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At least 109 records · Page 6Linked to original sources
USGS2019

Development of a flood-inundation map library and precipitation-runoff modeling for the Clear Fork Mohican River in and near Bellville, Ohio

The U.S. Geological Survey (USGS), in cooperation with the Muskingum Watershed Conservancy District, led hydrologic and hydraulic analyses within the Clear Fork Mohican River Basin in and near Bellville, Ohio. The analyses included the development of digital flood-inundation maps for an approximately 2.5-mile reach of the Clear Fork Mohican River and the development of a precipitation-runoff model for a portion of the Clear Fork Mohican River Basin. Data collection for the study involved the installation and operation of 2 streamgages (Clear Fork Mohican River at Bellville, Ohio, and Cedar Fork above Bellville, Ohio); 1 lake-level gage (Clear Fork Reservoir near Lexington, Ohio); 2 precipitation gages (Clear Fork Reservoir near Lexington, Ohio, and Rain Gage at Cedar Fork above Bellville, Ohio); and 12 submersible pressure transducers on Clear Fork Mohican River and 4 of its tributaries. Data collection also included field surveys of hydraulic structures and channel cross sections. Flood profiles were computed for the 2.5-mile reach of the Clear Fork Mohican River by means of a one-dimensional step-backwater model. The model was calibrated to 16 measured events and to a portion (stages 9 to 11 feet) of the current stage-streamflow relation at the USGS streamgage Clear Fork Mohican River at Bellville, Ohio, and to stage recorded at a submersible pressure transducer site near the downstream study limit. After calibration the step-backwater model was used to compute nine flood profiles for stages ranging from 9 to 17 feet. The flood profiles were then used in combination with a digital elevation model to delineate the area that would be inundated at each stage. A precipitation-runoff model was developed and calibrated using data from the streamgage, precipitation gage, and 11 submersible pressure transducers. The modeling included data during 10 runoff events that were used for model calibration and validation, with focus on 6 events. The Nash-Sutcliffe model efficiency coefficients for six peak streamflow events ranged from 0.459 to 0.851. The models produced by this study can be used to assess possible flood mitigation options and define flood hazard areas that could contribute to the protection of life and property. The availability of flood-inundation maps, internet information from USGS streamgages, and forecasted stages from the National Weather Service could provide emergency management personnel and residents with information on forecasting floods, appropriate flood response activities, and post-flood recovery efforts.

Ohio↗
USGS2020

Drought early warning and forecasting

Drought risk management involves three pillars: drought early warning, drought vulnerability and risk assessment, and drought preparedness, mitigation, and response. This book collects in one place a description of all the key components of the first pillar, and describes strategies for fitting these pieces together. The best modern drought early warning systems incorporate and integrate a broad array of environmental information sources: weather station observations, satellite imagery, land surface and crop model simulations, and weather and climate model forecasts, and analyze this information in context-relevant ways that take into account exposure and vulnerability. Drought Early Warning and Forecasting: Theory and Practice assembles a comprehensive overview of these components, providing examples drawn from the Famine Early Warning Systems Network and the United States Drought Monitor. This book simultaneously addresses the physical, social, and information management aspects of drought early warning, and informs readers about the tools, techniques, and conceptual models required to effectively identify, predict, and communicate potential drought-related disasters. This book is a key text for postgraduate scientists and graduate and advanced undergraduate students in hydrology, geography, earth sciences, meteorology, climatology, and environmental sciences programs. Professionals dealing with disaster management and drought forecasting will also find this book beneficial to their work.

