This web map is designed to provide an enriched geospatial platform to ascertain the flood potential status of our local place of residence and other land-use activities. Information on the flood risk distribution can be extracted by 5 major magnitudes (very high, high, moderate, low, and very low). The buildings, roads, and rail tracks that are susceptible to flooding based on the identified magnitudes are also included in the web map. In addition, the historical or flood inventory layer, which contains information on the previous flooding disasters that have occurred within the river basin, is included.

This web map is the result of extensive research using available data, open source and custom datasets that are extremely reliable.The collaborative study was done by Dr. Felix Ndidi Nkeki (GIS-Unit, BEDC Electricity PLC, 5, Akpakpava Road, Benin City, Nigeria and Department of Geography and Regional Planning, University of Benin, Nigeria), Dr. Ehiaguina Innocent Bello (National Space Research and Development Agency, Obasanjo Space Centre, FCT-Abuja, Nigeria) and Dr. Ishola Ganiy Agbaje (Centre for Space Science Technology Education, Obafemi Awolowo University, Ile-Ife, Nigeria). The study results are published in a reputable leading world-class journal known as the International Journal of Disaster Risk Reduction. The methodology, datasets, and full results of the study can be found in the paper.

The major sources of data are: ALOS PALSAR DEM; soil data from Harmonised World Soil Database-Food and Agriculture Organisation of the United Nations (FAO); land-use and surface geologic datasets from CSSTE, OAU Campus, Ile-Ife, Nigeria and Ibadan Urban Flood Management Project (IUFMP), Oyo State, Nigeria; transport network data was extracted from Open Street Map; building footprint data was mined from Google open building; and finally, rainfall grid data was downloaded from the Centre for Hydrometeorology and Remote Sensing (CHRS).

Robust risk assessment requires accurate flood intensity area mapping to allow for the identification of populations and elements at risk. However, available flood maps in West Africa lack spatial variability while global datasets have resolutions too coarse to be relevant for local scale risk assessment. Consequently, local disaster managers are forced to use traditional methods such as watermarks on buildings and media reports to identify flood hazard areas. In this study, remote sensing and Geographic Information System (GIS) techniques were combined with hydrological and statistical models to delineate the spatial limits of flood hazard zones in selected communities in Ghana, Burkina Faso and Benin. The approach involves estimating peak runoff concentrations at different elevations and then applying statistical methods to develop a Flood Hazard Index (FHI). Results show that about half of the study areas fall into high intensity flood zones. Empirical validation using statistical confusion matrix and the principles of Participatory GIS show that flood hazard areas could be mapped at an accuracy ranging from 77% to 81%. This was supported with local expert knowledge which accurately classified 79% of communities deemed to be highly susceptible to flood hazard. The results will assist disaster managers to reduce the risk to flood disasters at the community level where risk outcomes are first materialized.

Here we present the first-available global dataset that quantifies human alterations in 15 million sq km floodplains along the world’s 520 major river basins. We developed these data using a comprehensive 27-year (1992-2019) analysis of remotely sensed land use change at 250-m resolution. This new dataset reveals that the world has lost ~600,000 sq km floodplains in 27 years (1992-2019), moving from natural forest, grassland, and wetland conditions to 460,000 sq km of new agricultural and 140,000 sq km of new developed areas.

To ensure the maximum reuse of this dataset, we also developed three web-based semi-automatic programming tools partly supported with data-driven tutorials and step-by-step audiovisual instructions.

(1) Floodplain Mapping Tool - Web-based Python code that runs in any internet browser using Google's high performance computing resource: https://colab.research.google.com/drive/1xQlARZXKPexmDInYV-EMoJ-HZxmFL-eW?usp=sharing - A tutorial developed and published through an online data-driven geoscience education platform: https://serc.carleton.edu/hydromodules/steps/246320.html - A YouTube video with step-by-step instructions: https://youtu.be/TgMbkJdALig

(2) Land Use Change Tool - Web-based Python code that runs in any internet browser using Google's high performance computing resource: https://colab.research.google.com/drive/1vmIaUCkL66CoTv4rNRIWpJXYXp4TlAKd?usp=sharing - A tutorial developed and published through an online data-driven geoscience education platform: https://serc.carleton.edu/hydromodules/steps/241489.html - A YouTube video with step-by-step instructions: https://youtu.be/wH0gif_y15A

(3) Human Alteration Tool - Web-based Python code that runs in any internet browser using Google's high performance computing resource: https://colab.research.google.com/drive/1r2zNJNpd3aWSuDV2Kc792qSEjvDbFtBy?usp=sharing

Note, the floodplain dataset used in this analysis (GFPLAIN250m; Nardi et al., 2019) does not cover deserts and ice-covered regions. Hence, places like northern Africa, Persian Gulf, Tibetan plateau, and the region above 60 degrees north latitude are not included in this analysis.

