Flood Inundation Simulation Research Articles

The heterogeneity of human flood adaptation characteristics in Central Asia based on human-flood distance

The Central Asian region is subject to frequent seasonal floods, resulting in substantial losses. These recurrent floods have induced certain changes in human flood adaptation characteristics in Central Asia, an area that is currently under-researched. This study, grounded in meticulously simulated flood inundation outcomes, multisource population distribution data, and flood protection infrastructure data, introduces an improved approach to calculating the distance between human and flood (HFD) and investigates human flood adaptation characteristics in Central Asia. Our findings indicate that residents in Central Asia get close to flood-prone areas from 1970 to 2007, with the exceptions of Turkmenistan (TKM) and Kyrgyzstan (KGZ). Notably, this trend is particularly pronounced in regions such as Xinjiang, China, and Tajikistan (TJK). An intricate exponential increase is observed in the population exposed to floods as the HFD decreases. Under future scenarios, KGZ exhibits the most significant trend of staying away from flood-prone areas, while TJK presents the opposite trend, and TKM experiences minimal changes. Additionally, compared with urban areas, rural areas of Central Asia exhibit a trend towards approaching floods. This study underscores the optimum nature of human flood adaptation when the HFD reaches 4.23 km, showing the potential for substantial benefits by implementing population evacuation within this threshold buffer zone. These insights highlight the complex interplay between human behaviours and floods and can inform the formulation of effective flood risk mitigation strategies.

Urban flash floods modeling in Mzuzu City, Malawi based on Sentinel and MODIS data

Floods are major hazard in Mzuzu City, Malawi. This study applied geospatial and hydrological modeling techniques to map flood incidences and hazard in the city. Multi-sensor [Sentinel 1, Sentinel 2, and Moderate Resolution Imaging Spectroradiometer (MODIS)] Normalized Difference Vegetation Index (NDVI) datasets were used to determine the spatio-temporal variation of flood inundation. Ground control points collected using a participatory GIS mapping approach were used to validate the identified flood hazard areas. A Binary Logistic Regression (BLR) model was used to determine and predict the spatial variation of flood hazard as a function of selected environmental factors. The Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) was used to quantify the peak flow and runoff contribution needed for flood in the city. The runoff and peak flow from the HEC-HMS model were subjected to extreme value frequency analysis using the Gumbel Distribution approach before input into the Hydrologic Engineering Center River Analysis System (RAS) (HEC-RAS). The HEC-RAS model was then applied to map flood inundated areas producing flood extents maps for 100, 50, 20, and 10-year return periods, with rain-gauge and Climate Prediction Center MORPHed precipitation (CMORPH) satellite-based rainfall inputs. Results revealed that selected MODIS and Sentinel datasets were effective in delineating the spatial distribution of flood events. Distance from the river network and urban drainage are the most significant factors (p < 0.05) influencing flooding. Consequently, a relatively higher flood hazard probability and/susceptibility was noted in the south-eastern and western-most regions of the study area. The HEC-HMS model calibration (validation) showed satisfactory performance metrics of 0.7 (0.6) and similarly, the HEC-RAS model significantly performed satisfactorily as well (p < 0.05). We conclude that bias corrected satellite rainfall estimates and hydrological modeling tools can be used for flood inundation simulation especially in areas with scarce or poorly designed rain gauges such as Mzuzu City as well as those affected by climate change. These findings have important implications in informing and/updating designs of flood early warning systems and impacts mitigation plans and strategies in developing cities such as Mzuzu.

