Flood Volume Research Articles

Research progress of compound flood in coastal cities

Facilitating the development of cities in coastal areas is an economic strategy from a global perspective. Currently it can be seen that large developed cities in many countries are distributed along the coast. However, in recent years, with the influence of climate change, sea level rise, cyclone changes and land subsidence, the frequency of compound storm floods has been increasing. More academics begin work on compound storm flooding. It can be found that the development of the flood model has experienced a process from ordinary static simulation to one-Dimensional-three-Dimensional coupled model. At present, there are mainly hurricane model and hydrodynamic model to study the composite storm flood. Through current storm flood models, it is known that the intensity and frequency of storm floods will increase under current climate change conditions. At the same time, this paper also looks forward to the models, and proposes an improvement plan to increase the application scope of the model.

Strengthening weather forecast and dissemination capabilities in Central Africa: Case assessment of intense flooding in January 2020

The first dekad of January 2020 was characterised by heavy precipitation in the capital of the Republic of Congo, Brazzaville, which led to several localised landslides. Satellite-derived rainfall estimates and rain-gauge totals illustrate a strong wet spell between the 6th and 9th January 2020 across southern Congo. This study highlights the generation and implementation of user-driven weather and climate forecast bulletins, developed at the Economic Communities of Central African States (ECCAS) Climate Application and Prediction Centre (CAPC), to reduce the impacts associated with intense precipitation during this dekad. Through doing so, we document the current state of regional-scale climate services across Central Africa. Advisories and outlooks generated by CAPC use risk matrices developed by the World Meteorological Organisation (WMO) and are produced at hourly, daily, sub-seasonal, and seasonal timescales. To develop them, meteorologists and climate scientists at CAPC combine information from a wide range of meteorological observations and forecasts. Regional-scale forecasts are downscaled to individual countries to improve accessibility and relevance.Central African users have reported that bulletins provide support for mitigating against the impacts of extreme weather and have requested more reliable sub-seasonal and seasonal forecast products. In this paper we take the opportunity to discuss the resources obtained through the Satellite and Weather Information for Disaster Resilience (SAWIDRA) framework, which are often taken for granted in developed nations, including the procurement of a high performance computing system, satellite data and numerical models outputs receiving stations. This study is the first to highlight the current state of regional-scale climate services across Central Africa and motivates further co-production of climate services across the region.

Stochastic Flood Simulation Method Combining Flood Intensity and Morphological Indicators

The existing flood stochastic simulation methods are mostly applied to the stochastic simulation of flood intensity characteristics, with less consideration for the randomness of the flood hydrograph shape and its correlation with intensity characteristics. In view of this, this paper proposes a flood stochastic simulation method that combines intensity and morphological indicators. Using the Foziling and Xianghongdian reservoirs in the Pi River basin in China as examples, this method utilizes a three-dimensional asymmetric Archimedean M6 Copula to construct stochastic simulation models for peak flow, flood volume, and flood duration. Based on K-means clustering, a multivariate Gaussian Copula is employed to construct a dimensionless flood hydrograph stochastic simulation model. Furthermore, separate two-dimensional symmetric Copula stochastic simulation models are established to capture the correlations between flood intensity characteristics and shape variables such as peak shape coefficient, peak occurrence time, rising inflection point angle, and coefficient of variation. By evaluating the fit between the simulated flood characteristics and the dimensionless flood hydrograph, a complete flood hydrograph is synthesized, which can be applied in flood control dispatch simulations and other related fields. The feasibility and practicality of the proposed model are analyzed and demonstrated. The results indicate that the simulated floods closely resemble natural floods, making the simulation outcomes crucial for reservoir scheduling, risk assessment, and decision-making processes.

