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At least 1,189 records · Page 66Linked to original sources
USGS2017

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

Digital flood-inundation maps for a 7.5-mile reach of the White River at Noblesville, Indiana, were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Department of Transportation. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at https://water.usgs.gov/osw/flood_inundation/ , depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the White River at Noblesville, Ind., streamgage (USGS station number 03349000). Real-time stages at this streamgage may be obtained from the USGS National Water Information System at https://waterdata.usgs.gov/nwis or the National Weather Service (NWS) Advanced Hydrologic Prediction Service at http:/water.weather.gov/ahps/ , which also forecasts flood hydrographs at the same site as the USGS streamgage (NWS site NBLI3). Flood profiles were computed for the stream reach by means of a one-dimensional, step-backwater hydraulic modeling software developed by the U.S. Army Corps of Engineers. The hydraulic model was calibrated using the current (2016) stage-discharge rating at the USGS streamgage 03349000, White River at Noblesville, Ind., and documented high-water marks from the floods of September 4, 2003, and May 6, 2017. The hydraulic model was then used to compute 15 water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum ranging from 10.0 ft (the NWS “action stage”) to 24.0 ft, which is the highest stage interval of the current (2016) USGS stage-discharge rating curve and 2 ft higher than the NWS “major flood stage.” 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 0.98-ft vertical accuracy and 4.9-ft horizontal resolution) to delineate the area flooded at each stage. The availability of these maps, along with internet information regarding current stage from the USGS streamgage and forecasted high-flow 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 postflood recovery efforts.

Indiana↗
USGS1998

Combining satellite data with ancillary data to produce a refined land-use/land-cover map

As part of the U.S. Geological Survey's National Water-Quality Assessment Program in the Western Lake Michigan Drainages Study Unit, a current map of land use and land cover is needed to gain a better understanding of how land use and land cover may influence water quality. Satellite data from the Landsat Thematic Mapper provides a means to map and measure the type and amount of various land-cover types across the Study Unit and can be easily updated as changes occur in the landscape or in water quality. Translating these land cover categories to land use, however, requires the use of other thematic maps or ancillary data layers, such as wetland inventories, population data, or road networks. This report describes a process of (1) using satellite imagery to produce a land-cover map for the Fox/Wolf River basin, a portion of the Western Lake Michigan Drainages NAWQA Study Unit and (2) improving the satellite-derived land-cover map by using other thematic maps. The multiple data layers are processed in a geographic information system (GIS), and the combination provides more information than individual sources alone.

Michigan, Wisconsin↗
USGS2018

A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform

Mapping high resolution (30-m or better) cropland extent over very large areas such as continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of great importance for addressing the global food and water security challenges. Such cropland extent products capture individual farm fields, small or large, and are crucial for developing accurate higher-level cropland products such as cropping intensities, crop types, crop watering methods (irrigated or rainfed), crop productivity, and crop water productivity. It also brings many challenges that include handling massively large data volumes, computing power, and collecting resource intensive reference training and validation data over complex geographic and political boundaries. Thereby, this study developed a precise and accurate Landsat 30-m derived cropland extent product for two very important, distinct, diverse, and large countries: Australia and China. The study used of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR1, and NDVI) of Landsat-8 every 16-day Operational Land Imager (OLI) data for the years 2013–2015. The classification was performed by using a pixel-based supervised random forest (RF) machine learning algorithm (MLA) executed on the Google Earth Engine (GEE) cloud computing platform. Each band was time-composited over 4–6 time-periods over a year using median value for various agro-ecological zones (AEZs) of Australia and China. This resulted in a 32–48-layer mega-file data-cube (MFDC) for each of the AEZs. Reference training and validation data were gathered from: (a) field visits, (b) sub-meter to 5-m very high spatial resolution imagery (VHRI) data, and (c) ancillary sources such as from the National agriculture bureaus. Croplands versus non-croplands knowledge base for training the RF algorithm were derived from MFDC using 958 reference-training samples for Australia and 2130 reference-training samples for China. The resulting 30-m cropland extent product was assessed for accuracies using independent validation samples: 900 for Australia and 1972 for China. The 30-m cropland extent product of Australia showed an overall accuracy of 97.6% with a producer’s accuracy of 98.8% (errors of omissions = 1.2%), and user’s accuracy of 79% (errors of commissions = 21%) for the cropland class. For China, overall accuracies were 94% with a producer’s accuracy of 80% (errors of omissions = 20%), and user’s accuracy of 84.2% (errors of commissions = 15.8%) for cropland class. Total cropland areas of Australia were estimated as 35.1 million hectares and 165.2 million hectares for China. These estimates were higher by 8.6% for Australia and 3.9% for China when compared with the traditionally derived national statistics. The cropland extent product further demonstrated the ability to estimate sub-national cropland areas accurately by providing an R 2 value of 0.85 when compared with province-wise cropland areas of China. The study provides a paradigm-shift on how cropland maps are produced using multi-date remote sensing. These products can be browsed at www.croplands.org and made available for download at NASA’s Land Processes Distributed Active Archive Center (LP DAAC) https://www.lpdaac.usgs.gov/node/1282 .