Book↗
USGS2003

Estimated flood-inundation maps for Cowskin Creek in western Wichita, Kansas

The October 31, 1998, flood on Cowskin Creek in western Wichita, Kansas, caused millions of dollars in damages. Emergency management personnel and flood mitigation teams had difficulty in efficiently identifying areas affected by the flooding, and no warning was given to residents because flood-inundation information was not available. To provide detailed information about future flooding on Cowskin Creek, high-resolution estimated flood-inundation maps were developed using geographic information system technology and advanced hydraulic analysis. Two-foot-interval land-surface elevation data from a 1996 flood insurance study were used to create a three-dimensional topographic representation of the study area for hydraulic analysis. The data computed from the hydraulic analyses were converted into geographic information system format with software from the U.S. Army Corps of Engineers' Hydrologic Engineering Center. The results were overlaid on the three-dimensional topographic representation of the study area to produce maps of estimated flood-inundation areas and estimated depths of water in the inundated areas for 1-foot increments on the basis of stream stage at an index streamflow-gaging station. A Web site (http://ks.water.usgs.gov/Kansas/cowskin.floodwatch) was developed to provide the public with information pertaining to flooding in the study area. The Web site shows graphs of the real-time streamflow data for U.S. Geological Survey gaging stations in the area and monitors the National Weather Service Arkansas-Red Basin River Forecast Center for Cowskin Creek flood-forecast information. When a flood is forecast for the Cowskin Creek Basin, an estimated flood-inundation map is displayed for the stream stage closest to the National Weather Service's forecasted peak stage. Users of the Web site are able to view the estimated flood-inundation maps for selected stages at any time and to access information about this report and about flooding in general. Flood recovery teams also have the ability to view the estimated flood-inundation map pertaining to the most recent flood. The availability of these maps and the ability to monitor the real-time stream stage through the U.S. Geological Survey Web site provide emergency management personnel and residents with information that is critical for evacuation and rescue efforts in the event of a flood as well as for post-flood recovery efforts.

Water-Resources Investigations Report↗
USGS2020

Two ensemble approaches for forecasting sulfur dioxide concentrations from Kīlauea volcano

Kīlauea volcano, located on the island of Hawaii, is one of the most active volcanoes in the world. It was in a state of nearly continuous eruption from 1983 to 2018 with copious emissions of sulfur dioxide (SO 2 ) that affected public health, agriculture, and infrastructure over large portions of the island. Since 2010, the University of Hawaiʻi at Mānoa provides publicly available vog forecasts that began in 2010 to aid in the mitigation of volcanic smog (or “vog”) as a hazard. In September 2017, the forecast system began to produce operational ensemble forecasts. The months that preceded Kīlauea’s historic lower east rift zone eruption of 2018 provide an opportunity to evaluate the newly implemented air quality ensemble prediction system and compare it another approach to the generation of ensemble members. One of the two approaches generates perturbations in the wind field while the other perturbs the sulfur dioxide (SO 2 ) emission rate from the volcano. This comparison has implications for the limits of forecast predictability under the particularly dynamic conditions at Kīlauea volcano. We show that for ensemble forecasts of SO 2 generated under these conditions, the uncertainty associated with the SO 2 emission rate approaches that of the uncertainty in the wind field. However, the inclusion of a fluctuating SO 2 emission rate has the potential to improve the prediction of the changes in air quality downwind of the volcano with suitable postprocessing.

Hawaii↗
USGS2017

Extreme geomagnetic storms: Probabilistic forecasts and their uncertainties

Extreme space weather events are low-frequency, high-risk phenomena. Estimating their rates of occurrence, as well as their associated uncertainties, is difficult. In this study, we derive statistical estimates and uncertainties for the occurrence rate of an extreme geomagnetic storm on the scale of the Carrington event (or worse) occurring within the next decade. We model the distribution of events as either a power law or lognormal distribution and use (1) Kolmogorov-Smirnov statistic to estimate goodness of fit, (2) bootstrapping to quantify the uncertainty in the estimates, and (3) likelihood ratio tests to assess whether one distribution is preferred over another. Our best estimate for the probability of another extreme geomagnetic event comparable to the Carrington event occurring within the next 10 years is 10.3% 95% confidence interval (CI) [0.9,18.7] for a power law distribution but only 3.0% 95% CI [0.6,9.0] for a lognormal distribution. However, our results depend crucially on (1) how we define an extreme event, (2) the statistical model used to describe how the events are distributed in intensity, (3) the techniques used to infer the model parameters, and (4) the data and duration used for the analysis. We test a major assumption that the data represent time stationary processes and discuss the implications. If the current trends persist, suggesting that we are entering a period of lower activity, our forecasts may represent upper limits rather than best estimates.