An advanced tool integrating failure and sensitivity analysis into novel modeling of the stormwater flood volume

Abstract. An innovative tool for modeling the specific flood volume was presented that can be applied to assess the need for stormwater network modernization as well as for advanced flood risk assessment. Field measurements for a catchment area in Kielce, Poland, were used to apply the model and demonstrate its usefulness. This model extends the capability of recently developed statistical and machine learning hydrodynamic models developed from multiple runs of the US Environmental Protection Agency (EPA) Storm Water Management Model (SWMM). The extensions enable the inclusion of (1) the characteristics of the catchment and its stormwater network, calibrated model parameters expressing catchment retention, and the capacity of the sewer system; (2) extended sensitivity analysis; and (3) risk analysis. Sensitivity coefficients of calibrated model parameters include correction coefficients for percentage area, flow path, depth of storage, and impervious area; Manning roughness coefficients for impervious areas; and Manning roughness coefficients for sewer channels. Sensitivity coefficients were determined with respect to rainfall intensity and characteristics of the catchment and stormwater network. Extended sensitivity analysis enabled an evaluation of the variability in the specific flood volume and sensitivity coefficients within a catchment, in order to identify the most vulnerable areas threatened by flooding. Thus, the model can be used to identify areas particularly susceptible to stormwater network failure and the sections of the network where corrective action should be taken to reduce the probability of system failure. The simulator developed to determine the specific flood volume represents an alternative approach to the SWMM that, unlike current approaches, can be calibrated with limited topological data availability; therefore, the aforementioned simulator incurs a lower cost due to the lower number and lower specificity of data required.

Expressive fluxes over Amazon floodplain revealed by 2D hydrodynamic modelling

Water fluxes in the Amazon River floodplain affect hydrodynamic and ecological processes from local to global scales, but they remain poorly understood due to difficult accessibility and limited data. We characterized the hydrodynamics of eight floodplain units of the central Amazon River (40000 km2) using the 2D hydraulic model HEC-RAS. High resolution modelling (∼400 m) improved the representation of river and floodplain discharge, water surface elevation (77 cm accuracy) and flood extent (∼80% - high water period, ∼52% - low water period) compared to past modelling studies. Our results show that floodplain flows during floods are very intense with upstream inflow and downstream outflow of the floodplain units. These gross flows are much larger than the net flows, with values of up to 20% of the Amazon River discharge and residence time around 6 days during floods (several months during low water period). Water extent did not show strong interannual variability during floods as the volume stored in the floodplain did, possibly due to topographic constraints. Significant hysteresis in flood extent and volume, and active and storage zones on the floodplain highlight the complexity of floodplain hydrodynamics. Extreme floods strongly impacted the onset and duration of the flood by up to one month and, consequently on duration of high water renewal period with the river. Our characterization is important to assess the effects of extreme floods on riverine communities, understand nutrient and sediment variations in the floodplain, and characterize the export of water, sediment, and carbon flux to the ocean from the world's largest hydrological system.

Copula-Based Joint Flood Frequency Analysis: The Case of Guder River, Upper Blue Nile Basin, Ethiopia

The univariate analysis of hydrological extremes is a well-established practice in developing countries such as Ethiopia. However, for the design of hydrological and hydraulic systems, it is essential to have a thorough understanding of flood event characteristics, including volumes, peaks, time of occurrence, and duration. This study utilizes copula functions for bivariate modeling of flood peak and volume characteristics, examining the performance of four Archimedean copulas in the Guder basin located in Ethiopia from 1987 to 2017. Flood peak and volume were extracted using the theory of runs for analysis of their joint characteristics with the truncation level chosen as equal to the lowest annual maximum event. Univariate distributions with the best fitness on both variables were determined, and results showed that gamma and GEV-fitted flood peaks and lognormal-fitted flood volumes are the most suitable. Four Archimedean copulas were evaluated, and the Gumbel-Hougaard copula was found to be the best fit for the data based on graphical and measurable tests. Bivariate probability and return period were computed in “AND” and “OR” states. The joint return period for flood peak (97.49 m3/s) and volume (77.35 m3/s) was found to be 15 years in the “AND” state and approximately 4 years in the “OR” state. The study also evaluates univariate and conditional return periods, comparing them with the primary one. The copula method was an effective method for distributing marginal variables, highlighting its potential as a valuable tool in flood management.