This global floodplain alteration dataset is built off our recent work published in the Nature Scientific Data: Rajib et al. (2021). The changing face of floodplains in the Mississippi River Basin detected by a 60-year land use change dataset. https://doi.org/10.1038/s41597-021-01048-w

Two detailed geomorphological maps (1:2000) depicting landscape changes as a result of a glacial lake outburst flood were produced for the 2.1-km-long section of the Zackenberg river, NE Greenland. The maps document the riverscape before the flood (5 August 2017) and immediately after the flood (8 August 2017), illustrating changes to the riverbanks and morphology of the channel. A series of additional maps (1:800) represent case studies of different types of riverbank responses, emphasising the importance of the lateral thermo-erosion and bank collapsing as significant immediate effects of the flood. The average channel width increased from 40.75 m pre-flood to 44.59 m post-flood, whereas the length of active riverbanks decreased from 1729 to 1657 m. The new deposits related to 2017 flood covered 93,702 m2. The developed maps demonstrated the applicability of small Unmanned Aerial Vehicles (UAVs) for investigating the direct effects of floods, even in the harsh Arctic environment.

This dataset contains an explanation of data analysis for creating a flood vulnerability map of Samarinda Seberang District. The dataset contains sub-criteria for each flood parameter and its score value. In addition, this dataset contains the weight value of each parameter, flood vulnerability level and its coloring, and the results of calculating the area of each vulnerability level.

The data provide an estimate of flood extent following Hurricane Matthew (2016) across the Piedmont and Coastal Plain of North Carolina. Flooded and non-flooded regions were delineated using a random forest classification model leveraging pre- and post-storm synthetic aperture radar from the European Space Agency's Sentinel-1 sensor, in addition to topography, floodplain, and landcover data. The classification model was trained with USGS and NCDEMS high-water marks, in addition to flooded and non-flooded regions delineated from high-resolution NOAA aerial photography; the model achieved 92% accuracy against an independent withheld sample. This effort was aimed at identifying inland flooding and not storm surge. This dataset is not intended to replace North Carolina's Floodplain Mapping Program hazard projections. For additional details regarding the methods, please see the peer-reviewed publication and data and code archives referenced in the Credits below.Note: Do not download this raster using the map at the top of the page. Instead, click Download then "Download Matthew flood extent raster." The link will take you to a page where the raster can be downloaded.

Groundwater is the water that soaks into the ground from rain and can be stored beneath the ground. Groundwater floods occur when the water stored beneath the ground rises above the land surface. The Groundwater Flooding High Probability mapshows the expected flood extent of groundwater flooding in limestone regions for annual exceedance probabilities (AEP’s) of 10%, which correspond with a return period of every 10 years. The map was created using groundwater levels measured in the field, satellite images and hydrological models The map is a vector dataset. The floods are shown as polygons. Each polygon has info on the data source, and the area of the flood.The flood extents were calculated using remote sensing data and hydrological modelling techniques with various precision levels. As such, it should be used with caution.The Groundwater Flooding Medium Probability mapshows the expected flood extent of groundwater flooding in limestone regions for annual exceedance probabilities (AEP’s) of 1%, which correspond with a return period of every 100 years. The map was created using groundwater levels measured in the field, satellite images and hydrological models The map is a vector dataset. The floods are shown as polygons. Each polygon has info on the data source, and the area of the flood.The flood extents were calculated using remote sensing data and hydrological modelling techniques with various precision levels. As such, it should be used with caution.The Groundwater Flooding Low Probability mapshows the expected flood extent of groundwater flooding in limestone regions for annual exceedance probabilities (AEP’s) of 0.1%, which correspond with a return period of every 1000 years.The map was created using groundwater levels measured in the field, satellite images and hydrological models .The map is a vector dataset. Vector data portray the world using points, lines, and polygons (area). The floods are shown as polygons. Each polygon has info on the data source, and the area of the flood.The flood extents were calculated using remote sensing data and hydrological modelling techniques with various precision levels. As such, it should be used with caution.

