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At least 91 records · Page 5Linked to original sources
USGS2013

Flood-inundation maps for the Saluda River from Old Easley Bridge Road to Saluda Lake Dam near Greenville, South Carolina

Digital flood-inundation maps for a 3.95-mile reach of the Saluda River from approximately 815 feet downstream from Old Easley Bridge Road to approximately 150 feet downstream from Saluda Lake Dam near Greenville, South Carolina, were developed by the U.S. Geological Survey (USGS). 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 Saluda River near Greenville, South Carolina (station 02162500). Current conditions at the USGS streamgage may be obtained through the National Water Information System Web site at http://waterdata.usgs.gov/sc/nwis/uv/?site_no=02162500&PARAmeter_cd=00065,00060,00062. The National Weather Service (NWS) forecasts flood hydrographs at many places that are often collocated with USGS streamgages. Forecasted peak-stage information is available on the Internet at the NWS Advanced Hydrologic Prediction Service (AHPS) flood-warning system Web site (http://water.weather.gov/ahps/) and 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-streamflow relations at USGS streamgage station 02162500, Saluda River near Greenville, South Carolina. The hydraulic model was then used to determine water-surface profiles for flood stages at 1.0-foot intervals referenced to the streamgage datum and ranging from approximately bankfull to 2 feet higher than the highest recorded water level at the streamgage. The simulated water-surface profiles were then exported to a geographic information system, ArcGIS, and combined with a digital elevation model (derived from Light Detection and Ranging [LiDAR] data with a 0.6-foot vertical Root Mean Square Error [RMSE] and a 3.0-foot horizontal RMSE), using HEC-GeoRAS tools in order to delineate the area flooded at each water level. The availability of these maps, along with real-time stage data from the USGS streamgage station 02162500 and forecasted stream stages from the NWS, can provide emergency management personnel and residents with information that is critical during flood-response and flood-recovery activities, such as evacuations, road closures, and disaster declarations.

South Carolina↗
USGS2013

Flood-inundation maps for the Iroquois River at Rensselaer, Indiana

Digital flood-inundation maps for a 4.0-mile reach of the Iroquois River at Rensselaer, Indiana (Ind.), were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Department of Transportation. 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 USGS streamgage 05522500, Iroquois River at Rensselaer, Ind. 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/in/nwis/uv?site_no=05522500). In addition, the National Weather Service (NWS) forecasts flood hydrographs at the Rensselaer streamgage. That forecasted peak-stage information, also available on the Internet (http://water.weather.gov/ahps/), 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 Iroquois 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 (June 27, 2012) stage-discharge relations at USGS streamgage 05522500, Iroquois River at Rensselaer, Ind., and high-water marks from the flood of July 2003. The calibrated hydraulic model was then used to determine nine water-surface profiles for flood stages at 1-foot intervals referenced to the streamgage datum and ranging from bankfull to the highest stage of the current stage-discharge rating curve. 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 Rensselaer, Ind., and forecasted stream stages from the NWS, provides 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↗
USGS2013

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

Digital flood-inundation maps for a 5.3-mile reach of the White River at Spencer, Indiana, 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 and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage White River at Spencer, Indiana (sta. no. 03357000). 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/. National Weather Service (NWS)-forecasted peak-stage inforamation 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 by using the most current stage-discharge relation at the White River at Spencer, Indiana, streamgage and documented high-water marks from the flood of June 8, 2008. The hydraulic model was then used to compute 20 water-surface profiles for flood stages at 1-foot intervals referenced to the streamgage datum and ranging from the NWS action stage (9 feet) to the highest rated stage (28 feet) 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 the current stage from the Spencer USGS streamgage and forecasted stream stages from the NWS will 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↗
USGS2013

Flood-inundation maps for the East Fork White River at Columbus, Indiana

Digital flood-inundation maps for a 5.4-mile reach of the East Fork White River at Columbus, Indiana, from where the Flatrock and Driftwood Rivers combine to make up East Fork White River to just upstream of the confluence of Clifty Creek with the East Fork White River, were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Department of Transportation. 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 03364000, East Fork White River at Columbus, Indiana. Current conditions at the USGS streamgage may be obtained on the Internet from the USGS National Water Information System (http://waterdata.usgs.gov/in/nwis/uv/?site_no=03364000&agency_cd=USGS&). The National Weather Service (NWS) forecasts flood hydrographs for the East Fork White River at Columbus, Indiana at their Advanced Hydrologic Prediction Service (AHPS) flood warning system Website (http://water.weather.gov/ahps/), that 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 hydraulic model was calibrated by using the most current stage-discharge relation at USGS streamgage 03364000, East Fork White River at Columbus, Indiana. The calibrated hydraulic model was then used to determine 15 water-surface profiles for flood stages at 1-foot (ft) 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), having a 0.37-ft vertical accuracy and a 1.02 ft horizontal accuracy), 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 Columbus, Indiana, and forecasted stream stages from the NWS will 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↗
USGS2013