Preview of Causes, Frequency, Extent, Magnitude and Intensity of Flooding in Nigeria

This study previewed causes, frequency, extent, magnitude, intensity and charted a course of mitigating flood in Nigeria. This Study revealed the causes of flooding to include: climate change, unending rainfall, excess water from Cameroon’s Lagdo Dam, river overflow, expansion of floodplains and poor planning. In 2022 flooding affected 7 million Nigerians, 2 million internally displaced, 5,000 physically injured, 5,900 houses destroyed. The 2012 flood had the highest number of occurrences of 18 incidences, in 17 each in 2015 and 2017, in 2018 and 2019, 12 incidences. 2014 and 2016, had 1 incidence. These frequent floods were recorded in 31 out of the 36 and the FCT, severe in Benue, Kogi, Anambra, Niger, Adamawa, Oyo, Kano, Jigawa and Lagos States. The extent of flooding includes shortage of food supply, farmlands washed away; economy, health, social life and environment were also affected. Corpses floated in floods, the country was at high risks of waterborne diseases like diarrhoea, cholera and typhoid fever. However, the benefits of flooding include rendering terrorists incapable in some states; deposit rich, fertile alluvium, replenish irrigation channels, filter pollutants, nourish lands to support ecosystems and fertile areas for farming. This Study recommended that individuals should not build houses in floodplains; government should maintain dams; flood management policy and collaboration between agencies. Experts should develop awareness, enlightenment, educational and legal framework to checkmate flooding in Nigeria.

SatVITS‐Flood: Satellite Vegetation Index Time Series Flood Detection Model for Hyperarid Regions

AbstractWe present the satellite vegetation index time series model for detecting historical floods in ungauged hyperarid regions (SatVITS‐Flood). SatVITS‐Flood is based on observations that floods are the primary cause of local vegetation expansion in hyperarid regions. To detect such expansion, we used two time‐series metrics: (a) trend change detection from the Breaks For Additive Season and Trend and (b) a newly developed seasonal change metric based on Temporal Fourier Analysis (TFA) and the growing‐season integral anomaly (TFA‐GSIanom). The two metrics complement each other by capturing changes in perennial plant species following extreme, rare floods and ephemeral vegetation changes following more frequent floods. Metrics were derived from the time series of the normalized difference vegetation index, the modified soil‐adjusted vegetation index, and the normalized difference water index, acquired from MODIS, Landsat, and Advanced Very High‐Resolution Radiometer. The timing of the change was compared with the date of the flood and the magnitude of change with its volume and duration. We tested SatVITS‐Flood in three regions on different continents with 40‐year‐long, systematic, reliable gauge data. Our results indicate that SatVITS‐Flood can predict flood occurrence with an accuracy of 78% and precision of 67% (Recall = 0.69 and F1 = 0.68; p < 0.01), and the flood volume and duration with NSE of 0.79 (RMSE = 15.4 × 106 m3 event−1), and R2 of 0.69 (RMSE = 5.7 days), respectively. SatVITS‐Flood proved useful for detecting historical floods and may provide valuable long‐term hydrological information in poorly documented areas, which can help understand the impacts of climate change on the hydrology of hyperarid regions.

Seroprevalence of serovars of pathogenic leptospira in dogs and red foxes (Vulpes Vulpes) from bosnia and herzegovina

Abstract The aim of this study was to examine the seroprevalence of Leptospira spp. in dogs and red foxes in the entity of the Republic of Srpska, Bosnia and Herzegovina, after heavy rainfall and floods in 2014 and for the two years thereafter. The seroepidemiological study involved testing serum samples from dogs (n = 98) and foxes (n = 112) using MAT (microscopic agglutination test). Antibodies to at least one Leptospira spp. serovar were found in 52.04% of the tested dogs. The dog seroprevalence in 2014 (81.25%) was significantly higher than in 2015 (51.42% p <0.0001) and 2016 (22.5% p<0.05). The highest seroprevalences were for serovars Australis (76.47%), Bratislava (70.58%), Sejroe (66.67%) and Autumnalis (45.09%). Antibodies to at least one Leptospira spp. serovar were detected in 34.82% of the examined red foxes. In 2015, the fox seroprevalence was significantly higher (52.94%) than in 2016 (6.82%) (p <0.0001). The highest seroprevalences were for serovars Sejroe (64.10%), Bratislava (48.72%), Australis (43.59%) and Bataviae (25.64%). The high seroprevalence of Leptospira spp. in dogs and foxes determined during this study indicates the importance of these carnivores in maintaining leptospirosis in the study area, and the potential risk of infection for humans and other animal species that come into contact with these canids. The results obtained indicate that heavy rainfall and intense floods can result in increased Leptospira spp. infection in these canids.

