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These data are netcdf files of the projected timing of the onset of thermal stress severe enough (>8 Degree Heating Weeks) to cause coral bleaching 2x per decade and 10x per decade (annual) under emissions scenarios RCP8.5 and RCP4.5. The projected timing (a year between 2006 and 2100) is the data value. Values are only shown for the ~60,000 four-km pixels where coral reefs are known to occur.
This data release is provided in support of Arismendi, I., Dunham, J.B., Heck, M.P., Schultz, L.D., Hockman-Wert, D.P., 2017, A statistical method to predict flow permanence in dryland streams from time series of stream temperature: Water, v. 9, no. 12, p. 946, https://doi.org/10.3390/w9120946. This code release contains all of the source code from the "Hidden Markov Model" sections of the associated manuscript. The source code was written using the R programming language (www.r-project.org, version 3.3.1). Running the code requires knowlege of the R programming language. The code snippet requires the folder location containing the data, and the site being processed, to be updated. The code requires certain R packages,...
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Winter climate change has the potential to have a large impact on coastal wetlands in the southeastern U.S. Warmer winter temperatures and reductions in the intensity of freeze events would likely lead to mangrove forest range expansion and salt marsh displacement in parts of the U.S. Gulf of Mexico and Atlantic coast. The objective of this research was to better understand some of the ecological implications of mangrove forest migration and salt marsh displacement. The potential ecological effects of mangrove migration are diverse ranging from important biotic impacts (e.g., coastal fisheries, land bird migration; colonial nesting wading birds) to ecosystem stability (e.g., response to sea level rise and drought;...
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Winter climate change has the potential to have a large impact on coastal wetlands in the southeastern U.S. Warmer winter temperatures and reductions in the intensity of freeze events would likely lead to mangrove forest range expansion and salt marsh displacement in parts of the U.S. Gulf of Mexico and Atlantic coast. The objective of this research was to better understand some of the ecological implications of mangrove forest migration and salt marsh displacement. The potential ecological effects of mangrove migration are diverse ranging from important biotic impacts (e.g., coastal fisheries, land bird migration; colonial nesting wading birds) to ecosystem stability (e.g., response to sea level rise and drought;...
1) Raw parcel-level habitat data for the South Carolina Lowcountry surrounding Cape Romain NWR and Francis Marion NF, from current current conditions and for three projected sea-level rise futures based on SLAMM model outputs, NLCD land cover and the projected distribution of sea levels for 2050. 2) a table of parcel identification numbers (without georeference) with parcel size (Ha) and sub-group identity. 3) Optimization-model derived reserve design portfolios that define the Pareto-optimal frontier for each sub-group and for four budget scenarios along axes of reserve design benefits and risk.
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Within large-river ecosystems, floodplains serve a variety of important ecological functions. A recent survey of 80 managers of floodplain conservation lands along the Upper and Middle Mississippi and Lower Missouri Rivers in the central United States found that the most critical information needed to improve floodplain management centered on metrics for characterizing depth, extent, frequency, duration, and timing of inundation. These metrics can be delivered to managers efficiently through cloud-based interactive maps. To calculate these metrics, we interpolated an existing one-dimensional HEC-RAS hydraulic model for the Lower Missouri River, which simulated water surface elevations at cross sections spaced (<1...
(1) This written report summarizes and synthesizes results of literature review, interviews, and workshops, providing the scientific basis for and extension strategies for the management recommendations provided in the "green website" [Data Input New Collection]. The report includes an addendum regarding reference bibliographies and a references list with citations. (2) Selected, unusual references that are not readily available online or through standard academic sources were collected by the project. (3) Selected photographs are retained by the project in electronic form.
This code computes the analytical solution for the damping of sinusoidal infiltration in variably saturated soils described by Bakker and Neiber (2009) and implemented by Dickinson et al. (2014). The usage of the code is documented in the appendix of Dickinson et al. (2014). Bakker, M., Nieber, J.L., 2009. Damping of sinusoidal surface flux fluctuations with soil depth. Vadose Zone J. 8, 119–126,http://dx.doi.org/10.2136/vzj2008.0084. Dickinson, J.E., Ferré, T.P.A., Bakker, M., Crompton, B., 2014. A screening tool for delineating subregions of steady recharge within groundwater models.Vadose Zone J. 13, 15, http://dx.doi.org/10.2136/vzj2013.10.0184. The code can be obtained at http://az.water.usgs.gov/software/damp.html
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To assess the current topography of the tidal marshes we conducted survey-grade elevation surveys at all sites between 2009 and 2013 using a Leica RX1200 Real Time Kinematic (RTK)Global Positioning System (GPS) rover (±1 cm horizontal, ±2 cm vertical accuracy; Leica Geosystems Inc., Norcross, GA; Figure 4). At sites with RTK network coverage (San Pablo, Petaluma, Pt. Mugu, and Newport), rover positions were received in real time from the Leica Smartnet system via a CDMA modem (www.lecia-geosystems.com). At sites without network coverage (Humboldt, Bolinas, Morro and Tijuana), rover positions were received in real time from a Leica GS10 antenna base station via radio link. When using the base station, we adjusted...
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Mean modeled snow-water-equivalent (meters) on February 20, the date of peak basin-integrated mean modeled snow-water-equivalent (meters) for the T4 climate change scenario. Reference period: the period 1989 – 2011 for the Upper Deschutes River Basin domain, for which observed historical meteorology is used for model input. T4 scenario: the observed historical (reference period) meteorology is perturbed by adding +4°C to each daily temperature record in the reference period meteorology, and this data is then used as input to the model.