Investment decision support for business sustainability improvement based on flood inundation simulations with a variety of extreme rainfall scenarios

Investment decision support for business sustainability improvement based on flood inundation simulations with a variety of extreme rainfall scenarios

Simulating block-scale flood inundation and streamflow using the WRF-Hydro model in the New York City metropolitan area

This study assesses the performance of the Weather Research and Forecasting-Hydrological modeling system (WRF-Hydro) in the simulation of street-scale flood inundation. The case study is the Hackensack River Watershed in New Jersey, US, which is part of the operational Stevens Flood Advisory System (SFAS), a one-way coupled hydrodynamic-hydrologic system that currently uses the Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) to simulate streamflow. The performance of the 50-m gridded WRF-Hydro model was assessed for potential integration into the operational SFAS system. The model was calibrated with the dynamically dimensioned search algorithm using streamflow observations. The model performance was assessed using (i) streamflow observations, (ii) USGS HWMs, and (iii) crowdsourced data on street inundation. Results show that WRF-Hydro outperformed the HEC-HMS model. WRF-Hydro over and underestimated flood inundation extent due to the inaccuracy of the synthetic rating curves and the modeling structure errors. An agreement was noticed between WRF-Hydro and crowdsourced data on flood extent.

Quick large-scale spatiotemporal flood inundation computation using integrated Encoder-Decoder LSTM with time distributed spatial output models

Accurate spatiotemporal flood simulations are essential for making informed decisions regarding flood release in affected regions, such as flood detention areas. Traditional spatiotemporal flood simulation approach uses partial differential equation (PDE) models (or physics-based models), which need high computational time. Although many machine learning (ML) models for inundation are increasingly being used to emulate the PDE models to address this issue, utilizing conventional ML models to achieve large-scale spatiotemporal flood prediction (i.e., simulation output in tens of thousands of grids and time steps over the whole flood event) remains a significant challenge. Therefore, we developed a new inundation model (IM) using encoder-decoder long short-term memory (ED-LSTM) with Time Distributed Spatial output model (ED-LSTM-TDS) that can acquire accurate spatially distributed flood information more rapidly. In the new IM framework, each ED-LSTM-TDS is built to simultaneously generate output for multiple (K) cell grids and multiple ED-LSTM-TDS models are built for prediction at all grids of entire PDE model. This study is the first of its kind to employ the ED-LSTM-TDS method to address spatiotemporal flood inundation simulation problems for flood detention areas. A 1994 km2 flood detention area in northeastern China was used as a case study. ED-LSTM-TDS exhibited better performance in predicting flood characteristics (e.g., water depth, velocity) than alternative methods, including ordinary LSTM, artificial neural network (ANN), and multiple linear regression (MLR). In addition, we investigated the trade-off relationship between the accuracy of flood characteristic prediction and the computation time of the proposed model by considering different numbers of K cell grids in each ED-LSTM-TDS model. The final proposed inundation model could accurately predict the spatiotemporal flood characteristics within 1.5 min to acquire the same information that required approximately 1 h by the PDE models. Such rapid and accurate prediction by the proposed model is important for evacuation planning, upstream flood control decisions and flood damage reduction.

Hydraulic model for flood inundation in Diyala River Basin using HEC-RAS, PMP, and neural network

Abstract The Diyala River Basin in Iraq is vital for water supply to residential, agricultural, and the Tigris River (with approximately 4.5 billion cubic meters annually), but it faces frequent floods and droughts due to reliance on rainfall. This study aims to address these issues by simulating flood inundation using the hydrological engineering centre-river analysis system model and predicting high-intensity rainfall with artificial neural networks. ArcGIS and remote sensing tools aid model development with data from official sources and organizations such as national aeronautics and space administration and food and agriculture organization. The hydraulic model is calibrated using satellite imagery to depict a 2019 flood, and artificial intelligence predicts the precipitation patterns for the next 50 years based on historical data from 1981 to 2021. One of the challenges and difficulties encountered in the study is the scarcity of available data, as well as the absence of scientific research pertaining to the region regarding hydraulic modeling. The study identifies flood risks in March and April every year, notably for the Hemrin Dam, which may exceed permissible water levels (reach a level over 110 m where the Hemrin Crest level is 109.5 m). To mitigate this, an artificial canal is proposed to divert water annually, protecting the dam and downstream areas without disrupting operations. The diverted water could also augment the Tigris River in Kut Governorate during summer. The study demonstrates the value of integrating multiple modeling techniques and data sources for accurate hydraulic predictions. It offers insights for decision-makers in flood management and planning. This study contributes to efficient flood management strategies by adopting a multidisciplinary approach.