Flood-inundation maps for the Saddle River from Upper Saddle River Borough to Saddle River Borough, New Jersey, 2013

Digital flood-inundation maps for a 4.1-mile reach of the Saddle River from 0.6 miles downstream from the New Jersey-New York State boundary in Upper Saddle River Borough to 0.2 miles downstream from the East Allendale Road bridge in Saddle River Borough, 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 select water levels (stages) at the USGS streamgage 01390450, Saddle River at Upper Saddle River, New Jersey. 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=01390450. 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 by using the most current stage-discharge relations (in effect March 2013) at USGS streamgage 01390450, Saddle River at Upper Saddle River, New Jersey, and documented high-water marks from recent floods. The hydraulic model was then used to determine eight water-surface profiles for flood stages at 0.5-foot (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 upper 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 real-time streamflow data and 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↗
USGS2014

Flood-inundation maps for the West Branch Susquehanna River near the Boroughs of Lewisburg and Milton, Pennsylvania

Digital flood-inundation maps for an approximate 8-mile reach of the West Branch Susquehanna River from approximately 2 miles downstream from the Borough of Lewisburg, extending upstream to approximately 1 mile upstream from the Borough of Milton, Pennsylvania, were created by the U.S. Geological Survey (USGS) in cooperation with the Susquehanna River Basin Commission (SRBC). 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 the estimated areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage 01553500, West Branch Susquehanna River at Lewisburg, Pa. In addition, the information has been provided to the Susquehanna River Basin Commission (SRBC) for incorporation into their Susquehanna Inundation Map Viewer (SIMV) flood warning system ( http://maps.srbc.net/simv/ ). The National Weather Service (NWS) forecasted peak-stage information ( http://water.weather.gov/ahps ) for USGS streamgage 01553500, West Branch Susquehanna River at Lewisburg, Pa., 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. Calibration of the model was achieved using the most current stage-discharge relations (rating number 11.1) at USGS streamgage 01553500, West Branch Susquehanna River at Lewisburg, Pa., a documented water-surface profile from the December 2, 2010, flood, and recorded peak stage data. The hydraulic model was then used to determine 26 water-surface profiles for flood stages at 1-foot intervals referenced to the streamgage datum ranging from 14 feet (ft) to 39 ft. Modeled flood stages, as defined by NWS, include Action Stage, 14 ft; Flood Stage, 18 ft; Moderate Flood Stage, 23 ft; and Major Flood Stage, 28 ft. Geographic information system (GIS) technology was then used to combine the simulated water-surface profiles with a digital elevation model (DEM) derived from light detection and ranging (lidar) data to delineate the area flooded at each water level. The availability of these maps, along with World Wide Web 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.

Pennsylvania↗
USGS2016

Flood-inundation maps for a 9.1-mile reach of the Coast Fork Willamette River near Creswell and Goshen, Lane County, Oregon

Digital flood-inundation maps for a 9.1-mile reach of the Coast Fork Willamette River near Creswell and Goshen, Oregon, were developed by the U.S. Geological Survey (USGS) in cooperation with the U.S. Army Corps of Engineers (USACE). 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 stages at the USGS streamgage at Coast Fork Willamette River near Goshen, Oregon (14157500), at State Highway 58. Current stage at the streamgage for estimating near-real-time areas of inundation may be obtained at http://waterdata.usgs.gov/or/nwis/uv/?site_no=14157500&PARAmeter_cd=00065,00060 . In addition, the National Weather Service (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, areas of inundation were provided by USACE. The inundated areas were developed from flood profiles simulated by a one-dimensional unsteady step‑backwater hydraulic model. The profiles were checked by the USACE using documented high-water marks from a January 2006 flood. The model was compared and quality assured using several other methods. The hydraulic model was then used to determine eight water-surface profiles at various flood stages referenced to the streamgage datum and ranging from 11.8 to 19.8 ft, approximately 2.6 ft above the highest recorded stage at the streamgage (17.17 ft) since 1950. The intervals between stages are variable and based on annual exceedance probability discharges, some of which approximate NWS action stages. The areas of inundation and water depth grids provided to USGS by USACE were used to create interactive flood‑inundation maps. The availability of these maps with current stage from USGS streamgage 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.