Delineation of flooded areas obtained from SAR images (COSMO-Sky Med / Sentinel-1) for selected case studies occurred in Massa, Vilanova i la Geltrù and Oasoladea. These maps were use in SCORE WP 8 to validate the effectiveness of a Digital Twin of the three cities developped in SCORE.

Deliverable 8.11 titled 'Early warning and spatial Digital Twin Assessment Report'

Natural Disaster Detection IoT Market size was valued at USD 1.2 Billion in 2023 and is projected to reach USD 4.06 Billion by 2030, growing at a CAGR of 37.2 % during the forecast period 2024-2030.Global Natural Disaster Detection IoT Market DriversImproved Early Warning Systems: The Internet of Things (IoT) makes it possible to implement sophisticated early warning systems for natural disasters such hurricanes, floods, tsunamis, earthquakes, and wildfires. Sensors placed in disaster-prone locations are able to identify environmental anomalies and precursor signals, sending real-time data to central monitoring systems. This makes it easier to notify authorities and locals in a timely manner, lessening the effects of calamities and maybe saving lives.Enhanced Surveillance and Forecasting: Internet of Things-capable sensors and surveillance apparatuses furnish constant data gathering and examination capacities, imparting discernment into environmental factors like temperature, humidity, pressure, seismic activity, and meteorological trends. This data is processed using sophisticated analytics and machine learning algorithms to find patterns, trends, and early warning signs of impending disasters. This allows for more accurate forecasting and preparedness planning.Remote sensing and surveillance of disaster-prone locations are made possible by Internet of Things (IoT) devices outfitted with cameras, drones, and satellite imaging technology. Emergency responders and decision-makers can benefit greatly from the situational awareness that these sensors can provide by monitoring changes in the topography, vegetation, water levels, and integrity of infrastructure. Efforts to assess damage, prepare for emergencies, and conduct catastrophe assessments are improved by real-time imagery and video feeds.Integration with Geographic Information Systems (GIS): Spatial analysis, mapping, and visualization of disaster-related data are made easier by the integration of IoT data with GIS platforms. Decision-making processes are improved by geographic data overlays, risk maps, and geospatial modeling tools, which help authorities identify high-risk areas, allocate resources wisely, and schedule evacuation routes and shelter places.Developments in Sensor Technology: The spread of IoT devices for natural disaster detection is driven by ongoing developments in sensor technology, such as downsizing, enhanced sensitivity, and low power consumption. Highly weatherproof and resilient sensors can survive extreme weather conditions, which makes them appropriate for use in dangerous and remote areas that are vulnerable to natural disasters.Government Initiatives and Regulations: Across the globe, governments and regulatory agencies are investing more money and requiring the use of Internet of Things (IoT)-based technologies for resilience and disaster management. Adoption of IoT technologies to improve catastrophe warning, response, and recovery capacities is encouraged by national disaster preparedness programs, financing initiatives, and regulatory frameworks.Collaborations between the Public and Private Sectors: In the development of Internet of Things (IoT)-based solutions for natural disaster detection, cooperation between public agencies, private businesses, academic institutions, and non-governmental organizations (NGOs) promotes innovation and knowledge exchange. In order to improve community safety and catastrophe resilience, technological development, pilot projects, and field testing are driven by public-private partnerships (PPPs) and collaborative research activities.Growing Concern and Awareness of Climate Change: The need for Internet of Things (IoT) solutions for disaster detection and mitigation has increased as a result of growing global awareness of climate change and its effects on the frequency and intensity of natural catastrophes. The necessity for preventive actions to mitigate climate-related hazards is acknowledged by stakeholders from all industries, which motivates investments in IoT infrastructure, research, and innovation.

Flood-Resilient Road Elevation Analytics Market Outlook

According to our latest research, the global flood-resilient road elevation analytics market size reached USD 1.47 billion in 2024, demonstrating robust momentum driven by heightened climate adaptation investments and rapid urbanization. The market is set to expand at a CAGR of 10.8% through the forecast period, with the market size expected to reach USD 3.74 billion by 2033. This significant growth trajectory is primarily propelled by increasing government mandates for resilient infrastructure, the rising frequency of extreme weather events, and advancements in geospatial analytics technologies.