Simulation of urban environment flood inundation from potential dam break: case of Midimar Embankment Dam, Tigray, Northern Ethiopia

Simulation of urban environment flood inundation from potential dam break: case of Midimar Embankment Dam, Tigray, Northern Ethiopia

High efficiency integrated urban flood inundation simulation based on the urban hydrologic unit

As the impact of flooding intensifies globally in metropolitan areas, there is an urgent need for high-resolution flood forecasting models to facilitate preparedness and mitigate flood impacts. In response, we have proposed a high efficiency algorithm (HEA) designed to simulate urban flood inundation, leveraging the urban hydrologic unit (UHU) concept from an urban hydrological cycle perspective. The HEA significantly reduces the computational demands associated with high-resolution modeling by using a hydrologic unit derived from the digital elevation model (DEM). This approach effectively shifts the overwhelming burden of computational load to the hydrodynamic algorithm (HDA), which is grounded in the Shallow Water Equations (SWEs), thus enabling efficient flood inundation simulation crucial for time-critical decision making during extreme events. Our research also incorporated the HEA and HDA methods into a self-developed, distributed-framework basin modeling system, named the Taihu Basin Model (TBM). Both the HEA and HDA models were calibrated and validated with observed data on pipe level, flow rate, and inundation depth from our Shuangqiaobang experimental fields. Comparative analysis, focusing on simulated urban flood inundation distribution characteristics, simulation accuracy and timeliness, demonstrated the superior effieicney of the HEA method by three orders of magnitude. The consistency between observed data and the simulated results of both HEA and HDA models underscores the accuracy and reliablility of these models. The HEA model’s simulated urban flood inundation distribution appears more patchy and localized compared to the HDA model, due to the generalization of urban UHUs in the TBM.

A study on compound flood prediction and inundation simulation under future scenarios in a coastal city

Coastal cities are affected by the compound effects of flood hazard systems, including the ocean, river, and coastal land, within which the disaster-induced factors interact complexly. To explore the combined effects of multiple disaster-causing factors under changing environment, this study first constructed univariate distribution by maximum daily precipitation of the year at Zhuhai Station from 1962 to 2020 and maximum daily average river discharge of the year at Makou Station from 1951 to 2005. Based on the integrated model of the validated Delft3D-FLOW and HEC-RAS hydrodynamic models, the river level process of compound floods was simulated for 24 h, and the effects of extreme compound flood events involving storm surge, rainfall and river flood under future scenarios is analyzed. In this study, the Modaomen waterway in Zhuhai City of China is used as the study area. The results show: (1) The Lognormal distribution is selected as the optimal distribution, and the design values of precipitation and discharge in different recurrence periods are calculated based on Lognormal distribution. The precipitation and discharge for the 100-year return period were 450.3 mm and 51000 m3, respectively, which could be used in the compound flood simulation; (2) The application of Delft3D-FLOW model ensures the accurate simulation of the compound flood process. In the different future scenarios of rising sea levels and increasing wind intensity (WI), the effect of multiple disaster-causing factors results to a more serious inundation than that of a single disaster-causing factor, with the increase of maximum storm surge level from 8.09 % to 20.31 %; and (3) Fluvial flooding and storm surge became the major causes of compound flood when the city coastal protection is vulnerable, while in the city with robust coastal protection, precipitation serves as a main disaster-causing factor. Taking the inundation under scenario S1 as an example, when the seawall elevation is 2 m, the composite flooding causes significant inundation in the riparian area (the total inundation area is 15.29 km2), whereas when the seawall elevation is raised to 2.5 m, the inundation area in the riparian area caused by the compound flood decreases significantly (the total inundation area is 0.03 km2). This study is expected to provide scientific references on disaster prevention planning, compound disaster risk management and urban resilience construction along the coastline, as well as have a significant impact on modern economic growth, social stability, and the security in coastal cities.

