Intercomparison of Satellite Remote Sensing‐Based Flood Inundation Mapping Techniques

The objective of this study was to determine the accuracy of five different digital image processing techniques to map flood inundation extent with Landsat 8–Operational Land Imager satellite imagery. The May 2016 flooding event in the Hempstead region of the Brazos River, Texas is used as a case study for this first comprehensive comparison of classification techniques of its kind. Five flood water classification techniques (i.e., supervised classification, unsupervised classification, delta‐cue change detection, Normalized Difference Water Index [NDWI], modified NDWI [MNDWI]) were implemented to characterize flooded regions. To identify flood water obscured by cloud cover, a digital elevation model (DEM)–based approach was employed. Classified floods were compared using an Advanced Fitness Index to a “reference flood map” created based on manual digitization, as well as other data sources, using the same satellite image. Supervised classification yielded the highest accuracy of 86.4%, while unsupervised, MNDWI, and NDWI closely followed at 79.6%, 77.3%, and 77.1%, respectively. Delta‐cue change detection yielded the lowest accuracy with 70.1%. Thus, supervised classification is recommended for flood water classification and inundation map generation under these settings. The DEM‐based approach used to identify cloud‐obscured flood water pixels was found reliable and easy to apply. It is therefore recommended for regions with relatively flat topography.     

Probabilistic Flood Inundation Forecasting Using Rating Curve Libraries

One approach for performing uncertainty assessment in flood inundation modeling is to use an ensemble of models with different conceptualizations, parameters, and initial and boundary conditions that capture the factors contributing to uncertainty. However, the high computational expense of many hydraulic models renders their use impractical for ensemble forecasting. To address this challenge, we developed a rating curve library method for flood inundation forecasting. This method involves pre‐running a hydraulic model using multiple inflows and extracting rating curves, which prescribe a relation between streamflow and stage at various cross sections along a river reach. For a given streamflow, flood stage at each cross section is interpolated from the pre‐computed rating curve library to delineate flood inundation depths and extents at a lower computational cost. In this article, we describe the workflow for our rating curve library method and the Rating Curve based Automatic Flood Forecasting (RCAFF) software that automates this workflow. We also investigate the feasibility of using this method to transform ensemble streamflow forecasts into local, probabilistic flood inundation delineations for the Onion and Shoal Creeks in Austin, Texas. While our results show water surface elevations from RCAFF are comparable to those from the hydraulic models, the ensemble streamflow forecasts used as inputs to RCAFF are the largest source of uncertainty in predicting observed floods.     

Comparative Analysis of Inundation Mapping Approaches for the 2016 Flood in the Brazos River,Texas

Accurate and timely flood inundation maps serve as crucial information for hydrologists, first‐responders, and decision makers of natural disaster management agencies. In this study, two modeling approaches are applied to estimate the inundation area for a large flooding event that occurred in May 2016 in the Brazos River: (1) Height Above the Nearest Drainage combined with National Hydrograph Dataset Plus (NHDPlus‐HAND) and (2) International River Interface Cooperative — Flow and Sediment Transport with Morphological Evolution of Channels (iRIC‐FaSTMECH). The inundation extents simulated from these two modeling approaches are then compared against the observed inundation extents derived from a Landsat 8 satellite image. The simulated results from NHDPlus‐HAND and iRIC‐FaSTMECH show 56% and 70% of overlaps with the observed flood extents, respectively. A modified version of the NHDPlus‐HAND model, considering networked catchment behaviors, is also tested with an improved fitness of 67%. This study suggests that NHDPlus‐HAND has the potential for real‐time continental inundation forecast due to its low computational cost and ease to couple with the National Water Model. Better performance of NHDPlus‐HAND can be achieved by considering the inter‐catchment flows during extreme riverine flood events. Overall, this study presents a comprehensive examination made of remote sensing compared with HAND‐based inundation mapping in a region of complex topography.     