The primary growth driver for the flood-resilient road elevation analytics market is the escalating impact of climate change, which has resulted in more frequent and severe flooding incidents globally. Governments and municipal authorities are under immense pressure to safeguard critical infrastructure, particularly road networks, from flood-induced damages. As roads constitute the backbone of economic activities and emergency response, the need for robust, data-driven elevation analytics has never been more urgent. These analytics solutions leverage high-resolution topographical data, hydrological models, and real-time weather feeds to assess flood risks and recommend optimal elevation strategies for both new and existing roadways. The integration of artificial intelligence and machine learning in these analytics platforms further enhances predictive capabilities, enabling proactive and cost-effective flood mitigation measures.

Another key factor fueling the market is the substantial increase in public and private sector investments in infrastructure modernization. Many nations, especially those with aging transportation networks, are prioritizing flood resilience as a core objective in their infrastructure renewal programs. The adoption of flood-resilient road elevation analytics is being incentivized by regulatory frameworks, international funding agencies, and insurance requirements. Additionally, the proliferation of smart city initiatives is fostering the uptake of advanced analytics tools that enable urban planners and civil engineers to design road networks with built-in flood resistance. This trend is further supported by the availability of cloud-based platforms, which facilitate real-time collaboration among stakeholders and reduce the need for costly on-premises infrastructure.

Technological innovation also plays a crucial role in the expansion of the flood-resilient road elevation analytics market. The convergence of remote sensing, Geographic Information Systems (GIS), and big data analytics has revolutionized the way road elevation planning is conducted. High-resolution satellite imagery, LiDAR (Light Detection and Ranging), and drone-based surveys provide unprecedented accuracy in mapping flood-prone areas and simulating various flood scenarios. These technological advancements not only improve the precision of risk assessments but also reduce project timelines and costs. Furthermore, the growing ecosystem of third-party data providers and analytics service firms is making it easier for end-users to access customized solutions tailored to specific geographic and regulatory contexts.

Regionally, Asia Pacific stands out as the fastest-growing market for flood-resilient road elevation analytics, owing to its high population density, rapid urbanization, and vulnerability to monsoon-related flooding. Countries such as China, India, and Indonesia are investing heavily in resilient infrastructure to mitigate the socio-economic impacts of recurring floods. North America and Europe, with their mature infrastructure and stringent regulatory standards, continue to lead in technology adoption and innovation. Meanwhile, Latin America and the Middle East & Africa are gradually increasing their market share, driven by international funding and a growing awareness of climate adaptation needs. The regional dynamics underscore the global imperative to build resilient transportation networks capable of withstanding future climate uncertainties.

Component Analysis

The component segment of the flood-resilient road elevation analytics market is divided into software, hardware, and services, each playing a critical role in delivering comprehensive flood risk management solutions. Software solutions encompass advanced analytics platforms that integrate GIS, hydro