Cost-Benefit Analysis of the Wuxikou Integrated Flood Management Project Considering the Effects of Flood Risk Reduction and Resettlement

AbstractEvaluation of the economic costs and benefits of flood disaster risk management projects is crucial. However, current cost-benefit analyses (CBA) often lack reliable estimates of the expected loss reduction from flood control measures and ignore quantitative assessments of resettlement. To address these limitations, this study incorporated a probabilistic risk analysis method and quantitative resettlement benefits assessment into the CBA framework, using the Wuxikou Integrated Flood Management Project (WIFMP) in Jiangxi Province, China, as a case study. The direct economic benefits of flood control were estimated by integrating hydrological statistics, numerical flood inundation simulation, and quantitative damage analysis with exposure and vulnerability data. Furthermore, the resettlement benefits were quantified by measuring the annual income growth of migrants based on assumptions about household employment. Our analysis shows that the total WIFMP investment is RMB 3546.1 million yuan (USD 1 = RMB 6.976 yuan), including loan principal and interest of 244.4 million yuan, and operations and maintenance of 605.5 million yuan at 2020 prices. Annual project benefits are estimated at 351.3 million yuan in flood risk reduction, 155.7–191.9 million yuan from increased resettlement income, and 42.7 million yuan in power and water revenues. Considering the costs and benefits across the entire project lifecycle, the internal rate of return ranges from 13.7 to 14.2%, and the net present value ranges from 31.8 to 352.6 billion yuan. Through improved benefit estimation methodology, this research enables a more reliable and holistic evaluation of costs and benefits for flood risk management projects. It provides insights for policymakers and practitioners involved in similar projects, contributing to more informed decision making and better allocation of resources in flood disaster risk management.

Rainfall Based Dynamic Flood Inundation Simulation for Kelani River Basin of Sri Lanka


 
 
 Kelani river basin is inundated annually, primarily by heavy precipitation and rapid unplanned development, causing physical, social, and economic impacts. Two major attribut- able factors are inefficient rainfall forecasting and flood inundation modeling to facilitate flood management in this region. This study aims to simulate flood inundation in the Kelani River Basin in Sri Lanka using the Hydrological Engineering Center – River Analysis System (HEC-RAS). The model was used to create the scenarios for the elevation of the Kelani River basin, river network, water levels of the five hydrometric stations in the upper stream, land use and the land cover of the river basin were produced to identify the flood inundation and to suggest the possible solutions. A digital terrain model of the river basin was developed from elevation data using GIS techniques. HEC-RAS is the suitable analysis system to identify the flood inundation area by the receiving rainfall on every scale of the rain gauge, and the received rainfall can make the prediction earlier. According to the models of HEC-RAS on terrain, rainfall, river network, and water level of this study, following areas such as Kolonnawa, Hanwella, Homagama, and Kaduwela areas were inundated in the Colombo district after receiving above 85mm rainfall in the catchment areas of Kelani river basin and Wattala, Kelani, Biyagama, Ja-Ela in Gampaha district inundated after receiving 110 mm. It is proposed to build a reservoir in the Glanhouse area, along with a water retention bund in Hanwella, Dompe, Kolonnawa, Biyagama, and Kelani, to protect the vulnerable areas from frequent floods.
 