ISPRS Journal of Photogrammetry and Remote Sensing↗
USGS2022

Weakly supervised spatial deep learning for Earth image segmentation based on imperfect polyline labels

In recent years, deep learning has achieved tremendous success in image segmentation for computer vision applications. The performance of these models heavily relies on the availability of large-scale high-quality training labels (e.g., PASCAL VOC 2012). Unfortunately, such large-scale high-quality training data are often unavailable in many real-world spatial or spatiotemporal problems in earth science and remote sensing (e.g., mapping the nationwide river streams for water resource management). Although extensive efforts have been made to reduce the reliance on labeled data (e.g., semi-supervised or unsupervised learning, few-shot learning), the complex nature of geographic data such as spatial heterogeneity still requires sufficient training labels when transferring a pre-trained model from one region to another. On the other hand, it is often much easier to collect lower-quality training labels with imperfect alignment with earth imagery pixels (e.g., through interpreting coarse imagery by non-expert volunteers). However, directly training a deep neural network on imperfect labels with geometric annotation errors could significantly impact model performance. Existing research that overcomes imperfect training labels either focuses on errors in label class semantics or characterizes label location errors at the pixel level. These methods do not fully incorporate the geometric properties of label location errors in the vector representation. To fill the gap, this article proposes a weakly supervised learning framework to simultaneously update deep learning model parameters and infer hidden true vector label locations. Specifically, we model label location errors in the vector representation to partially reserve geometric properties (e.g., spatial contiguity within line segments). Evaluations on real-world datasets in the National Hydrography Dataset (NHD) refinement application illustrate that the proposed framework outperforms baseline methods in classification accuracy.

ACM Transactions on Intelligent Systems and Techno↗
USGS2012

Flood-inundation maps for the Leaf River at Hattiesburg, Mississippi

Digital flood-inundation maps for a 1.7-mile reach of the Leaf River were developed by the U.S. Geological Survey (USGS) in cooperation with the City of Hattiesburg, City of Petal, Forrest County, Mississippi Emergency Management Agency, Mississippi Department of Homeland Security, and the Emergency Management District. The Leaf River study reach extends from just upstream of the U.S. Highway 11 crossing to just downstream of East Hardy/South Main Street and separates the cities of Hattiesburg and Petal, Mississippi. 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-surface elevations (stages) at the USGS streamgage at Leaf River at Hattiesburg, Mississippi (02473000). Current conditions at the USGS streamgage may be obtained through the National Water Information System Web site at http://waterdata.usgs.gov/ms/nwis/uv/?site_no=02473000&PARAmeter_cd=00065,00060 . In addition, the information has been provided to the National Weather Service (NWS) for incorporation into their Advanced Hydrologic Prediction Service (AHPS) flood-warning system ( http://water.weather.gov/ahps/ ). The NWS forecasts flood hydrographs at many places that are often collocated at USGS streamgages. The forecasted peak-stage information, available on the AHPS Web site, 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 Leaf River at Hattiesburg, Mississippi, streamgage and documented high-water marks from recent and historical floods. The hydraulic model was then used to determine 13 water-surface profiles for flood stages at 1.0-foot intervals referenced to the streamgage datum and ranging from bankfull to approximately the highest recorded water-surface elevation 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 0.6-foot vertical accuracy and 9.84-foot horizontal resolution] in order to delineate the area flooded at each 1-foot increment of stream stage. The availability of these maps, when combined with real-time stage information from USGS streamgages and forecasted stream stage from the NWS, provides emergency management personnel and residents with critical information during flood-response activities, such as evacuations and road closures, as well as for post-flood recovery efforts.

Mississippi↗
USGS2018

Flood-inundation maps for the North Fork Kentucky River at Hazard, Kentucky

Digital flood-inundation maps for a 7.1-mile reach of the North Fork Kentucky River at Hazard, Kentucky (Ky.), were created by the U.S. Geological Survey (USGS) in cooperation with the Kentucky Silver Jackets and the U.S. Army Corps of Engineers Louisville District. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at https://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 on the North Fork Kentucky River at Hazard, Ky. (USGS station number 03277500). 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/ or the National Weather Service (NWS) Advanced Hydrologic Prediction Service (AHPS) at https://water.weather.gov/ahps/ , which also forecasts flood hydrographs at this site (NWS AHPS site HAZK2). NWS AHPS forecast peak stage information may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation. Flood profiles were computed for the North Fork Kentucky 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 current stage-discharge relation (USGS rating no. 24.0) at USGS streamgage 03277500, North Fork Kentucky River at Hazard, Ky. The calibrated hydraulic model was then used to compute 26 water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum and ranging from approximately bankfull (14 ft) to the highest even-foot increment stage (39 ft) 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 data, to delineate the area flooded at each water level. The availability of these maps, along with information on the internet regarding current stage from the USGS streamgage at North Fork Kentucky River at Hazard, Ky., and forecasted stream stages from the NWS AHPS, 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 postflood recovery efforts.

