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USGS2002

Near-real-time simulation and internet-based delivery of forecast-flood inundation maps using two-dimensional hydraulic modeling--A pilot study for the Snoqualmie River, Washington

A system of numerical hydraulic modeling, geographic information system processing, and Internet map serving, supported by new data sources and application automation, was developed that generates inundation maps for forecast floods in near real time and makes them available through the Internet. Forecasts for flooding are generated by the National Weather Service (NWS) River Forecast Center (RFC); these forecasts are retrieved automatically by the system and prepared for input to a hydraulic model. The model, TrimR2D, is a new, robust, two-dimensional model capable of simulating wide varieties of discharge hydrographs and relatively long stream reaches. TrimR2D was calibrated for a 28-kilometer reach of the Snoqualmie River in Washington State, and is used to estimate flood extent, depth, arrival time, and peak time for the RFC forecast. The results of the model are processed automatically by a Geographic Information System (GIS) into maps of flood extent, depth, and arrival and peak times. These maps subsequently are processed into formats acceptable by an Internet map server (IMS). The IMS application is a user-friendly interface to access the maps over the Internet; it allows users to select what information they wish to see presented and allows the authors to define scale-dependent availability of map layers and their symbology (appearance of map features). For example, the IMS presents a background of a digital USGS 1:100,000-scale quadrangle at smaller scales, and automatically switches to an ortho-rectified aerial photograph (a digital photograph that has camera angle and tilt distortions removed) at larger scales so viewers can see ground features that help them identify their area of interest more effectively. For the user, the option exists to select either background at any scale. Similar options are provided for both the map creator and the viewer for the various flood maps. This combination of a robust model, emerging IMS software, and application interface programming should allow the technology developed in the pilot study to be applied to other river systems where NWS forecasts are provided routinely.

Water-Resources Investigations Report↗

USGS2023

Comparing snowpack meteorological inputs to support regional wet snow avalanche forecasting

Wet snow avalanches are predicted to increase in frequency with climate change and are often difficult to forecast. Improving our understanding of wet snow avalanche timing will help with current forecasting challenges. The onset of wet snow avalanching is closely tied to the temporal progression of liquid water flow through the seasonal snowpack. Measuring the flow of water through the snowpack in-situ is difficult due to the spatial variability of snow depth and structure. However, physical snowpack models can potentially simulate this process. The accuracy of snowpack models is heavily dependent upon the quality of the meteorological input data. A thorough investigation of model output differences using several different meteorological inputs for forecasting water movement and wet snow avalanches has not yet been thoroughly investigated. Here, we evaluate indicators of regional wet snow avalanches produced by the SNOWPACK model using different meteorological input. We compare the accuracy of SNOWPACK modeled outputs driven by two different numerical weather prediction (NWP) forecast models: the High-Resolution Deterministic Prediction System (HRDPS) and the North American Model (NAMnest). We leverage hourly automated weather station data, daily operational avalanche observations along the Going-to-the-Sun Road in Glacier National Park, Montana, United States, and in-situ snow stratigraphy and wetness profile observations to validate the SNOWPACK modeled outputs. This research is directly applicable to avalanche forecasting operations and future avalanche research as wet snow avalanche timing evolves due to climate change.

Montana↗

USGS2024

Advanced quantitative precipitation information: Improving monitoring and forecasts of precipitation, streamflow, and coastal flooding in the San Francisco Bay area

Advanced Quantitative Precipitation Information (AQPI) is a synergistic project that combines observations and models to improve monitoring and forecasts of precipitation, streamflow, and coastal flooding in the San Francisco Bay Area. As an experimental system, AQPI leverages more than a decade of research, innovation, and implementation of a statewide, state-of-the-art network of observations, and development of the next generation of weather and coastal forecast models. AQPI was developed as a prototype in response to requests from the water management community for improved information on precipitation, riverine, and coastal conditions to inform their decision-making processes. Observation of precipitation in the complex Bay Area landscape of California’s coastal mountain ranges is known to be a challenging problem. But, with new advanced radar network techniques, AQPI is helping fill an important observational gap for this highly populated and vulnerable metropolitan area. The prototype AQPI system consists of improved weather radar data for precipitation estimation; additional surface measurements of precipitation, streamflow, and soil moisture; and a suite of integrated forecast modeling systems to improve situational awareness about current and future water conditions from sky to sea. Together these tools will help improve emergency preparedness and public response to prevent loss of life and destruction of property during extreme storms accompanied by heavy precipitation and high coastal water levels—especially high-moisture laden atmospheric rivers. The Bay Area AQPI system could potentially be replicated in other urban regions in California, the United States, and worldwide.

