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BWSN Network for Transient Simulation
No full textThis is an EPANET input file for the BWSN network adapted from https://cs.stanford.edu/people/jure/pubs/bwsn-jwrpm09.pdf
The model is compatible with EPANET 2.00.1
Sentinel-1 InSAR Surface Deformation Data over the Permian Basin
No full textThis Sentinel-1 InSAR dataset contains surface deformation that occurred between Nov. 2014 and Jan. 2019 associated with the Permian Basin oil and gas production. For further details of the processing method and uncertainty analysis, please see the associated paper of Staniewicz et al., 2020.
Note: click the "tree" viewing option to see proper organizational layout of files, not the "table" layout.
When using this data for research, please cite:
Staniewicz, S., Chen, J., Lee, H., Olson, J., Savvaidis, A., Reedy, R., et al. (2020). InSAR reveals complex surface deformation patterns over an 80,000 square kilometer oil-producing region in the Permian Basin. Geophysical Research Letters, 47, e2020GL090151.
Details of generation and data attributes
Two paths of Sentinel 1 data were used in the analysis: the ascending path 78 and the descending path 85. For each path, the cumulative radar line-of-sight (LOS) deformation between (1) Nov. 2014 and Jan. 2017; (2) Nov. 2014 and Jan. 2018; and (3) Nov. 2014 and Jan. 2019 are included. Here the pixel spacing for all InSAR grids is 120 meters. All deformation data units are in centimeters. Each of the maps' cumulative results have an uncertainty of ~ 1 cm or less. All the maps using ascending (or descending) Sentinel data are coregistered the same latitude/longitude grid as the digital elevation model (DEM) covering the ascending (or descending) path.
Note: We used the SRTM DEM data to generate the interferograms. These DEM data can be found in geotiffs/ascending_path78/dem.tif and geotiffs/descending_path85/dem.tif.
The units of the DEMs are in meters.
For each path, we provided the names and locations of the GPS stations with continuous coverage between Nov. 2014 and Jan. 2019 as CSV files. The GPS east, north, and vertical daily time series are available through the Nevada Geodetic Laboratory (http://geodesy.unr.edu/ ). For this example, the NMHB station's NA plate-fixed solutions are available at http://geodesy.unr.edu/NGLStationPages/stations/NMHB.sta
The file geotiffs/ascending_path78/gps_locations.csv contains the name, latitude, and longitude of the stations within the ascending path, as well as the row and column of that location within the ascending latitude/longitude grid. The GPS stations TXKM was used as the spatial reference point to calibrate all LOS InSAR maps, the rest of GPS stations were used as independent validations for the InSAR results.
In addition to providing the data in GeoTIFF format, we have also loaded the data into MATLAB provded .mat files (located in the matlab_version/ folder). We have divided the .mat files into data coregistered on the ascending grid, the descending grid, and the vertical/east deformation solutions in the region where the ascending and descending paths overlap.
The definition of the radar LOS direction
We note that InSAR measures surface deformation along the radar LOS direction. In the region where the ascending and descending paths overlap, we decomposed the the two LOS deformation solutions (Nov. 2014 to Jan. 2019) using the ascending and descending LOS maps (unitless, as in geotiffs/ascending_path78/los_enu.tif and geotiffs/descending_path85/los_enu.tif) into their horizontal and vertical components. These vertical/horizontal solutions are contained in geotiffs/vertical_horizontal_decomposition/.
Further details of the LOS decomposition can be found in the associated paper and supplement of Staniewicz et al., 2020.
