Journal of Flood Risk Management最新文献

The US Army Corps of Engineers (USACE) prescribes flood control operations for reservoirs it regulates in watershed-specific water control manuals (WCMs), which can be decades-old and may not capture changed conditions in the watersheds or include the benefit of state-of-the-science weather and streamflow prediction. Considering the specific characteristics of a reservoir, forecast-informed reservoir operations (FIRO) may be used to enhance flood risk reduction, improve water availability, and achieve other benefits. The first FIRO pilot project at Lake Mendocino in California focused on determining if water supply reliability could be improved using FIRO without increasing flood risk. The final report concluded that FIRO concepts could indeed improve water supply reliability while enhancing flood risk reduction. Subsequently, USACE chose additional reservoir systems in California with different characteristics as additional pilot study locations to further investigate FIRO concepts. These successful FIRO efforts have provided justification to continue its expansion beyond the initial pilot sites. The lessons learned from the FIRO pilot projects are being used to inform the development of the FIRO Screening Process, a screening level framework intended to scale up the implementation of FIRO. The lessons learned could support FIRO implementation at suitable USACE reservoirs by updating WCMs.

TOPMODEL has been widely employed in hydrology research, undergoing continuous modifications to broaden its practical applicability and enhance its simulation accuracy. To encompass spatial discretization, diffusion-wave characteristics, depth-dependent flow velocity, and flux estimation in the unsaturated zone, a generalized dynamic TOPMODEL is developed by introducing a greater number of physical parameters. The present study aims to evaluate the optimal combination of these parameters within the dynamic TOPMODEL framework using machine learning techniques to improve the accuracy of runoff predictions and bolster the model's reliability. An innovative training method is suggested to elevate the model's performance by integrating the Long Short-Term Memory (LSTM) algorithm and a topological classification, which relies on the evolving spatial distribution of runoff conditions during floods. The research findings show that the proposed methodology achieves the lowest mean relative error (MRE) at 0.106, the highest Pearson correlation coefficient (PC) at 0.938, and the highest coefficient of determination (R²) at 0.906 among the three dynamic TOPMODEL types adopted in this study. The effective implementation of a case study in a river basin showcases the feasibility of the proposed method in conjunction with dynamic TOPMODEL and underscores the importance of employing the suggested training procedure.

Flood modeling is essential to determine and protect vulnerable areas. However, due to complexity of flooding, it is challenging to model floods with a high level of sensitivity. While many factors affect flood models' accuracy, topography is among the most critical. With developing technologies, designing high-accuracy topographical data is becoming more feasible, especially for small catchments. In this study, the authors focus on macro-scale modeling using different types of satellite data across the Amik Plain; a large plain with a complex stream network. SRTM, Aster, and Alos Palsar satellite data were used to create digital terrain models (DTMs). The pre-evaluation of the results showed that even the main streams in the Amik Plain were not visible. So, the geometry of the streams was created and added to the digital elevation models using the HEC-RAS software RAS Mapper tool. A flood in 2012 was simulated using all three improved DTMs. As a result, it is seen that an enhanced version of the DTM created from SRTM data provides the best performance for use in macro-scale flood modeling. The usage of the RAS Mapper tool as a GIS tool also performed well in the case of DTM improvements. The DTM improvements on the satellite data for the large plains can give a fairly reasonable output instead of using high-cost sensitive data.

Understanding the impacts of climate change and conversion of paddy field areas in the future on agricultural production is an essential part of flood-risk management. However, the quantitative impact of flood on agricultural crops in the far-future under climate change, considering prospective changes in paddy area, is still not clearly understandable. This study thus focused on quantitative analysis of flood impact on rice crops under climate change using MRI-AGCM climate model outputs for the past (1979–2002) and far-future (2075–2098) periods for the Solo River basin in Indonesia. We developed a quantitative damage assessment method by coupling water and energy budget-based rainfall-runoff-inundation model outputs and a depth-duration-damage flood loss model. We also analyzed land-use and land cover changes to project future paddy areas. The future rice production in the study basin may decrease by 21% by 2048 and by 24.6% by 2076 compared with that in 2020, due to the conversion of paddy fields to other land cover classes. The average annual flood damage value of rice crops may increase in the future period (2075–2098) by 93.7% (average damage: 666.08 billion IDR) compared with that in the past period (1979–2002) (average damage: 343.7 billion IDR), due to climate change impacts alone.