A Probabilistic,Parcel‐Level Inundation Prediction Tool for Medium‐Range Flood Forecasting in Large Lake Systems

This study contributes a bathtub‐style inundation prediction model with abstractions of coastal processes (i.e., storm surge and wave runup) for flood forecasting at medium‐range (weekly to monthly) timescales along the coastline of large lakes. Uncertainty from multiple data sources are propagated through the model to establish probabilistic bounds of inundation, providing a conservative measure of risk. The model is developed in a case study of the New York Lake Ontario shoreline, which has experienced two record‐setting floods over the course of three years (2017–2019). Predictions are developed at a parcel‐level and are validated using inundation accounts from an online survey and flyover imagery taken during the recent flood events. Model predictions are compared against a baseline, deterministic model that accounts for the same processes but does not propagate forward data uncertainties. Results suggest that a probabilistic approach helps capture observed instances of inundation that would otherwise be missed by a deterministic inundation model. However, downward biases are still present in probabilistic predictions, especially for parcels impacted by wave runup. The goal of the tool is to provide community planners and property owners with a conservative, parcel‐level assessment of flood risk to help inform short‐term emergency response and better prepare for future flood events.     

Coastal Flood Inundation Monitoring with Satellite C‐band and L‐band Synthetic Aperture Radar Data

Satellite Synthetic Aperture Radar (SAR) was evaluated as a method to operationally monitor the occurrence and distribution of storm‐ and tidal‐related flooding of spatially extensive coastal marshes within the north‐central Gulf of Mexico. Maps representing the occurrence of marsh surface inundation were created from available Advanced Land Observation Satellite (ALOS) Phased Array type L‐Band SAR (PALSAR) (L‐band) (21 scenes with HH polarizations in Wide Beam [100 m]) data and Environmental Satellite (ENVISAT) Advanced SAR (ASAR) (C‐band) data (24 scenes with VV and HH polarizations in Wide Swath [150 m]) during 2006‐2009 covering 500 km of the Louisiana coastal zone. Mapping was primarily based on a decrease in backscatter between reference and target scenes, and as an extension of previous studies, the flood inundation mapping performance was assessed by the degree of correspondence between inundation mapping and inland water levels. Both PALSAR‐ and ASAR‐based mapping at times were based on suboptimal reference scenes; however, ASAR performance seemed more sensitive to reference‐scene quality and other types of scene variability. Related to water depth, PALSAR and ASAR mapping accuracies tended to be lower when water depths were shallow and increased as water levels decreased below or increased above the ground surface, but this pattern was more pronounced with ASAR. Overall, PALSAR‐based inundation accuracies averaged 84% (n = 160), while ASAR‐based mapping accuracies averaged 62% (n = 245).     

Demonstrating Floodplain Uncertainty Using Flood Probability Maps1

Abstract: The U.S. Federal Emergency Management Agency (FEMA) flood maps depict the 100‐year recurrence interval floodplain boundary as a single line. However, because of natural variability and model uncertainty, no floodplain extents can be accurately defined by a single line. This article presents a new approach to floodplain mapping that takes advantage of accepted methodologies in hydrologic and hydraulic analysis while including the effects of uncertainty. In this approach, the extents of computed floodplain boundaries are defined as a continuous map of flood probabilities, rather than as a single line. Engineers and planners can use these flood probability maps for viewing the uncertainty of a floodplain boundary at any recurrence interval. Such a flood probability map is a useful tool for visualizing the uncertainty of a floodplain boundary and represents greater honesty in engineering technologies that are used for flood mapping. While institutional barriers may prevent adoption of such definitions for use in graduated flood insurance rates (as most other insurance industries use to account for relative risks), the methods open the door technically to such a reality.     

FLOOD HAZARD STUDIES IN THE MISSISSIPPI RIVER BASIN USING REMOTE SENSING1

ABSTRACT. The Spring 1973 Mississippi River flood was investigated using remotely sensed data from ERTS-1. Both manual and automatic analyses of the data indicate that ERTS-I is extremely useful as a regional tool for flood management. Quantitative estimates of area flooded were made in St. Charles County, Missouri and Arkansas. Flood hazard mapping was conducted in three study areas along the Mississippi River using pre-flood ERTS-1 imagery enlarged to 1:250,000 and 1:100,000 scale. The flood prone areas delineated on these maps correspond to areas that would be inundated by significant flooding (approximately the 100 year flood). The flood prone area boundaries were generally in agreement with flood hazard maps produced by the U. S. Army Corps of Engineers and U. S. Geological Survey although the latter are somewhat more detailed because of their larger scale. Initial results indicate that ERTS-1 digital mapping of flood prone areas can be performed at 1:62,500 which is comparable to some conventional flood hazard map scales.     