Oregon↗
USGS2022

Development of projected depth-duration frequency curves (2050–89) for south Florida

Planning stormwater projects requires estimates of current and future extreme precipitation depths for events with specified return periods and durations. In this study, precipitation data from four downscaled climate datasets are used to determine changes in precipitation depth-duration-frequency curves from the period 1966–2005 to the period 2050–89 primarily on the basis of Representative Concentration Pathways 4.5 and 8.5 emission scenarios from the Coupled Model Intercomparison Project Phase 5. The four downscaled climate datasets are (1) the Coordinated Regional Downscaling Experiment (CORDEX) dataset, (2) the Localized Constructed Analogs (LOCA) dataset, (3) the Multivariate Adaptive Constructed Analogs (MACA) dataset, and (4) the Jupiter Intelligence Weather Research and Forecasting Model (JupiterWRF) dataset. Change factors—multiplicative changes in expected extreme precipitation magnitude from current to future period—were computed for grid cells from the downscaled climate datasets containing National Oceanic and Atmospheric Administration Atlas 14 stations in central and south Florida. Change factors for specific durations and return periods may be used to scale the National Oceanic and Atmospheric Administration Atlas 14 historical depth-duration-frequency values to the period 2050–89 on the basis of changes in extreme precipitation derived from downscaled climate datasets. Model culling was implemented to select downscaled climate models that best captured observed historical patterns of precipitation extremes in central and south Florida. Overall, a large variation in change factors across downscaled climate datasets was found, with change factors generally greater than one and increasing with return period. In general, median change factors were higher for the south-central Florida climate region (1.05–1.55 depending on downscaled climate dataset, duration, and return period) than for the south Florida climate region (1–1.4 depending on downscaled climate dataset, duration, and return period) when considering best performing models for both areas, indicating a projected overall increase in future extreme precipitation events.

Florida↗
USGS2026

Development of projected depth-duration-frequency curves for precipitation in Florida, 2020–59 and 2050–89

The planning, permitting, and design of stormwater-management projects require estimates of the depths of extreme precipitation for current and future events with specified durations and return periods. In this project, precipitation data from six downscaled climate datasets were used to determine changes in precipitation depth-duration-frequency curves from the period 1966–2005 to the periods 2020–59 and 2050–89. The downscaled climate datasets are from the Coupled Model Intercomparison Project Phases 5 and 6 and include (1) Coordinated Regional Downscaling Experiment (CORDEX), (2) Localized Constructed Analogs (LOCA), (3) Multivariate Adaptive Constructed Analogs (MACA), (4) Jupiter Intelligence Weather Research and Forecasting model (JupiterWRF), (5) LOCA version 2 (LOCA2), and (6) National Aeronautics and Space Administration Earth Exchange Global Daily Downscaled Projections (NEX-GDDP). Change factors—multiplicative changes in expected extreme precipitation magnitude from a historical to future period—were computed for grid cells containing National Oceanic and Atmospheric Administration Atlas 14 stations in Florida. Change factors for specific durations and return periods were developed to scale the National Oceanic and Atmospheric Administration Atlas 14 historical depth-duration-frequency values to the periods 2020–59 and 2050–89 on the basis of changes in extreme precipitation derived from six downscaled climate datasets. Overall, a large variation in change factors across downscaled climate datasets was found, with change factors generally being greater than 1 and increasing with return period. In general, median change factors were found to range within 1.01–1.58 for 2020–59 and 1.01–1.63 for 2050–89, depending on the downscaled climate dataset, region, duration, and return period, indicating a projected overall increase in future extreme-precipitation events. When data from all datasets are considered together, median change factors range within 1.04–1.18 for the period 2020–59 and within 1.04–1.23 for the period 2050–89, depending on the region, duration, and return period. Spatial patterns in median change factors were found to vary by dataset.