 

Unraveling the 2021 Central Tennessee flood event using a hierarchical multi-model inundation modeling framework

Flood prediction systems need hierarchical atmospheric, hydrologic, and hydraulic models to predict rainfall, runoff, streamflow, and floodplain inundation. The accuracy of such systems depends on the error propagation through the modeling chain, sensitivity to input data, and choice of models. In this study, we used multiple precipitation forcings (hindcast and forecast) to drive hydrologic and hydrodynamic models to analyze the impacts of various drivers on the estimates of flood inundation depth and extent. We implement this framework to unravel the August 2021 extreme flooding event that occurred in Central Tennessee, USA. We used two radar-based quantitative precipitation estimates (STAGE4 and MRMS) as well as quantitative precipitation forecasts (QPF) from the National Weather Service Weather Prediction Center (WPC) to drive a series of models in the hierarchical framework, including the Variable Infiltration Capacity (VIC) land surface model, the Routing Application for Parallel Computation of Discharge (RAPID) river routing model, and the AutoRoute and TRITON inundation models. An evaluation with observed high-water marks demonstrates that the framework can reasonably simulate flood inundation. Despite the complex error propagation mechanism of the modeling chain, we show that inundation estimates are most sensitive to rainfall estimates. Most notably, QPF significantly underestimates flood magnitudes and inundations leading to unanticipated severe flooding for all stakeholders involved in the event. Finally, we discuss the implications of the hydrodynamic modeling framework for real-time flood forecasting.

Subgrid Model of Fluid Force Acting on Buildings for Three-Dimensional Flood Inundation Simulations

In recent years, large-scale heavy rainfall disasters have occurred frequently in several parts of the world. Therefore, a quantitative approach to understanding how buildings are damaged during floods is necessary to develop appropriate flood-resistant technologies. In flood inundation simulations for the quantitative evaluation of a building’s resistance to flooding, a subgrid model is necessary to appropriately evaluate the resistance of buildings smaller than the grid size at a medium grid resolution. In this study, a new subgrid (SG) 3D inundation model is constructed to evaluate the fluid force acting on buildings and assess the damage to individual buildings during flood inundation. The proposed method does not increase the computational load. The model is incorporated into a 2D and 3D hybrid model with high computational efficiency to construct a 3D river and inundation flow model. Its validity and effectiveness are evaluated through comparisons with field observations and the conventional equivalent roughness model. Considering horizontal and vertical velocity distributions, the proposed model showed statistically significant improvements in performance in terms of building loss indices such as velocity and fluid force. These results suggest that the SG model can effectively evaluate the fluid force acting on buildings, including the vertical distribution of flow velocities.

Analysing the Impact of Land Subsidence on the Flooding Risk: Evaluation Through InSAR and Modelling

Floods greatly impact human settlements in flood risk areas, such as floodplains and coastal lowlands, following heavy rainfall. The Alto Guadalentin valley, an orogenic tectonic depression, experiences extreme flash floods and land subsidence due to groundwater withdrawal, rendering it one of Europe's fastest subsiding regions. In this study, we compared two 2D flood event models representing different land subsidence scenarios for 1992 and 2016. To determine the flooded area and water depth variations due to land subsidence, the Hydrologic Engineering Centre River Analysis System 2D (HEC-RAS 2D) model was used to simulate flood inundation by the Alto Guadalentin River and its tributaries. Synthetic aperture radar (SAR) satellite (ERS, ENVISAT, and Cosmo-SkyMED) images were employed, along with the interferometric SAR (InSAR) technique, to calculate the magnitude and spatial distribution of land subsidence. By analysing the accumulated subsidence distributions obtained from InSAR, the original topography of the valley in 1992 and 2016 was reconstructed. These digital surface models (DSMs) were then used to generate 2D hydraulic models, simulating flood scenarios in the unsteady mode. The results demonstrated significant changes in the water surface elevation over the 14-year period, with a 2.04 km2 increase in areas with water depths exceeding 0.7 m. These findings were utilized to create a flood risk map and assess the economic flood risk. The data highlight the crucial role of land subsidence in determining the inundation risk in the Alto Guadalentin valley, providing valuable insights for emergency management and civil protection against future potential flooding events.