Mathematical Model on Flow Regime and Water Harvesting in Inundation Plains1

Abstract: A mathematical model on flow regime and water harvesting in inundation plains is presented. The flow profile is a free over‐fall at the end of the desired inundation. The flow front in the plain is on‐line for the entire coverage, in a sense that there is initiation of flow mass after each small reach of the flow traverse, and it is continuing to the extreme point of coverage. The water‐harvesting phenomenon depends upon the occurrences of the hydrologic events, the nature of surface flows in the valley, the expected favorable time of flood incidence, and the soil characteristics of the plains. The model has been tested for three micro‐watersheds of different soil characteristics. It is best suited to platykurtic nature of flood phenomenon in the study area, with the correlation co‐efficient in‐between computed and observed amount of water harvesting above 0.90.     

Application of Satellite Microwave Images in Estimating Snow Water Equivalent1

Abstract: Flood forecast and water resource management requires reliable estimates of snow pack properties [snow depth and snow water equivalent (SWE)]. This study focuses on application of satellite microwave images to estimate the spatial distribution of snow depth and SWE over the Great Lakes area. To estimate SWE, we have proposed the algorithm which uses microwave brightness temperatures (Tb) measured by the Special Sensor Microwave Imager (SSM/I) radiometer along with information on the Normalized Difference Vegetation Index (NDVI). The algorithm was developed and tested over 19 test sites characterized by different seasonal average snow depth and land cover type. Three spectral signatures derived from SSM/I data, namely T19V‐T37V (GTV), T19H‐T37H (GTH), and T22V‐T85V (SSI), were examined for correlation with the snow depth and SWE. To avoid melting snow conditions, we have used observations taken only during the period from December 1‐February 28. It was found that GTH, and GTV exhibit similar correlation with the snow depth/SWE and are most should be used over deep snowpack. In the same time, SSI is more sensitive to snow depth variations over a shallow snow pack. To account for the effect of dense forests on the scattering signal of snow we established the slope of the regression line between GTV and the snow depth as a function of NDVI. The accuracy of the new technique was evaluated through its comparison with ground‐based measurements and with results of SWE analysis prepared by the National Operational Hydrological Remote Sensing Center (NOHRSC) of the National Weather Service. The proposed algorithm was found to be superior to previously developed global microwave SWE retrieval techniques.     

A CyberGIS Integration and Computation Framework for High‐Resolution Continental‐Scale Flood Inundation Mapping

We present a Digital Elevation Model‐based hydrologic analysis methodology for continental flood inundation mapping (CFIM), implemented as a cyberGIS scientific workflow in which a 1/3rd arc‐second (10 m) height above nearest drainage (HAND) raster data for the conterminous United States (CONUS) was computed and employed for subsequent inundation mapping. A cyberGIS framework was developed to enable spatiotemporal integration and scalable computing of the entire inundation mapping process on a hybrid supercomputing architecture. The first 1/3rd arc‐second CONUS HAND raster dataset was computed in 1.5 days on the cyberGIS Resourcing Open Geospatial Education and Research supercomputer. The inundation mapping process developed in our exploratory study couples HAND with National Water Model forecast data to enable near real‐time inundation forecasts for CONUS. The computational performance of HAND and the inundation mapping process were profiled to gain insights into the computational characteristics in high‐performance parallel computing scenarios. The establishment of the CFIM computational framework has broad and significant research implications that may lead to further development and improvement of flood inundation mapping methodologies.     