Kentucky↗
USGS2020

Freezing in a warming climate: Marked declines of a subnivean hibernator after a snow drought

Recent snow droughts associated with unusually warm winters are predicted to increase in frequency and affect species dependent upon snowpack for winter survival. Changes in populations of some cold‐adapted species have been attributed to heat stress or indirect effects on habitat from unusually warm summers, but little is known about the importance of winter weather to population dynamics and how responses to snow drought vary among sympatric species. We evaluated changes in abundance of hoary marmots ( Marmota caligata ) over a period that included a year of record‐low snowpack to identify mechanisms associated with weather and snowpack. To consider interspecies comparisons, our analysis used the same a priori model set as a concurrent study that evaluated responses of American pikas ( Ochotona princeps ) to weather and snowpack in the same study area of North Cascades National Park, Washington, USA. We hypothesized that marmot abundance reflected mechanisms related to heat stress, cold stress, cold exposure without an insulating snowpack, snowpack duration, atmospheric moisture, growing‐season precipitation, or select combinations of these mechanisms. Changes in marmot abundances included a 74% decline from 2007 to 2016 and were best explained by an interaction of chronic dryness with exposure to acute cold without snowpack in winter. Physiological stress during hibernation from exposure to cold, dry air appeared to be the most likely mechanism of change in marmot abundance. Alternative mechanisms associated with changes to winter weather, including early emergence from hibernation or altered vegetation dynamics, had less support. A post hoc assessment of vegetative phenology and productivity did not support vegetation dynamics as a primary driver of marmot abundance across years. Although marmot and pika abundances were explained by strikingly similar models over periods of many years, details of the mechanisms involved likely differ between species because pika abundances increased in areas where marmots declined. Such differences may lead to diverging geographic distributions of these species as global change continues.

Washington↗
USGS1994

Analysis of data on nutrients and organic compounds in ground water in the upper Snake River basin, Idaho and western Wyoming, 1980-91

Nutrient and organic compound data from the U.S. Geological Survey and the U.S. Environmental Protection Agency STORET data bases provided information for development of a preliminary conceptual model of spatial and temporal ground-water quality in the upper Snake River Basin. Nitrite plus nitrate (as nitrogen; hereafter referred to as nitrate) concentrations exceeded the Federal drinking-water regulation of 10 milligrams per liter in three areas in Idaho" the Idaho National Engineering Laboratory, the area north of Pocatello (Fort Hall area), and the area surrounding Burley. Water from many wells in the Twin Falls area also contained elevated (greater than two milligrams per liter) nitrate concentrations. Water from domestic wells contained the highest median nitrate concentrations; water from industrial and public supply wells contained the lowest. Nitrate concentrations decreased with increasing well depth, increasing depth to water (unsaturated thickness), and increasing depth below water table (saturated thickness). Kjeldahl nitrogen concentrations decreased with increasing well depth and depth below water table. The relation between kjeldahl nitrogen concentrations and depth to water was poor. Nitrate and total phosphorus concentrations in water from wells were correlated among three hydrogeomorphic regions in the upper Snake River Basin, Concentrations of nitrate were statistically higher in the eastern Snake River Plain and local aquifers than in the tributary valleys. There was no statistical difference in total phosphorus concentrations among the three hydrogeomorphic regions. Nitrate and total phosphorus concentrations were correlated with land-use classifications developed using the Geographic Information Retrieval and Analysis System. Concentrations of nitrate were statistically higher in area of agricultural land than in areas of rangeland. There was no statistical difference in concentrations between rangeland and urban land and between urban land and agricultural land. There was no statistical difference in total phosphorus concentrations among any of the land-use classifications. Nitrate and total phosphorus concentrations also were correlated with land-use classifications developed by the Idaho Department of Water Resources for the Idaho part of the upper Snake River Basin. Nitrate concentrations were statistically higher in areas of irrigated agriculture than in areas of dryland agriculture and rangeland. There was no statistical difference in total phosphorus concentrations among any of the Idaho Department of Water Resources land-use classifications. Data were sufficient to assess long-term trends of nitrate concentrations in water from only eight wells: four wells north of Burley and four wells northwest of Pocatello. The trend in nitrate concentrations in water from all wells in upward. The following organic compounds were detected in ground water in the upper Snake River Basin: cyanazine, 2,4-D DDT, dacthal, diazinon, dichloropropane, dieldrin, malathion, and metribuzin. Of 211 wells sampled for organic compounds, water from 17 contained detectable concentrations.

