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opencc-by-4.0Jun 2023View details →

S106 | AQUAlity19 | EU AQUAlity's water analysis list 2019

This is the collection associated with list S106 AQUAlity19 EU AQUAlity's water analysis list 2019 on the NORMAN Suspect List Exchange. <a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a> List of compounds found in surface and wastewater sampling and analysis by LC-HRMS (Orbitrap) within the European project AQUAlity in 2019.<a href="http://https://www.aquality-etn.eu/"> AQUAlity</a> is a project funded by the European Union under the Marie Skłodowska-Curie Actions (MSCA) –  Innovative Training Networks (Call: H2020-MSCA-ITN-2017) Project N. 765860. The list was kindly provided by Azziz Assoumani (INERIS, France)

opencc-by-4.0Jun 2023View details →

S107 | AQUAlity20 | EU AQUAlity's water analysis list 2020

This is the collection associated with list S107 AQUAlity20 EU AQUAlity's water analysis list 2020 on the NORMAN Suspect List Exchange. <a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a> List of compounds found in surface and wastewater sampling and analysis by LC-QTOF within the European project AQUAlity in 2020.<a href="http://https://www.aquality-etn.eu/"> AQUAlity</a> is a project funded by the European Union under the Marie Skłodowska-Curie Actions (MSCA) –  Innovative Training Networks (Call: H2020-MSCA-ITN-2017) Project N. 765860. The list was kindly provided by Azziz Assoumani (INERIS, France)

opencc-by-4.0Nov 2022View details →

The role of the intraspecific variability of hydraulic traits for modelling the plant water use in different European forest ecosystems: scripts, model output, and parameter files

This repository contains the model outputs and R scripts used to process the data to analyze the impact of the plant hydraulic parameterization of the manuscript: "The role of the intraspecific variability of hydraulic traits for modelling the plant water use in different European forest ecosystems". The following is a detailed description of the content of this repository: model_output.zip: This compressed file contains the results of all the individual numerical experiments per experimental site as produced by the Comunity Land Model version 5. The files are stored in NETCDF format per year. The folder is arranged with subfolders containing the individual results from each experimental site as follows: <ul> <li>rc: model output with the results of the resistant configuration of experiment 1 (RC)</li> <li>vc: model output with the results of the vulnerable configuration of experiment 1 (VC)</li> <li>k_dc: model output with the results of the default configuration used for experiments 1 and 2 (DC or DCkmax)</li> <li>k_rc: model output with the results of the low plant hydraulic conductance (Lkmax) for experiment 2</li> <li>k_irc: model output with the results of the intermediate low plant hydraulic conductance (ILkmax) for experiment 2</li> <li>k_vc: model output with the results of the high plant hydraulic conductance (Hkmax) for experiment 2</li> <li>k_ivc: model output with the results of the intermediate high plant hydraulic conductance (IHkmax) for experiment 2</li> <li>k_iirc: model output with the results of the additional intermediate low plant hydraulic conductance (IILkmax) for experiment 2</li> <li>ko_dc: model output with the results of the best kmax and the default configuration of the PVC used in experiment 3</li> <li>ko_rc: model output with the results of the best kmax and the resistant configuration of the PVC used in experiment 3</li> <li>ko_vc: model output with the results of the best kmax and the vulnerable configuration of the PVC used in experiment 3</li> </ul> The scripts were written for use in RStudio, and each contains a detailed description of the data requirements and outputs. Each script was developed to read directly the netcdf files of the model output and the csv files containing the transpiration estimates calculated from the SAPFLUXNET per experimental site (script 1).

opencc-by-4.0Feb 2023View details →

Corrections for Geostationary Cloud Liquid Water Path Using Microwave Imagery

Netcdf files containing a set of correctional factors for GOES-16 and GOES-17 cloud liquid-water path (LWP). The correctional factors for both satellites are fractional corrections of microwave imager LWP divided by GOES-16/17 LWP at a given solar zenith, GOES sensor zenith, relative azimuth (solar - sensor zenith), and low-cloud fraction. Uncorrected GOES-16/17 LWP is derived from GOES retrieved cloud-optical thickness and cloud-top effective radius, and it is multiplied by the corresponding correctional factor (i.e. the bins which the uncorrected values are in).