California↗

USGS2013

New service interface for River Forecasting Center derived quantitative precipitation estimates

For more than a decade, the National Weather Service (NWS) River Forecast Centers (RFCs) have been estimating spatially distributed rainfall by applying quality-control procedures to radar-indicated rainfall estimates in the eastern United States and other best practices in the western United States to producea national Quantitative Precipitation Estimate (QPE) (National Weather Service, 2013). The availability of archives of QPE information for analytical purposes has been limited to manual requests for access to raw binary file formats that are difficult for scientists who are not in the climatic sciences to work with. The NWS provided the QPE archives to the U.S. Geological Survey (USGS), and the contents of the real-time feed from the RFCs are being saved by the USGS for incorporation into the archives. The USGS has applied time-series aggregation and added latitude-longitude coordinate variables to publish the RFC QPE data. Web services provide users with direct (index-based) data access, rendered visualizations of the data, and resampled raster representations of the source data in common geographic information formats.

Fact Sheet↗

USGS2020

InFRM Flood Decision Support Toolbox user guide

Digital geospatial flood inundation mapping can be a powerful tool for flood risk management. Flood preparedness, communication, warning, response and mitigation can be enhanced by flood inundation mapping that shows floodwater extent and depth over the land surface. Flood inundation maps that accurately reflect observed and forecasted hydrodynamic conditions enable officials to make timely operational and public safety decisions before and during flood events. Real-time inundation maps, based on U.S. Geological Survey (USGS) real-time streamgage observations, National Weather Service (NWS) forecasts and US Army Corps of Engineers (USACE) flood operations, can significantly enhance a community’s flood warning and response operations and systems. These maps enable local officials to make more informed flood risk management decisions and enhance the communication of these decisions to the public, thereby reducing loss of life and property. In addition, flood inundation maps and scenario analysis can inform all parties of the potential risk associated with various flood management options, prior to an actual flood event.

Report↗

USGS2016

Flood-inundation maps for the Schoharie Creek at Prattsville, New York, 2014

Digital flood-inundation maps for a 2.6-mile reach of the Schoharie Creek at Prattsville, New York, were created by the U.S. Geological Survey (USGS) in cooperation with the New York State Department of Environmental Conservation. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/ , depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage at Schoharie Creek at Prattsville (station number 01350000). Near-real-time stages at this streamgage may be obtained online from the USGS National Water Information System ( http://waterdata.usgs.gov/ ) or the National Weather Service Advanced Hydrologic Prediction Service ( http://water.weather.gov/ahps/ ), which also forecasts flood hydrographs at this site. National Weather Service-forecasted peak-stage information may be used in conjunction with the maps developed in this study to show predicted areas and depths of flood inundation. Flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The model was calibrated by using the stage-discharge relation (rating 82.0) at the Schoharie Creek at Prattsville streamgage (station 01350000) and high-water marks from the flood of August 28, 2011. The hydraulic model was then used to compute 17 water-surface profiles for flood stages at 1-foot intervals referenced to the streamgage datum and ranging from bankfull to greater than the highest recorded water level at the streamgage. The simulated water-surface profiles were then combined with a geographic information system digital elevation model, derived from light detection and ranging (lidar) data having a 0.61-foot vertical root-mean squared error and 6.6-foot horizontal resolution, in order to delineate the area flooded at each water level. These flood-inundation maps, along with near-real-time stage data from USGS streamgages and forecasted stage data from the National Weather Service, can provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures as well as for postflood recovery efforts.