Idaho;Montana;Nevada;Utah;Wyoming↗
USGS2013

Flood-inundation maps for the Tippecanoe River near Delphi, Indiana

Digital flood-inundation maps for an 11-mile reach of the Tippecanoe River that extends from County Road W725N to State Road 18 below Oakdale Dam, 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 of flooding corresponding to selected water levels (stages) at USGS streamgage 03333050, Tippecanoe River near Delphi, Ind. Current conditions at the USGS streamgages in Indiana may be obtained online at http://waterdata.usgs.gov/in/nwis/current/?type=flow. In addition, the information has been provided to the National Weather Service (NWS) for incorporation into their Advanced Hydrologic Prediction Service (AHPS) flood warning system (http://water.weather.gov/ahps/). The NWS forecasts flood hydrographs at many places that are often co-located at USGS streamgages. 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. In this study, water-surface profiles were simulated for the stream reach by means of a hydraulic one-dimensional step-backwater model. The model was calibrated by using the most current stage-discharge relation at USGS streamgage 03333050, Tippecanoe River near Delphi, Ind., and USGS streamgage 03332605, Tippecanoe River below Oakdale Dam, Ind. The hydraulic model was then used to simulate 13 water-surface profiles for flood stages at 1-foot intervals reference 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. A flood inundation map was generated for each water-surface profile stage (13 maps in all) so that, for any given flood stage, users will be able to view the estimated area of inundation. The availability of these maps, along with current stage from USGS streamgages 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↗
USGS2016

Flood-Inundation Maps for Sugar Creek at Crawfordsville, Indiana

Digital flood-inundation maps for a 6.5-mile reach of Sugar Creek at Crawfordsville, Indiana, were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Office of Community and Rural Affairs. 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 03339500, Sugar Creek at Crawfordsville, Ind. Near-real-time stages at this streamgage may be obtained on the Internet from the USGS National Water Information System at http://waterdata.usgs.gov/ or the National Weather Service (NWS) Advanced Hydrologic Prediction Service at http://water.weather.gov/ahps/ , which also forecasts flood hydrographs at this site (NWS site CRWI3). Flood profiles were computed for the USGS streamgage 03339500, Sugar Creek at Crawfordsville, Ind., reach by means of a one-dimensional step-backwater hydraulic modeling software developed by the U.S. Army Corps of Engineers. The hydraulic model was calibrated using the current stage-discharge rating at the USGS streamgage 03339500, Sugar Creek at Crawfordsville, Ind., and high-water marks from the flood of April 19, 2013, which reached a stage of 15.3 feet. The hydraulic model was then used to compute 13 water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum ranging from 4.0 ft (the NWS “action stage”) to 16.0 ft, which is the highest stage interval of the current USGS stage-discharge rating curve and 2 ft higher than the NWS “major flood stage.” 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 0.49-ft root mean squared error and 4.9-ft horizontal resolution) to delineate the area flooded at each stage. The availability of these maps, along with Internet information regarding current stage from the USGS streamgage and forecasted high-flow 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↗
USGS1992

Approach to data exchange: the spatial data transfer standard

Significant developments have taken place in the disciplines of cartography and geography in recent years with the advent of computer hardware and software that manipulate and process digital cartographic and geographic data more efficiently. The availability of inexpensive and powerful hardware and software systems offers the capability of displaying and analyzing spatial data to a growing number of users. As a result, developing and using existing digital cartographic databases are becoming very popular. However, the absence of uniform standards for the transfer of digital spatial data is hindering the exchange of data and increasing costs. Several agencies of the U.S. government and the academic community have been working hard over the last few years to develop a spatial data transfer standard that includes definitions of standard terminology, a spatial data transfer specification, recommendations on reporting digital cartographic data quality, and standard topographic and hydrographic entity terms and definitions. This proposed standard was published in the January 1988 issue of The American Cartographer. Efforts to test and promote this standard were coordinated by the U.S. Geological Survey. A Technical Review Board was appointed with representatives from the U.S. government, the private sector, and the academic community to complete the standard for submittal to the National Institute of Standards and Technology for approval as a Federal Information Processing Standard. The proposed standard was submitted in February 1992 for final approval.