Florida↗
USGS2018

The Polar WRF downscaled historical and projected twenty-first century climate for the coast and foothills of Arctic Alaska

Climate change is most pronounced in the northern high latitude region. Yet, climate observations are unable to fully capture regional-scale dynamics due to the sparse weather station coverage, which limits our ability to make reliable climate-based assessments. A set of simulated data products was therefore developed for the North Slope of Alaska through a dynamical downscaling approach. The polar-optimized Weather Research and Forecast (Polar WRF) model was forced by three sources: The ERA-interim reanalysis data (for 1979–2014), the Community Earth System Model 1.0 (CESM1.0) historical simulation (for 1950–2005), and the CESM1.0 projected (for 2006–2100) simulations in two Representative Concentration Pathways (RCP4.5 and RCP8.5) scenarios. Climatic variables were produced in a 10-km grid spacing and a 3-h interval. The ERA-interim forced WRF (ERA-WRF) proves the value of dynamical downscaling, which yields more realistic topographical-induced precipitation and air temperature, as well as corrects underestimations in observed precipitation. In summary, dry and cold biases to the north of the Brooks Range are presented in ERA-WRF, while CESM forced WRF (CESM-WRF) holds wet and warm biases in its historical period. A linear scaling method allowed for an adjustment of the biases, while keeping the majority of the variability and extreme values of modeled precipitation and air temperature. CESM-WRF under RCP 4.5 scenario projects smaller increase in precipitation and air temperature than observed in the historical CESM-WRF product, while the CESM-WRF under RCP 8.5 scenario shows larger changes. The fine spatial and temporal resolution, long temporal coverage, and multi-scenario projections jointly make the dataset appropriate to address a myriad of physical and biological changes occurring on the North Slope of Alaska.

Alaska↗
USGS2020

Soil moisture product validation good practices protocol, version 1.0

The Global Climate Observing System (GCOS) included soil moisture in the list of Essential Climate Variables (ECVs) to express its important role in Earth’s water, energy and carbon cycle. Soil moisture has a major impact on agriculture, land surface hydrology, weather, and climate forecasting. This document is a community-based effort to provide recommendations on good practices for the validation of global to regional soil moisture products. Definitions are given and metrics to adequately describe the quality of soil moisture products are presented. Spaceborne active and passive microwave sensors are listed with their characteristics, and the typical soil moisture retrieval methods are explained, including dielectric mixing models and optical methods. Spatial scaling, root zone soil moisture estimation, and operational implementations are addressed, as these issues continue to gain more and more importance. Standard and advanced in situ measurement techniques are described as well as sensor calibration, spatial representativity, sampling strategies, and the benefit of airborne campaigns.The community has agreed upon the utilization of the International Soil Moisture Network (ISMN) as the main online repository for in situ soil moisture measurements. Different validation methods such as ground-based validation, satellite product intercomparison, and time series analyses are presented. We provide strategies to evaluate the long-term quality of soil moisture products, and give advice on how to handle typical temporal and spatial-scale mismatches and how to effectively report validation results. Moreover, the benefit of blind tests is discussed to gain objective validation results. We encourage data providers, scientists and practitioners to use this Soil Moisture Product Validation Good Practices Protocol to provide, analyze, and improve high quality Earth Observation results.

Report↗
USGS2022

Analysis of ocean dynamics during the impact of Hurricane Matthew using ocean-atmosphere coupling

The main goal of this investigation is to improve the understanding of ocean-atmosphere coupling during hurricanes. The present work involves the integration of the ocean-atmosphere coupled components of the Coupled Ocean-Atmosphere-Wave-Sediment Transport Modeling System in the Very Short Term Prediction System (SisPI). Three experiments are performed: First, using a dynamic sea surface temperature, consistent with the daily updated atmospheric model Weather Research and Forecast (SisPI); second, using the Regional Oceanic Modeling System and third, using a dynamic coupling between the atmospheric and the oceanic models. The coupled system improves the tracks of the hurricane simulations respect to the SisPI. The use of the oceanic model allows a more detailed representation of the sea surface temperature. Using the coupled model, a more precise diurnal cycle of the surface net heat fluxes is obtained.