Simulated discharge statistics in Central and Southwestern Europe considering water use under 2K global warming

We provide a novel, high-resolution hydrological modelling dataset using pseudo-global warming climate data as forcing to the Community Water Model (CWatM). CWatM is a state-of-the-art large-scale rainfall-runoff and channel routing water resources model that is process-based and used to quantify water supply, as well as human water withdrawals from different sectors (industry, domestic, agriculture) and multiple sources representing the effects of water infrastructure, including reservoirs, groundwater pumping and irrigation canals. CWatM is forced by a pseudo-global warming (PGW) experiment from 1981 to 2010. PGW simulations resemble historical weather patterns and events under globally warmer conditions (here, 2 K global warming) by perturbing historical, reanalysis-driven regional climate simulations. We performed simulations considering regular incremental adjustments of the historical water withdrawals (ranging between +/- 50% of historic water withdrawals) under PGW conditions. That range represents an ad hoc and simplified representation of multiple possible future water management scenarios across Southwestern and Central Europe. The approach allows us to investigate the effects of changing water withdrawals under 2 K global warming. Especially in Western and Central Europe, the projected impacts on low flows highly depend on the chosen water withdrawal assumption. The data highlights the importance of accounting for future water withdrawals in discharge projections.   Discharge statistics based on daily output from CWatM within 1981-2010: <ul> <li>Q1 - 1st percentile</li> <li>Q5 - 5th percentile</li> <li>Q10 - 10th percentile</li> <li>Qavg - average discharge</li> <li>Q90 - 90th percentile</li> <li>Q95 - 95th percentile</li> <li>Q99 - 99th percentile</li> </ul> Files: <ul> <li>Qxx_reference: CWatM considering historical water use forced by RACMO-ERA5</li> <li>Qxx_PGW: CWatM considering historical water use forced by RACMO-ERA5 + climate pertubations under 2 K global warming. In the reference experiment, RACMO is forced at the lateral and sea surface boundaries of the model domain by unperturbed ERA5 reanalysis data, while in the pseudo-global warming experiment, the forcing data consist of perturbed reanalysis data. Perturbations are added to the ERA5 reference data corresponding to climate change patterns of surface pressure and sea surface temperature, and atmospheric profiles of temperature, relative humidity, and wind speed components that are retrieved from a 16-member single model initial condition ensemble of EC-EARTH global climate simulations.</li> <li>Qxx_PGW_adjusted_demand: We have performed 11 additional hydrological simulations adjusting the historical water demand (ranging between +/- 50% of historic water withdrawals) to enable sensitivity assessments of low and high flows under 2 K global warming.</li> </ul> An upcoming publication will be made available and linked to this research very soon.

Water Quality Features' Changes - WQeMS Raster Products

This dataset contains samples of the Water Quality Features' Changes service of the WQeMS H2020 project.

The global water resources and use model WaterGAP v2.2e: location and attributes of reservoirs and regulated lakes

This dataset contain the location and attributes of the reservoirs and regulated lakes in WaterGAP v2.2e. This dataset is provided to be transparent how the reservoirs are included in this WaterGAP version and e.g. to check deviations from the locations as provided by ISIMIP (www.isimip.org). Please see the readme.md for furhter details and please consider the license terms from the data sources listed in the readme.md.

