Conceptual Framework for the National Flood Interoperability Experiment

The National Flood Interoperability Experiment is a research collaboration among academia, National Oceanic and Atmospheric Administration National Weather Service, and government and commercial partners to advance the application of the National Water Model for flood forecasting. In preparation for a Summer Institute at the National Water Center in June‐July 2015, a demonstration version of a near real‐time, high spatial resolution flood forecasting model was developed for the continental United States. The river and stream network was divided into 2.7 million reaches using the National Hydrography Dataset Plus geospatial dataset and it was demonstrated that the runoff into these stream reaches and the discharge within them could be computed in 10 min at the Texas Advanced Computing Center. This study presents a conceptual framework to connect information from high‐resolution flood forecasting with real‐time observations and flood inundation mapping and planning for local flood emergency response.     

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.     

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.     

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.     

Impacts of Human Behavioral Heterogeneity on the Benefits of Probabilistic Flood Warnings: An Agent‐Based Modeling Framework

Flood forecasts and warnings are intended to reduce flood‐related property damages and loss of human life. Considerable research has improved flood forecasting accuracy (e.g., more accurate prediction of the occurrence of flood events) and lead time. However, the delivery of improved forecast information alone is not necessarily sufficient to reduce flood damage and loss of life, as people have varying responses and reactions to flood warnings. This study develops an agent‐based modeling framework that evaluates the impacts of heterogeneity in human behaviors (i.e., variation in behaviors in response to flood warnings), as well as residential density, on the benefits of flood warnings. The framework is coupled with a traffic model to simulate evacuation processes within a road network under various flood warning scenarios. The results show the marginal benefit associated with providing better flood warnings is significantly constrained if people behave in a more risk‐tolerant manner, especially in high‐density residential areas. The results also show significant impacts of human behavioral heterogeneity on the benefits of flood warnings, and thus stress the importance of considering human behavioral heterogeneity in simulating flood warning‐response systems. Further study is suggested to more accurately model human responses and behavioral heterogeneity, as well as to include more attributes of residential areas to estimate and improve the benefits of flood warnings.     

A Comparison of Bayesian Methods for Uncertainty Analysis in Hydraulic and Hydrodynamic Modeling

We evaluate and compare the performance of Bayesian Monte Carlo (BMC), Markov chain Monte Carlo (MCMC), and the Generalized Likelihood Uncertainty Estimation (GLUE) for uncertainty analysis in hydraulic and hydrodynamic modeling (HHM) studies. The methods are evaluated in a synthetic 1D wave routing exercise based on the diffusion wave model, and in a multidimensional hydrodynamic study based on the Environmental Fluid Dynamics Code to simulate estuarine circulation processes in Weeks Bay, Alabama. Results show that BMC and MCMC provide similar estimates of uncertainty. The posterior parameter densities computed by both methods are highly consistent, as well as the calibrated parameter estimates and uncertainty bounds. Although some studies suggest that MCMC is more efficient than BMC, our results did not show a clear difference between the performance of the two methods. This seems to be due to the low number of model parameters typically involved in HHM studies, and the use of the same likelihood function. In fact, for these studies, the implementation of BMC results simpler and provides similar results to MCMC. The results of GLUE are, on the other hand, less consistent to the results of BMC and MCMC in both applications. The posterior probability densities tend to be flat and similar to the uniform priors, which can result in calibrated parameter estimates centered in the parametric space.     

Multi‐Criteria Decision Support Systems for Flood Hazard Mitigation and Emergency Response in Urban Watersheds1

Abstract: Flood management problems are inherently complex, time‐bound and multi‐faceted, involving many decision makers (with conflicting priorities and dynamic preferences), high decision stakes, limited technical information (both in terms of quality and quantity), and difficult tradeoffs. Multi‐Criteria Decision Support Systems (MCDSS) can help to manage this complexity and decision load by combining value judgments and technical information in a structured decision framework. A brief overview of MCDSS is presented, an original MCDSS architecture is put forth, and future research directions are discussed, including extensions to Multi‐Criteria Spatial Decision Support Systems and group MCDSS (as flood management involves shared resources and broad constituencies). With application to the September 11‐12, 2000 Tokai floods in Japan, the proposed multi‐criteria decision support instruments enhance communication among stakeholders and improve emergency management resource allocation. In summary, by making the links among flood knowledge, assumptions and choices more explicit, MCDSS increases stakeholder satisfaction, saves lives, and reduces flood management costs, thereby increasing decision‐making effectiveness, efficiency and transparency.     