Flood characteristics analyses in the Jratunseluna river basin due to the impact of Kedungsepur national strategic area development

The area of the Jratunseluna river basin is categorized as a high-risk flood vulnerability. The urban development plan can worsen this condition through Kedungsepur national strategic area. The objective of this study is to identify flood characteristics in Jratunseluna river basin caused by the alteration of land use due to Kedungsepur national strategic area development through rainfall-runoff 2D simulation. The RRI 2D model was chosen because it could simulate flood inundation comprehensively in all river systems while the other models such as HEC-HMS, TOPMODEL, and Tank models have a limitation on it. The input data used for the RRI model were rainfall data, topography (DEM), and land use maps. The rain data used in the simulation was the Global Precipitation Measurement (GPM) from 2010 to 2020. The study results indicate that the flood risk level would increase during the implementation of the Kedungsepur national strategic area development. The flood duration and peak discharge increase mainly characterize the increase in flood risk. The most significant increase in peak discharge among the four outlets is obtained in the Garang watershed. In the case of a 2-year flood, the peak discharge increased by 44.91% from 262.18 m3/s to 379.92 m3/s and for the case of a 50-year flood, the peak discharge increased by 54.34% from 331.63 m3/s to 511.83 m3/.

Evaluating geomorphological changes and coastal flood vulnerability of the Nijhum Dwip Island using remote sensing techniques

This research critically assesses the spatiotemporal changes in morphology of the Nijhum Dwip Island caused by the dynamic wave action of the Bay of Bengal. Using image analysis techniques on Landsat Satellite Data (ETM+, OLI), the nature of shoreline alterations, erosion-accretion, and lob shifting from 2003 to 2021 has been identified. The findings reveal that since 2013, the island's morphology and geographical extent have undergone considerable changes, and it has gradually reached its current stable state in the Bay of Bengal. The fluvial sediments brought by the Ganges-Brahmaputra-Meghna (GBM) river system influenced by tide is mainly responsible for the sedimentary accretion in and around the island. Due to the complex wave action, the island has experienced alternating erosion and accretion domination scenarios. Compared with its extent in 2003, up until 2021, it has lost approximately 2.57 km2 area, erosion and accretion mostly occurring in the southern part, geometrically transforming the island from a rectangular to a subrounded shape. The depocenter shifting of the island was predominantly in the south-eastern direction during the whole period but has shifted to reverse direction in 2021. The minimum and maximum lob shifting occurred in 2006 and 2015, respectively, at approximately 600 m and 1800 m. ALOS PALSAR DEM (12.5m) in combination with secondary data from the Bangladesh Meteorological Department has been used to develop and simulate flood inundation scenarios under different surge heights. The projections illustrate that in the case of 2 m, 3 m, and 4 m surge heights, approximately 46.45%, 56.51%, and 61.42% of the total land will go underwater, respectively. In all scenarios, cropland and bare ground are the most affected classes.

Validating the Quality of Volunteered Geographic Information (VGI) for Flood Modeling of Hurricane Harvey in Houston, Texas

The primary objective of this study was to examine the quality of volunteered geographic information (VGI) data for flood mapping of Hurricane Harvey. As a crowdsourcing platform, the U-Flood project mapped flooded streets in the Houston metro area. This research examines the following: (1) If there are any significant differences in water depth (WD) among the hydraulic and hydrologic (H&H) model, the Federal Emergency Management Agency (FEMA) reference floodplain map, and the VGI? (2) Are there any significant differences in the inundated areas between the floodplain modeled by the VGI and hydraulic simulation? This study used HEC-RAS to simulate flood inundation maps and validated the results with high water marks (HWM) and the FEMA-modeled floodplain after Hurricane Harvey. The statistical results showed that there were significant differences in the WD, the inundated road count, and the length inside/outside of HEC-RAS-modeled floodplain. The results also showed that a less consistent decreasing trend between the U-Flood data and the modeled floodplain over time and space. This study empirically evaluated the data quality of the VGI based on observed and modeled data in flood monitoring. The findings from this study fill the gaps in the literature by assessing the uncertainty and data quality of VGI, providing insights into using supplementary data in flood mapping research.