This raster represents a continuous surface of sage-grouse habitat suitability index (HSI, created using ArcGIS 10.2.2) values for Nevada during spring, which is a surrogate for habitat conditions during the sage-grouse breeding and nesting period. Summary of steps to create Habitat Categories: HABITAT SUITABILITY INDEX: The HSI was derived from a generalized linear mixed model (specified by binomial distribution) that contrasted data from multiple environmental factors at used sites (telemetry locations) and available sites (random locations). Predictor variables for the model represented vegetation communities at multiple spatial scales, water resources, habitat configuration, urbanization, roads, elevation, ruggedness, and slope. Vegetation data was derived from various mapping products, which included NV SynthMap (Petersen 2008, SageStitch (Comer et al. 2002, LANDFIRE (Landfire 2010), and the CA Fire and Resource Assessment Program (CFRAP 2006). The analysis was updated to include high resolution percent cover within 30 x 30 m pixels for Sagebrush, non-sagebrush, herbaceous vegetation, and bare ground (C. Homer, unpublished; based on the methods of Homer et al. 2014, Xian et al. 2015 ) and conifer (primarily pinyon-juniper, P. Coates, unpublished). The pool of telemetry data included the same data from 1998 - 2013 used by Coates et al. (2014); additional telemetry location data from field sites in 2014 were added to the dataset. The dataset was then split according calendar date into three seasons (spring, summer, winter). Summer included telemetry locations (n = 14,058) from mid-March to June. All age and sex classes of marked grouse were used in the analysis. Sufficient data (i.e., a minimum of 100 locations from at least 20 marked Sage-grouse) for modeling existed in 10 subregions for spring and summer, and seven subregions in winter, using all age and sex classes of marked grouse. It is important to note that although this map is composed of HSI values derived from the seasonal data, it does not explicitly represent habitat suitability for reproductive females (i.e., nesting and with broods). Insufficient data were available to allow for estimation of this habitat type for all seasons throughout the study area extent. A Resource Selection Function (RSF) was calculated using R Software (v 3.13) for each subregion and using generalized linear models to derive model-averaged parameter estimates for each covariate across a set of additive models. Subregional RSFs were transformed into Habitat Suitability Indices, and averaged together to produce an overall statewide HSI whereby a relative probability of occurrence was calculated for each raster cell during the spring. In order to account for discrepancies in HSI values caused by varying ecoregions within Nevada, the HSI was divided into north and south extents using a slightly modified flood region boundary (Mason 1999) that was designed to represent respective mesic and xeric regions of the state. North and south HSI rasters were each relativized according to their maximum value to rescale between zero and one, then mosaicked once more into a state-wide extent. REFERENCES: California Forest and Resource Assessment Program (CFRAP). 2006. Statewide Land Use / Land Cover Mosaic. [Geospatial data.] California Department of Forestry and Fire Protection, http://frap.cdf.ca.gov/data/frapgisdata-sw-rangeland-assessment_data.php Census 2010. TIGER/Line Shapefiles. Urban Areas [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2014. TIGER/Line Shapefiles. Roads [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2015. TIGER/Line Shapefiles. Blocks [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online) Comer, P., Kagen, J., Heiner, M., and Tobalske, C. 2002. Current distribution of sagebrush and associated vegetation in the western United States (excluding NM). [Geospatial data.] Interagency Sagebrush Working Group, http://sagemap.wr.usgs.gov Homer, C.G., Aldridge, C.L., Meyer, D.K., and Schell, S.J. 2014. Multi-Scale Remote Sensing Sagebrush Characterization with Regression Trees over Wyoming, USA; Laying a Foundation for Monitoring. International Journal of Applied Earth Observation and Geoinformation 14, Elsevier, US. LANDFIRE. 2010. 1.2.0 Existing Vegetation Type Layer. [Geospatial data.] U.S. Department of the Interior, Geological Survey, http://landfire.cr.usgs.gov/viewer/ Mason, R.R. 1999. The National Flood-Frequency Program—Methods For Estimating Flood Magnitude And Frequency In Rural Areas In Nevada U.S. Geological Survey Fact Sheet 123-98 September, 1999, Prepared by Robert R. Mason, Jr. and Kernell G. Ries III, of the U.S. Geological Survey; and Jeffrey N. King and Wilbert O. Thomas, Jr., of Michael Baker, Jr., Inc. http://pubs.usgs.gov/fs/fs-123-98/ Peterson, E. B. 2008. A Synthesis of Vegetation Maps for Nevada (Initiating a 'Living' Vegetation Map). Documentation and geospatial data, Nevada Natural Heritage Program, Carson City, Nevada, http://www.heritage.nv.gov/gis Xian, G., Homer, C., Rigge, M., Shi, H., and Meyer, D. 2015. Characterization of shrubland ecosystem components as continuous fields in the northwest United States. Remote Sensing of Environment 168:286-300. NOTE: This file does not include habitat areas for the Bi-State management area and the spatial extent is modified in comparison to Coates et al. 2014

• Calibration (Historical Flood Marks and Historical Rainfall Data);
• Miscellaneous (Collected data, peak discharge locations, potential flood mitigation options, questionnaire responses, rainfall and stream gauges, remote sensing land use map, etc)