New York↗

USGS2020

High‐resolution dynamically downscaled rainfall and temperature projections for ecological life zones within Puerto Rico and for the U.S. Virgin Islands

The weather research and forecasting (WRF) model and a combination of the regional spectral model (RSM) and the Japanese Meteorological Agency Non‐Hydrostatic Model (NHM) were used to dynamically downscale selected CMIP5 global climate models to provide 2‐km projections with hourly model output for Puerto Rico and the U.S. Virgin Islands. Two 20‐year time slices were downscaled for historical (1986–2005) and future (2041–2060) periods following RCP8.5. Projected changes to mean and extreme temperature and precipitation were quantified for Holdridge life zones within Puerto Rico and for the U.S. Virgin Islands. The evaluation reveals a persistent cold bias for all islands in the U.S. Caribbean, a dry bias across Puerto Rico, and a wet bias on the windward side of mountains within the U.S. Virgin Islands. Despite these biases, model simulations show a robust drying pattern for all islands that is generally larger for Puerto Rico (25% annual rainfall reduction for some life zones) than the U.S. Virgin Islands (12% island average). The largest precipitation reductions are found during the more convectively active afternoon and evening hours. Within Puerto Rico, the model uncertainty increases for the wetter life zones, especially for precipitation. Across the life zones, both models project unprecedented maximum and minimum temperatures that may exceed 200 days annually above the historical baseline with only small changes to the frequency of extreme rainfall. By contrast, in the U.S. Virgin Islands, there is no consensus on the location of the largest drying relative to the windward and leeward side of the islands. However, the models project the largest increases in maximum temperature on the southern side of St. Croix and in higher elevations of St. Thomas and St. John.

Puerto Rico↗

USGS2014

Air-sea interactions during strong winter extratropical storms

A high-resolution, regional coupled atmosphere–ocean model is used to investigate strong air–sea interactions during a rapidly developing extratropical cyclone (ETC) off the east coast of the USA. In this two-way coupled system, surface momentum and heat fluxes derived from the Weather Research and Forecasting model and sea surface temperature (SST) from the Regional Ocean Modeling System are exchanged via the Model Coupling Toolkit. Comparisons are made between the modeled and observed wind velocity, sea level pressure, 10 m air temperature, and sea surface temperature time series, as well as a comparison between the model and one glider transect. Vertical profiles of modeled air temperature and winds in the marine atmospheric boundary layer and temperature variations in the upper ocean during a 3-day storm period are examined at various cross-shelf transects along the eastern seaboard. It is found that the air–sea interactions near the Gulf Stream are important for generating and sustaining the ETC. In particular, locally enhanced winds over a warm sea (relative to the land temperature) induce large surface heat fluxes which cool the upper ocean by up to 2 °C, mainly during the cold air outbreak period after the storm passage. Detailed heat budget analyses show the ocean-to-atmosphere heat flux dominates the upper ocean heat content variations. Results clearly show that dynamic air–sea interactions affecting momentum and buoyancy flux exchanges in ETCs need to be resolved accurately in a coupled atmosphere–ocean modeling framework.

Ocean Dynamics↗

USGS2022

Bioclimatic variables dataset for baseline and future climate scenarios for climate change studies in Hawai'i

Gridded bioclimatic variables representing yearly, seasonal, and monthly means and extremes in temperature and precipitation have been widely used for ecological modeling purposes and in broader climate change impact and biogeographical studies. As a result of their utility, numerous sets of bioclimatic variables have been developed on a global scale (e.g., WorldClim) but rarely represent the finer regional scale pattern of climate in Hawai'i. Recognizing the value of having such regionally downscaled products, we integrated more detailed projections from recent climate models developed for Hawai'i with current climatological datasets to generate updated regionally defined bioclimatic variables. We derived updated bioclimatic variables from new projections of baseline and future monthly minimum, mean, and maximum temperature (T min , T mean , T max ) and mean precipitation (P mean ) data at 250 m resolution. We used the most up-to-date dynamically downscaled projections based on the Weather Research and Forecasting (WRF) model from the International Pacific Research Center (IPRC) and the National Center for Atmospheric Research (NCAR). We summarized the monthly data from these two climate projections into a suite of 19 standard bioclimatic variables that provide detailed information about annual and seasonal mean climatic conditions for the Hawaiian Islands. These bioclimatic variables are available for three climate scenarios: baseline climate (1990-2009) and future climate (2080-2099) under representative concentration pathway (RCP) 4.5 (IPRC projections only) and RCP 8.5 (both IPRC and NCAR projections) climate scenarios. The resulting dataset provides a more robust set of climate products that can be used for modeling purposes, impact studies, and management planning.