Variation characteristics of hydrological response to water conservancy construction in the Qinhe River Basin of the Loess Plateau, China

AbstractInvestigating the response characteristics of various hydrological factors to water conservancy projects area in basin and evaluating their impact on the ecological environment are crucial for ecological protection and restoration on the Loess Plateau, China, with a complex environment. In this study, we employed a geomorphology‐based hydrological model to simulate the hydrological elements of the Qinhe River Basin on the Loess Plateau. We explored the response characteristics of the water cycle and hydrological processes to the construction of reservoirs in the basin. Additionally, we also examined multiyear changes in peak flood volume and sediment discharge during flood seasons influenced by reservoirs. The sub‐basins hosting reservoirs initialled an increase in evaporation, followed by a decrease. During the change periods, both runoff and soil water decreased, but remained higher than the mean values for the basin during the same period. The Normalized Difference Vegetation Index of sub‐basins associated with five reservoirs was obviously higher than the mean value for the basin during the same period. The peak flood volume and sediment discharge in the basin were characterized by decreasing trends, with the latter showing weak sustainability. The value of each index for a sub‐basin associated with a reservoir was higher than the average value for the basin. The construction and operation of reservoirs had a positive impact on the ecology of the basin. Water and soil conservation measures, including sediment regulation and storage using reservoirs, effectively decreased water hazard and soil erosion in the basin. This study provides a scientific basis for the construction of water conservancy projects and ecological governance in the basin.

Whole Watershed-based Estimation of FEWS Installation Site Using UAV Photogrammetry

For small rivers and reservoirs, a flood early warning system (FEWS) is generally used to prepare for sudden flood events whose impact is augmented from homonized environment and climate change. Currently, a FEWS requires the installation of a water gauge, typically on a bridge. This is a limitation of the FEWS owing to the possibility of existence of areas that are more vulnerable than the bridge. Therefore, to analyze the flood risk over the entire target basin, a procedure to install a FEWS was proposed. First, unmanned aerial vehicle (UAV)-based photogrammetry information is analyzed to preselect flood-prone areas. Second, further investigation including the ground surveying is followed for the preselected areas. Finally, the flood amount is estimated and the UAV photogrammetry information for issuing the alarm levels of the FEWS. For this purpose, Malgol Reservoir, a reservoir with high-risk level from floods, containing residential areas was selected as the current study area. Eleven flood-prone points were identified by analyzing the UAV photogrammetry information. Then, the flooding point with the lowest flood volume among the 11 points was selected as the most vulnerable point; this section was located at 464 m from the Malgol Reservoir spillway in the downstream direction. The warning alarm for the basin-wide FEWS could be issued according to the embankment of the most vulnerable section. This ensured the safety over the entire basin including the reservoir and its downstream area. The proposed procedure can overcome the limitations of the current bridge water level-based analysis by employing UAV-photogrammetry information of the entire basin and considering the entire flood-prone cross-sections.

Hybrid deep learning model for flood frequency assessment and flood forecasting

The most common and persistent natural hazard to people across the globe is flooding. The frequency of floods in a given place is defined as the likelihood and intensity of floods occurring there within a certain period. Examining historical flood data and using techniques are often used to determine the likelihood that a flood of a certain size would occur in a specific location. The method of flood prediction involves making forecasts on the frequency and severity of flooding. It may be influenced by a number of factors, including the topography, river flow, soil moisture content, and the period of rainfall. In this research, we provide a novel Cat Swarm Optimized Spatial Adversarial Network (CSO-SAN) technique for predicting and assessing flood frequency. This technique simulates the yearly greatest flow at the river Mahanadi measurement sites at Andhiyarkore, Bamanidhi, Baronda, and Kurubhatta over 60 years. The CSO-SAN model is adapted for the flood forecasting component to predict the frequency and size of future floods. The model incorporates real-time data from various sources, such as meteorological predictions and information on river flow, to anticipate the probability and severity of upcoming floods. Compared to other conventional statistical techniques and forecasting models, the CSO-SAN model outperformed them in tests conducted on the Mahanadi river basins. The model offers a viable method for improving the precision of flood frequency evaluation and flood forecasting, with significant advantages for managing and reducing flood risk.