Cuban Journal of Meteorology (Revista Cubana de Me↗
USGS2015

Real-time validation of the Dst Predictor model

The Dst Predictor model, which has been running real-time in the Space Weather Analysis and Forecast System (SWAFS), provides 1-hour and 4-hour forecasts of the Dst index. This is useful for awareness of impending geomagnetic activity, as well as driving other real-time models that use Dst as an input. In this report, we examine the performance of this forecast model in detail. When validating indices it should be noted that performance is only with respect to a reference index as they are derived quantities assumed to reflect a state of the magnetosphere that cannot be directly measured. In this case U.S. Geological Survey (USGS) Definitive Dst is the reference index (Section 3). Whether or not the model better reflects the actual activity level is nearly impossible to discern and is outside the scope of this report. We evaluate the performance of the model by computing continuous predictant skill scores against USGS Definitive Dst values as “observations” (Section 4.2). The two sets of data are not well-correlated for both 1-hour and 4-hour forecasts. The Dst Predictor Prediction Efficiency for both the 1- and 4-hour forecasts suggests poor performance versus the climatological mean. However, the skill score against a nowcast persistence model is positive, suggesting value added by the Dst Predictor model. We further examine statistics for storm times (Section 4.3) with similar results: nowcast persistence performs worse than Dst Predictor. Dst Predictor is superior to the nowcast persistence model for the metric used in this study. We recommend continued use of the DstPredictor model for 1-and4-hour Dst predictions along with active study of other Dst forecast models that do not rely on nowcast inputs (Section 6). The lack of certified requirements makes further recommendations difficult. A study of how the error in Dst translates to error in models and a better understanding of operational needs for magnetic storm warning are needed to determine such requirements. Nowcast persistence is often hard to beat for short term forecasts and specification and Dst Predictor clearly performs well against that standard (with 1-hour and 4-hour skill-scores of 0.233 and 0.485 respectively), although poor in absolute terms (with1-hourand4-hour prediction efficiencies of-64.6and-43.1, respectively).

Air Force Research Laboratory Technical Report↗
USGS2006

Estimated flood-inundation mapping for the Lower Blue River in Kansas City, Missouri, 2003-2005

The U.S. Geological Survey, in cooperation with the city of Kansas City, Missouri, began a study in 2003 of the lower Blue River in Kansas City, Missouri, from Gregory Boulevard to the mouth at the Missouri River to determine the estimated extent of flood inundation in the Blue River valley from flooding on the lower Blue River and from Missouri River backwater. Much of the lower Blue River flood plain is covered by industrial development. Rapid development in the upper end of the watershed has increased the volume of runoff, and thus the discharge of flood events for the Blue River. Modifications to the channel of the Blue River began in late 1983 in response to the need for flood control. By 2004, the channel had been widened and straightened from the mouth to immediately downstream from Blue Parkway to convey a 30-year flood. A two-dimensional depth-averaged flow model was used to simulate flooding within a 2-mile study reach of the Blue River between 63rd Street and Blue Parkway. Hydraulic simulation of the study reach provided information for the design and performance of proposed hydraulic structures and channel improvements and for the production of estimated flood-inundation maps and maps representing an areal distribution of water velocity, both magnitude and direction. Flood profiles of the Blue River were developed between Gregory Boulevard and 63rd Street from stage elevations calculated from high water marks from the flood of May 19, 2004; between 63rd Street and Blue Parkway from two-dimensional hydraulic modeling conducted for this study; and between Blue Parkway and the mouth from an existing one-dimensional hydraulic model by the U.S. Army Corps of Engineers. Twelve inundation maps were produced at 2-foot intervals for Blue Parkway stage elevations from 750 to 772 feet. Each map is associated with National Weather Service flood-peak forecast locations at 63rd Street, Blue Parkway, Stadium Drive, U.S. Highway 40, 12th Street, and the Missouri River at the Hannibal railroad bridge in Kansas City. The National Weather Service issues peak-stage forecasts for these locations during times of flooding. Missouri River backwater inundation profiles were developed using interpolated Missouri River stage elevations at the mouth of the Blue River. Twelve backwater-inundation maps were produced at 2-foot intervals for the mouth of the Blue River from 730.9 to 752.9. To provide public access to the information presented in this report, a World Wide Web site (http://mo.water.usgs.gov/indep/kelly/blueriver/index.htm) was created that displays the results of two-dimensional modeling between 63rd Street and Blue Parkway, estimated flood-inundation maps, estimated backwater-inundation maps, and the latest gage heights and National Weather Service stage forecast for each forecast location within the study area. In addition, the full text of this report, all tables, and all plates are available for download at http://pubs.water.usgs.gov/sir2006-5089.