Space Weather↗
USGS2023

Landslide initiation thresholds in data-sparse regions: Application to landslide early warning criteria in Sitka, Alaska, USA

Probabilistic models to inform landslide early warning systems often rely on rainfall totals observed during past events with landslides. However, these models are generally developed for broad regions using large catalogs, with dozens, hundreds, or even thousands of landslide occurrences. This study evaluates strategies for training landslide forecasting models with a scanty record of landslide-triggering events, which is a typical limitation in remote, sparsely populated regions. We evaluate 136 statistical models trained on a precipitation dataset with five landslide-triggering precipitation events recorded near Sitka, Alaska, USA, as well as > 6000 d of non-triggering rainfall (2002–2020). We also conduct extensive statistical evaluation for three primary purposes: (1) to select the best-fitting models, (2) to evaluate performance of the preferred models, and (3) to select and evaluate warning thresholds. We use Akaike, Bayesian, and leave-one-out information criteria to compare the 136 models, which are trained on different cumulative precipitation variables at time intervals ranging from 1 h to 2 weeks, using both frequentist and Bayesian methods to estimate the daily probability and intensity of potential landslide occurrence (logistic regression and Poisson regression). We evaluate the best-fit models using leave-one-out validation as well as by testing a subset of the data. Despite this sparse landslide inventory, we find that probabilistic models can effectively distinguish days with landslides from days without slide activity. Our statistical analyses show that 3 h precipitation totals are the best predictor of elevated landslide hazard, and adding antecedent precipitation (days to weeks) did not improve model performance. This relatively short timescale of precipitation combined with the limited role of antecedent conditions likely reflects the rapid draining of porous colluvial soils on the very steep hillslopes around Sitka. Although frequentist and Bayesian inferences produce similar estimates of landslide hazard, they do have different implications for use and interpretation: frequentist models are familiar and easy to implement, but Bayesian models capture the rare-events problem more explicitly and allow for better understanding of parameter uncertainty given the available data. We use the resulting estimates of daily landslide probability to establish two decision boundaries that define three levels of warning. With these decision boundaries, the frequentist logistic regression model incorporates National Weather Service quantitative precipitation forecasts into a real-time landslide early warning “dashboard” system ( https://sitkalandslide.org/ , last access: 9 October 2023). This dashboard provides accessible and data-driven situational awareness for community members and emergency managers.

Alaska↗
USGS2007

Population influences on tornado reports in the United States

The number of tornadoes reported in the United States is believed to be less than the actual incidence of tornadoes, especially prior to the 1990s, because tornadoes may be undetectable by human witnesses in sparsely populated areas and areas in which obstructions limit the line of sight. A hierarchical Bayesian model is used to simultaneously correct for population-based sampling bias and estimate tornado density using historical tornado report data. The expected result is that F2-F5 compared with F0-F1 tornado reports would vary less with population density. The results agree with this hypothesis for the following population centers: Atlanta, Georgia; Champaign, Illinois; and Des Moines, Iowa. However, the results indicated just the opposite in Oklahoma. It is hypothesized that the result is explained by the misclassification of tornadoes that were worthy of F2-F5 rating but were classified as F0-F1 tornadoes, thereby artificially decreasing the number of F2-F5 and increasing the number of F0-F1 reports in rural Oklahoma.