Using Steady‐State Backwater Analysis to Predict Inundated Area from National Water Model Streamflow Simulations

National Water Model (NWM) simulates the hydrologic cycle and produces streamflow forecasts for 2.7 million reaches in the National Hydrography Dataset for continental United States (U.S.). NWM uses Muskingum–Cunge channel routing, which is based on the continuity equation. However, the momentum equation also needs to be considered to obtain more accurate estimates of streamflow and stage in rivers, especially for applications such as flood‐inundation mapping. Here, we used a steady‐state backwater version of Simulation Program for River NeTworks (SPRNT) model. We evaluated SPRNT’s and NWM’s abilities to predict inundated area for the record flood of Hurricane Matthew in October 2016. The Neuse River experienced record‐breaking floods and was well‐documented by U.S. Geological Survey. Streamflow simulations from NWM retrospective analysis were used as input for the SPRNT simulation. Retrospective NWM discharge predictions were converted to stage. The stages (from both SPRNT and NWM) were utilized to produce flood‐inundation maps using the Height Above Nearest Drainage method which uses the local relative heights to find out the local draining potentials and provide spatial representation of inundated area. The inundated‐area accuracies for NWM and SPRNT (based on comparison to a remotely sensed dataset) were 65.1% and 67.6%, respectively. These results show using steady‐state SPRNT results in a modest improvement of inundation‐forecast accuracy compared to NWM.     

Measuring the Effects of a Flood Control Project: Hedonic Land Price Approach

This article aims to measure the effects of a flood control project planned for the Chitose River Basin in Hokkaido Prefecture, Japan, using hedonic land price functions. In these functions, "annual expected depth of flood water" is introduced as an explanatory variable to represent the effect of the flood control project. Comparing the approach with the method of "the economic analysis of flood control projects", which has been a conventional evaluation method widely used in Japan, the efficiency and limitations of our approach are discussed.     

AutoRAPID: A Model for Prompt Streamflow Estimation and Flood Inundation Mapping over Regional to Continental Extents

This article couples two existing models to quickly generate flow and flood‐inundation estimates at high resolutions over large spatial extents for use in emergency response situations. Input data are gridded runoff values from a climate model, which are used by the Routing Application for Parallel computatIon of Discharge (RAPID) model to simulate flow rates within a vector river network. Peak flows in each river reach are then supplied to the AutoRoute model, which produces raster flood inundation maps. The coupled tool (AutoRAPID) is tested for the June 2008 floods in the Midwest and the April‐June 2011 floods in the Mississippi Delta. RAPID was implemented from 2005 to 2014 for the entire Mississippi River Basin (1.2 million river reaches) in approximately 45 min. Discretizing a 230,000‐km² area in the Midwest and a 109,500‐km² area in the Mississippi Delta into thirty‐nine 1° by 1° tiles, AutoRoute simulated a high‐resolution (~10 m) flood inundation map in 20 min for each tile. The hydrographs simulated by RAPID are found to perform better in reaches without influences from unrepresented dams and without backwater effects. Flood inundation maps using the RAPID peak flows vary in accuracy with F‐statistic values between 38.1 and 90.9%. Better performance is observed in regions with more accurate peak flows from RAPID and moderate to high topographic relief.     

Assessing Calibration Uncertainty and Automation for Estimating Evapotranspiration from Agricultural Areas Using METRIC

Agricultural irrigation accounts for a large fraction of the total water use in the western United States. The Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) remote sensing energy balance model is being used to estimate historical agricultural water use in western Nevada to evaluate basin‐wide water budgets. Each METRIC evapotranspiration (ET) estimate must be calibrated by a trained user, which requires some iterative time investment and results in variation in ET estimates between users. An automated calibration algorithm for the METRIC model was designed to generate ET estimates comparable to those from trained users by mimicking the manual calibration process. Automated calibration allows for rapid generation of METRIC ET estimates with minimal manual intervention, as well as uncertainty and sensitivity analysis of the model. The variation in ET estimates generated by the automated calibration algorithm was found to be similar to the variation in manual ET estimates. Results indicate that uncertainty was highest for fields with low ET levels and lowest for fields with high ET levels, with a seasonal mean uncertainty of approximately 5% for all fields. In addition, in a blind comparison, automated daily and seasonal ET estimates compared well with flux tower measurement ET data at multiple sites. Automated methods can generate first‐order ET estimates that are similar to time intensive manual efforts with less time investment.     