Conference Paper↗
USGS2006

Scoping of flood hazard mapping needs for Cumberland County, Maine

This report was prepared by the U.S. Geological Survey (USGS) Maine Water Science Center as the deliverable for scoping of flood hazard mapping needs for Cumberland County, Maine, under Federal Emergency Management Agency (FEMA) Inter-Agency Agreement Number HSFE01-05-X-0018. This section of the report explains the objective of the task and the purpose of the report. The Federal Emergency Management Agency (FEMA) developed a plan in 1997 to modernize the FEMA flood mapping program. FEMA flood maps delineate flood hazard areas in support of the National Flood Insurance Program (NFIP). FEMA's plan outlined the steps necessary to update FEMA's flood maps for the nation to a seamless digital format and streamline FEMA's operations in raising public awareness of the importance of the maps and responding to requests to revise them. The modernization of flood maps involves conversion of existing information to digital format and integration of improved flood hazard data as needed. To determine flood mapping modernization needs, FEMA has established specific scoping activities to be done on a county-by-county basis for identifying and prioritizing requisite flood-mapping activities for map modernization. The U.S. Geological Survey (USGS), in cooperation with FEMA and the Maine State Planning Office Floodplain Management Program, began scoping work in 2005 for Cumberland County. Scoping activities included assembling existing data and map needs information for communities in Cumberland County, documentation of data, contacts, community meetings, and prioritized mapping needs in a final scoping report (this document), and updating the Mapping Needs Update Support System (MNUSS) Database or its successor with information gathered during the scoping process. The average age of the FEMA floodplain maps in Cumberland County, Maine is 21 years. Most of these studies were in the early to mid 1980s. However, in the ensuing 20-25 years, development has occurred in many of the watersheds, and the characteristics of the watersheds have changed with time. Therefore, many of the older studies may not depict current conditions nor accurately estimate risk in terms of flood heights. The following is the scope of work as defined in the FEMA/USGS Statement of Work: Task 1: Collect data from a variety of sources including community surveys, other Federal and State Agencies, National Flood Insurance Program (NFIP) State Coordinators, Community Assistance Visits (CAVs) and FEMA archives. Lists of mapping needs will be obtained from the MNUSS database, community surveys, and CAVs, if available. FEMA archives will be inventoried for effective FIRM panels, FIS reports, and other flood-hazard data or existing study data. Best available base map information, topographic data, flood-hazard data, and hydrologic and hydraulic data will be identified. Data from the Maine Floodplain Management Program database also will be utilized. Task 2: Contact communities in Cumberland County to notify them that FEMA and the State have selected them for a map update, and that a project scope will be developed with their input. Topics to be reviewed with the communities include (1) Purpose of the Flood Map Project (for example, the update needs that have prompted the map update); (2) The community's mapping needs; (3) The community's available mapping, hydrologic, hydraulic, and flooding information; (4) target schedule for completing the project; and (5) The community's engineering, planning, and geographic information system (GIS) capabilities. On the basis of the collected information from Task 1 and community contacts/meetings in Task 2, the USGS will develop a Draft Project Scope for the identified mapping needs of the communities in Cumberland County. The following items will be addressed in the Draft Project Scope: review of available information, determine if and how effective FIS data can be used in new project, and identify other data needed to

Open-File Report↗
USGS2006

Scoping of flood hazard mapping needs for Kennebec County, Maine

This report was prepared by the U.S. Geological Survey (USGS) Maine Water Science Center as the deliverable for scoping of flood hazard mapping needs for Kennebec County, Maine, under Federal Emergency Management Agency (FEMA) Inter-Agency Agreement Number HSFE01-05-X-0018. This section of the report explains the objective of the task and the purpose of the report. The Federal Emergency Management Agency (FEMA) developed a plan in 1997 to modernize the FEMA flood mapping program. FEMA flood maps delineate flood hazard areas in support of the National Flood Insurance Program (NFIP). FEMA's plan outlined the steps necessary to update FEMA's flood maps for the nation to a seamless digital format and streamline FEMA's operations in raising public awareness of the importance of the maps and responding to requests to revise them. The modernization of flood maps involves conversion of existing information to digital format and integration of improved flood hazard data as needed. To determine flood mapping modernization needs, FEMA has established specific scoping activities to be done on a county-by-county basis for identifying and prioritizing requisite flood-mapping activities for map modernization. The U.S. Geological Survey (USGS), in cooperation with FEMA and the Maine State Planning Office Floodplain Management Program, began scoping work in 2005 for Kennebec County. Scoping activities included assembling existing data and map needs information for communities in Kennebec County (efforts were made to not duplicate those of pre-scoping completed in March 2005), documentation of data, contacts, community meetings, and prioritized mapping needs in a final scoping report (this document), and updating the Mapping Needs Update Support System (MNUSS) Database or its successor with information gathered during the scoping process. The average age of the FEMA floodplain maps in Kennebec County, Maine is 16 years. Most of these studies were in the late 1970's to the mid 1980s. However, in the ensuing 20-30 years, development has occurred in many of the watersheds, and the characteristics of the watersheds have changed with time. Therefore, many of the older studies may not depict current conditions nor accurately estimate risk in terms of flood heights. The following is the scope of work as defined in the FEMA/USGS Statement of Work: Task 1: Collect data from a variety of sources including community surveys, other Federal and State Agencies, National Flood Insurance Program (NFIP) State Coordinators, Community Assistance Visits (CAVs) and FEMA archives. Lists of mapping needs will be obtained from the MNUSS database, community surveys, and CAVs, if available. FEMA archives will be inventoried for effective FIRM panels, FIS reports, and other flood-hazard data or existing study data. Best available base map information, topographic data, flood-hazard data, and hydrologic and hydraulic data will be identified. Data from the Maine Floodplain Management Program database also will be utilized. Task 2: Contact communities in Kennebec County to notify them that FEMA and the State have selected them for a map update, and that a project scope will be developed with their input. Topics to be reviewed with the communities include (1) Purpose of the Flood Map Project (for example, the update needs that have prompted the map update); (2) The community's mapping needs; (3) The community's available mapping, hydrologic, hydraulic, and flooding information; (4) target schedule for completing the project; and (5) The community's engineering, planning, and geographic information system (GIS) capabilities. On the basis of the collected information from Task 1 and community contacts/meetings in Task 2, the USGS will develop a Draft Project Scope for the identified mapping needs of the communities in Kennebec County. The following items will be addressed in the Draft Project Scope: review of available information, determine if and how e