Advancing operational flood forecasting, early warning and risk management with new emerging science: Gaps, opportunities and barriers in Kenya

AbstractKenya and the wider East African region suffer from significant flood risk, as illustrated by major losses of lives, livelihoods and assets in the most recent years. This is likely to increase in future as exposure rises and rainfall intensifies under climate change. Accordingly, flood risk management is a priority action area in Kenya's national climate change adaptation planning. Here, we outline the opportunities and challenges to improve end‐to‐end flood early warning systems, considering the scientific, technical and institutional/governance dimensions. We demonstrate improvements in rainfall forecasts, river flow, inundation and baseline flood risk information. Notably, East Africa is a ‘sweetspot’ for rainfall predictability at sub‐seasonal to seasonal timescales for extending forecast lead times beyond a few days and for ensemble flood forecasting. Further, we demonstrate coupled ensemble flow forecasting, new flood inundation simulation, vulnerability and exposure data to support Impact based Forecasting (IbF). We illustrate these advances in the case of fluvial and urban flooding and reflect on the potential for improved flood preparedness action. However, we note that, unlike for drought, there remains no national flood risk management framework in Kenya and there is need to enhance institutional capacities and arrangements to take full advantage of these scientific advances.

Impacts of retention basins on downstream flood peak attenuation in the Odaw river basin, Ghana

Study regionOdaw River Basin (ORB) in Ghana Study focusWe simulated and examined the impacts of retention basin as a hydraulic structure in attenuating the flood peaks downstream of the ORB with a coupled HEC-HMS and HEC-RAS models for different flood scenarios. Calibrated and validated HEC-HMS models were used to forecast floods in terms of peak flows and results forced into the 2D Unsteady flow HEC-RAS model to simulate flood inundation areas.New hydrological insight for the region: The calibrated and validated HEC-HMS model indicated good performance (NSE > 0.65) in simulating the runoff characteristics of the basin. Forecasted peak flows with the calibrated model for return periods of 30-to-1-year were 131.1 m3/s, 121.6 m3/s, 106.4 m3/s, 88.8 m3/s and 59.3 m3/s respectively for the “without” a retention basin scenario within the ORB. In the “with” retention basin scenario, attenuated peak flows were observed downstream the ORB by 8.1 % (120.5 m3/s), 8.4 % (111.4 m3/s), 9.1 % (96.7 m3/s), 13.6 % (76.7 m3/s) and 13.2 % (51.5 m3/s) respectively for the different return periods. Flood volumes reaching downstream of the basin reduced by 2.3 %, 2.5 %, 2.7 %, 3.0% and 3.8 % respectively due to the presence of the retention basin. The retention basin increased the basin lag time by an average of 4 h across all different flood scenarios. The introduction of retention basins will serve a pivotal role in flood management and planning in the ORB.

Multiscale Observation Product (MOP) for Temporal Flood Inundation Mapping of the 2015 Dallas Texas Flood

This paper presents a new data fusion multiscale observation product (MOP) for flood emergencies. The MOP was created by integrating multiple sources of contributed open-source data with traditional spaceborne remote sensing imagery in order to provide a sequence of high spatial and temporal resolution flood inundation maps. The study focuses on the 2015 Memorial Day floods that caused up to USD 61 million dollars of damage. The Hydraulic Engineering Center River Analysis System (HEC-RAS) model was used to simulate water surfaces for the northern part of the Trinity River in Dallas, using reservoir surcharge releases and topographic data provided by the U.S. Army Corps of Engineers. A measure of fit assessment is performed on the MOP flood maps with the HEC-RAS simulated flood inundation output to quantify spatial differences. Estimating possible flood inundation using individual datasets that vary spatially and temporally allow to gain an understanding of how much each observational dataset contributes to the overall water estimation. Results show that water surfaces estimated by MOP are comparable with the simulated output for the duration of the flood event. Additionally, contributed data, such as Civil Air Patrol, although they may be geographically sparse, become an important data source when fused with other observation data.