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This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs....
This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs....
This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs....
This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs....
This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs....
This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs....
This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs....
This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs....
This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs....
This data set contains imagery from the National Agriculture Imagery Program (NAIP). The NAIP program is administered by USDA FSA and has been established to support two main FSA strategic goals centered on agricultural production. These are, increase stewardship of America's natural resources while enhancing the environment, and to ensure commodities are procured and distributed effectively and efficiently to increase food security. The NAIP program supports these goals by acquiring and providing ortho imagery that has been collected during the agricultural growing season in the U.S. The NAIP ortho imagery is tailored to meet FSA requirements and is a fundamental tool used to support FSA farm and conservation programs....
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This dataset contains monthly crop irrigation requirement (CIR) values from March 1940 through 2014 for the 20 virtual land-use units, including the seven canal service units, in the Rio Grande Transboundary Integrated Hydrologic Model (RGTIHM). CIR values are presented in units of feet per day.
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This point vector dataset represents 10 climate stations used for analysis of annual and seasonal precipitation, analysis of monthly measured reference evapotranspiration, and comparison of simulated potential evapotranspiration with measured reference evapotranspiration within the Rio Grande transboundary integrated hydrologic model and water-availability analysis, New Mexico and Texas, United States, and Northern Chihuahua, Mexico.
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Burn probability (BP) raster dataset predicted for the 2080-2100 period in the Rio Grande area was generated using: 1) data developed from the 2014 Fire Program Analysis (FPA) system; 2) geospatial Fire Simulation (FSim) system developed by the US Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk (Finney et al. 2011); and 3) climate predictions developed using the Multivariate Adaptive Constructed Analogs (MACA) method (Abatzoglou and Brown 2011) which downscaled model output from the GFDL-ESM-2m global climate model of the Coupled Model Inter-Comparison Project 5 for the 8.5 Representative Concentration Pathway.
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Burn probability (BP) for Fireline Intensity Class 2 (FIL2) with flame lengths in the range of 0.6-1.2 m predicted for the 2050-2070 period in the Rio Grande area. This raster dataset was generated using: 1) data developed from the 2014 Fire Program Analysis (FPA) system; 2) geospatial Fire Simulation (FSim) system developed by the US Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk (Finney et al. 2011); and 3) climate predictions developed using the Multivariate Adaptive Constructed Analogs (MACA) method (Abatzoglou and Brown 2011) which downscaled model output from the GFDL-ESM-2m global climate model of the Coupled Model Inter-Comparison Project 5 for the...
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Burn probability (BP) for Fireline Intensity Class 6 (FIL6) with flame lengths in the range of 3.7-15 m predicted for the 2080-2100 period in the Rio Grande area. This raster dataset was generated using: 1) data developed from the 2014 Fire Program Analysis (FPA) system; 2) geospatial Fire Simulation (FSim) system developed by the US Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk (Finney et al. 2011); and 3) climate predictions developed using the Multivariate Adaptive Constructed Analogs (MACA) method (Abatzoglou and Brown 2011) which downscaled model output from the GFDL-ESM-2m global climate model of the Coupled Model Inter-Comparison Project 5 for the 8.5...
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There is a need to understand how alteration of physical processes on the Rio Grande River have impacted aquatic biota and their habitats, and a need to predict potential future effects of climate change on biotic resources in order to prescribe research and management activities that will enhance conservation of aquatic species. We propose a project with the goal of developing monitoring recommendations and identifying research needs for aquatic ecological resources in the Big Bend region of the Rio Grande. This goal will be targeted by synthesizing and analyzing available data and literature for aquatic species in the project region. In particular, we will work to develop time series of abundance and population...
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Conclusions: In fragmented watersheds, macrohabitat attributes measured at the patch scale were far more effective in predicting trout translocation success than measurements taken at the landscape scale Thresholds/Learnings: As a course filter indicator of cutthroat trout translocation success, the study found that translocations have a greater than 50% chance of fruitful establishment in watersheds >14.7km2 in area. Synopsis: This study aimed to identify stream-scale and basin-scale macrohabitat attributes limiting successful translocation and persistence of native cutthroat trout populations in fragmented landscapes along the Rio Grande. The study developed models of habitat attributes measured at two scales...
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This child page contains the model input and output data used in the model validation process for one Program for Predicting Polluting Particle Passage through Pits, Puddles and Ponds (P8) model during the validation period of the study detailed in the associated Scientific Investigations Report "Comparison of Storm Runoff Models for a Small Watershed in an Urban Metropolitan Area, Albuquerque, New Mexico" (Shephard and Douglas-Mankin, 2020). This model was used to simulate storm runoff in the Hahn Arroyo Watershed, an urbanized watershed with concrete lined channels in the northeastern quadrant of Albuquerque that exhibits flashy, monsoonal-driven storm runoff events. The model is described in detail in the associated...
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This part of the Data Release contains the raster representation of the water-level altitude and water-level change maps developed every 5 years from 1980-2015 for the upper Rio Grande Focus Area Study. The input point data used to generate the water-level altitude maps can be found in the "Groundwater level measurement data used to develop water-level altitude maps in the upper Rio Grande Alluvial Basins" child item of this data release. These digital data accompany Houston, N.A., Thomas, J.V., Foster, L.K., Pedraza, D.E., and Welborn, T.L., 2020, Hydrogeologic framework, groundwater-level altitudes, groundwater-level changes, and groundwater-storage changes in selected alluvial basins of the upper Rio Grande...