Hawaii↗

USGS2016

Arctic sea ice decline contributes to thinning lake ice trend in northern Alaska

Field measurements, satellite observations, and models document a thinning trend in seasonal Arctic lake ice growth, causing a shift from bedfast to floating ice conditions. September sea ice concentrations in the Arctic Ocean since 1991 correlate well ( r = +0.69, p < 0.001) to this lake regime shift. To understand how and to what extent sea ice affects lakes, we conducted model experiments to simulate winters with years of high (1991/92) and low (2007/08) sea ice extent for which we also had field measurements and satellite imagery characterizing lake ice conditions. A lake ice growth model forced with Weather Research and Forecasting model output produced a 7% decrease in lake ice growth when 2007/08 sea ice was imposed on 1991/92 climatology and a 9% increase in lake ice growth for the opposing experiment. Here, we clearly link early winter 'ocean-effect' snowfall and warming to reduced lake ice growth. Future reductions in sea ice extent will alter hydrological, biogeochemical, and habitat functioning of Arctic lakes and cause sub-lake permafrost thaw.

Alaska↗

USGS1987

Real-time landslide warning during heavy rainfall

A real-time system for issuing warnings of landslides during major storms is being developed for the San Francisco Bay region, California. The system is based on empirical and theoretical relations between rainfall and landslide initiation, geologic determination of areas susceptible to landslides, real-time monitoring of a regional network of telemetering rain gages, and National Weather Service precipitation forecasts. This system was used to issue warnings during the storms of 12 to 21 February 1986, which produced 800 millimeters of rainfall in the region. Although analysis after the storms suggests that modifications and additional developments are needed, the system successfully predicted the times of major landslide events. It could be used as a prototype for systems in other landslide-prone regions.

California↗

USGS2021

Simulated atmospheric response to four projected land-use land-cover change scenarios for 2050 in the north-central United States

Land-use land-cover change (LULCC) has become an important topic of research for the central United States because of the extensive conversion of the natural prairie into agricultural land, especially in the northern Great Plains. As a result, shifts in the natural climate (minimum/maximum temperature, precipitation, etc.) across the north-central United States have been observed, as noted within the Fourth National Climate Assessment (NCA4) report. Thus, it is necessary to understand how further LULCC will affect the near-surface atmosphere, the lower troposphere, and the planetary boundary layer (PBL) atmosphere over this region. The goal of this work was to investigate the utility of a new future land-use land-cover (LULC) dataset within the Weather Research and Forecasting (WRF) modeling system. The present study utilizes a modeled future land-use dataset developed by the Forecasting Scenarios of Land-Use Change (FORE-SCE) model to investigate the influence of future (2050) land use on a simulated PBL development within the WRF Model. Three primary areas of LULCC were identified within the FORE-SCE future LULC dataset across Nebraska and South Dakota. Variations in LULC between the 2005 LULC control simulation and four FORE-SCE simulations affected near-surface temperature (0.5°–1°C) and specific humidity (0.3–0.5 g kg −1 ). The differences noted in the temperature and moisture fields affected the development of the simulated PBL, leading to variations in PBL height and convective available potential energy. Overall, utilizing the FORE-SCE dataset within WRF produced notable differences relative to the control simulation over areas of LULCC represented in the FORE-SCE dataset.

Nebraska, South Dakota↗

USGS2016

The sensitivity of WRF downscaled precipitation in Puerto Rico to cumulus parameterization and interior grid nudging

The sensitivity of the Weather Research and Forecasting Model (WRF) simulated precipitation over Puerto Rico is evaluated using multiple combinations of cumulus parameterization (CP) schemes and interior grid nudging. NCEP-DOE AMIP-II reanalysis (R-2) is downscaled to 2- km horizontal grid spacing with both convective permitting simulations (CP active only in the 49 middle and outer domains) and CP schemes active in all domains. The results generally show lower simulated precipitation amounts compared to the observations, regardless of WRF configuration. However, activating the CP schemes in the inner domain improves the annual cycle, intensity, and placement of rainfall compared to the convective permitting simulations. Furthermore, the use of interior grid nudging techniques in the outer domains improves the placement and intensity of rainfall in the inner domain. Incorporating a CP scheme at convective permitting scales (< 4 km) and grid nudging at non-convective permitting scales (> 4 km) improves the island average correlation of precipitation by 0.05 to 0.2 and reduces the island average RMSE by up to 40 mm on average over relying on the explicit microphysics at convective permitting scales with grid nudging. Projected changes in summer precipitation between 2040-2042 and 1985-1987 using WRF to downscale CCSM4 ranges from a 2.6 mm average increase to 81.9 mm average decrease, depending on the choice of CP scheme. The differences are only associated with differences between WRF configurations, which indicates the importance of CP scheme for projected precipitation change as well as historical accuracy.