Modeling abrupt excursions in water vapor isotopic variability during cold fronts at the Pointe Benedicte observatory in Amsterdam Island / Model dataset

ECHAM6wiso and LMDZ6iso simulations, and python script analyzing model outputs, associated with the article : <ul> <li>Amaelle Landais, Cécile Agosta, Françoise Vimeux, Olivier Magand, Cyrielle Solis, Alexandre Cauquoin, Niels Dutrievoz, Camille Risi, Christophe Leroy Dos Santos, Elise Fourré, Olivier Cattani, Bénédicte Minster, Frédéric Prié, Mathieu Casado, Aurélien Dommergue, Yann Bertrand, and Martin Werner (submitted to <a href="https://www.atmospheric-chemistry-and-physics.net/">Atmospheric Chemistry and Physics</a>, 2023) Modeling abrupt excursions in water vapor isotopic variability during cold fronts at the Pointe Benedicte observatory in Amsterdam Island.</li> </ul> If you use the data or the python script, please cite the last version of this article available on <a href="https://www.egusphere.net/">https://www.egusphere.net/</a> or <a href="https://acp.copernicus.org/">https://acp.copernicus.org/</a>. Please also cite the articles related to the model simulations: <ul> <li> Risi, C., Bony, S., Vimeux, F., and Jouzel, J.: Water-stable isotopes in the LMDZ4 general circulation model: Model evaluation for present-day and past climates and applications to climatic interpretations of tropical isotopic records, Journal of Geophysical Research Atmospheres, 115, https://doi.org/10.1029/2009JD013255, 2010. </li> <li> Cauquoin, A. and Werner, M.: High-Resolution Nudged Isotope Modeling With ECHAM6-Wiso: Impacts of Updated Model Physics and ERA5 Reanalysis Data, Journal of Advances in Modeling Earth Systems, 13, e2021MS002532, https://doi.org/10.1029/2021MS002532, 2021. </li> <li> Cauquoin, A., Werner, M., and Lohmann, G.: Water isotopes -- climate relationships for the mid-Holocene and preindustrial period simulated with an isotope-enabled version of MPI-ESM, Climate of the Past, 15, 1913–1937, https://doi.org/10.5194/cp-15-1913-2019, 2019. </li> </ul>

Doodleverse/Segmentation Gym SegFormer models for 4-class (other, water, sediment, wood) segmentation of RGB aerial orthomosaic imagery

Doodleverse/Segmentation Gym SegFormer models for 4-class (other, water, sediment, wood) segmentation of RGB aerial orthomosaic imagery This model release is part of the Doodleverse: https://github.com/Doodleverse These Residual-UNet model data are based on RGB (red, green, and blue) images of alluvial river corridors and associated labels. Models are designed to identify water, wood, sediment, and other in orthomosaic imagery. Models have been created using Segmentation Gym* using a dataset of images published here: Ritchie, A.C., Curran, C.A., Magirl, C.S., Bountry, J.A., Hilldale, R.C., Randle, T.J., and Duda, J.J., 2018, Data in support of 5-year sediment budget and morphodynamic analysis of Elwha River following dam removals: U.S. Geological Survey data release, https://doi.org/10.5066/F7PG1QWC. Classes: {0=other, 1=water, 2=sediment, 3=large woody debris / driftwood}. See https://github.com/Doodleverse for more information about how this model was trained, and how to use it for inference File descriptions 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally,  1. BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU 2. sample_images.zip contains a few example input files, for model testing References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym

opencc-by-4.0May 2023View details →

Doodleverse/CoastSeg Segformer models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 3-band (RGB) images of coasts.