Types: Map Service, OGC WFS Layer, OGC WMS Layer, OGC WMS Service; Tags: Abiquiu Reservoir, Ahumada, Alamosa, Alamosa County, Alamosa Creek, All tags...
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This dataset contains monthly pumping rates for municipal and industrial (MnI) wells in New Mexico within the Rio Grande Transboundary Integrated Hydrologic Model (RGTIHM). In RGTIHM, these wells are considered the Other New Mexico (ONM) group. Monthly pumping rates are presented in units of cubic feet per day for the period from March 1940 through December 2014.


map background search result map search result map Minimum habitat requirements for establishing translocated cutthroat trout populations. Ecological changes in aquatic communities in the Big Bend reach of the Rio Grande: Synthesis and future monitoring needs Burn Probability for Fireline Intensity Class 2, predicted for 2050 to 2070 for Rio Grande study area Burn Probability for Fireline Intensity Class 6, predicted for 2080 to 2100 for Rio Grande study area Burn Probability predicted for 2080 to 2100 for Rio Grande study area FSA 10:1 NAIP Imagery m_3710619_se_13_1_20150918_20151102 3.75 x 3.75 minute JPEG2000 from The National Map FSA 10:1 NAIP Imagery m_3710620_se_13_1_20150912_20151102 3.75 x 3.75 minute JPEG2000 from The National Map FSA 10:1 NAIP Imagery m_3710622_sw_13_1_20150912_20151102 3.75 x 3.75 minute JPEG2000 from The National Map FSA 10:1 NAIP Imagery m_3710630_ne_13_1_20150912_20151102 3.75 x 3.75 minute JPEG2000 from The National Map FSA 10:1 NAIP Imagery m_3710632_sw_13_1_20150911_20151102 3.75 x 3.75 minute JPEG2000 from The National Map FSA 10:1 NAIP Imagery m_3710636_nw_13_1_20150912_20151102 3.75 x 3.75 minute JPEG2000 from The National Map FSA 10:1 NAIP Imagery m_3710636_se_13_1_20150912_20151102 3.75 x 3.75 minute JPEG2000 from The National Map FSA 10:1 NAIP Imagery m_3710637_se_13_1_20150912_20151102 3.75 x 3.75 minute JPEG2000 from The National Map FSA 10:1 NAIP Imagery m_3710639_nw_13_1_20150911_20151102 3.75 x 3.75 minute JPEG2000 from The National Map FSA 10:1 NAIP Imagery m_3710640_nw_13_1_20150911_20151102 3.75 x 3.75 minute JPEG2000 from The National Map Climate Stations for the Rio Grande transboundary integrated hydrologic model and water-availability analysis, New Mexico and Texas, United States, and Northern Chihuahua, Mexico RGTIHM CIR Other New Mexico Wells: Municipal and Industrial Monthly Pumping Rates for the Rio Grande transboundary integrated hydrologic model and water-availability analysis, New Mexico and Texas, United States, and Northern Chihuahua, Mexico Groundwater-level altitude and groundwater-level change maps developed for the groundwater component of the upper Rio Grande Focus Area Study P8 Validation Period Input and Output Data P8 Validation Period Input and Output Data FSA 10:1 NAIP Imagery m_3710619_se_13_1_20150918_20151102 3.75 x 3.75 minute JPEG2000 from The National Map FSA 10:1 NAIP Imagery m_3710620_se_13_1_20150912_20151102 3.75 x 3.75 minute JPEG2000 from The National Map FSA 10:1 NAIP Imagery m_3710622_sw_13_1_20150912_20151102 3.75 x 3.75 minute JPEG2000 from The National Map FSA 10:1 NAIP Imagery m_3710630_ne_13_1_20150912_20151102 3.75 x 3.75 minute JPEG2000 from The National Map FSA 10:1 NAIP Imagery m_3710632_sw_13_1_20150911_20151102 3.75 x 3.75 minute JPEG2000 from The National Map FSA 10:1 NAIP Imagery m_3710636_nw_13_1_20150912_20151102 3.75 x 3.75 minute JPEG2000 from The National Map FSA 10:1 NAIP Imagery m_3710636_se_13_1_20150912_20151102 3.75 x 3.75 minute JPEG2000 from The National Map FSA 10:1 NAIP Imagery m_3710637_se_13_1_20150912_20151102 3.75 x 3.75 minute JPEG2000 from The National Map FSA 10:1 NAIP Imagery m_3710639_nw_13_1_20150911_20151102 3.75 x 3.75 minute JPEG2000 from The National Map FSA 10:1 NAIP Imagery m_3710640_nw_13_1_20150911_20151102 3.75 x 3.75 minute JPEG2000 from The National Map Ecological changes in aquatic communities in the Big Bend reach of the Rio Grande: Synthesis and future monitoring needs Climate Stations for the Rio Grande transboundary integrated hydrologic model and water-availability analysis, New Mexico and Texas, United States, and Northern Chihuahua, Mexico RGTIHM CIR Other New Mexico Wells: Municipal and Industrial Monthly Pumping Rates for the Rio Grande transboundary integrated hydrologic model and water-availability analysis, New Mexico and Texas, United States, and Northern Chihuahua, Mexico Minimum habitat requirements for establishing translocated cutthroat trout populations. Burn Probability for Fireline Intensity Class 2, predicted for 2050 to 2070 for Rio Grande study area Burn Probability for Fireline Intensity Class 6, predicted for 2080 to 2100 for Rio Grande study area Burn Probability predicted for 2080 to 2100 for Rio Grande study area Groundwater-level altitude and groundwater-level change maps developed for the groundwater component of the upper Rio Grande Focus Area Study