Gas channeling control is key to improving COsub.2-flooding efficiency. A traditional plugging system has disadvantages, such as poor adaptability and stability, leading to the poor plugging effect ...of COsub.2 channeling in heterogeneous reservoirs and difficulty in controlling the subsequent COsub.2 injection pressure. To achieve a significant plugging effect and effectively control the subsequent COsub.2 injection pressure, a heterogeneous physical model of gas channeling in a horizontal well was established, and plugging experiments were conducted using four different combinations of plugging agents during COsub.2 flooding. Three evaluation parameters were defined, including the temperature field variation coefficient (TFVC), medium-permeability diversion rate (MPDR), and subsequent injection pressure coefficient (SIPC). The plugging effect of different combinations of plugging agents during COsub.2 flooding in heterogeneous reservoirs was analyzed. The results show that the plugging effect after using a combination of plugging agents was significantly better than after using a single plugging agent, and different plugging agent combinations had distinct characteristics. The strong–medium–weak (S-M-W) combination had the best MPDR for subsequent COsub.2 flooding, but the SIPC was the highest. The strong–weak–strong–weak (S-W-S-W) and weak–strong–weak–strong (W-S-W-S) combinations could effectively control the SIPC. These results indicate that plugging using the S-W-S-W and W-S-W-S combinations can achieve an effective plugging effect and reasonably control the subsequent COsub.2 injection pressure. This work provides a personalized design scheme for effective gas channeling control and maintenance of appropriate injection pressure during COsub.2 flooding in heterogeneous reservoirs.
Abstract Timely, accurate, and reliable information is essential for decision-makers, emergency managers, and infrastructure operators during flood events. This study demonstrates that a proposed ...machine learning model, MaxFloodCast , trained on physics-based hydrodynamic simulations in Harris County, offers efficient and interpretable flood inundation depth predictions. Achieving an average $$R^2$$ R 2 of 0.949 and a Root Mean Square Error of 0.61 ft (0.19 m) on unseen data, it proves reliable in forecasting peak flood inundation depths. Validated against Hurricane Harvey and Tropical Storm Imelda, MaxFloodCast shows the potential in supporting near-time floodplain management and emergency operations. The model’s interpretability aids decision-makers in offering critical information to inform flood mitigation strategies, to prioritize areas with critical facilities and to examine how rainfall in other watersheds influences flood exposure in one area. The MaxFloodCast model enables accurate and interpretable inundation depth predictions while significantly reducing computational time, thereby supporting emergency response efforts and flood risk management more effectively.
The efficacy of low‐impact development (LID) toward the attenuation of urban flood at the catchment scale is still an unsolved question. This work aims at providing insight into the effectiveness of ...LID at the catchment scale by a simple hydrological modeling approach that allows for an analytical solution. The paper focuses on LID design, investigating the peak discharge at the catchment outlet as a function of the return period of the rainfall event under unaltered and retrofitted conditions. The model captures the most important effects of LID on flood, that is, the temporal shift and attenuation of water discharge and the decrease of water volume conveyed by the drainage network due to increased hydrological losses. It is found that LID is likely more effective in reducing peak discharge at the local scale, while the effectiveness decreases with increasing catchment size. For some configurations, for example, when retrofit is performed in downstream areas, closer to the outlet, LID may even worsen the flood risk. The effectiveness of LID depends on the combination of several factors, including the fractional area covered by the LID infrastructures, their distribution within the catchment, and their hydraulic properties, as compared with the general hydraulic features of the catchment (network dispersion, detention, lag time, etc.). As a consequence, the LID design should be carried out considering all the relevant scales of the problem, from the local one, at which the LID infrastructure is implemented, to the catchment scale, and the discharge at the outlet in particular, in a comprehensive perspective.