Projections of flood regime changes over the upper-middle Huaihe River Basin in China based on CMIP6 models

Introduction: Climate change alters the hydrological cycle to different extents, in particular the intensification of extreme precipitation and floods, which has garnered more attention as a significant scientific issue in the last few decades. The last Coupled Model Inter-comparison Project 6 (CMIP6) was designed with new shared socioeconomic pathways (SSPs) to combine socioeconomic development with greenhouse gas emissions to project future climate.Method: In this study, we used 22 global climate models (GCMs) from CMIP6 to investigate future variations in extreme precipitation and temperature under SSP2-4.5 and SSP5-8.5 scenarios over the upper-middle Huaihe River Basin (UMHRB). Then, the modified Xinanjiang model integrating the flood control module was driven to obtain projections of the daily streamflow and to evaluate the future variations in flood regimes.Results: The results show that 1) the characteristics of future extreme precipitation, such as the average intensity and amount of annual precipitation and extreme precipitation, are projected to increase, and the average, maximum, and minimum temperature values also display substantial increasing trends in the future over the UMHRB; 2) warmer climate will lead to a more severe flood magnitude under the SSP5-8.5 scenario in the far future (2071–2100) over the UMHRB. The results of the multi-model ensemble show that the annual maximum flood peaks (15-day flood volumes) of Wangjiaba and Wujiadu stations are projected to increase by 46.4% (43.1%) and 45.4% (51.1%), respectively, in the far future (2071–2100) under the SSP5-8.5 scenario; and 3) variations in the flood frequency tend to resemble variations in flood magnitude, and the return period of the design flood will obviously decrease under future climate scenarios. For instance, in the far future, under SSP5-8.5 scenarios, the return period of the design flood with a 100-year return period will become 38 years and 31 years for Wangjiaba and Wujiadu stations, respectively.Discussion: The study enhances a more realistic understanding that the occurrence of future extreme precipitation and floods is projected to be more frequent and severe, thereby resulting in an urgent imperative to develop pertinent adaptation strategies to enhance social resilience toward the warming climate.

Monthly-scale hydro-climatic forecasting and climate change impact evaluation based on a novel DCNN-Transformer network

Climate change has emerged as one of the foremost global challenges confronting humanity today, leading to a heightened frequency and intensity of extreme weather phenomena, including droughts, floods, and erratic rainfall patterns. Accurately predicting changes in runoff patterns under future climate conditions holds significant importance for effective regional water resource planning and management. Recent research on runoff forecast has centered on optimizing hyperparameters of ELM, RNN, LSTM models using PSO, GWO, SSA, and other algorithms. Additionally, key features are extracted through input variable decomposition and preprocessing methods like EMD, EEMD, and VMD. However, these approaches have difficulties in extracting the long-term dependencies information of sequence units, parallel computing, and hyperparameter sensitivity. To address these shortcomings, this study proposes a novel end-to-end deep runoff prediction model based on deep convolutional neural network and Transformer (DCTN). The deep convolutional modules of DCTN employs the deep convolutional operation to extract local features of climate data while the Transformer of DCTN makes full use of self-attention to capture the long-term dependencies, which can achieve more accurate runoff predictions. Experiments on historical runoff forecasting at the Shanjiaodi hydrology station in the Dagu River Basin show that the proposed DCTN obtains a notable improvement of approximately 30.9% compared to traditional models. Based on the prediction results of three shared socioeconomic pathways, the potential impacts of climate change on runoff in Dagu River Basin were evaluated using the DCTN model. The results reveal that the likelihood of spring floods is substantially amplified in the mid-century and late-century, while the probability of extreme summer runoff diminishes. This study advances the understanding of runoff prediction and its implications under changing climate scenarios, paving the way for more informed decision-making and effective water resource management strategies.

