IWFM (Integrated Water Flow Model) - Version 2015.0.961 (released February 5, 2020)

This digital dataset consists of monthly climate data from the Basin Characterization Model v8 (BCMv8) for the updated Central Valley Hydrologic Model (CVHM2) for water years 1922 to 2019. The BCMv8 data are available in a separate data release titled "The Basin Characterization Model - A regional water balance software package (BCMv8) data release and model archive for hydrologic California, water years 1896-2020" that accompanies the USGS Techniques and Methods report titled: "The Basin Characterization Model - A Regional Water Balance Software Package". The BCMv8 data are extracted from the state-wide data for the CVHM2 modeled area for water years 1922 to 2019. Climate data for CVHM2 are presented in two child items: precipitation and potential evapotranspiration extracted for the modeled area within the CVHM2 model boundary; recharge and runoff are extracted for the small watersheds surrounding the CVHM2 model boundary. Each child items contain metadata, a shapefile, a file containing the data.

Version 3

## Overview

Version 3 is a dataset for object detection tasks - it contains Tableau annotations for 1,798 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Supplementary data for McKane et al. "Estimation of Flint Hills Tallgrass Prairie Productivity and Fuel Loads: A Model-Based Synthesis and Extrapolation of Experimental Data

Motorcut Version 1.0

## Overview

Motorcut Version 1.0 is a dataset for instance segmentation tasks - it contains NumberPlate annotations for 523 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

These data encompass the geologic framework model for the Central Valley Hydrologic Model Version 2 (CVHM2) study. This includes (1) the Well Log Database which contains borehole information and lithology used in creating the geologic framework, (2) Well Logs with Classification Information which explains how percent coarse values were determined for each borehole, and (3) the Three-Dimensional Framework Model.

These files contain the Version 30 of the Social Policy Simulation Database and Model (SPSD/M). This release is based on 2018 microdata. This model includes a dynamic labour adjustment model to reflect employment patterns from 2020 to 2022. It incorporates changes to income taxes and government transfers that were announced prior to December 31, 2022. The SPSD/M must be installed subject to the terms of your licence agreement. Operating System: This version of the SPSD/M is compatible with Windows 10. SPSD/M is not compatible with Apple Mac OS.

Dataset

This dataset was created by Gerwyn Ng

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Dataset

This dataset was created by phattiennguyen

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Dataset

This dataset was created by Sean Zhai

Released under MIT

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BDCP ANNs 64 bit

Dataset

This dataset was created by Justin Chae

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Supplemenary files for "Fitting Multiple Models to Multiple Data" Goncalo M. Marques, Dina Lika, Starrlight Augustine, Laure Pecquerie and Sebastiaan A.L.M. Kooijman.
Journal of Sea Research, 2018 compatible with DEBtool_M v.2017 https://github.com/add-my-pet/DEBtool_M/releases/tag/v.DEB2017 and are most likely compatible with the latest version.

To use these files, dowload the latest version of DEBtool_M from GitHub and set the matlab path to it. Keep these files together in a separate directory. Excecute the run_test file to obtain parameter estimates and view model prediction VS data. We refer the reader to the study to read about how the data are generated.

There are four files: run_test - this is the executable file mydata_test pars_init_test predict_test

To understand the set-up of the files we refer the reader to the open access publication "The AmP project: Comparing species on the basis of dynamic energy budget parameters" https://doi.org/10.1371/journal.pcbi.1006100

The user should also consult the mediawiki powered AmP estimation manual: http://www.debtheory.org/wiki/index.php?title=Add-my-Pet_manual which provides technical information on the files (run, mydata, predict, pars_init).

This digital dataset contains the municipal pumping dataset used to develop the Multi Node Well (MNW2) Package in the updated Central Valley Hydrologic Model (CVHM2). It includes well locations, well properties, and pumping rates for Municipal Pumping.

This dataset includes the models (fortran code, matlab code, data) used to simulate the heating and photolysis rates in a spherical atmosphere. Both Solar-J v7.5 and Cloud-J v7.3c have been published previously, but with the publication of Prather and Hsu, "A round Earth for climate models" in PNAS (2019) both models have been merged in terms of overlapping subroutines and both now include option for (0) a flat-Earth atmosphere, (1) solar ray tracing through a spherical atmosphere, (2) refracted solar rays in a spherical atmosphere, and (3) a geometically expanding spherical atmosphere that includes the extra area, volumen and mass for a spherical hydrostatic atmosphere.