Key Points
We develop a simple hydrological model, based on the IUH framework, to model LID effects on peak discharge quantiles at the catchment scale
We found that LID are effective in reducing urban flooding at the local scale, while effectiveness decreases with increasing catchment size
When retrofit is performed through LID implementation in areas close to the catchment outlet, LID may even worsen the flood risk
This article presents a geographic information system (GIS)-based artificial neural network (GANN) model for flood susceptibility assessment of Keelung City, Taiwan. Various factors, including ...elevation, slope angle, slope aspect, flow accumulation, flow direction, topographic wetness index (TWI), drainage density, rainfall, and normalized difference vegetation index, were generated using a digital elevation model and LANDSAT 8 imagery. Historical flood data from 2015 to 2019, including 307 flood events, were adopted for a comparison of flood susceptibility. Using these factors, the GANN model, based on the back-propagation neural network (BPNN), was employed to provide flood susceptibility. The validation results indicate that a satisfactory result, with a correlation coefficient of 0.814, was obtained. A comparison of the GANN model with those from the SOBEK model was conducted. The comparative results demonstrated that the proposed method can provide good accuracy in predicting flood susceptibility. The results of flood susceptibility are categorized into five classes: Very low, low, moderate, high, and very high, with coverage areas of 60.5%, 27.4%, 8.6%, 2.5%, and 1%, respectively. The results demonstrate that nearly 3.5% of the study area, including the core district of the city and an exceedingly populated area including the financial center of the city, can be categorized as high to very high flood susceptibility zones.
•The use of statistics improves vulnerability assessment based on qualitative data.•Correspondence analysis reveals strong relationships among vulnerability parameters.•Damage probability at the ...building scale was calculated using logistic regression.•Calculated damage probabilities correspond well to post-flood field observations.•Damage probabilities can also be extrapolated for non-field-surveyed buildings.
The focus of this study is an analysis of building vulnerability through investigating impacts from the 8 February 2013 flash flood event along the Avenida Venezuela channel in the city of Arequipa, Peru. On this day, 124.5mm of rain fell within 3h (monthly mean: 29.3mm) triggering a flash flood that inundated at least 0.4km2 of urban settlements along the channel, affecting more than 280 buildings, 23 of a total of 53 bridges (pedestrian, vehicle and railway), and leading to the partial collapse of sections of the main road, paralyzing central parts of the city for more than one week.
This study assesses the aspects of building design and site specific environmental characteristics that render a building vulnerable by considering the example of a flash flood event in February 2013. A statistical methodology is developed that enables estimation of damage probability for buildings. The applied method uses observed inundation height as a hazard proxy in areas where more detailed hydrodynamic modeling data is not available. Building design and site-specific environmental conditions determine the physical vulnerability. The mathematical approach considers both physical vulnerability and hazard related parameters and helps to reduce uncertainty in the determination of descriptive parameters, parameter interdependency and respective contributions to damage. This study aims to (1) enable the estimation of damage probability for a certain hazard intensity, and (2) obtain data to visualize variations in damage susceptibility for buildings in flood prone areas. Data collection is based on a post-flood event field survey and the analysis of high (sub-metric) spatial resolution images (Pléiades 2012, 2013). An inventory of 30 city blocks was collated in a GIS database in order to estimate the physical vulnerability of buildings. As many as 1103 buildings were surveyed along the affected drainage and 898 buildings were included in the statistical analysis. Univariate and bivariate analyses were applied to better characterize each vulnerability parameter. Multiple corresponding analyses revealed strong relationships between the “Distance to channel or bridges”, “Structural building type”, “Building footprint” and the observed damage. Logistic regression enabled quantification of the contribution of each explanatory parameter to potential damage, and determination of the significant parameters that express the damage susceptibility of a building. The model was applied 200 times on different calibration and validation data sets in order to examine performance. Results show that 90% of these tests have a success rate of more than 67%. Probabilities (at building scale) of experiencing different damage levels during a future event similar to the 8 February 2013 flash flood are the major outcomes of this study.
The development of a flash flood severity index Schroeder, Amanda J.; Gourley, Jonathan J.; Hardy, Jill ...
Journal of hydrology (Amsterdam),
October 2016, 2016-10-00, 20161001, Letnik:
541
Journal Article
Recenzirano
Odprti dostop
•First index proposed to characterize flash flood impacts.•Flash flood severity index based on multidisciplinary research and interviews.•Examples for three categories illustrated using ...photographs.•Potential to use index to communicate severity in future forecasts.