Doodleverse/CoastSeg Segformer models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 3-band (RGB) images of coasts. These Segformer model data are based on RGB (red, green, and blue) images of coasts and associated labels. Models have been created using Segmentation Gym* using the following datasets**: <a href="https://doi.org/10.5281/zenodo.7335647">https://doi.org/10.5281/zenodo.7335647</a> and ***https://doi.org/10.5281/zenodo.8011926. Those datasets have been combined and the training and validation images and labels are provided here. Classes: {0=water, 1=whitewater, 2=sediment, 3=other} Model validation accuracy statistics model name: overall accuracy, mean frequency weighted IoU, mean IoU, Matthews correlation. Bold indicates best overall <ul> <li>v5: .94, .90, .64, .87</li> <li>v6: .93, .89, .63, .87</li> <li>v7: .92, .88, .61, .84</li> <li>v8: .93, .89, .63, .87</li> <li>v9: .92, .88, .62, .85</li> <li>v10: .93, .89, .63, .86</li> </ul>   File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU This is a sister model to this set of Residual UNets: Buscombe, Daniel. (2022). Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 3-band (RGB) images of coasts. (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6950472 References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a> ** Buscombe, Daniel, Goldstein, Evan, Bernier, Julie, Bosse, Stephen, Colacicco, Rosa, Corak, Nick, Fitzpatrick, Sharon, del Jesús González Guillén, Anais, Ku, Venus, Paprocki, Julie, Platt, Lindsay, Steele, Bethel, Wright, Kyle, & Yasin, Brandon. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7335647">https://doi.org/10.5281/zenodo.7335647</a> ***Buscombe, Daniel. (2023). June 2023 Supplement Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8011926

water vole m1 geometric morphometrics

Code and data for the geometric morphometric analysis of water vole (A amphibius) lower first molars. The files include: 1) the complete information on the specimens included in the geometric morphometric analysis (“./redig_specinfo_20230222.csv”) 2) the landmarks file (“./lmrks_20230222.TPS”) 3) R code (“./gmm_pipeline_vole_20230727.R”) For any questions, please contact nimrod.arch@gmail.com

Gene annotations of Amphibolurus muricatus (jacky dragon), Intellagama lesueurii (Australian water dragon), Phrynocephalus przewalskii (Przewalski's toadhead agama), and Phrynocephalus vlangalii (Ching Hai toadhead agama)

Annotation file and associated FASTA files for A. muricatus assembly AmpMurF_3.0 • AmpMurF3.gff3.tar.gz: EVidenceModeler (EVM) gene model annotation file. • AmpMurF3.cds.tar.gz: EVM gene models coding sequences. • AmpMurF3.pep.tar.gz: EVM gene models coding sequences translated into amino acid sequences. Annotation file and associated FASTA files for A. muricatus assembly AmpMurM_3.0 • AmpMurM3.gff3.tar.gz: EVidenceModeler (EVM) gene model annotation file. • AmpMurM3.cds.tar.gz: EVM gene models coding sequences. • AmpMurM3.pep.tar.gz: EVM gene models coding sequences translated into amino acid sequences. Annotation file and associated FASTA files for I. lesueurii (Australian water dragon; assembly EWD_hifiasm_HiC generated as part of the AusARG consortium) • Intellagama_lesueurii.evm.final.add_replace_buscoV5_homolog.final.gff3.tar.gz: EVidenceModeler (EVM) gene model annotation file. • Intellagama_lesueurii.evm.final.add_replace_buscoV5_homolog.final.cds.fa.tar.gz: EVM gene models coding sequences. • Intellagama_lesueurii.evm.final.add_replace_buscoV5_homolog.final.pep.fa.tar.gz: EVM gene models coding sequences translated into amino acid sequences. Annotation file and associated FASTA files for P. przewalskii (Przewalski’s toadhead agama; see PMID ID 30808754 and CNGBdb accession no. CNP0000203)  • Phrynocephalus_przewalskii.evm.final.add_replace_buscoV5_homolog.gff3.tar.gz: EVidenceModeler (EVM) gene model annotation file. • Phrynocephalus_przewalskii.evm.final.add_replace_buscoV5_homolog.cds.fa.tar.gz: EVM gene models coding sequences. • Phrynocephalus_przewalskii.evm.final.add_replace_buscoV5_homolog.pep.fa.tar.gz: EVM gene models coding sequences translated into amino acid sequences. Annotation file and associated FASTA files for P. vlangalii (Ching Hai toadhead agama; see PMID ID 30808754 and CNGBdb accession no. CNP0000203) • Phrynocephalus_vlangalii.evm.final.add_replace_busco_homolog.gff3.tar.gz: EVidenceModeler (EVM) gene model annotation file. • Phrynocephalus_vlangalii.evm.final.add_replace_busco_homolog.cds.fa.tar.gz: EVM gene models coding sequences. • Phrynocephalus_vlangalii.evm.final.add_replace_busco_homolog.pep.fa.tar.gz: EVM gene models coding sequences translated into amino acid sequences.