Social Dimensions of Urban Flood Experience,Exposure, and Concern

With growing urban populations and climate change, urban flooding is an important global issue, even in dryland regions. Flood risk assessments are usually used to identify vulnerable locations and populations, flooding experience patterns, or levels of concern about flooding, but rarely are all of these approaches combined. Furthermore, the social dynamics of flood concerns, exposure, and experience are underexplored. We combined geographic and survey data on household‐level measures of flood experience, concern, and exposure in Utah's urbanizing Wasatch Front. We asked: (1) Are socially vulnerable groups more likely to be exposed to flood risk? (2) How common are flooding experiences among urban residents, and how are these experiences related to sociodemographic characteristics and exposure? and (3) How concerned are urban residents about flooding, and does concern vary by exposure, flood experience, and sociodemographic characteristics? Although floodplain residents were more likely to be White and have higher incomes, respondents who were of a racial/ethnic minority, were older, had less education, and were living in floodplains were more likely to report flood experiences and concern about flooding. Flood risk management approaches need to address social as well as physical sources of vulnerability to floods and recognize social sources of variation in flood experiences and concern.     

Geospatial and Temporal Analysis of a 20‐Year Record of Landsat‐Based Water Clarity in Minnesota's 10,000 Lakes

A large 20‐year database on water clarity for all Minnesota lakes ≥8 ha was analyzed statistically for spatial distributions, temporal trends, and relationships with in‐lake and watershed factors that potentially affect lake clarity. The database includes Landsat‐based water clarity estimates expressed in terms of Secchi depth (SD_Landsat), an integrative measure of water quality, for more than 10,500 lakes for time periods centered around 1985, 1990, 1995, 2000, and 2005. Minnesota lake clarity is lower (more turbid) in the south and southwest and clearer in the north and northeast; this pattern is evident at the levels of individual lakes and ecoregions. Temporal trends in clarity were detected in ~11% of the lakes: 4.6% had improving clarity and 6.2% had decreasing clarity. Ecoregions in southern and western Minnesota, where agriculture is the predominant land use, had higher percentages of lakes with decreasing clarity than the rest of the state, and small and shallow lakes had higher percentages of decreasing clarity trends than large and deep lakes. The mean SD_Landsat statewide remained stable from 1985 to 2005 but decreased in ecoregions dominated by agricultural land use. Deep lakes had higher clarity than shallow lakes statewide and for lakes grouped by land cover. SD_Landsat decreased as the percentage of agriculture and/or urban area increased at county and catchment levels and it increased with increasing forested land.     

Estimating Annual Groundwater Evapotranspiration from Phreatophytes in the Great Basin Using Landsat and Flux Tower Measurements

Escalating concerns about water supplies in the Great Basin have prompted numerous water budget studies focused on groundwater recharge and discharge. For many hydrographic areas (HAs) in the Great Basin, most of the recharge is discharged by bare soil evaporation and evapotranspiration (ET) from phreatophyte vegetation. Estimating recharge from precipitation in a given HA is difficult and often has significant uncertainty, therefore it is often quantified by estimating the natural discharge. As such, remote sensing applications for spatially distributing flux tower estimates of ET and groundwater ET (ET_g) across phreatophyte areas are becoming more common. We build on previous studies and develop a transferable empirical relationship with uncertainty bounds between flux tower estimates of ET and a remotely sensed vegetation index, Enhanced Vegetation Index (EVI). Energy balance‐corrected ET measured from 40 flux tower site‐year combinations in the Great Basin was statistically correlated with EVI derived from Landsat imagery (r² = 0.97). Application of the relationship to estimate mean‐annual ET_g from four HAs in western and eastern Nevada is highlighted and results are compared with previous estimates. Uncertainty bounds about the estimated mean ET_g allow investigators to evaluate if independent groundwater discharge estimates are “believable” and will ultimately assist local, state, and federal agencies to evaluate expert witness reports of ET_g, along with providing new first‐order estimates of ET_g.     

