly the northern regions and the low‐lying Haor basin, fall into the “high” flood susceptibility categories. These areas are highly vulnerable due to their flat topography, proximity to major rivers, and inadequate drainage systems. In contrast, the southern and southwestern areas, accounting for around 7.45% of the region, exhibit “low” flood susceptibility, benefiting from higher elevations and better natural drainage. This flood susceptibility map serves as an essential tool for identifying high‐risk areas, supporting targeted flood mitigation efforts, and enhancing disaster preparedness. By providing a scientific foundation for effective flood management, the study aids decision‐makers in reducing flood impacts and promoting the sustainable development of flood‐prone regions in northern Bangladesh.

El Mehdi Chagdali, Kamal El Kadi Abderrezzak, Sébastien Erpicum et al.

This study experimentally assesses the influence of varying the inlet boundary condition on the flow patterns in rectangular shallow reservoirs. Two types of inlet boundary conditions were compared: a free surface inlet channel, and a pressurized circular inlet jet positioned at three different elevations over the flow depth (centroid of the inlet jet situated at 25%, 50%, or 75% of the flow depth). The outlet boundary condition was a free surface channel in all cases. Twenty-two experiments were done with two distinct reservoir lengths (length-to-width ratios of 1.1 and 2.0) and three hydraulic boundary conditions (Froude numbers of 0.14, 0.16, and 0.21). Velocity fields were measured with Large-Scale Particle Image Velocimetry (LSPIV) at the surface, and with an Acoustic Doppler Velocity Profiler (ADVP) at several cross sections. The flow patterns are greatly influenced by the inlet boundary condition and the reservoir geometry, but to a lesser extent by the hydraulic boundary condition. For an inlet circular jet located near the reservoir bottom, an unstable flow type, changing over time in a chaotic manner, was observed regardless of the reservoir length and of the inlet flow rate. The same type of unstable flow pattern was observed for a relatively long reservoir and the lowest tested flow rate, irrespective of the vertical positioning of the inlet jet. In all other tested configurations, a steady reattached jet was found in the reservoir equipped with a pressurized inlet jet. In addition to providing new knowledge on flow patterns in shallow reservoirs with an inlet jet, the experimental data presented here will prove valuable for evaluating flow computational models.

Lisa Burghardt, Elena‐Maria Klopries, Holger Schüttrumpf

Abstract During the flood event in 2021 within Western Europe, many bridges were severely damaged, particularly in North Rhine‐Westphalia and Rhineland‐Palatinate in Germany. Within this study, a statistical analysis of the damages caused to bridges by the flood event was carried out. First, locations and damages of bridges along the rivers Inde, Vicht and Ahr were mapped. Based on these data, statistical correlations among the damage patterns were analyzed. Approximately 25 bridges along both rivers Inde and Vicht were damaged, while over 80 bridges along the Ahr were damaged. Notably, bridges located near residential areas suffered more severe damage than those in rural areas. In addition, the presence of debris played a significant role in damaging bridges. Although the bridge design did not emerge as the crucial factor, the bridge height could be determined as a contributing factor influencing the extent of damage along all three rivers. Also, the extent of damage increased as soon as overtopping of bridges occurred. Based on these findings, recommendations for the reconstruction of the numerous destroyed bridges could be identified which agree with existing literature. Additionally, recommendations regarding the estimation of 100‐year design floods and the implementation of clogging into flood hazard maps were derived.

Mahdi Valikhan Anaraki, Saeed Farzin

ABSTRACT This study presents a new flood routing method integrating the modified Muskingum (NLM7_Aqlat) method with hybrid natural optimization algorithms (hybrid of Humboldt squid optimization algorithm [HSOA] and gradient‐based optimizer [GBO] and hybrid of Pine cone optimization algorithm [PCOA] and GBO). In the NLM7_Aqlat, the lateral flow is applied to a seven‐parameter nonlinear Muskingum model (NLM7), and hybrid natural‐based optimization algorithms optimize the parameters. In Karahan flood routing, the standard value of the mean sum of squared deviations (SSQmean) for integrating the NLM7_Aqlat model and PCOA_GBO was calculated to be 96.06% less than the other 10 algorithms (such as GA and GBO). In Wilson flood routing, the PCOA_GBO algorithm in the NLM7 model calculated the SSQmean criterion value 99% lower than other optimization algorithms. The HSOA_GBO algorithm in the NLM7_Aqlat model provided the best flood routing for Weisman‐Lewis, enhancing hydrograph accuracy. In Karun flood routing, the PCOA algorithm estimated the SSQmean in the NLM7 model to be 89% lower than other algorithms. The new flood routing method showed competitive results versus NLM7. Hybrid optimization algorithms outperformed standalone ones, prompting authors to recommend this methodology for enhancing early flood warning systems.