This raster represents a continuous surface of sage-grouse habitat suitability index (HSI) values for Nevada during summer, which is a surrogate for habitat conditions during the sage-grouse brood-rearing period. Summary of steps to create Habitat Categories: HABITAT SUITABILITY INDEX: The HSI was derived from a generalized linear mixed model (specified by binomial distribution and created using ArcGIS 10.2.2) that contrasted data from multiple environmental factors at used sites (telemetry locations) and available sites (random locations). Predictor variables for the model represented vegetation communities at multiple spatial scales, water resources, habitat configuration, urbanization, roads, elevation, ruggedness, and slope. Vegetation data was derived from various mapping products, which included NV SynthMap (Petersen 2008, SageStitch (Comer et al. 2002, LANDFIRE (Landfire 2010), and the CA Fire and Resource Assessment Program (CFRAP 2006). The analysis was updated to include high resolution percent cover within 30 x 30 m pixels for Sagebrush, non-sagebrush, herbaceous vegetation, and bare ground (C. Homer, unpublished; based on the methods of Homer et al. 2014, Xian et al. 2015 ) and conifer (primarily pinyon-juniper, P. Coates, unpublished). The pool of telemetry data included the same data from 1998 - 2013 used by Coates et al. (2014); additional telemetry location data from field sites in 2014 were added to the dataset. The dataset was then split according calendar date into three seasons (spring, summer, winter). Summer included locations (n = 11,743) from July to mid-October. All age and sex classes of marked grouse were used in the analysis. Sufficient data (i.e., a minimum of 100 locations from at least 20 marked Sage-grouse) for modeling existed in 10 subregions for spring and summer, and seven subregions in winter, using all age and sex classes of marked grouse. It is important to note that although this map is composed of HSI values derived from the seasonal data, it does not explicitly represent habitat suitability for reproductive females (i.e., nesting and with broods). Insufficient data were available to allow for estimation of this habitat type for all seasons throughout the study area extent. A Resource Selection Function (RSF) was calculated was calculated for each subregion using R software (v 3.13) for each subregion using generalized linear models to derive model-averaged parameter estimates for each covariate across a set of additive models. Subregional RSFs were transformed into Habitat Suitability Indices, and averaged together to produce an overall statewide HSI whereby a relative probability of occurrence was calculated for each raster cell during the breeding season. In order to account for discrepancies in HSI values caused by varying ecoregions within Nevada, the HSI was divided into north and south extents using a slightly modified flood region boundary (Mason 1999) that was designed to represent respective mesic and xeric regions of the state. North and south HSI rasters were each relativized according to their maximum value to rescale between zero and one, then mosaicked once more into a state-wide extent. REFERENCES: California Forest and Resource Assessment Program (CFRAP). 2006. Statewide Land Use / Land Cover Mosaic. [Geospatial data.] California Department of Forestry and Fire Protection, http://frap.cdf.ca.gov/data/frapgisdata-sw-rangeland-assessment_data.php Census 2010. TIGER/Line Shapefiles. Urban Areas [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2014. TIGER/Line Shapefiles. Roads [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2015. TIGER/Line Shapefiles. Blocks [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online) Comer, P., Kagen, J., Heiner, M., and Tobalske, C. 2002. Current distribution of sagebrush and associated vegetation in the western United States (excluding NM). [Geospatial data.] Interagency Sagebrush Working Group, http://sagemap.wr.usgs.gov Homer, C.G., Aldridge, C.L., Meyer, D.K., and Schell, S.J. 2014. Multi-Scale Remote Sensing Sagebrush Characterization with Regression Trees over Wyoming, USA; Laying a Foundation for Monitoring. International Journal of Applied Earth Observation and Geoinformation 14, Elsevier, US. LANDFIRE. 2010. 1.2.0 Existing Vegetation Type Layer. [Geospatial data.] U.S. Department of the Interior, Geological Survey, http://landfire.cr.usgs.gov/viewer/ Mason, R.R. 1999. The National Flood-Frequency Program—Methods For Estimating Flood Magnitude And Frequency In Rural Areas In Nevada U.S. Geological Survey Fact Sheet 123-98 September, 1999, Prepared by Robert R. Mason, Jr. and Kernell G. Ries III, of the U.S. Geological Survey; and Jeffrey N. King and Wilbert O. Thomas, Jr., of Michael Baker, Jr., Inc. http://pubs.usgs.gov/fs/fs-123-98/ Peterson, E. B. 2008. A Synthesis of Vegetation Maps for Nevada (Initiating a 'Living' Vegetation Map). Documentation and geospatial data, Nevada Natural Heritage Program, Carson City, Nevada, http://www.heritage.nv.gov/gis Xian, G., Homer, C., Rigge, M., Shi, H., and Meyer, D. 2015. Characterization of shrubland ecosystem components as continuous fields in the northwest United States. Remote Sensing of Environment 168:286-300. NOTE: This file does not include habitat areas for the Bi-State management area and the spatial extent is modified in comparison to Coates et al. 2014

Peach Tree and Lower Surveyors Creek Flood Study All Other Required Data Peach Tree and Lower Surveyors Creek Flood Study - GIS Calibration (Historical Flood Marks and Historical Rainfall Data); Miscellaneous (Collected data, peak discharge locations, potential flood mitigation options, questionnaire responses, rainfall and stream gauges, remote sensing land use map, etc)