Estimating Crop Water Use via Remote Sensing Techniques vs. Conventional Methods in the South Platte River Basin,Colorado

We compared two methods of estimating crop water consumption to assess whether remote sensing techniques provide consumptive use (CU) estimates commensurate with conventional methods. Using available historical satellite and meteorological data, we applied Mapping EvapoTranspiration at high Resolution using Internalized Calibration (METRIC) to 317,455 ha in the South Platte basin, in northeastern Colorado, for the 2001 irrigation season. We then compared these derived CU estimates with values calculated by using the Colorado Water Conservation Board's South Platte Decision Support System StateCU model. Evaluating the data by irrigation ditch service area, we disaggregated the output to allow for comparison by service area size, crop type, irrigation method, water supply source, and water availability. We concluded that METRIC is a suitable alternative to StateCU in the South Platte basin and could help to identify areas with inhibited crop growth or deficit irrigation practices. In addition, METRIC could be used as a complement to StateCU to refine StateCU model parameters, allowing for more accurate estimates of crop water shortages and groundwater recharge associated with irrigation delivery and application.     

Local Responses to Inundation and De-Farming in the Reservoir Region of the Three Gorges Project (China)

Large-scale infrastructural developments in rural areas often impose significant direct and indirect impacts on environment and people. The Three Gorges Project to dam the Yangtze River in China will create a huge reservoir, inundate farmlands and villages, and incur large-scale resettlement. The concurrent de-farming program to reforest marginal farmlands on steep slopes imposes additional stresses on local people. This study evaluates the ecological and economic adjustments in rural areas affected by both projects, and explores villagers’ knowledge, attitudes, perceptions, and expectations vis-à-vis the drastic changes. Eleven villages in Yunyang County in Sichuan Province, stratified into three zones based on topography and agriculture, were assessed by field studies, questionnaire surveys, maps, satellite imagery, and census and government reports. Multiple regressions identified predictors for 17 dependent variables. Spatial variations in the difficult terrain imposed zone-differentiated agricultural constraints, ecological impacts, and human responses. The dominant farming population—mainly young adults working as migrant laborers in cities—has adopted some nonagricultural work to supplement incomes. Expected per-capita standardized farmland (SF) exceeded threshold SF, which surpasses existing SF. Motivations to reclaim more farmlands, de-farm marginal lands, and become migrant laborers were explained by different multiple-regression predictors. Reduction in farmland stock by inundation and de-farming, aggravated by unwillingness towards nonlocal resettlement, would impose ecological pressures and stimulate demands for nonfarming incomes. Common anticipation of better future income and occupation has been subdued by unfavorable feedbacks from early relocatees. Future environmental and landscape changes are hinged upon changing human responses. Government policies could be informed by research findings to match economic, ecological, and social realities.     

A Framework to Develop Nationwide Flooding Extents Using Climate Models and Assess Forecast Potential for Flood Resilience

The methods used to simulate flood inundation extents can be significantly improved by high‐resolution spatial data captured over a large area. This paper presents a hydraulic analysis methodology and framework to estimate national‐level floodplain changes likely to be generated by climate change. The hydraulic analysis was performed using existing published Federal Emergency Management Agency 100‐year floodplains and estimated 100‐ and 10‐year return period peak flow discharges. The discharges were estimated using climate variables from global climate models for two future growth scenarios: Representative Concentration Pathways 2.6 and 8.5. River channel dimensions were developed based on existing regional United States Geological Survey publications relating bankfull discharges with channel characteristics. Mathematic relationships for channel bankfull topwidth, depth, and side slope to contributing drainage area measured at model cross sections were developed. The proposed framework can be utilized at a national level to identify critical areas for flood risk assessment. Existing hydraulic models at these “hot spots” could be repurposed for near–real‐time flood forecasting operations. Revitalizing these models for use in simulating flood scenarios in near–real time through the use of meteorological forecasts could provide useful information for first responders of flood emergencies.