Flash flooding is a high impact weather event that requires clear communication regarding severity and potential hazards among forecasters, researchers, emergency managers, and the general public. Current standards used to communicate these characteristics include return periods and the United States (U.S.) National Weather Service (NWS) 4-tiered river flooding severity scale. Return periods are largely misunderstood, and the NWS scale is limited to flooding on gauged streams and rivers, often leaving out heavily populated urban corridors. To address these shortcomings, a student-led group of interdisciplinary researchers came together in a collaborative effort to develop an impact-based Flash Flood Severity Index (FFSI). The index was proposed as a damage-based, post-event assessment tool, and preliminary work toward the creation of this index has been completed and presented here. Numerous case studies were analyzed to develop the preliminary outline for the FFSI, and three examples of such cases are included in this paper. The scale includes five impact-based categories ranging from Category 1 very minor flooding to Category 5 catastrophic flooding. Along with the numerous case studies used to develop the initial outline of the scale, empirical data in the form of semi-structured interviews were conducted with multiple NWS forecasters across the country and their responses were analyzed to gain more perspective on the complicated nature of flash flood definitions and which tools were found to be most useful. The feedback from these interviews suggests the potential for acceptance of such an index if it can account for specific challenges.
How do people respond to the ways in which insurance mediates environmental risks? Socio‐cultural risk research has characterized and analyzed the experiential dimension of risk, but has yet to focus ...on insurance, which is a key institution shaping how people understand and relate to risk. Insurance not only assesses and communicates risk; it also economizes it, making the problem on the ground not just one of risk, but also of value. This article addresses these issues with an investigation of the social life of the flood insurance rate map, the central technology of the U.S. National Flood Insurance Program (NFIP), as it grafts a new landscape of ‘value at risk’ onto the physical and social world of New York City in the aftermath of Hurricane Sandy. Like other risk technologies, ubiquitous in modern societies as decision‐making and planning tools, the map disseminates information about value and risk in order to tame uncertainty and enable prudent action oriented toward the future. However, drawing together interview, ethnographic, and documentary data, I find that for its users on the ground, the map does not simply measure ‘value at risk’ in ways that produce clear strategies for protecting property values from flooding. Instead, it puts values‐beyond simply the financial worth of places‐at risk, as well as implicates past, present, and future risks beyond simply flooding. By informing and enlarging the stakes of what needs protecting, and from what, I argue that plural and interacting ‘values at risk’ shape how people live with and respond to environmental risks that are mediated by insurance technologies.
Abstract
A ‘roadmap’ for the future of UK flood hydrology over the next 25 years has been published, based on a wide-ranging and inclusive co-creation process involving more than 270 individuals and ...50 organisations from different sectors and disciplines. This paper highlights key features of the roadmap and its development as a community-owned initiative. The roadmap's relationship with hydrological research and practice is discussed, as is its context within the wider flood risk management innovation landscape, including funding. While the paper has a focus on UK flood hydrology, reflecting the scope of the roadmap, it is also considered in the context of advances in hydrology internationally.
The estimation of design flood is mainly focused on the peak flow and the volume, ignoring the underlying surface factor and flood rising and falling process. Three basic conceptual hydrological ...models, XAJ, TANK and SCS, are selected and applied for design flood estimation in two small-scale basins of northern China. Model parameter calibration is based on both the optimization algorithm SCE-UA and artificial adjusting, by using a combined objecting function of flood peak, volume and process. Each model singles out a set of optimal parameters as input to simulate the design flood process. The simulation results are compared with original engineering design standards and instantaneous unit hydrograph method. The results show that the XAJ model has the best performance in simulating the 100-year design flood in study basins. The SCS model also gives acceptable results, but the TANK model on the other hand in an underestimated flood peak with a prolonged recession period, which is not likely to be applicable. This study is to test the applicability of the conceptual hydrological models in simulating the design flood process in small-scale watersheds and should be a supplement to the traditional methods and further deliberation to a ungauged basin. Starting from the most basic models with simple structures, it is hoped that the methodology can be transferred to more complicated and physically based models with more realistic description of the rainfall-runoff transformation mechanism and dynamic mechanism for climate change.
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