Wenbo Zhu, Yan Lu, Chengyang Zhou et al.

The current study investigates the impact of burrowing activities by crab species in the tidal flats of the Yellow River Delta in China on the hydraulic resistance characteristics of water flow, particularly the regulatory effect of biological activity on hydraulic parameters. Although there are many models that attempt to describe the resistance to water flow, these models tend to ignore the influence of such things as biological structures, geomorphological features, and artificial constructs in complex natural water bodies, resulting in insufficient predictive accuracy of the resistance coefficients and Manning's roughness coefficients. In this paper, a new theoretical model is developed to achieve the construction of a model for predicting the hydrodynamic resistance characteristics of crab-hole regions affected by water flow by introducing a cross-sectional area correction coefficient to improve the accuracy of the calculation. The experimental results show that there is a significant positive correlation between the drag coefficient, and the hydraulic radius, and cave density, and a negative correlation with the Reynolds number, and the modification for the sidewall and bed effect greatly improves the representativeness of the measured data. In addition, a new theoretical model is proposed to improve the prediction of drag and Manning's roughness coefficient, and the prediction results are in good agreement with the measured data. The improved drag coefficient calculation model proposed in this paper improves the applicability to the research object and helps to establish a more accurate hydrodynamic model.

Addie McCurdy, Emily Jensen

The linear quadratic Gaussian (LQG) control problem for the linear wave equation on the unit circle with fully distributed actuation and partial state measurements is considered. An analytical solution to a spatial discretization of the problem is obtained. The main result of this work illustrates that for specific parameter values, the optimal LQG policy is completely decentralized, meaning only a measurement at spatial location $i$ is needed to compute an optimal control signal to actuate at this location. The relationship between performance and decentralization as a function of parameters is explored. Conditions for complete decentralization are related to metrics of kinetic and potential energy quantities and control effort.

Lijun Bo, Jingfei Wang, Xiang Yu

This paper studies a class of mean-field control (MFC) problems with singular control under general dynamic state-control-law constraints. We first propose a customized relaxed control formulation to cope with the dynamic mixed constraints and establish the existence of an optimal control using compactification argument in the proper canonical spaces to accommodate the singular control. To characterize the optimal pair of regular and singular controls, we treat the controlled McKean-Vlasov process as an infinite-dimensional equality constraint and recast the MFC problem as an optimization problem on canonical spaces with constraints on Banach space, allowing us to derive the stochastic maximum principle (SMP) and a constrained BSDE using a novel Lagrange multipliers method. Additionally, we investigate the uniqueness and the stability result of the solution to the constrained FBSDE associated to the constrained MFC problem with singular control.

Irina Stefanović, Ratko Ristić, Nada Dragović et al.

The aim of this research was to analyze the impact of implemented erosion control works (ECW) on soil erosion intensity in the watershed of the Ćelije reservoir (Rasina River) in the period between 1968 and 2022. The Erosion Potential Method was used to calculate the annual gross erosion (W), sediment transport (G), and erosion coefficient (Z) in the study area. As a result of the performed ECW there was a general decreasing trend in the intensity of soil erosion processes in the last 54 years. The specific annual gross erosion was 1189.12 m3/km−2/year−1 in 1968, while in 2022 it was 554.20 m3/km−2/year−1. The specific sediment transport was 540.18 m3/km−2/year−1 in 1968 and 253.55 m3/km−2/year−1 in 2022. Due to the changes in the intensity of erosion processes, the specific annual gross erosion decreased by 634.92 m3/km−2/year−1 and the specific sediment transport decreased by 286.63 m3/km−2/year−1. The erosion coefficient was reduced from Z = 0.62 to Z = 0.35. A dependence between the slope of siltation and the natural bed slope was defined. The results show a significant correlation between erosion intensity and performed ECW, providing a basis for future watershed management and defining a strategy for soil erosion control in the Ćelije reservoir watershed.

Balqis M. Rehan, Jim W. Hall, Edmund C. Penning‐Rowsell et al.