Weather and Forecasting↗
USGS2019

Flood warning toolset for the Medina River in Bandera County, Texas

Overview Floods are the most common natural disaster in the United States. The Medina River in Bandera County, Texas, is in the Edwards Plateau, where high-intensity rain rates and steep terrain frequently contribute to severe flash flooding capable of causing loss of life and property. For example, the July 5, 2002, flood claimed a total of 12 lives in the central Texas area. The estimated peak discharge during this flood at U.S. Geological Survey (USGS) streamflow-gaging station 08178880 Medina River at Bandera, Tex., was 159,000 cubic feet per second (corresponding to a stage or gage height of 38.91 feet), causing significant flooding in Bandera near Mud Creek and farther downstream. In 2018, the USGS, in cooperation with the Bandera County River Authority and Groundwater District and the Texas Water Development Board, developed a flood early-warning toolset to enhance the communication of flood risk and provide emergency management with additional information to improve flood response and mitigation. This toolset consists of a continuous streamflow-gage monitoring network, a well-calibrated hydraulic model of the Medina River, and a flood-inundation mapper application for the study area. A library of flood-inundation maps tied to the National Weather Service river stage forecast capability is included with the toolset.

Texas↗
USGS2014

Flood-inundation maps and updated components for a flood-warning system or the City of Marietta, Ohio and selected communities along the Lower Muskingum River and Ohio River

Digital flood-inundation maps for lower reaches of the Muskingum River and a reach of the Ohio River in southeast Ohio were created by the U.S. Geological Survey (USGS), in cooperation with the Muskingum Watershed Conservancy District and the City of Marietta, Ohio. To complete the inundation maps, Ohio River and lower Muskingum River bathymetry was updated and two streamgages, one on the Ohio River upstream of Marietta near Sardis, Ohio, and one on the Muskingum River in Beverly, Ohio, were added as basic components of the flood-warning system. An updated hydraulic model component also led to the new flood-inundation maps. The 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 water levels (stages) at one or more of the following USGS streamgages: Muskingum River at McConnelsville, Ohio (03150000); Muskingum River at Beverly, Ohio (03150500); and Ohio River at Marietta, Ohio (03150700). The maps can be used in conjunction with National Weather Service flood-forecast data to show areas of estimated flood inundation associated with forecasted flood-peak stages. Flood profiles for selected reaches were prepared by calibrating steady-state step-backwater models to selected streamgage rating curves. The step-backwater models were used to determine water-surface-elevation profiles for up to 12 flood stages at a streamgage with corresponding stream-flows ranging from approximately the 10- to 0.2-percent chance annual-exceedance probabilities for each of the 3 streamgages that correspond to the flood-inundation maps. Additional hydraulic modeling was used to account for the effects of backwater from the Ohio River on water levels in the Muskingum River. The computed longitudinal profiles of flood levels were used with a Geographic Information System digital elevation model (derived from light detection and ranging) to delineate flood-inundation areas. Digital maps showing flood-inundation areas overlain on digital orthophotographs were prepared for the selected floods.

Ohio↗
USGS2014

Flood-inundation maps for the White River near Edwardsport, Indiana

Digital flood-inundation maps for a 3.3-mile reach of the White River near Edwardsport, (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 03360730, White River near Edwardsport, Ind. Near-real-time stages at this streamgage may be obtained from the USGS National Water Information System at http://waterdata.usgs.gov/ or the National Weather Service Advanced Hydrologic Prediction Service at http://water.weather.gov/ahps/, which also forecasts flood hydrographs at this site (site EDWI3.) Flood profiles were computed for the White River near Edwardsport reach by means of a one-dimensional step-back-water model developed by the U.S. Army Corps of Engineers. The hydraulic model was calibrated by using the most current stage-discharge relations at USGS streamgage 03360730, White River near Edwardsport, Ind., and high-water marks from the flood of April 2013. The calibrated hydraulic model was then used to determine 19 water-surface profiles for flood stages at approximately 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 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 03360730 White River near Edwardsport, Ind., and forecasted stream stages from the National Weather Service, 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↗
USGS2023