Flash floods (FFs) are a leading cause of natural hazard-related fatalities in the US, posing unique challenges due to their localized impact and rapid onset. Traditional FF susceptibility assessments often fail to account for regional variations. Addressing this, we introduce Dynamic Flash Flood Susceptibility (DFFS), a GIS-based solution designed for dynamic, region-specific FF assessment. DFFS operates through four key steps: extracting FF data from the NOAA Storm Events Database for census tracts (CTs) in any region of interest, conducting spatial hotspot analysis to identify areas of high and low FF occurrences, applying causal discovery to identify region-specific causal factors (from potential factors such as geology, terrain, and meteorology), and using machine learning to calculate susceptibility scores, resulting in a detailed FF susceptibility map. Our case studies in three Texas regions—Dallas-Fort Worth, Greater Austin, and Greater Houston—revealed distinct causal relationships, with factors like storm duration consistently influential across all regions, while others, such as population density specific to Greater Austin. Furthermore, DFFS demonstrated high accuracy (0.87, 0.86, 0.94) and F1-scores (0.88, 0.86, 0.96) in computing community susceptibility scores for these regions. We demonstrate DFFS's tangible value in FF risk management and policy-making, providing a data-driven and generalizable tool for FF assessment.

Flood susceptibility mapping (FSM) is crucial for effective flood risk management, particularly in flood-prone regions like Pakistan. This study addresses the need for accurate and scalable FSM by systematically evaluating the performance of 14 machine learning (ML) models in high-risk areas of Pakistan. The novelty lies in the comprehensive comparison of these models and the use of explainable artificial intelligence (XAI) techniques. We employed XAI to identify significant conditioning factors for flood susceptibility at both the model training and prediction stages. The models were assessed for both accuracy and scalability, with specific focus on computational efficiency. Our findings indicate that LGBM and XGBoost are the top performers in terms of accuracy, with XGBoost also excelling in scalability, achieving a prediction time of ~18 s compared to LGBM's 22 s and random forest's 31 s. The evaluation framework presented is applicable to other flood-prone regions and highlights that LGBM is superior for accuracy-focused applications, while XGBoost is optimal for scenarios with computational constraints. The findings of this study can assist in accurate FSM in different regions and can also assist in scaling up the analysis to a larger geographical region which could assist in better decision-making and informed policy production for flood risk management.

Off-stream reservoirs are artificial water storage structures that increase the flood risk of an area. In some places, related risk reduction plans are based on a risk classification of these structures, which follows local water resource management regulations. These classification methods typically follow deterministic qualitative guidelines that do not account for uncertainties. This study introduces a fourth-step probabilistic approach that accounts for uncertainties related to simultaneous breach formation and breaking point location of off-stream reservoirs, and proposes an alternative visualisation for their classification. The methodology is applied to a set of Spanish off-stream reservoirs that are classified according to the Spanish normative. Results show that different breaking points and breach formations generate diverse classifications that can affect risk reduction plans. Additionally, we demonstrate that the proposed visualisation can be used for various purposes, including the case of the evolution of the categorisation in time, due to land use changes, which could be used by decision-makers to understand which off-stream reservoir requires a category update. These findings introduce a novel approach to managing uncertainties, which is crucial for developing resilient flood management strategies and contributes to the innovation discourse in flood risk management.

To improve the effectiveness of flood disaster relief operations, by ensuring timely and accurate delivery of urgently needed supplies to affected areas, this study focuses on the problem of emergency material distribution during floods. With the objective of minimizing the overall delivery time of emergency materials, we propose a coordinated optimization model that integrates trucks, speedboats, and drones for effective distribution of emergency supplies in flood-affected areas. To solve this optimization problem, we introduce an improved adaptive large neighborhood search (IALNS) algorithm, which builds on the traditional ALNS framework through refined tuning of deletion and insertion operators. Comparative analyses are conducted with a genetic algorithm, simulated annealing algorithm, and tabu search algorithm. The results reveal that the average performance gap of IALNS compared to these methods is 91.13%, 152.72%, and 16.92%, respectively. The experimental results demonstrate that the efficiency of the proposed model and algorithm in addressing the emergency supply distribution problem during flood disasters, highlighting the superior performance of IALNS. This research contributes to enhancing disaster response strategies, ultimately leading to improved outcomes for flood-affected communities.