Abstract Adaptations to flood‐proof individual properties (referred to here as property‐level adaptation, PLA) provide a potential means of reducing flood risk at isolated properties, whilst conventional community‐scale flood protection (CSFP) is usually more economical in protecting high‐density urban spaces. This paper develops a risk‐based framework to identify the tipping point when PLA measures become more cost‐beneficial when compared to CSFP in different urban densities. The framework was demonstrated using a hypothetical case study based on a residential area in Teddington, London. Sensitivity analysis was performed by varying the building densities in the urban space. Results show that PLA can have a role to supplement CSFP even in dense urban areas if the CSFP standard of protection is low. However, adding some element of CSFP to PLA can be more cost‐effective than implementing a single higher protection standard of PLA. Given the unique flood risk condition of most urban spaces, and the sensitivity of cost‐effectiveness of flood adaptation measures such as those demonstrated in this work, this approach can provide additional information to assist decisions in finding a sensible portfolio of measures that match that risk condition.

Sally Priest

Priyabrata Saha, Saibal Mukhopadhyay

Modeling and controlling complex spatiotemporal dynamical systems driven by partial differential equations (PDEs) often necessitate dimensionality reduction techniques to construct lower-order models for computational efficiency. This paper explores a deep autoencoding learning method for reduced-order modeling and control of dynamical systems governed by spatiotemporal PDEs. We first analytically show that an optimization objective for learning a linear autoencoding reduced-order model can be formulated to yield a solution closely resembling the result obtained through the dynamic mode decomposition with control algorithm. We then extend this linear autoencoding architecture to a deep autoencoding framework, enabling the development of a nonlinear reduced-order model. Furthermore, we leverage the learned reduced-order model to design controllers using stability-constrained deep neural networks. Numerical experiments are presented to validate the efficacy of our approach in both modeling and control using the example of a reaction-diffusion system.

Geovana Franca dos Santos, Eugenio B. Castelan, Walter Lucia

This paper proposes an output feedback controller capable of ensuring steady-state offset-free tracking for ramp and sinusoidal reference signals while ensuring local stability and state and input constraints fulfillment. The proposed solution is derived by jointly exploiting the internal model principle, polyhedral robust positively invariant arguments, and the Extended Farkas' Lemma. In particular, by considering a generic class of output feedback controller equipped with a feedforward term, a proportional effect, and a double integrator, we offline design the controller's gains by means of a single bilinear optimization problem. A peculiar feature of the proposed design is that the sets of all the admissible reference signals and the plant's initial conditions are also offline determined. Simulation results are provided to testify to the effectiveness of the proposed tracking controller and its capability to deal with both state and input constraints.

Fethi Bencherki, Anders Rantzer

An adaptive controller is proposed and analyzed for the class of infinite-horizon optimal control problems in positive linear systems presented in (Ohlin et al., 2024b). This controller is derived from the solution of a "data-driven algebraic equation" constructed using the model-free Bellman equation from Q-learning. The equation is driven by data correlation matrices that do not scale with the number of data points, enabling efficient online implementation. Consequently, a sufficient condition guaranteeing stability and robustness to unmodeled dynamics is established. The derived results also provide a quantitative characterization of the interplay between excitation level and robustness to unmodeled dynamics. The class of optimal control problems considered here is equivalent to Stochastic Shortest Path (SSP) problems, allowing for a performance comparison between the proposed adaptive policy and model-free algorithms for learning the stochastic shortest path, as demonstrated in the numerical experiment.

Qi Zhuang, Zhengzheng Zhou, Shuguang Liu et al.

Abstract The Global Precipitation Measurement (GPM) Integrated Multi‐satellitE Retrievals for GPM products (i.e., IMERG) provide new‐generation satellite precipitation measurements. For urban contexts, however, the issues of its bias and insufficient resolutions still exist. This study aims to develop high‐precision and high‐resolution (e.g., 0.01°/1 h) data for a metropolitan region based on IMERG and gauge precipitations. The original IMERG product is evaluated using hourly in situ precipitations from 47 gauges. A spatial downscaling‐calibration (DC) technique is then developed to enhance the IMERG using the Normalized Difference Vegetation Index. The results show the limited capability of IMERG to capture sub‐daily precipitation and high‐intensity precipitation. The proposed DC method significantly improves IMERG performance, with correlation coefficient (CC) increasing from 0.07 to 0.75, and probability of detection improving from 0.34 to 0.90 at the hourly scale. In terms of spatial rainfall distribution, 86% of mean absolute error and 80% of RMSE are improved with CC increasing from 0.07 to 0.91 on average. Additionally, the calibrated downscaled product provides finer information in local areas, capturing three times more spatial variabilities of urban precipitation against the original IMERG input data. The results highlight the necessity of improving urban observations for flood risk management at fine spatiotemporal resolutions.