Missouri↗
USGS2020

Applications of knowledge and predictions of atmospheric rivers

This chapter reviews how AR research is being applied in real-world situations to address issues of flood planning and emergency intervention. It includes water supply management case studies. Examples comprise five distinct sections that show how AR research is being directly applied to the challenges that water managers, dam operators, crisis-management engineers such as USACE, National Weather Service (NWS) personnel, the media, and others face. These topics include how decision-makers on the ground must iteratively alternate between forecasts and their own field observations, especially in unfolding emergency-response conditions, and the trade-offs necessitated between acting on competing priorities such as flood-risk management and water supply management. Ultimately, almost all AR studies have the potential to directly benefit the public’s need for ongoing water supply as well as for accurate weather forecasts and deployable emergency protocols for natural hazards that necessitate municipal, state, and federal government personnel to collaborate.

Book chapter↗
USGS2021

Impact of SST and surface waves on Hurricane Florence (2018): A coupled modeling investigation

Hurricane Florence (2018) devastated the coastal communities of the Carolinas through heavy rainfall that resulted in massive flooding. Florence was characterized by an abrupt reduction in intensity (Saffir-Simpson Category 4 to Category 1) just prior to landfall and synoptic-scale interactions that stalled the storm over the Carolinas for several days. We conducted a series of numerical modeling experiments in coupled and uncoupled configurations to examine the impact of sea surface temperature (SST) and ocean waves on storm characteristics. In addition to experiments using a fully coupled atmosphere-ocean-wave model, we introduced the capability of the atmospheric model to modulate wind stress and surface fluxes by oceanwaves through data from an uncoupled wave model. We examined these experiments by comparing track, intensity, strength, SST, storm structure, wave height, surface roughness, heat fluxes, and precipitation in order to determine the impacts of resolving ocean conditions with varying degrees of coupling. We found differences in the storm’s intensity and strength, with the best correlation coefficient of intensity (r=0.89) and strength (r=0.95) coming from the fully-coupled simulations. Further analysis into surface roughness parameterizations added to the atmospheric model revealed differences in the spatial distribution and magnitude of the largest roughness lengths. Adding ocean andwave features to the model further modified the fluxes due to more realistic cooling beneath the stormwhich in turn modified the precipitation field. Our experiments highlight significant differences in how air-sea processes impact hurricane modeling. The storm characteristics of track, intensity, strength, and precipitation at landfall are crucial to predictability and forecasting of future landfalling hurricanes.

Weather and Forecasting↗
USGS2022

Towards real-time probabilistic ash deposition forecasting for New Zealand

Volcanic ashfall forecasts are highly dependent on eruption source parameters (ESPs) and synoptic weather conditions at the time and location of the eruption. In New Zealand, MetService and GNS Science have been jointly developing an ashfall forecast system that incorporates four-dimensional high-resolution numerical weather prediction (NWP) and ESPs into the HYSPLIT model, a state-of-the art hybrid Eulerian and Lagrangian dispersion model widely used for volcanic ash. However, these forecasts are based on discrete ESPs combined with a deterministic weather forecast and thus provide no information on output uncertainty. This shortcoming hinders stakeholder decision making, particularly near the geographical margin of forecasted ashfall and in areas with large gradients in forecasted ash deposition. Our study presents a new approach that incorporates uncertainty from both eruptive and meteorological inputs to deliver uncertainty in the model output. To this end, we developed probability density functions (PDFs) for three key ESPs (plume height, mass eruption rate, eruption duration) tailored to New Zealand’s volcanoes and combine them with NWP ensemble datasets to generate probabilistic ashfall forecasts using the HYSPLIT model. We show that the Latin Hypercube Sampling (LHS) technique can be used to representatively span this four-dimensional parameter space and allow us to add uncertainty quantification to rapid response forecast systems. For a case study of a hypothetical eruption at Tongariro, New Zealand we suggest that large parts of New Zealand’s North Island would not receive adequate warning for potential ashfall if uncertainties were not included in the forecasts. We also propose new probabilistic summary products to support public information and emergency responders decision making.

Journal of Applied Volcanology↗
USGS2009

Forecasting distributions of large federal-lands fires utilizing satellite and gridded weather information

The current study presents a statistical model for assessing the skill of fire danger indices and for forecasting the distribution of the expected numbers of large fires over a given region and for the upcoming week. The procedure permits development of daily maps that forecast, for the forthcoming week and within federal lands, percentiles of the distributions of (i) number of ignitions; (ii) number of fires above a given size; (iii) conditional probabilities of fires greater than a specified size, given ignition. As an illustration, we used the methods to study the skill of the Fire Potential Index an index that incorporates satellite and surface observations to map fire potential at a national scale in forecasting distributions of large fires. ?? 2009 IAWF.

International Journal of Wildland Fire↗