Soil Moisture and Streamflow Data Assimilation for Streamflow Prediction in the Narmada River Basin

Abstract An accurate streamflow forecast is vital for flood prediction and early warning systems. Notwithstanding the rising frequency and intensity of floods during the summer monsoon season in India, efforts to examine the utility of data assimilation (DA) for streamflow prediction remain limited. We examine soil moisture and streamflow DA to improve streamflow simulations in the Narmada River basin, considered a testbed. Data assimilation was performed using the Variable Infiltration Capacity (VIC) model at four gauge stations in the basin. First, we used the ensemble Kalman filter (EnKF) to assimilate the satellite soil moisture from the European Space Agency Climate Change Initiative (ESA-CCI) to the initial state of the VIC model. We examined the usefulness of observed streamflow from the India Water Resources Information System (India-WRIS) to improve the initial hydrological conditions of the VIC model in the streamflow DA during the summer monsoon (June–September) season from 1980 to 2018. The assimilation of ESA-CCI soil moisture showed less improvement in percent error reduction (PER) and efficiency index (EFF) (less than 2%) than the streamflow DA at all of the four gauge locations in the Narmada basin. On the other hand, the streamflow DA showed a significant improvement in PER and EFF (more than 10%) at all the gauge stations for both mean and high-flow conditions. Streamflow data assimilation improved errors in the magnitude and timing for the major floods in 1994 and 2013. Significance Statement India is considerably affected by floods during the summer monsoon. Floods have increased and are projected to become more frequent under the warming climate. Flood prediction and early warning systems are essential in reducing their impacts and exposure. In the present study, we are interested in examining the role of soil moisture and streamflow data assimilation on flood prediction in the Narmada River basin. Our results show that streamflow data assimilation is more effective in reducing errors and bias in streamflow prediction than soil moisture data assimilation. The hydrologic framework used for the Narmada basin can be extended and evaluated to other flood-prone basins in India.

Risk assessment of flash flood and soil erosion impacts on electrical infrastructures in overcrowded mountainous urban areas under climate change

Risk management of flash floods in arid mountainous cities is challenged by the lack of proper data and the unreadiness of infrastructures to handle large floods. Climate projections predict increasing frequency of extreme droughts and floods over these arid zones aggravating the impacts of flash floods by increasing the hardness of the topsoil, making it less efficient at absorbing rain water. This study assesses the hazards of steadily increasing flash floods on voltage towers around Makkah city using hydrological modeling to simulate flow velocity and volume and erosion intensity of floods. Hydrological modeling estimated the maximum discharge rates at Wadi Numan and Wadi Al-Sharaya outlets as 3142 and 2543 m3/s, respectively. Extreme soil erosion rates are encountered in the lower reaches of these basins (11% of the total area) and severe erosion rates (3.6%) were reported in the planned voltage tower path. Catchment's lower reaches are proved highly vulnerable to soil erosion due to the lack of vegetation cover and high flow accumulations. The study alarms revisiting the risk management policies for infrastructure projects in arid mountainous cities considering the climate change impacts on increasing the frequency of unprecedented droughts and floods and their aggravated destructive impacts.

An Integrative Framework for Assessment of Urban Flood Response to Changing Climate

AbstractIncreasing frequency of extreme rainfall induced catastrophic urban flood events in recent decades demands proactive efforts to assess flood risk and vulnerability. Here, we develop an integrative framework for fine spatiotemporal scale assessment of urban flood response to historical and future projected changes in extreme precipitation. The framework includes three main components—nonstationary modeling of historical extreme precipitation, modeling of future precipitation, and urban flood simulations. It also provides robust estimates of uncertainty in design precipitation from statistical modeling, multiple climate models and stochastic uncertainty, estimated using machine learning techniques. We demonstrate the proposed framework for White Oak Bayou watershed in Houston, Texas, US. Two‐dimensional hydrologic‐hydraulic Interconnected Channel and Pond Routing model is used to simulate flood response from design precipitation for historical (1986–2020) and two future (2021–2050 and 2071–2100) periods in three (SSP1‐2.5, SSP2‐4.5, and SSP5‐8.5) future climate scenarios. Results show that nonstationary design estimates for historical precipitation are 14%–25% higher than the stationary estimates for 100‐year event of 1‐ to 24‐hr duration. Contrary to general global trends, we found a significant reduction in future design precipitation in all three emission scenarios. Additionally, stochastic uncertainty in future design precipitations is found to be larger than the modeling uncertainty in historical estimates and climate model uncertainty in future estimates. Flood response in terms of peak flood and total flood volume suggests that the difference in stationary versus nonstationary historical and future design precipitation are substantial to cause considerable change in simulated flood.