Monthly lake water quality data (May-September 2022) following the Greenwood Fire in northeastern Minnesota v1.0

This repository contains lake water quality responses to the 2021 Greenwood Fire in Superior National Forest, Minnesota, USA (near Isabella, MN in northeastern MN). Thirty lakes (15 burned watershed, 15 control) were sampled monthly from May-September 2022 along various fire disturbance gradients (e.g., % watershed burned) and in relation to hydrologic connectivity (i.e., drainage vs. isolated lakes). Much of the non-water quality data we used came from published sources, which are described and referenced below. This repository also contains R code used to analyze and visualize data, as well as some output figures.

Doodleverse/CoastSeg Segformer models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 MNDWI images of coasts.

Doodleverse/CoastSeg Segformer models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 MNDWI images of coasts. Models have been created using Segmentation Gym* using the following datasets ** https://zenodo.org/record/7384263 and ***: https://doi.org/10.5281/zenodo.7335647. Those datasets have been combined and the training and validation images and labels are provided here. Classes: {0=water, 1=whitewater, 2=sediment, 3=other} Model validation accuracy statistics model name: overall accuracy, mean frequency weighted IoU, mean IoU, Matthews correlation. Bold indicates best overall     v2: 0.808, 0.7309,    0.47864, 0.656     v3: 0.809, 0.7302, 0.4982, 0.664 File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU This is a sister model to these sets of Residual UNets:     https://zenodo.org/record/7352850     https://zenodo.org/record/7557080 References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym ** Buscombe, Daniel. (2022). Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7384263 ***Coast Train data release: Wernette, P.A., Buscombe, D.D., Favela, J., Fitzpatrick, S., and Goldstein E., 2022, Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation: U.S. Geological Survey data release, https://doi.org/10.5066/P91NP87I. See https://coasttrain.github.io/CoastTrain/ for more information ***Buscombe, Daniel. (2023). June 2023 Supplement Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8011926

Doodleverse/CoastSeg Segformer models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 NDWI images of coasts.

Doodleverse/CoastSeg Segformer models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 NDWI images of coasts. Models have been created using Segmentation Gym* using the following datasets ** https://zenodo.org/record/7384263 and ***: https://doi.org/10.5281/zenodo.7335647. Those datasets have been combined and the training and validation images and labels are provided here. Classes: {0=water, 1=whitewater, 2=sediment, 3=other} Model validation accuracy statistics model name: overall accuracy, mean frequency weighted IoU, mean IoU, Matthews correlation. Bold indicates best overall <table> <tbody> <tr> <td>0.896016693115234</td> <td>0.832759195999637</td> <td>0.565748652153519</td> <td>0.806139409136944</td> </tr> </tbody> </table> <table> <tbody> <tr> <td>0.906008201175266</td> <td>0.847625161837392</td> <td>0.593821675882991</td> <td>0.819790462192222</td> </tr> </tbody> </table> <table> <tbody> <tr> <td>0.903999212053087</td> <td>0.844255821722932</td> <td>0.577444030164045</td> <td>0.813646408575</td> </tr> </tbody> </table>   File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU This is a sister model to these sets of Residual UNets: https://zenodo.org/record/7557072 https://zenodo.org/record/7352859 References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym ** https://zenodo.org/record/7384263 ***Buscombe, Daniel. (2023). June 2023 Supplement Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8011926

Terrestrial water data synthesis for hydrological catchments around the world in the Anthropocene