Converting GPS ENU data to the radar LOS
Here we show an example of how you would convert GPS east, north, up (ENU) time series data into measurements comparable to the ascending LOS InSAR measurements using the LOS unit vector coefficients. We use station NMHB as an example, whose metadata is contained in the geotiffs/ascending_path78/gps_locations.csv file. The LOS vector coefficients are in the geotiffs/ascending_path78/los_enu.tif image (or path78_data.mat), which is a 3 band GeoTIFF containing the look vector coefficients. To extract the 3 LOS coefficients from the matrix los_enu we could do the following in MATLAB:
load path78_data.mat
r = gps_locations.row(1);
c = gps_locations.col(1);
enu_coeffs = los_enu(r, c, :);
alpha_east = enu_coeffs(1); alpha_north = enu_coeffs(2); alpha_up = enu_coeffs(3);
Calling the east, north, up time series ts_east, ts_north, ts_up respectively, we can convert this to ts_LOS as follows:
ts_LOS = alpha_east * ts_east + alpha_north * ts_north + alpha_up * ts_up
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CDOM in Galveston Bay
No full textResults for two years of monitoring CDOM in Galveston Bay. Includes date, geographic coordinates, PARAFAC components, fluorescence indices, salinity, dissolved oxygen, Chlorophyll-a, and nutrients
Maximum Entropy machine learning model output using digitized downed trees (structure) with three variables and regularization multiplier of 3.0
No full text This dataset (multiple outputs files in a compresssed folder) is output from a run of the Maxent ML Model (Phillips et.al., 2006, url: https://biodiversityinformatics.amnh.org/open_source/maxent/) using trees with structure (N=4283) and includes the three environmental variables with most contribution according to MaxentVariableSelection (Jueterbock, et.al. 2016) (vegetation community, inundation probability, and soil classification) and implemented as hinge features with a regularization multiplier (beta parameter) of 3.0. The value of 0p150 in the dataset file names refer to the fact that it uses digitized downed trees with structure.
Phillips, S.J., Anderson, R.P., Schapire, R.E. (2006). Maximum Entropy modelling of species geographic distribution. Ecological Modelling, 190, 231-259.
Jueterbock, A. Smolina, I., Cover, J.A., Hoaru, C. (2016) The fate of arctic seaweed Fucus disticus under climate change: an ecological niche modelling approach. Ecology and Evolution, 6(6), 1712-1724.</p
Replication Data for: Switchgrass Genome Paper
No full textR script containing the code to replicate the results of the switchgrass genome paper. Includes code to: a) run the neural network to assign ecotypes to switchgrass individuals, identified by PLANT_ID's (six character strings that typically begin with J), based on individuals with confidently assigned ecotypes; b) run the discriminant analysis of principle components to assign individuals to the three genetic subpopulations; 1) Run genome-wide association (GWAS); 2) Find gene annotations for top GWAS hits; 3) Make multivariate adaptive shrinkage input dataframes from GWAS hits; 4) Run multivariate adaptive shrinkage; 5) Find heritabilities for GWAS hits and the polygenic background, and do regional heritability mapping for biomass phenotypes
Free Energy Computation & Results Display Scripts
No full textThese are the MATLAB scripts to compute free energy from behavior and EEG measures and then display the results. Includes data summary files in XLS format. Requires MATLAB 2017b or higher
Water Surface Elevations: Landcover +10% Roughness Scenario
No full text Raster maps of water surface elevations (WSEs) that are output from the HEC-RAS 5.0.7 2D hydrodynamic model (Brunner, 2016), and associated with flow stages (0.1m – 11.63m) at 0.25m interval (N=44). These flow stages were output from simulations coincident with the scenario that contains patches that are derived from locations of downed trees expanded by 2.0 m. At these patches, roughness is 10% higher than the roughness that is only associated with land cover informed by the Ecological Mapping Systems of Texas (Elliott et al. 2014, url: https://tpwd.texas.gov/landwater/land/programs/landscape-ecology/ems/) and calibrated to WSEs found in flood insurance studies (FEMA, 2014), which found an error of 20cm. Only WSE’s that have a depth above 20cm and is connected to the main channel are preserved.
Brunner, G.W. (2016). HEC-RAS: River Analysis System, 2D Modeling User’s Manual Version 5.0. Rep., Hydrological Engineering Center – U.S. Army Corp of Engineers, Davis, CA.
Elliott, L.F., Treuer-Kuehn, A., Blodgett, C.F., True. C.D., German, D. Diamond, D.D. (2014). Ecological System of Texas: 391 Mapped Types, edited by Texas Parks and Wildlife Department and Texas Water Development Board, Austin, TX.
FEMA (2014). Flood Insurance Study: Refugio County, Texas and Incorporated Areas. Rep., 101 pp, Federal Emergency Management Agency, Denton, TX.</p
Carta a Ignacio Aguilar y Marocho, 1868 Agosto 20
No full textSanta Anna writes to Ignacio Aguilar y Marocho, Mexican poet, politician and journalist. He states that sad times have caused their communication to be interrupted for too long. He expresses his hope that, "as the storm clouds clear," they can continue their relationship as before. —— Santa Anna escribe a Ignacio Aguilar y Marocho, poeta, político y periodista mexicano. Afirma que tiempos tristes han provocado que su comunicación se interrumpa durante demasiado tiempo. Expresa su esperanza de que, "como los nubarrones van desapareciendo," puedan continuar su relación como antes. Extent: 2 pages