Reducing flood risk and improving system resiliency in Sacramento, California: overcoming obstacles and emerging solutions

Sacramento, the capital of California, has a population of over 2 million and is one of the most flood prone regions in the nation. Its problems exemplify those of many urban communities built near riverine and deltaic systems, that are subject to climate change. The city and its surrounding communities are protected by an elaborate system of levees and flood bypasses; but aging infrastructure, expected increases in extreme wet weather, and projected sea level rise are increasing the risk of levee failures. We explore how flood management approaches including social/institutional (non-structural), traditional structural, and ecological based approaches are being implemented in the Lower Sacramento/North Delta Region amid significant obstacles, to build resilient flood management systems. We review four case studies, one structural levee project and three multi-benefit projects that are only recently being implemented. We also examine the barriers, constraints, and challenges for implementing flood protection projects, and how project proponents are collectively working through these obstacles. We conclude that significant progress has been made in building flood resiliency since the 2008 Central Valley Flood Protection Act and the release of the 2012 Central Valley Flood Protection Plan. Informational tools and policies are being developed to educate the public and prepare for floods. Structural levee investments are substantial and are being implemented through partnerships. Statewide policies and investments are increasingly supporting multi-benefit projects that incorporate ecological restoration/enhancement while expanding flood volume capacity. Progress on implementing multi-benefit projects has been slow, due to land acquisition, easements, funding, regulatory and construction challenges; however, solutions to these impediments are emerging to facilitate more rapid progress. It is essential to continue and intensify the progress made in the last two decades, by learning from past projects, and improving on existing pathways to implement sustainable projects at a faster rate.

Evaluating the effect of building patterns on urban flooding based on a boosted regression tree: A case study of Beijing, China

AbstractRapid urbanization and global climate change are likely to exacerbate urban flooding intensity, frequency and uncertainty. Thus, it is fundamental and crucial to investigate the dominant influencing factors for the mitigation of urban flooding. However, knowledge about the influence of building patterns on urban flooding remains limited. Taking Beijing, a typical megacity, as a case study area, we quantified the importance of building patterns and their interaction effect at the subwatershed scale using the boosted regression tree and geographical detector model. The results indicated that (1) the landscape shape index, slope, green space ratio and waterbody ratio were the most important influencing factors determining urban flooding, with a total relative contribution of 67.23%; (2) building metrics had a certain impact on urban flooding, and the sum of the relative contribution reached 21.03%; (3) the landscape shape index, slope, and green space ratio exhibited a combination of negative and positive correlations with urban flooding density; and (4) an enhancement effect existed between building metrics, especially the building congestion degree and building density. These findings provide quantitative insights on rational urban morphology planning which could be applied to improve stormwater management and promote urban sustainability in megacities.

A Framework of Dependence Modeling and Evaluation System for Compound Flood Events

AbstractThe coincidence and superposition of flood processes from different rivers and regions tend to form compound flood events, determined by spatial relationship between diverse flood processes that cannot be accurately depicted and evaluated by existing dependence analysis methods. A framework, integrating multi‐dimensional vine copula model and dependence evaluation system, was developed with a testing‐oriented application to explore underlying dependence between two kinds of extreme runoff series (peak discharge and flood volume) extracted from the identified compound flood events in the upper reaches of the Yangtze River. Multi‐dimensional regular vine (R‐vine) copula models were established to depict the complex and diverse dependence, and corresponding vine structure was specified by the vine structure array that can reflect the sequence of tributaries flowing into the main stream and the spatial locations of different hydrometric stations. Dependence magnitude and association status were calculated and compared according to the optimal R‐vine copula models and information theory. Comparison with existing methods demonstrated that dependence evaluation system could reflect nonlinear and local dependence characteristics and eliminate the effect of extreme runoff series from other hydrometric station on the dependence. The association status between different extreme runoff series of the upper Yangtze River and its tributaries were diverse in view of the impact of tributary flood inflow. The proposed framework can be regarded as an effective way for dependence modeling and evaluation of compound floods, thus providing a scientific reference for the risk analysis of water resources systems.