This raster represents a continuous surface of sage-grouse habitat suitability index (HSI) values for Nevada during the winter season, and is a surrogate for habitat conditions during periods of cold and snow. Summary of steps to create Habitat Categories: HABITAT SUITABILITY INDEX: The HSI was derived from a generalized linear mixed model (specified by binomial distribution and created using ArcGIS 10.2.2) that contrasted data from multiple environmental factors at used sites (telemetry locations) and available sites (random locations). Predictor variables for the model represented vegetation communities at multiple spatial scales, water resources, habitat configuration, urbanization, roads, elevation, ruggedness, and slope. Vegetation data was derived from various mapping products, which included NV SynthMap (Petersen 2008, SageStitch (Comer et al. 2002, LANDFIRE (Landfire 2010), and the CA Fire and Resource Assessment Program (CFRAP 2006). The analysis was updated to include high resolution percent cover within 30 x 30 m pixels for Sagebrush, non-sagebrush, herbaceous vegetation, and bare ground (C. Homer, unpublished; based on the methods of Homer et al. 2014, Xian et al. 2015 ) and conifer (primarily pinyon-juniper, P. Coates, unpublished). The pool of telemetry data included the same data from 1998 - 2013 used by Coates et al. (2014); additional telemetry location data from field sites in 2014 were added to the dataset. The dataset was then split according calendar date into three seasons (spring, summer, winter). Winter included telemetry locations (n = 4862) from November to March. All age and sex classes of marked grouse were used in the analysis. Sufficient data (i.e., a minimum of 100 locations from at least 20 marked Sage-grouse) for modeling existed in 10 subregions for spring and summer, and seven subregions in winter, using all age and sex classes of marked grouse. It is important to note that although this map is composed of HSI values derived from the seasonal data, it does not explicitly represent habitat suitability for reproductive females (i.e., nesting and with broods). Insufficient data were available to allow for estimation of this habitat type for all seasons throughout the study area extent. A Resource Selection Function (RSF) was calculated for each subregion using R software (v 3.13) using generalized linear models to derive model-averaged parameter estimates for each covariate across a set of additive models. Subregional RSFs were transformed into Habitat Suitability Indices, and averaged together to produce an overall statewide HSI whereby a relative probability of occurrence was calculated for each raster cell during the winter season. In order to account for discrepancies in HSI values caused by varying ecoregions within Nevada, the HSI was divided into north and south extents using a slightly modified flood region boundary (Mason 1999) that was designed to represent respective mesic and xeric regions of the state. North and south HSI rasters were each relativized according to their maximum value to rescale between zero and one, then mosaicked once more into a state-wide extent. REFERENCES: California Forest and Resource Assessment Program (CFRAP). 2006. Statewide Land Use / Land Cover Mosaic. [Geospatial data.] California Department of Forestry and Fire Protection, http://frap.cdf.ca.gov/data/frapgisdata-sw-rangeland-assessment_data.php Census 2010. TIGER/Line Shapefiles. Urban Areas [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2014. TIGER/Line Shapefiles. Roads [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2015. TIGER/Line Shapefiles. Blocks [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online) Comer, P., Kagen, J., Heiner, M., and Tobalske, C. 2002. Current distribution of sagebrush and associated vegetation in the western United States (excluding NM). [Geospatial data.] Interagency Sagebrush Working Group, http://sagemap.wr.usgs.gov Homer, C.G., Aldridge, C.L., Meyer, D.K., and Schell, S.J. 2014. Multi-Scale Remote Sensing Sagebrush Characterization with Regression Trees over Wyoming, USA; Laying a Foundation for Monitoring. International Journal of Applied Earth Observation and Geoinformation 14, Elsevier, US. LANDFIRE. 2010. 1.2.0 Existing Vegetation Type Layer. [Geospatial data.] U.S. Department of the Interior, Geological Survey, http://landfire.cr.usgs.gov/viewer/ Mason, R.R. 1999. The National Flood-Frequency Program—Methods For Estimating Flood Magnitude And Frequency In Rural Areas In Nevada U.S. Geological Survey Fact Sheet 123-98 September, 1999, Prepared by Robert R. Mason, Jr. and Kernell G. Ries III, of the U.S. Geological Survey; and Jeffrey N. King and Wilbert O. Thomas, Jr., of Michael Baker, Jr., Inc. http://pubs.usgs.gov/fs/fs-123-98/ Peterson, E. B. 2008. A Synthesis of Vegetation Maps for Nevada (Initiating a 'Living' Vegetation Map). Documentation and geospatial data, Nevada Natural Heritage Program, Carson City, Nevada, http://www.heritage.nv.gov/gis Xian, G., Homer, C., Rigge, M., Shi, H., and Meyer, D. 2015. Characterization of shrubland ecosystem components as continuous fields in the northwest United States. Remote Sensing of Environment 168:286-300. NOTE: This file does not include habitat areas for the Bi-State management area and the spatial extent is modified in comparison to Coates et al. 2014