Puerto Rico↗

USGS2009

Remote sensing of global croplands for food security: Way forward

This book opens a new pathway for global mapping that is focused on a specific land use theme, such as irrigated or rain-fed croplands and classes within these themes. Since croplands use most of the water consumed by humans, specific knowledge of irrigated and rain-fed croplands will be critical for precise estimates of water use. At present and in the coming decades, irrigated and rain-fed cropland area mapping is crucial for food security studies. Throughout this book, various subjects pertaining to global croplands are discussed comprehensively.

Book chapter↗

USGS2020

Real-time performance of the PLUM earthquake early warning method during the 2019 M6.4 and M7.1 Ridgecrest, California, Earthquakes

We evaluate the timeliness and accuracy of ground‐motion‐based earthquake early warning (EEW) during the July 2019 M 6.4 and 7.1 Ridgecrest earthquakes. In 2018, we began retrospective and internal real‐time testing of the propagation of local undamped motion (PLUM) method for earthquake warning in California, Oregon, and Washington, with the potential that PLUM might one day be included in the ShakeAlert EEW system. A real‐time version of PLUM was running on one of the ShakeAlert EEW system’s development servers at the time of the 2019 Ridgecrest sequence, allowing us to evaluate the timeliness and accuracy of PLUM’s warnings for the M 6.4 and 7.1 mainshocks in real time with the actual data availability and latencies of the operational ShakeAlert EEW system. The latter is especially important because high‐data latencies during the M 7.1 earthquake degraded ShakeAlert’s performance. PLUM proved to be largely immune to these latencies. In this article, we present a retrospective analysis of PLUM performance and explore three potential regional alerting strategies ranging from spatially large regions (counties), to moderate‐size regions (National Weather Service public forecast zones), to high‐spatial specificity (50 km regular geographic grid). PLUM generated initial shaking forecasts for the two mainshocks 5 and 6 s after their respective origin times, and faster than the ShakeAlert system’s first alerts. PLUM was also able to accurately forecast shaking across southern California for all three alerting strategies studied. As would be expected, a cost‐benefit analysis of each approach illustrates trade‐offs between increasing warning time and minimizing the area receiving unneeded alerts. Choosing an optimal alerting strategy requires knowledge of users’ false alarm tolerance and minimum required warning time for taking protective action, as well as the time required to distribute alerts to users.

California↗

USGS2025

Water supply in the conterminous United States, Alaska, Hawaii, and Puerto Rico, water years 2010–20

We present an assessment of water supply across the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico covering water years 2010–20. Our analysis drew on two national hydrologic models, the National Hydrologic Model Precipitation-Runoff Modeling System and the Weather Research and Forecasting model hydrologic modeling system. Both models produced estimates of streamflow, evapotranspiration, soil moisture, snow water equivalent, and other hydrologic states and fluxes. The models were driven by the bias-adjusted 4-kilometer-resolution, long-term regional hydroclimate simulation over the conterminous United States dataset (CONUS404). We assessed spatial and temporal error distributions by comparing monthly simulations at the 12-digit hydrologic unit code and regional scale from both models against external benchmarking datasets. Results showed that average annual rainfall across the CONUS was 857 millimeters per year for the period of analysis, with water year 2012 the driest year (729 millimeters) and water year 2019 the wettest year (995 millimeters). Key interannual variability results included the following: (1) the California–Nevada hydrologic region had the highest variability in precipitation and snow accumulation, and (2) the Texas hydrologic region was among hydrologic regions with the highest variability in precipitation. We related interannual variability in precipitation to storage volumes in soil moisture, snow water equivalent, and lakes and reservoirs to highlight areas with little storage and large year-to-year variability in precipitation. These areas included the Southern High Plains, Central High Plains, Texas, Souris–Red–Rainy, Mississippi Embayment, and Midwest regions. Our analysis of groundwater-level data showed that several of these areas overlap aquifers where groundwater levels were considerably lower than historical averages, including the Colorado Plateaus aquifers, the Rio Grande aquifer system, and the Central and Southern regions of the High Plains aquifer. Many of these lowered groundwater levels are continuations of decades-long declines from overpumping that started well before the assessment period. The resulting water budgets and their analyses provide a high-resolution foundational assessment of the mean state and variability of the terrestrial hydrologic cycle across the CONUS and Alaska, Hawaii, and Puerto Rico to support a wide range of water resource management applications.