Assessing Effectiveness of National Flood Policy Through Spatiotemporal Monitoring of Socioeconomic Exposure1

Abstract: After a century of evolving flood policies, there has been a steady increase in flood losses, which has partly been driven by development in flood prone areas. National flood policy was revised in 1994 to focus on limiting and reducing the amount of development inside the 100‐year floodplain, with the goal of decreasing flood losses, which can be measured and quantified in terms of population and property value inside the 100‐year floodplain. Monitoring changes in these measurable indicators can inform where and how effective national floodplain management strategies have been. National flood policies are restricted to the spatial extent of the 100‐year floodplain, thus there are no development regulations to protect against flooding adjacent to this boundary. No consistent monitoring has been undertaken to examine the effect of flood policy on development immediately outside the 100‐year floodplain. We developed a standardized methodology, which leveraged national data to quantify changes in population and building tax value (exposure). We applied this approach to counties in North Carolina to assess (1) temporal changes, before and after the 1994 policy and (2) spatial changes, inside and adjacent to the 100‐year floodplain. Temporal results indicate the Piedmont and Mountain Region had limited success at reducing exposure within the 100‐year floodplain, while the Coastal Plain successfully reduced exposure. Spatially, there was a significant increase in exposure immediately outside the 100‐year floodplain throughout North Carolina. The lack of consistent monitoring has resulted in the continuation of this unintended consequence, which could be a significant driver of increased flood losses as any flood even slightly higher than the 100‐year floodplain will have a disproportionately large impact since development is outside the legal boundary of national flood policy.     

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.     

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.     

A data mining approach to simulating farmers' crop choices for integrated water resources management

Water and land resources in Thailand are increasingly under pressure from development. In particular, there are many resource conflicts associated with agricultural production in northern Thailand. Communities in these areas are significantly constrained in the land and water management decisions they are able to make. This paper describes the application of a data mining approach to describing and simulating farmers' decision rules in a catchment in northern Thailand. This approach is being applied to simulate social, economic and biophysical constraints on farmers' decisions in these areas as part of an integrated water management model.     

Rapid Flood Damage Prediction and Forecasting Using Public Domain Cadastral and Address Point Data with Fuzzy Logic Algorithms

National Flood Interoperability Experiment (NFIE) derived technologies and workflows will offer the ability to rapidly forecast flood damages. Address Points used by emergency management personnel approximate the locations of buildings, and they are a common operating picture for emergency responders. Most United States (U.S.) county tax assessment offices throughout the contiguous U.S. (CONUS) produce georeferenced cadastral data. To varying degrees, these parcel data describe building characteristics of structures within the parcel. Address Point data with cadastral data offers the ability to rapidly develop building inventories for flood damage estimation. Flood damage forecasts can expedite recovery and improve short‐term flood resilience. In this work the authors evaluate Flood Damage Wizard, a proposed open source platform independent methodology. Flood Damage Wizard uses point shapefile building information to estimate flood damage to buildings by finding the appropriate depth‐damage function using fuzzy‐text matching. The authors apply Flood Damage Wizard using Address Point and parcel datasets to demonstrate a method of estimating flood damage to buildings nearly anywhere within the CONUS. Results indicate using Address Point and cadastral datasets can generate total flood damage estimates approximate to those estimated using existing software solutions Hazus‐MH and HEC‐FIA with minimal manual processing of input data.     

Consideration of User Needs and Spatial Accuracy in Drought Mapping1

Abstract: The increasing availability of web mapping tools creates new opportunities to bridge decision‐makers’ climate information needs with technical capabilities. These new tools, however, raise familiar, unresolved issues related to cartographic representation. Using an on‐line drought mapping tool, this study seeks to understand which spatial unit best meets the desire drought managers have for “local” information, their comprehension of uncertainties introduced in mapping information at local scales, and their willingness to trade off accuracy for information at a desired unit. We found that the most useful local map information includes regional context and boundaries which present their local area of interest. Even among this experienced, well‐educated, professional group, those who had not taken a GIS or cartography class did not fully recognize the role of interpolation in creating and introducing uncertainty to some drought maps. Those who did recognize the uncertainty introduced by interpolation still strongly favored maps that provided estimated values for all areas vs. station point accuracy. Mapping poses a unique set of challenges to communicating risk and uncertainty. As more decision‐support efforts incorporate web mapping, greater attention is needed to assure that users understand the tradeoffs between accuracy and precision in creating local information, the imprecision of boundaries, as well as the limits of forecasts. Clearly conveying spatial accuracy and uncertainty is a challenge that merits greater attention in using maps to communicate drought and other environmental risk information.     