Open-File Report↗
USGS2006

Scoping of flood hazard mapping needs for Somerset County, Maine

This report was prepared by the U.S. Geological Survey (USGS) Maine Water Science Center as the deliverable for scoping of flood hazard mapping needs for Somerset County, Maine, under Federal Emergency Management Agency (FEMA) Inter-Agency Agreement Number HSFE01-05-X-0018. This section of the report explains the objective of the task and the purpose of the report. The Federal Emergency Management Agency (FEMA) developed a plan in 1997 to modernize the FEMA flood mapping program. FEMA flood maps delineate flood hazard areas in support of the National Flood Insurance Program (NFIP). FEMA's plan outlined the steps necessary to update FEMA's flood maps for the nation to a seamless digital format and streamline FEMA's operations in raising public awareness of the importance of the maps and responding to requests to revise them. The modernization of flood maps involves conversion of existing information to digital format and integration of improved flood hazard data as needed. To determine flood mapping modernization needs, FEMA has established specific scoping activities to be done on a county-by-county basis for identifying and prioritizing requisite flood-mapping activities for map modernization. The U.S. Geological Survey (USGS), in cooperation with FEMA and the Maine State Planning Office Floodplain Management Program, began scoping work in 2005 for Somerset County. Scoping activities included assembling existing data and map needs information for communities in Somerset County (efforts were made to not duplicate those of pre-scoping completed in March 2005), documentation of data, contacts, community meetings, and prioritized mapping needs in a final scoping report (this document), and updating the Mapping Needs Update Support System (MNUSS) Database or its successor with information gathered during the scoping process. The average age of the FEMA floodplain maps in Somerset County, Maine is 18.1 years. Most of these studies were in the late 1970's to the mid 1980s. However, in the ensuing 20-30 years, development has occurred in many of the watersheds, and the characteristics of the watersheds have changed with time. Therefore, many of the older studies may not depict current conditions nor accurately estimate risk in terms of flood heights. The following is the scope of work as defined in the FEMA/USGS Statement of Work: Task 1: Collect data from a variety of sources including community surveys, other Federal and State Agencies, National Flood Insurance Program (NFIP) State Coordinators, Community Assistance Visits (CAVs) and FEMA archives. Lists of mapping needs will be obtained from the MNUSS database, community surveys, and CAVs, if available. FEMA archives will be inventoried for effective FIRM panels, FIS reports, and other flood-hazard data or existing study data. Best available base map information, topographic data, flood-hazard data, and hydrologic and hydraulic data will be identified. Data from the Maine Floodplain Management Program database also will be utilized. Task 2: Contact communities in Somerset County to notify them that FEMA and the State have selected them for a map update, and that a project scope will be developed with their input. Topics to be reviewed with the communities include (1) Purpose of the Flood Map Project (for example, the update needs that have prompted the map update); (2) The community's mapping needs; (3) The community's available mapping, hydrologic, hydraulic, and flooding information; (4) target schedule for completing the project; and (5) The community's engineering, planning, and geographic information system (GIS) capabilities. On the basis of the collected information from Task 1 and community contacts/meetings in Task 2, the USGS will develop a Draft Project Scope for the identified mapping needs of the communities in Somerset County. The following items will be addressed in the Draft Project Scope: review of available information, determine if and ho

Open-File Report↗
USGS2017

Climate change and tree-line ecosystems in the Sierra Nevada: Habitat suitability modelling to inform high-elevation forest dynamics monitoring