We here synthesise hydro-climatic data reported by previous studies for 65 hydrological catchments around the world [1-7] for further meta-analysis of how water fluxes of precipitation (P), runoff (R), and actual evapotranspiration (ET) on land change in the Anthropocene epoch, from before to after its start in the 1950's [8]. These water flux changes alter how much water ends up sustaining crops and other plants (evapotranspiration) and how much remains for the lateral water flows (runoff) through the landscape and the societal water uses and ecosystems they support. How this partitioning changes as integral part of global change is key for water and food security, and life on land and below water around the world’s land areas and coasts.  To distinguish the impacts of direct human drivers on evapotranspiration and runoff changes based on instrumental data for wide-ranging water, human-activity and climate conditions around the world, the 65 study catchments are divided in two comparative sets. One set includes 52 large catchments, selected from two previous data compilations and studies [1,2] and is in the data files referred to as the world set of catchments. The study periods are 1901–2008 for 21 [2] and 1930–1979 for the other 31 [1] of these catchments. The second set includes 13 catchments selected from previous studies [3-7] of both the water flux changes and associated dominant human drivers of these for time periods that are largely consistent with those for the world set of catchments (within 1901-2016); this is in the data files referred to as the known human-shifted set of catchments. The dominant direct human drivers of water-flux changes in these catchments include expanded/intensified rainfed agriculture (RA), irrigated agriculture (IA), or dams and reservoirs for engineered flow regulation (FR), as listed and cited for each known human-shifted catchment in the data files.  For each study catchment, we have quantified and report in the data files long-term average P, R and ET values over the total study period and changes in period-average values between two subperiods within it. The subperiods are 1901–1954 and 1955–2008 for 21 [2], and 1930–1954 and 1955–1979 for the other 31 [1] world catchments, and largely consistent for the known human-shifted catchments [3-7] (as listed in the data files). Each study catchment is also classified as water or energy limited by quantifying the associated Budyko-based aridity index PET/P, where PET is potential evapotranspiration. Average PET is estimated as PET≈325+21T+0.9T2 where T is long-term annual and catchment average surface temperature in °C, calculated from monthly temperature data in the previous catchment studies [1-7]. The resulting PET/P index classifies actual ET/P in each catchment as energy limited for average PET/P < 1 or water limited for average PET/P > 1, as listed in the data files. Other catchment information included in the data files, uploaded in both pdf and xlsx formats, include source references, catchment name, area and station ID and continent and latitude location. Overall, this dataset provides a wide-ranging sample of catchment-wise related, water balance-constrained local-regional changes in average P, R and ET under major RA, IA, FR developments (known for the second human-shifted set of catchments [3-7]) and various other human-activity and climate developments around the world from before to after the Anthropocene start in the 1950’s [8].

UFLUX 100m half-yearly carbon, water, and energy fluxes in Europe in 2018

<div> <h3>UFLUX Ensemble Europe100m6monthly (European 100 6-monthly) in 2018</h3> Overview The UFLUX ensemble dataset offers European fluxes at 100 m spatial resolution, generated using Deep Forest machine learning models. It integrates satellite-based Sentinel-2 vegetation proxies NIRv with ERA5 climate reanalysis, and is trained against ICOS eddy covariance observations. The UFLUX project includes five core flux components: <ul> <li> Gross Primary Production (GPP) </li> <li> Ecosystem Respiration (RECO) </li> <li> Net Ecosystem Exchange (NEE) </li> <li> Sensible Heat Flux (H) </li> <li> Latent Energy Flux (LE) </li> </ul> Background and Methodology The Unified FLUXes (UFLUX) initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change. Key innovations of UFLUX include: <ol> <li> Consistent Upscaling Framework: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data. </li> <li> Hybrid Explainable ML: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2). </li> <li> Uncertainty Quantification: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner. </li> <li> Multisource Integration: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches. </li> <li> Superior Gap-Filling: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods. </li> <li> High Performance: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop. </li> <li> Community Adoption: Already used by other global upscaling projects, highlighting its reliability and impact. </li> </ol> Applications UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements. Resources <ul> <li>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></li> <li> Code Repository: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a> </li> <li> Technical & Descriptive Publication: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a> </li> </ul> </div>