Development and Operational Testing of a Super‐Ensemble Artificial Intelligence Flood‐Forecast Model for a Pacific Northwest River

Coastal catchments in British Columbia, Canada, experience a complex mixture of rainfall‐ and snowmelt‐driven contributions to flood events. Few operational flood‐forecast models are available in the region. Here, we integrated a number of proven technologies in a novel way to produce a super‐ensemble forecast system for the Englishman River, a flood‐prone stream on Vancouver Island. This three‐day‐ahead modeling system utilizes up to 42 numerical weather prediction model outputs from the North American Ensemble Forecast System, combined with six artificial neural network‐based streamflow models representing various slightly different system conceptualizations, all of which were trained exclusively on historical high‐flow data. As such, the system combines relatively low model development times and costs with the generation of fully probabilistic forecasts reflecting uncertainty in the simulation of both atmospheric and terrestrial hydrologic dynamics. Results from operational testing by British Columbia's flood forecasting agency during the 2013‐2014 storm season suggest that the prediction system is operationally useful and robust.     

Growth of the Decision Tree: Advances in Bottom‐Up Climate Change Risk Management

There has recently been a return in climate change risk management practice to bottom‐up, robustness‐based planning paradigms introduced 40 years ago. The World Bank's decision tree framework (DTF) for “confronting climate uncertainty” is one incarnation of those paradigms. In order to better represent the state of the art in climate change risk assessment and evaluation techniques, this paper proposes: (1) an update to the DTF, replacing its “climate change stress test” with a multidimensional stress test; and (2) the addition of a Bayesian network framework that represents joint probabilistic behavior of uncertain parameters as sensitivity factors to aid in the weighting of scenarios of concern (the combination of conditions under which a water system fails to meet its performance targets). Using the updated DTF, water system planners and project managers would be better able to understand the relative magnitudes of the varied risks they face, and target investments in adaptation measures to best reduce their vulnerabilities to change. Next steps for the DTF include enhancements in: modeling of extreme event risks; coupling of human‐hydrologic systems; integration of surface water and groundwater systems; the generation of tradeoffs between economic, social, and ecological factors; incorporation of water quality considerations; and interactive data visualization.     

Cyberinfrastructure and Web Apps for Managing and Disseminating the National Water Model

Hydrologic modeling can be used to provide warnings before, and to support operations during and after floods. Recent technological advances have increased our ability to create hydrologic models over large areas. In the United States (U.S.), a new National Water Model (NWM) that generates hydrologic variables at a national scale was released in August 2016. This model represents a substantial step forward in our ability to predict hydrologic events in a consistent fashion across the entire U.S. Nevertheless, for these hydrologic results to be effectively communicated, they need to be put in context and be presented in a way that is straightforward and facilitates management‐related decisions. The large amounts of data produced by the NWM present one of the major challenges to fulfill this goal. We created a cyberinfrastructure to store NWM results, “accessibility” web applications to retrieve NWM results, and a REST API to access NWM results programmatically. To demonstrate the utility of this cyberinfrastructure, we created additional web apps that illustrate how to use our REST API and communicate hydrologic forecasts with the aid of dynamic flood maps. This work offers a starting point for the development of a more comprehensive toolset to validate the NWM while also improving the ability to access and visualize NWM forecasts, and develop additional national‐scale‐derived products such as flood maps.     

Development of a Decision Support System for Assessing Farm Animal Welfare in Relation to Husbandry Systems: Strategy and Prototype

Due to increasing empiricalinformation on farm animal welfare since the1960s, the prospects for sound decisionmakingconcerning welfare have improved. This paperdescribes a strategy to develop adecision-making aid, a decision support system,for assessment of farm-animal welfare based onavailable scientific knowledge. Such a decisionsupport system allows many factors to be takeninto account. It is to be developed accordingto the Evolutionary Prototyping Method, inwhich an initial prototype is improved inreiterative updating cycles. This initialprototype has been constructed. It useshierarchical representations to analysescientific statements and statements describingthe housing system. Welfare is assessed fromwhat is known about the biological needs of theanimals, using a welfare model in the form of atree that contains these needs as welfarecomponents. Each state of need is assessedusing welfare relevant attributes of thehousing system and weighting factors.Attributes are measurable properties of thehousing system. Weighting factors are assignedaccording to heuristic rules based on theprinciple of weighting all components(attributes and needs) equally, unless thereare strong reasons to do otherwise. Preliminarytests of the prototype indicate that it may bepossible to perform assessment of farm-animalwelfare in an explicit way and based onempirical findings. The procedure needs to berefined, but its prospects are promising.     