Professional Paper↗

USGS2012

Flood-inundation maps for the Saddle River from Rochelle Park to Lodi, New Jersey, 2012

Digital flood-inundation maps for a 2.75-mile reach of the Saddle River from 0.2 mile upstream from the Interstate 80 bridge in Rochelle Park to 1.5 miles downstream from the U.S. Route 46 bridge in Lodi, New Jersey, were created by the U.S. Geological Survey (USGS) in cooperation with the New Jersey Department of Environmental Protection (NJDEP). The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation , depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage at Saddle River at Lodi, New Jersey (station 01391500). Current conditions for estimating near real-time areas of inundation using USGS streamgage information may be obtained on the Internet at http://waterdata.usgs.gov/nwis/uv?site_no=01391500 . The National Weather Service (NWS) forecasts flood hydrographs at many places that are often collocated with USGS streamgages. NWS-forecasted peak-stage information may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation. In this study, flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The model was calibrated using the most current stage-discharge relations at the Saddle River at Lodi, New Jersey streamgage and documented high-water marks from recent floods. The hydraulic model was then used to determine 11 water-surface profiles for flood stages at the Saddle River streamgage at 1-ft intervals referenced to the streamgage datum, North American Vertical Datum of 1988 (NAVD 88), and ranging from bankfull, 0.5 ft below NWS Action Stage, to the extent of the stage-discharge rating, which is approximately 1 ft higher than the highest recorded water level at the streamgage. Action Stage is the stage which when reached by a rising stream the NWS or a partner needs to take some type of mitigation action in preparation for possible significant hydrologic activity. The simulated water-surface profiles were then combined with a geographic information system 3-meter (9.84-ft) digital elevation model (derived from Light Detection and Ranging (LiDAR) data) in order to delineate the area flooded at each water level. The availability of these maps, along with Internet information regarding current stage from USGS streamgages and forecasted stream stages from the NWS, provide emergency management personnel and residents with information that is critical for flood response activities, such as evacuations and road closures, as well as for post-flood recovery efforts.

New Jersey↗

USGS2012

Flood-inundation maps for the White River at Newberry, Indiana

Digital flood-inundation maps for a 4.9-mile reach of the White River at Newberry, Indiana (Ind.), were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Office of Community and Rural Affairs. The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation , depict estimates of the areal extent of flooding corresponding to selected water levels (stages) at USGS streamgage 03360500, White River at Newberry, Ind. Current conditions at the USGS streamgage may be obtained on the Internet ( http://waterdata.usgs.gov/in/nwis/uv?site_no=03360500 ). The National Weather Service (NWS) forecasts flood hydrographs at the Newberry streamgage. That forecasted peak-stage information, also available on the Internet, may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation. For this study, flood profiles were computed for the White River reach by means of a one-dimensional step-backwater model developed by the U.S. Army Corps of Engineers. The hydraulic model was calibrated by using the most current stage-discharge relation at USGS streamgage 03360500, White River at Newberry, Ind., and high-water marks from a flood in June 2008.The calibrated hydraulic model was then used to determine 22 water-surface profiles for flood stages a1-foot intervals referenced to the streamgage datum and ranging from bankfull to approximately the highest recorded water level at the streamgage. The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from Light Detection and Ranging (LiDAR) data) in order to delineate the area flooded at each water level. The availability of these maps, along with Internet information regarding current stage from the USGS streamgage at Newberry, Ind., and forecasted stream stages from the NWS, provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures as well as for post-flood recovery efforts.

Indiana↗