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UW_Olallie_photo_metadata & image files: These are the raw timelapse photographs. The date/time stamp is inaccurate for the camera deployed in the open (at the SNOTEL) due to a programming error. This timestamp is one day early (i.e., subtract 1 day from the timestamp when using these data). Also available is metadata for two timelapse cameras and their associated snow depth poles (two visible in each camera's field of view) deployed at Olallie Meadows SNOTEL during water year 2015. One camera was deployed in the open area that is the Olallie Meadows SNOTEL station (the snow pillow is in the field of view). The other camera was deployed in the adjacent forest, approximately 60 m to the southeast of the SNOTEL....
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The percentage difference between mean modeled snow-water-equivalent (meters) on April 1 for the reference (1989-2011) climate period and mean modeled snow-water-equivalent on April 1 for the T4 climate change scenario. Reference period: the period 1989 – 2011 for the Upper Deschutes River Basin domain, for which observed historical meteorology is used for model input. T4 scenario: the observed historical (reference period) meteorology is perturbed by adding +4°C to each daily temperature record in the reference period meteorology, and this data is then used as input to the model.
This dataset is a continuous parameter grid (CPG) of normal (average) annual precipitation data for the years 1981 through 2010 in the Pacific Northwest. Source precipitation data was produced by the PRISM Climate Group at Oregon State University.
These datasets are continuous parameter grids (CPG) of permeability (and impermeability) of surface geology in the Pacific Northwest. Source data come from work by Chris Konrad, U.S. Geological Survey (USGS), and geologic map databases produced by USGS scientists.
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Information about these images can be found in the Final Report for Sea-level Rise Response Modeling for San Francisco Bay Estuary Tidal Marshes. Site-specific data are available by request. Contact: Dr. John Y. Takekawa, USGS Western Ecological Research Center, San Francisco Bay Estuary Field Station, 505 Azuar Dr. Vallejo, Calif. 94592, 707-562-2000
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This dataset includes electrical resistance data from a network of 50 data loggers that was installed throughout the Willow-Whitehorse watershed of SE Oregon in September 2014. Data loggers were downloaded in August 2015 and September 2016. These data loggers were used as “electrical resistance” (ER) sensors, following Chapin et al. 2014. The sensors were Onset HOBO Pendant temperature data loggers that were modified to monitor streamflow intermittency and determine the timing of stream drying.
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This dataset provides model specifications used to estimate water temperature from a process-based model (Hipsey et al. 2019). The format is a single JSON file indexed for each lake based on the "site_id". This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).
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This dataset includes model inputs that describe local weather conditions for Sparkling Lake, WI. Weather data comes from two sources: locally measured (2009-2017) and gridded estimates (all other time periods). There are two comma-delimited files, one for weather data (one row per model timestep) and one for ice-flags, which are used by the process-guided deep learning model to determine whether to apply the energy conservation constraint (the constraint is not applied when the lake is presumed to be ice-covered). The ice-cover flag is a modeled output and therefore not a true measurement (see "Predictions" and "pb0" model type for the source of this prediction). This dataset is part of a larger data release of...
map background search result map search result map New soil data collection: subplot-level shear strength New porewater data collection: subplot-level physicochemical Modeled snow-water-equivalent, percent difference between historical and projected April 1 values under T4 climate change scenario, Upper Deschutes River Basin, Oregon [full and clipped versions] Modeled snow-water-equivalent, projected seasonal peak values under T4 climate change scenario, Upper Deschutes River Basin, Oregon [full and clipped versions] Tijuana: Tidal Marsh Digital Elevation Model Timelapse photos at SNOTEL station, locations, and associated metadata, Ollalie Meadows, Wash., 2015 Electrical resistance data from the Willow-Whitehorse watersheds of southeast Oregon, USA, 2014-2016 Climate Change Scenario Inundation Metrics along the Upper and Middle Mississippi and Lower Missouri Rivers Streamflow Permanence Probability rasters, 2004-2011, Version 2.0 (PROSPER) Process-guided deep learning water temperature predictions: 2 Model configurations (lake metadata and parameter values) Process-guided deep learning water temperature predictions: 3b Sparkling Lake inputs Process-guided deep learning water temperature predictions: 3b Sparkling Lake inputs Tijuana: Tidal Marsh Digital Elevation Model Timelapse photos at SNOTEL station, locations, and associated metadata, Ollalie Meadows, Wash., 2015 Electrical resistance data from the Willow-Whitehorse watersheds of southeast Oregon, USA, 2014-2016 Modeled snow-water-equivalent, percent difference between historical and projected April 1 values under T4 climate change scenario, Upper Deschutes River Basin, Oregon [full and clipped versions] Modeled snow-water-equivalent, projected seasonal peak values under T4 climate change scenario, Upper Deschutes River Basin, Oregon [full and clipped versions] Climate Change Scenario Inundation Metrics along the Upper and Middle Mississippi and Lower Missouri Rivers New soil data collection: subplot-level shear strength New porewater data collection: subplot-level physicochemical Process-guided deep learning water temperature predictions: 2 Model configurations (lake metadata and parameter values) Streamflow Permanence Probability rasters, 2004-2011, Version 2.0 (PROSPER)