Whitebark pine and foxtail pine serve foundational roles in the subalpine zone of the Sierra Nevada. They provide the dominant structure in tree-line forests and regulate key ecosystem processes and community dynamics. Climate change models suggest that there will be changes in temperature regimes and in the timing and magnitude of precipitation within the current distribution of these species, and these changes may alter the species’ distributional limits. Other stressors include the non-native pathogen white pine blister rust and mountain pine beetle, which have played a role in the decline of whitebark pine throughout much of its range. The National Park Service is monitoring status and trends of these species. This report provides complementary information in the form of habitat suitability models to predict climate change impacts on the future distribution of these species within Sierra Nevada national parks. We used maximum entropy modeling to build habitat suitability models by relating species occurrence to environmental variables. Species occurrence was available from 328 locations for whitebark pine and 244 for foxtail pine across the species’ distributions within the parks. We constructed current climate surfaces for modeling by interpolating data from weather stations. Climate surfaces included mean, minimum, and maximum temperature and total precipitation for January, April, July, and October. We downscaled five general circulation models for the 2050s and the 2090s from ~125 km2 to 1 km2 under both an optimistic and an extreme climate scenario to bracket potential climatic change and its influence on projected suitable habitat. To describe anticipated changes in the distribution of suitable habitat, we compared, for each species, climate scenario, and time period, the current models with future models in terms of proportional change in habitat size, elevation distribution, model center points, and where habitat is predicted to expand or contract. Overall, models indicated that suitable habitats for whitebark and foxtail pine are more likely to shift geographically within the parks by 2100 rather than decline precipitously. This implies park managers might focus conservation efforts on stressors other than climate change, working toward species resilience in the face of threats from introduced disease and elevated native insect damage. More specifically, further understanding of the incidence and severity of white pine blister rust and other stressors in high elevation white pines would help assess vulnerability from threats other than climate change.

California↗
USGS2021

Flood-inundation maps for the Blue River near Red Bridge Road, Kansas City, Missouri, 2019

Digital flood-inundation maps for a 4.6-mile reach of the Blue River near Red Bridge Road in Kansas City, Missouri, were created by the U.S. Geological Survey (USGS), in cooperation with the City of Kansas City, 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 06893195, Blue River at Red Bridge Road, Kansas City, Mo. Near-real-time stages at this streamgage may be obtained from the USGS National Water Information System at https://doi.org/10.5066/F7P55KJN or the Johnson County, Kansas, StormWatch Automated Local Elevation in Real Time Flood Warning System at https://www.stormwatch.com . Flood profiles were computed for the Blue River reach by means of a one-dimensional model for simulating water-surface profiles with steady-state flow computations. The model was calibrated by using the current stage-streamflow relations at the upstream USGS streamgage 06893150, Blue River at Blue Ridge Boulevard Extension, Kansas City, Mo., and the downstream streamgage 06893500, Blue River at Kansas City, Mo. The hydraulic model was then used to compute 37 water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum and ranging from 11 ft, or near bankfull, to 47 ft at the reference streamgage 06893195. The upper stage for the map library exceeds the stage corresponding to the estimated 0.2-percent annual exceedance probability flood (500-year recurrence interval flood) in the model reach. The simulated water-surface profiles were then combined with a geographic information system digital elevation model with a maximum 10-centimeter vertical root mean square error and 4.0-ft horizontal resolution to delineate the area flooded at each water level. The availability of these maps, along with real-time internet information regarding current stage from the USGS streamgage, will help guide emergency management personnel and residents in flood mitigation, preparedness and planning, flood-response activities such as evacuations and road closures, and any postflood recovery efforts.