We constructed a time-series spatial dataset of parcel boundaries for the period 1962-2005, in roughly 4-year intervals, by digitizing historical plat maps for Dane County and combining them with the 2005 GIS digital parcel dataset. The resulting datasets enable the consistent tracking of subdivision and development for all parcels over a given time frame. The process involved 1) dissolving and merging the 2005 digital Dane County parcel dataset based on contiguity and name, 2) further merging 2005 parcels based on the hard copy 2005 Plat book, and then 3) the reverse chronological merging of parcels to reconstruct previous years, at 4-year intervals, based on historical plat books. Additional land use information such as 1) whether a structure was actually constructed (using the companion digitized aerial photo dataset), 2) cover crop, and 3) permeable surface area, can be added to these datasets at a later date.

Authority: Executive Order No. 149: FEMA and Flood Plain Use. Jurisdiction: Floodplains in the Commonwealth of Massachusetts. Applicability: Construction by state agencies must avoid floodplains. State-administered grant and loan programs must avoid supporting construction in flood plains, to the extent possible. Regulatory Designates the Massachusetts Department of Conservation and Recreation (DCR, formerly the Department of Environmental Management) as the state coordinating agency to implement the National Flood Insurance Program. Requires all state agencies, to the extent possible, to avoid construction, provision of loans or grants, conveying, or permitting projects in floodplains. Provides for Massachusetts participation in the National Flood Insurance Program. Review Process: Contact the Federal Emergency Management Agency (FEMA) to determine if a proposed project is in a floodplain. Projects proposed in floodplains are reviewed in conjunction with Massachusetts Environmental Policy Act (15), Massachusetts Wetlands Protection Act (17), and Massachusetts Office of Coastal Zone Management (22) reviews. Technical assistance is also available from the DCR Flood Hazard Management Program. Forms: No additional forms for floodplain review. Fees No additional fees for floodplain review. Website: FEMA at http://store.msc.fema.gov/webapp/wcs/stores/servlet/FemaWelcomeView?storeId=10001&catalogId=10001&langId=-1 DCR Flood Hazard Management Program at http://www.mass.gov/dcr/stewardship/mitigate/.

This data set contains chlorophyll concentration maps of the Amazon River floodplain region from Parintins (Amazonas) to Almeirim (Para). These chlorophyll fraction maps were derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product (MOD09) for 19 months from April 2002 to December 2003.

The study was conducted in a floodplain reach upstream from Santarem, Para, in order to assess seasonal changes in phytoplanktonic chlorophyll-a distributions in the floodplain Lake Curuai. MODIS reflectance data were acquired at four river stages: rising (April), high (June), decreasing (September), and low (November). Chlorophyll maps were derived and used to compute the weighted average of chlorophyll concentration from MODIS images in the region. Field measurements of suspended inorganic matter and chlorophyll-a in Lake Curuai were made almost concurrently with satellite overpasses (Barbosa, 2005). The images and the estimated chlorophyll concentrations were compared to measured chlorophyll concentrations at control points for different hydrological states. This data set may be applied to better understand the seasonal dynamics of primary production of the Amazon floodplains. The maps of chlorophyll-a concentration may be used to model spatial and temporal variations of primary production in this region.

The monthly chlorophyll-a maps are provided as GeoTIFF files. There are two formats: (1) color-mapped pixels and (2) pixels as chlorophyll-a concentrations. These latter images are not intended for browsing. These images have pixel values that are the chlorophyll-a concentration in mg/m3 and need to be download and opened in GIS software.

• Calibration (Historical Flood Marks and Historical Rainfall Data);\r
• Miscellaneous (Collected data, peak discharge locations, potential flood mitigation options, questionnaire responses, rainfall and stream gauges, remote sensing land use map, etc)