Salinity - water level relationships in shallow lakes: Data

Data consists of water levels and salinity of 159 lakes across southwest Australia, collected as part of the South West Wetlands Monitoring Program (Lane et al., 2017). The data was collected between 1977 and 2019 by staff from the Department of Biodiversity Conservation and Attractions, Western Australia, and its predecessors.  Description of files: SiteSummary.xlsx <ul> <li>An Excel file with basic lake descriptions, including names and geographic coordinates. </li> </ul> WaterData.csv <ul> <li>A comma delimited ascii file consisting of a lake identifier, a date of observation, a lake level relative to the deepest bed elevation, the measured salinity (g/L).</li> </ul> Bathymetry.xlsx <ul> <li>An Excel file with bathymetries of a subset of lakes</li> </ul>

opencc-by-4.0Mar 2021View details →

Global Water Body Levels Derived from ICESat-2

This dataset contains water level records derived from ICESat-2 for 227,386 lakes spanning Oct 14, 2018 to July 16, 2020. These records have been updated to reflect an error in the calculation of lake area in the previous version which led to an overestimation of lake area at high latitudes. For details on how this correction was performed, please see the 2023 Addenda to Cooley et al (2021). A complete description of the method used to derive water level from ICESat-2 can be found in Cooley et al (2021), but is briefly summarized below: <ol> <li>We create a conservative water mask modified from the Global Surface Water Occurrence (GSWO) product (Pekel et al., 2016);</li> <li>We intersect ATL08 mean terrain height returns with this water mask, requiring water bodies to receive at least three ICESat-2 point observations on the same day to be included in the analysis;</li> <li>We filter observations based on the mean standard deviation of returns, among other factors;</li> <li>We aggregate observations to monthly timesteps;</li> <li>We calculate seasonal variability in water level as the maximum minus the minimum monthly water level over the 22-month period.</li> </ol> This dataset contains: <ol> <li>ICESat2_lake_variability_v2_updated.shp: A shapefile containing lake points and summary statistics (i.e. height variability, storage variability, etc) *updated to include corrected lake area and storage values</li> <li>ICESat2_lake_height_time_series_v2_updated.csv: A csv file of the monthly water height time series used to calculate the global lake level variability *updated to include corrected lake area values</li> <li>265 water mask GeoTiffs: Water masks created from GSWO which we intersect with ICESat-2 data to produce the water level time series *unchanged from previous version</li> <li>ICESat2_mask_reference_v2_updated.csv – A csv file which lists the corresponding water mask for each water body in the dataset *updated to include corrected lake area values</li> <li>USGS_height_validation_v2_updated.csv – A csv file containing the height comparison between ICESat-2 and USGS gauges used for validation and uncertainty analyses *updated to include corrected lake area values</li> <li>USGS_range_validation_ v2_updated.csv – A csv file containing the range comparison between ICESat-2 and USGS gauges used for validation and uncertainty analyses *updated to include corrected lake area values</li> <li>California_storage_validation_v2_updated.csv – A csv file containing the storage comparison between ICESat-2 and California Department of Water Resource gauges used for validation and uncertainty analyses *updated to include corrected lake area and storage values</li> </ol> See the README file for a more detailed description of this dataset. Anyone wishing to use this dataset should cite Cooley et al. 2021) and contact Sarah Cooley at <a href="mailto:scooley2@uoregon.edu">scooley2@uoregon.edu</a> with a description of the work and any questions so that we may offer guidance in regards to the best usage of our dataset.  Cooley, S.W., Ryan, J.C., and Smith, L.C., (2021), Human alteration of global surface water storage variability, Nature, https://doi.org/10.1038/s41586-021-03262-3