USGS installs 2022 high-water markers to provide flood information

Historic flooding on June 12-13, 2022 occurred in the Gallatin, Absaroka and Beartooth Mountains of Montana and Wyoming, near Yellowstone National Park. The flooding was initiated by rainstorms that produced between 1-5 inches of rain on top of an above-average snowpack, causing the snow to melt faster and rush downstream. The combined rain and melted snow led to record floods on the Yellowstone, Boulder, and Gallatin Rivers and Rock Creek near Red Lodge, Montana, as well as many other streams and rivers in the area. The US Geological Survey, in cooperation with the Montana Silver Jackets and local communities, plans to add high water mark signs to mark the highest level the rivers reached during the flooding. These signs will provide the date of flooding, site information, the maximum flood depth (known as river stage, in feet), and links to the National Weather Service’s river forecasting website and US Geological Survey’s streamgage website. Streamflow has been measured along the Yellowstone River for over 100 years and these data are used to estimate the frequency of large flood events. The floods of 2022 ranged from once in 100 years to once in 500 years likelihood. Though the chances of these floods are rare, they have an equal chance of happening each year, so it is always possible to have historic flood events back-to-back.

Montana Highground↗
USGS1996

Meeting summary - Coastal meteorology and oceanography: Report of the third prospectus development team of the U.S. Weather Research Program to NOAA and NSF

U.S. Weather Research Program (USWRP) prospectus development teams (PDTs) are small groups of scientists that are convened by the USWRP lead scientist on a one-time basis to discuss critical issues and to provide advice related to future directions of the program. PDTs are a principal source of information for the Science Advisory Committee, which is a standing committee charged with the duty of making recommendations to the Program Office based upon overall program objectives. PDT-1 focused on theoretical issues, and PDT-2 on observational issues; PDT-3 is the first of several to focus on more specialized topics. PDT-3 was convened to identify forecasting problems related to U.S. coastal weather and oceanic conditions, and to suggest likely solution strategies. There were several overriding themes that emerged from the discussion. First, the lack of data in and over critical regions of the ocean, particularly in the atmospheric boundary layer, and the upper-ocean mixed layer were identified as major impediments to coastal weather prediction. Strategies for data collection and dissemination, as well as new instrument implementation, were discussed. Second, fundamental knowledge of air-sea fluxes and boundary layer structure in situations where there is significant mesoscale variability in the atmosphere and ocean is needed. Companion field studies and numerical prediction experiments were discussed. Third, research prognostic models suggest that future operational forecast models pertaining to coastal weather will be high resolution and site specific, and will properly treat effects of local coastal geography, orography, and ocean state. The view was expressed that the exploration of coupled air-sea models of the coastal zone would be a particularly fruitful area of research. PDT-3 felt that forecasts of land-impacting tropical cyclones, Great Lakes-affected weather, and coastal cyclogenesis, in particular, would benefit from such coordinated modeling and field efforts. Fourth, forecasting for Arctic coastal zones is limited by our understanding of how sea ice forms. The importance of understanding air-sea fluxes and boundary layers in the presence of ice formation was discussed. Finally, coastal flash flood forecasting via hydrologic models is limited by the present accuracy of measured and predicted precipitation and storm surge events. Strategies for better ways to improve the latter were discussed.

Conference Paper↗
USGS2010

A model to forecast short-term snowmelt runoff using synoptic observations of streamflow, temperature, and precipitation

Snowmelt runoff is forecast with a statistical model that utilizes daily values of stream discharge, gaged precipitation, and maximum and minimum observations of air temperature. Synoptic observations of these variables are made at existing low- and medium-altitude weather stations, thus eliminating the difficulties and expense of new, high-altitude installations. Four model development steps are used to demonstrate the influence on prediction accuracy of basin storage, a preforecast test season, air temperature (to estimate ablation), and a prediction based on storage. Daily ablation is determined by a technique that employs both mean temperature and a radiative index. Radiation (both long- and short-wave components) is approximated by using the range in daily temperature, which is shown to be closely related to mean cloud cover. A technique based on the relationship between prediction error and prediction season weather utilizes short-term forecasts of precipitation and temperature to improve the final prediction. Verification of the model is accomplished by a split sampling technique for the 1960–1977 period. Short- term (5–15 days) predictions of runoff throughout the main snowmelt season are demonstrated for mountain drainages in western Washington, south-central Arizona, western Montana, and central California. The coefficient of prediction ( C p ) based on actual, short-term predictions for 18 years is for Thunder Creek (Washington), 0.69; for South Fork Flathead River (Montana), 0.45; for the Black River (Arizona), 0.80; and for the Kings River (California), 0.80.