"Sunlight drives the Earth's weather, climate, chemistry and biosphere. Recent efforts to improve solar heating codes in climate models focused on more accurate treatment of the absorption spectrum or fractional clouds. A mostly forgotten assumption in climate models is that of a flat-Earth atmosphere. Spherical atmospheres intercept 2.5 W m-2 more sunlight and heat the climate by an additional 1.5 W m-2 globally. Such a systematic shift, being comparable to the radiative forcing change from preindustrial to present, is likely to produce a discernible climate shift that would alter a model's skill in simulating current climate. Regional heating errors, particularly at high latitudes, are several times larger. Unlike flat atmospheres, constituents in a spherical atmosphere, such as clouds and aerosols, alter the total amount of energy received by the Earth. To calculate the net cooling of aerosols in a spherical framework, one must count the increases in both incident and reflected sunlight, thus reducing the aerosol effect by 10-14% relative to using just the increase in reflected. Simple fixes to the current flat-earth climate models can correct much of this oversight, although some inconsistencies will remain."

Early climate and weather models, constrained by computing resources, made numerical approximations on modeling the real world. One process, the radiative transfer of sunlight through the atmosphere, has always been a costly component. As computational ability expanded, these models added resolution, processes, and numerical methods to reduce errors and become the Earth system models that we use today. While many of the original approximations have since been improved, one -- that the Earth's surface and atmosphere are flat – remains in current models. Correcting from flat to spherical atmospheres leads to regionally differential solar heating at rates comparable to the climate forcing by greenhouse gases and aerosols. In addition, spherical atmospheres change how we evaluate the aerosol direct radiative forcing.

Seasonal hindcasts are performed by Norwegian Climate Prediction Model (NorCPM, Counillon et al. 2014). The hindcasts start on the 15th of January, April, July and October each year during 1985–2010. Totally, there are 104 hindcasts (26 years and 4 season starts per year). Each hindcast consists of 9 realisations (ensemble members) and is 13 months long. The hindcasts are forced by CMIP5 historical forcings (Taylor et al. 2012) before 2005 and the representative Concentration Pathway 8.5 (RCP8.5, van Vuuren et al. 2011) forcings after 2005. Initial conditions are taken from the first 9 out of the 30 ensemble members of the NorCPM_V1 reanalysis (https://doi.org/10.11582/2019.00029). For full parameter list see https://doi.org/10.11582/2019.00028 Citation: Wang, Y., Counillon, F., Kimmritz, M., Nansen Environmental and Remote Sensing Centre, Bjerknes Centre for Climate Research (2019).Norwegian Climate Prediction Model version 1: seasonal and decadal hindcasts 1985-2010 [Data set]. Norstore. https://doi.org/10.11582/2019.00028

This data release serves to provide supporting datasets used by Genmod and GRTD as part of the supplemental material used to generate 30 general models from which water-particle residence time distributions were determined. Descriptions of files found in the “Attached Files” section below (files with the “.7z” extension are 7-Zip files, http://www.7-zip.org/): “General_Groundwater-Model_Construction_System_Version_0point1_Metadata.xml”: Metadata file describing contents of the shapefiles. “Genmod_directory_structure.png”: A screen capture of the directory structure where the components of the General Groundwater-Model Construction System Version 0.1 (Genmod) are organized on a users' computer system. “shapefiles_30_model_domains.7z”: A zip file containing 30 individual shapefiles, one for each model domain. “GWSW_points.7z”: One shapefile of points with water-elevation data for groundwater and surface-water sites from the USGS National Water Information System (NWIS). “Watershed ...

The Multi-Model Large Ensemble Archive version 2 (MMLEAv2) was compiled as an extension of the original archive. It includes 18 models (12 CMIP6 and 6 CMIP5) and 15 monthly variables. ... While a consistent RCP8.5 future forcing scenario was available for the CMIP5 class models, the CMIP6 class models use one or both of the historical plus SSP370 or SSP585 scenarios. The data is provided remapped to a common 2.5 x 2.5 degree grid. Observational reference products are included in the archive also remapped to the same common grid. Additionally, the NetCDF output from the Climate Variability Diagnostics Package version 6 (CVDP v6) run on the model for the time periods 1950-2022 and 2027-2099 is provided for both detrended and non-detrended MMLEAv2 data.