A Decision‐Analytic Approach to Managing Climate Risks: Application to the Upper Great Lakes1

Brown, Casey, William Werick, Wendy Leger, and David Fay, 2011. A Decision‐Analytic Approach to Managing Climate Risks: Application to the Upper Great Lakes. Journal of the American Water Resources Association (JAWRA) 47(3):524‐534. DOI: 10.1111/j.1752‐1688.2011.00552.x Abstract: In this paper, we present a risk analysis and management process designed for use in water resources planning and management under climate change. The process incorporates climate information through a method called decision‐scaling, whereby information related to climate projections is tailored for use in a decision‐analytic framework. The climate risk management process begins with the identification of vulnerabilities by asking stakeholders and resource experts what water conditions they could cope with and which would require substantial policy or investment shifts. The identified vulnerabilities and thresholds are formalized with a water resources systems model that relates changes in the physical climate conditions to the performance metrics corresponding to vulnerabilities. The irreducible uncertainty of climate change projections is addressed through a dynamic management plan embedded within an adaptive management process. Implementation of the process is described as applied in the ongoing International Upper Great Lakes Study.     

Assessing Parameter Uncertainty of a Semi‐Distributed Hydrology Model for a Shallow Aquifer Dominated Environmental System

This paper examines the performance of a semi‐distributed hydrology model (i.e., Soil and Water Assessment Tool [SWAT]) using Sequential Uncertainty FItting (SUFI‐2), generalized likelihood uncertainty estimation (GLUE), parameter solution (ParaSol), and particle swarm optimization (PSO). We applied SWAT to the Waccamaw watershed, a shallow aquifer dominated Coastal Plain watershed in the Southeastern United States (U.S.). The model was calibrated (2003‐2005) and validated (2006‐2007) at two U.S. Geological Survey gaging stations, using significant parameters related to surface hydrology, hydrogeology, hydraulics, and physical properties. SWAT performed best during intervals with wet and normal antecedent conditions with varying sensitivity to effluent channel shape and characteristics. In addition, the calibration of all algorithms depended mostly on Manning's n‐value for the tributary channels as the surface friction resistance factor to generate runoff. SUFI‐2 and PSO simulated the same relative probability distribution tails to those observed at an upstream outlet, while all methods (except ParaSol) exhibited longer tails at a downstream outlet. The ParaSol model exhibited large skewness suggesting a global search algorithm was less capable of characterizing parameter uncertainty. Our findings provide insights regarding parameter sensitivity and uncertainty as well as modeling diagnostic analysis that can improve hydrologic theory and prediction in complex watersheds. Editor's note : This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.     

Water Prism: A Tool to Assess Water Availability Risk and Investigate Water Management Strategies

Water availability risk is a local issue best understood with watershed‐scale quantification of both withdrawal and consumptive demands in the context of available supply. Collectively, all water use sectors must identify, understand, and respond to this risk. A highly visual and computationally robust decision support tool, Water Prism, quantitatively explores mitigation responses to water risk on both a facility‐level and basin‐aggregated basis. Water Prism examines a basin water balance for a 40‐ to 60‐year planning horizon, distinguishes among water use sectors, and accounts for ecosystem water needs. The 2012 Texas State Water Plan was used to apply Water Prism to the Big Cypress‐Sulphur Basin (Texas). The case study showed Water Prism to be an accurate and convenient tool to provide fine‐scale understanding of water use in the context of available supply, evaluate multi‐sector combinations of conservation strategies, and quantify the effects of future demands and water availability. Analyses demonstrated water availability risks for rivers and reservoirs can vary within a basin and must be calculated independently, simulation of water balance conditions can help illuminate potential impacts of increasing demands, and scenario simulations can be used to evaluate relative conservation efficacy of different water resource management strategies for each sector. Based on case study findings, Water Prism can serve as a useful assessment tool for regional water planners.