Water Resources Research↗
USGS2013

The Anemomilos prediction methodology for Dst

This paper describes new capabilities for operational geomagnetic D isturbance s torm t ime (Dst) index forecasts. We present a data‐driven, deterministic algorithm called Anemomilos for forecasting Dst out to a maximum of 6 days for large, medium, and small storms, depending upon transit time to the Earth. This capability is used for operational satellite management and debris avoidance in Low Earth Orbit (LEO). Anemomilos has a 15 min cadence, 1 h time granularity, 144 h prediction window (+6 days), and up to 1 h latency. A new finding is that nearly all flare events above a certain irradiance threshold, occurring within a defined solar longitude/latitude region and having sufficient estimated liftoff velocity of ejected material, will produce a geoeffective Dst perturbation. Three solar observables are used for operational Dst forecasting: flare magnitude, integrated flare irradiance through time, and event location. Magnitude is a proxy for ejecta quantity or mass and, combined with speed derived from the integrated flare irradiance, represents the kinetic energy. Speed is estimated as the line‐of‐sight velocity for events within 45° radial of solar disk center. Storms resulting from high‐speed streams emanating from coronal holes are not modeled or predicted. A new result is that solar disk, not limb, observable features are used for predictive techniques. Comparisons between Anemomilos predicted and measured Dst for every hour over 25 months in three continuous time frames between 2001 (high solar activity), 2005 (low solar activity), and 2012 (rising solar activity) are shown. The Anemomilos operational algorithm was developed for a specific customer use related to thermospheric mass density forecasting. It is an operational space weather technology breakthrough using solar disk observables to predict geomagnetically effective Dst up to several days at 1 h time granularity. Real‐time forecasts are presented at http://sol.spacenvironment.net/~sam_ops/index.html?

Space Weather↗
USGS2017

Development and evaluation of a reservoir model for the Chain of Lakes in Illinois

Forecasts of flows entering and leaving the Chain of Lakes reservoir on the Fox River in northeastern Illinois are critical information to water-resource managers who determine the optimal operation of the dam at McHenry, Illinois, to help minimize damages to property and loss of life because of flooding on the Fox River. In 2014, the U.S. Geological Survey; the Illinois Department of Natural Resources, Office of Water Resources; and National Weather Service, North Central River Forecast Center began a cooperative study to develop a system to enable engineers and planners to simulate and communicate flows and to prepare proactively for precipitation events in near real time in the upper Fox River watershed. The purpose of this report is to document the development and evaluation of the Chain of Lakes reservoir model developed in this study. The reservoir model for the Chain of Lakes was developed using the Hydrologic Engineering Center–Reservoir System Simulation program. Because of the complex relation between the dam headwater and reservoir pool elevations, the reservoir model uses a linear regression model that relates dam headwater elevation to reservoir pool elevation. The linear regression model was developed using 17 U.S. Geological Survey streamflow measurements, along with the gage height in the reservoir pool and the gage height at the dam headwater. The Nash-Sutcliffe model efficiency coefficients for all three linear regression model variables ranged from 0.90 to 0.98. The reservoir model performance was evaluated by graphically comparing simulated and observed reservoir pool elevation time series during nine periods of high pool elevation. In addition, the peak elevations during these time periods were graphically compared to the closest-in-time observed pool elevation peak. The mean difference in the simulated and observed peak elevations was -0.03 feet, with a standard deviation of 0.19 feet. The Nash-Sutcliffe coefficient for peak prediction was calculated as 0.94. Evaluation of the model based on accuracy of peak prediction and the ability to simulate an elevation time series showed the performance of the model was satisfactory.