IWFM (Integrated Water Flow Model) - Version 2015.0.1113 (released January 13, 2021), updated to Version 2015.1.1113 on February 12, 2021 to incorporate a bug fix (see Release Notes for the description of bug fixes and modifications)

The Aboriginal Sites Decision Support Tool ASDST extends the Aboriginal Heritage Information Management System (AHIMS) by illustrating the potential distribution of site features recorded in AHIMS.\r ASDST was first developed in 2012 by the Office of Environment and Heritage (OEH) to support landscape planning of Aboriginal Heritage. The Tool produces a suite of raster GIS modelled outputs and is held in Esri GRID format. The first suite was published in 2016 as Version 7 at 100m resolution and in Lamberts Conic Conformal Projection (LCC). The current Version 7.5 was produced by the now Department of Planning, Industry and Environment (DPIE) in 2020 at 50m resolution in Geographic Coordinate System (GCS). Each layer covers the extent of NSW. \r \r The suite of layers includes separate predictive layers for different Aboriginal site feature types. The feature codes used in layer naming conventions are:\r \r * ALL = model for all feature types combined \r * AFT = predicted likelihood for stone artefacts \r * ART = predicted likelihood for rock art \r * BUR = predicted likelihood of burials \r * ETM = predicted likelihood of western mounds and shell \r * GDG = predicted likelihood of grinding grooves \r * HTH = predicted likelihood of hearths \r * SHL = predicted likelihood of coastal middens \r * STQ = predicted likelihood of stone quarries and \r * TRE = predicted likelihood of scarred trees. \r \r The feature models have been derived in two forms:\r \r * The first form (“p1750XXX” where XXX denotes three letter feature code) predicts likelihood of feature distribution prior to European colonisation of NSW. \r \r * The second form (“curr_XXX” where XXX denotes three letter feature code) predicts feature likelihood in the current landscape. \r \r For both sets of feature likelihood layers, cell values range from 0 – 1000, where 0 indicates low likelihood and 1000 is high likelihood. \r \r Please note the scale is likelihood and NOT probability. Likelihood is defined as a relative measure indicating the likelihood that a grid cell may contain the feature of interest relative to all other cells in the layer. \r \r Additionally, there are other derived products as part of the suite. These are: \r \r * drvd_imp = which is a model of accumulated impacts, derived by summing the difference between the pre colonisation and current version of all feature models. Cell values range from 0 – 1000, where 1000 is a high accumulated impact.\r \r * drvd_rel = which is a model of the reliability of predictions based on an environmental distance algorithm that looks at recorded site density across the variables used in the models.\r \r * drvd_srv = which is a survey priority map, which considers model reliability (data gap), current likelihood and accumulated impact. Cell values range from 0 – 1000 where 1000 indicates highest survey priority relative to the rest of the layer.\r \r For more details see the technical reference on the ASDST website.\r \r NB. Old layers with a suffix of “_v7” indicate they are part of ASDST Version 7 produced in 2016. The current models (Version 7.5) do not contain a version number in their name and will continue to be named generically in future versions for seamless access.\r \r Updates applied to ASDST version 7.5\r \r For all ASDST 7.5 data sets, the resolution was increased from a 100m cell to a 50m cell. All data sets were clipped and cleaned to a refined coastal mask. Cell gaps in the mask were filled using a Nibble algorithm. The pre-settlement data sets were derived by resampling the version 7 pre-settlement data sets to 50m cell size. The present-day data sets were derived from the version 7.5 pre-settlement layers and 2017-18 land-use mapping and applying the same version 7 parameters for estimating the preservation of each feature type on each land-use. For version 7.5, the model reliability data set was derived by resampling the version 7 data set to 50m cell size. Accumulated impact and survey priority version 7.5 data sets were derived by applying the version 7 processing algorithm but substituting the version 7.5 pre-settlement and present-day ASDST models.\r