Kansas, Missouri↗
USGS2002

Sea otter studies in Glacier Bay National Park and Preserve

Following translocations to the outer coast of Southeast Alaska in 1965, sea otters have been expanding their range and increasing in abundance. We began conducting surveys for sea otters in Cross Sound, Icy Strait, and Glacier Bay, Alaska in 1994, following initial reports (in 1993) of their presence in Glacier Bay. Since 1995, the number of sea otters in Glacier Bay proper has increased from around 5 to more than 1500. Between 1993 and 1997 sea otters were apparently only occasional visitors to Glacier Bay, but in 1998 long-term residence was established as indicated by the presence of adult females and their dependent pups. Sea otter distribution is limited to the Lower Bay, south of Sandy Cove, and is not continuous within that area. Concentrations occur in the vicinity of Sita Reef and Boulder Island and between Pt. Carolus and Rush Pt. on the west side of the Bay (Figure 1). We describe the diet of sea otters during 2001 in Glacier Bay based on visual observations of prey during 456 successful forage dives. In Glacier Bay, diet consisted of 62% clam, 15% mussel, 9% crab, 7% unidentified, 4& urchins, and 4% other. Most prey recovered by sea otters are commercially, socially, or ecologically important species. Species of clam include Saxidomus gigantea, Protothaca staminea, and Mya truncata. Urchins are primarily Strongylocentrotus droebachiensis and the mussel is Modiolus modiolus. Crabs include species of three genera: Cancer, Chinoecetes, and Telmessus. Although we characterize diet at broad geographic scales, we found diet to vary between sites separated by as little as several hundred meters. Dietary variation among and within sites can reflect differences in prey availability and individual specialization. We estimated species composition, density, biomass, and sizes of subtidal clams, urchins, and mussels at 9 sites in lower Glacier Bay. All sites were selected based on the presence of abundant clam siphons. Sites were not selected to allow inference to any area larger than the sampling area (approx 400 m^2). Sites were selected to achieve a broad geographic sample of dense subtidal clam beds within Glacier Bay prior to occupation and foraging by sea otters. There was no direct evidence of otter foraging at any of our clam sampling sites. We sampled 11,568 bivalves representing 14 speces of clam and 2 species of mussel. We sampled 4,981 urchins, all Strongylocentrotus droeobachiensis. Only four species of clam (littleneck clams, Protothaca staminea; butter clams, Saxidomus gigantea; soft-shell claims, Mya truncata; and Macoma sp.) accounted for 91.6% of all clams sampled. Mean total clam density (#/0.25 m^2) across the 9 sites was 62.3. Densities (and se ) of P. staminea averaged 22.6 (1.6) and ranged from 0 to 97. Densities of S. gigantea averaged 14.4 (1.0) and ranged from 0 to 63. Densities of Macoma sp. averaged 14.5 (1.2) and ranged from 0 to 78. Densities of S. droebachiensis averaged 27.3 (1.7) and ranged from 0 to 109. Mean S. droebachiensis sizes ranged from 16 to 30 mm by site. Mean P. staminea sizes ranged from 30 to 53 mm, mean S. gigantea sizes ranged from 51 to 85 mm, and mean Macoma sp. sizes ranged from 14 to 19 mm. Although not the most abundant clam, S. gigantea contributed the greatest proportion to total clam biomass (63%), followed by P. staminea (24%). Sea otters are now well established in limited areas of the lower portions of Glacier Bay. It is likely that distribution and numbers of sea otters will continue to increase in Glacier Bay in the near future. Glacier Bay supports large and diverse populations of clams that are largely unexploited by sea otters presently. It is predictable that the density and sizes of clam populations will decline in response to otter predation. This will result in fewer opportunities for human harvest, but will also trigger ecosystem level changes, as prey for other predators, such as octopus, sea stars, fishes, birds and mammals are modified. Sea ott

Annual Report↗
USGS2012

Archive of single-beam bathymetry data collected during USGS cruise 07CCT01 nearshore of Fort Massachusetts and within Camille Cut, West and East Ship Islands, Gulf Islands National Seashore, Mississippi, July 2007

The Gulf Islands National Seashore (GUIS) is composed of a series of barrier islands along the Mississippi - Alabama coastline. Historically these islands have undergone long-term shoreline change. The devastation of Hurricane Katrina in 2005 prompted questions about the stability of the barrier islands and their potential response to future storm impacts. Additionally, there was concern from the National Park Service (NPS) about the preservation of the historical Fort Massachusetts, located on West Ship Island. During the early 1900s, Ship Island was an individual island. In 1969 Hurricane Camille breached Ship Island, widening the cut and splitting it into what is now known as West Ship Island and East Ship Island. In July of 2007, the U.S. Geological Survey (USGS) was able to provide the NPS with a small bathymetric survey of Camille Cut using high-resolution single-beam bathymetry. This provided GUIS with a post-Katrina assessment of the bathymetry in Camille Cut and along the northern shoreline directly in front of Fort Massachusetts. Ultimately, this survey became an initial bathymetry dataset toward a larger USGS effort included in the Northern Gulf of Mexico (NGOM) Ecosystem Change and Hazard Susceptibility Project ( http://ngom.usgs.gov/gomsc/mscip/ ). This report serves as an archive of the processed single-beam bathymetry. Data products herein include gridded and interpolated digital depth surfaces and x,y,z data products. Additional files include trackline maps, navigation files, geographic information system (GIS) files, Field Activity Collection System (FACS) logs, and formal Federal Geographic Data Committee (FGDC) metadata. Scanned images of the handwritten FACS logs and digital FACS logs are also provided as PDF files. Refer to the Acronyms page for description of acronyms and abbreviations used in this report or hold the cursor over an acronym for a pop-up explanation. The USGS St. Petersburg Coastal and Marine Science Center assigns a unique identifier to each cruise or field activity. For example, 07CCT01 tells us the data were collected in 2007 for the Coastal Change and Transport (CCT) study and the data were collected during the first (01) field activity for that project in that calendar year. Refer to http://walrus.wr.usgs.gov/infobank/programs/html/definition/activity.html for a detailed description of the method used to assign the field activity ID. Data were collected using a 26-foot (ft) Glacier Bay catamaran. The single-beam transducers were sled mounted on a rail attached between the catamaran hulls. Navigation was acquired using HYPACK, Inc., Hypack version 4.3a.7.1 and differentially corrected using land-based GPS stations. See the digital FACS equipment log for details about the acquisition equipment used. Raw datasets were stored digitally and processed systematically using NovAtel's Waypoint GrafNav version 7.6, SANDS version 3.7, and ESRI ArcGIS version 9.3.1. For more information on processing refer to the Equipment and Processing page.

Mississippi↗