Illinois↗
USGS2016

Flood-inundation maps for Indian Creek and Tomahawk Creek, Johnson County, Kansas, 2014

Digital flood-inundation maps for a 6.4-mile upper reach of Indian Creek from College Boulevard to the confluence with Tomahawk Creek, a 3.9-mile reach of Tomahawk Creek from 127th Street to the confluence with Indian Creek, and a 1.9-mile lower reach of Indian Creek from the confluence with Tomahawk Creek to just beyond the Kansas/Missouri border at State Line Road in Johnson County, Kansas, were created by the U.S. Geological Survey in cooperation with the city of Overland Park, Kansas. The flood-inundation maps, which can be accessed through the U.S. Geological Survey 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 U.S. Geological Survey streamgages on Indian Creek at Overland Park, Kansas; Indian Creek at State Line Road, Leawood, Kansas; and Tomahawk Creek near Overland Park, Kansas. Near real time stages at these streamgages may be obtained on the Web from the U.S. Geological Survey National Water Information System at http://waterdata.usgs.gov/nwis or the National Weather Service Advanced Hydrologic Prediction Service at http://water.weather.gov/ahps/ , which also forecasts flood hydrographs at these sites. Flood profiles were computed for the stream reaches by means of a one-dimensional step-backwater model. The model was calibrated for each reach by using the most current stage-discharge relations at the streamgages. The hydraulic models were then used to determine 15 water-surface profiles for Indian Creek at Overland Park, Kansas; 17 water-surface profiles for Indian Creek at State Line Road, Leawood, Kansas; and 14 water-surface profiles for Tomahawk Creek near Overland Park, Kansas, for flood stages at 1-foot intervals referenced to the streamgage datum and ranging from bankfull to the next interval above the 0.2-percent annual exceedance probability flood level (500-year recurrence interval). The simulated water-surface profiles were then combined in a geographic information system with a digital elevation model derived from light detection and ranging data (having a 0.429-foot vertical and 0.228-foot horizontal accuracy) to delineate the area flooded at each water level. The availability of these maps, along with Web information regarding current stage from the U.S. Geological Survey streamgages and forecasted high-flow stages from the National Weather Service, will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations, road closures, and postflood recovery efforts.

Kansas↗
USGS2019

Flood-inundation maps for Joachim Creek, De Soto, Missouri, 2018

Digital flood-inundation maps for a 6.7-mile reach of Joachim Creek, De Soto, Missouri, were created by the U.S. Geological Survey (USGS) in cooperation with the city of De Soto and Jefferson County, Missouri. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Program website at https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program , depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage Joachim Creek at De Soto, Missouri (station number 07019500). Near-real-time stages at this streamgage may be obtained on the internet from the USGS National Water Information System at https://waterdata.usgs.gov/nwis or the National Weather Service Advanced Hydrologic Prediction Service at https://water.weather.gov/ahps2/hydrograph.php?wfo=lsx&gage=desm7 , which also forecasts flood hydrographs at this site (site DESM7). Flood profiles were computed for the stream reach using a one-dimensional model for simulation of water-surface profiles with steady-state (gradually varied) or unsteady-state flow computation options. The model was calibrated by using the theoretical stage-discharge relation at the USGS streamgage Joachim Creek at De Soto, Missouri (station number 07019500), and documented high-water marks from the flood of April 18, 2013. The hydraulic model was then used to compute 10 water surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum. The profiles ranged from 8.0 ft, or near bankfull, to 17.0 ft, which exceeds the stage that corresponds to the estimated 0.2-percent annual exceedance probability flood (500-year recurrence interval flood). The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.60-ft vertical accuracy and 1.97-ft horizontal resolution) 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 and forecasted high-flow stages from the National Weather Service, will provide emergency management personnel and residents with information that is critical for flood-response activities such as evacuations and road closures and for post-flood recovery efforts.

Missouri↗