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101 results for “Land surface model”
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2016_2020)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change.</p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling
<p><strong>Summary</strong>: Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change.</p> <p><br><strong>Format</strong>: NetCDF.<br><strong>Institution</strong>: Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory<br><strong>Contacts</strong>: Lingcheng Li (lingcheng.li@pnnl.gov; lingchengliwhu@gmail.com), Gautam Bisht (gautam.bisht@pnnl.gov)</p> <p><strong>Description</strong>: This dataset provides land surface parameters specifically designed for global kilometer scale earth system modeling.<br><strong>Spatial resolution</strong>: ~1 km, corresponding to 1/120 degree.<br><strong>Temporal resolution</strong>: includes yearly (2001-2020), monthly (2001-2020), and static data for different parameters.</p> <p><br><strong>Reference</strong>: <strong>Li, L., Bisht, G., Hao, D., and Leung, L.-Y. R.: Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-242, Acceptance, 2023.</strong></p> <p>It includes four categories of parameters, Please refer to the readme file for details:<br>1. LULC: land use and land cover parameters<br>2. VEGE: vegetation paramertes<br>3. SOIL: soil parameters<br>4. TOPO: topography parameters</p> <p>Due to storage limitations, the LAI and SAI files are stored in the following repositories:</p> <p>1) LAI 2001-2005: <a href="../records/10815637" target="_blank" rel="noopener">https://zenodo.org/records/10815637</a>; 2) LAI 2006-2010: <a href="../records/10815649" target="_blank" rel="noopener">https://zenodo.org/records/10815649</a>; 3) LAI 2011-2015: <a href="../records/10815658" target="_blank" rel="noopener">https://zenodo.org/records/10815658</a>; 4) LAI 2016-2020: <a href="../records/10815662" target="_blank" rel="noopener">https://zenodo.org/records/10815662</a>;</p> <p>5) SAI 2001-2005: <a href="../records/10815623" target="_blank" rel="noopener">https://zenodo.org/records/10815623</a>; 6) SAI 2006-2010: <a href="../records/10815629" target="_blank" rel="noopener">https://zenodo.org/records/10815629</a>; 7) SAI 2011-2015: <a href="../records/10790724" target="_blank" rel="noopener">https://zenodo.org/records/10790724</a>; 8) SAI 2016-2020: <a href="../records/10790758" target="_blank" rel="noopener">https://zenodo.org/records/10790758</a></p>
Datasets from study: "Land surface observations boost temperature forecast skill: experiments using Long Short-Term Memory surrogate for physics-based models to assess potential predictability"
<p>This repository contains the datasets needed to reproduce the figures from manuscript: Land surface observations boost temperature forecast skill: experiments using Long Short-Term Memory surrogate for physics-based models to assess potential predictability.</p> <p>In this study, we examine the potential of land surface temperature and vegetation data, which are not routinely assimilated in NWP models, for enhancing temperature forecast skill. We build surrogate models for NWP using Long Short-Term Memory.</p>
FluxDataKit v3.4.2: A comprehensive data set of ecosystem fluxes for land surface modelling
<p>The Flux data kit is an effort to expand upon the existing work by Ukkola et a. (2022) to synthesize various sources of ecosystem flux data (i.e. the PLUMBER2 data set, gathered from all major networks). We further expand upon the original data set by integrating data which was either expanded upon (temporally) or where sites were added (e.g. the integration of ICOS data).</p> <p>The effort uses the FluxnetLSM package by the above mentioned authors, as well as their general workflow. In contrast to the PLUMBER2 data set we do not apply stringent quality control, and all quality control on the availability of variables and/or their duration <em>should be done by the user</em>. Furthermore, we include both leaf area index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) in the netcdf output, where PLUMBER2 only provided LAI. On all other parts the formatting and naming conventions as well as quality control specifications remain the same as in PLUMBER2. We therefore refer to Ukkola et al. (2022) for details.</p> <p><strong>Data included</strong></p> <p>The data included consists of the following files, containing different versions of the same data and site meta information.</p> <ul> <li><code>FLUXDATAKIT_LSM.tar.gz</code> file contains compressed NetCDF files compatible with the ALMA scheme for land surface modelling. </li> <li><code>FLUXDATAKIT_FLUXNET.tar.gz</code> file contains data in a CSV format according to the FLUXNET specifications.</li> <li><code>rsofun_driver_data_v3.3.rds</code> file is a compressed serialized R file containing data formatted for use with the {rsofun} R package.</li> <li><code><a href="../api/records/11370417/draft/files/fdk_site_info.csv/content" target="_blank" rel="noopener noreferrer">fdk_site_info.csv</a></code> contains site meta information in tabular form</li> <li><a href="../api/records/11370417/draft/files/fdk_site_fullyearsequence.csv/content" target="_blank" rel="noopener noreferrer"><code>fdk_site_fullyearsequence.csv</code></a> contains information about complete sequences of good-quality data by site (see also <a href="https://geco-bern.github.io/FluxDataKit/articles/04_data_use.html">here</a>).</li> </ul> <p><strong>Data generation</strong></p> <p>Data is generated using the FluxDataKit project. Although this project is not meant for continuous releases, and no support is provided in using this code with data provided AS IS, it might still be useful to some:</p> <p><a href="https://github.com/geco-bern/FluxDataKit">https://github.com/geco-bern/FluxDataKit</a></p> <p>The data can be further complimented using the FluxnetEO dataset, which is accessible through the package with the same name as found here:</p> <p><a href="https://github.com/geco-bern/FluxnetEO">https://github.com/geco-bern/FluxnetEO</a></p> <p><strong>Acknowledgements</strong></p> <p>The flux data kit is part of the LEMONTREE project and funded by Schmidt Futures and under the umbrella of the Virtual Earth System Research Institute (VESRI).</p> <p><strong>References:</strong></p> <ul> <li>Ukkola, Anna M., Gab Abramowitz, and Martin G. De Kauwe. "A flux tower dataset tailored for land model evaluation." Earth System Science Data 14.2 (2022): 449-461.</li> </ul>
Data Repository: Land surface modelling activities at Weierbach catchment.
<p>The data in this repository comes from the modelling activities with the Community Land Model version 5.0 (CLM5) carried out at the Weierbach catchment, Luxembourg. The repository contains:</p> <ol> <li>A list of matric potentials of <em>Fagus sylvatica </em>at which it experiences a specific loss of conductivity (i.e., 12%, 50%, 88%) obtained from published data [File: additional_PHT_Fagus_sylvatica_Europe.csv].</li> <li>The hourly atmospheric forcing used during the simulations with CLM 5.0 in a NetCDF format [File: atmospheric_forcing.zip].</li> <li>All model results per experiment [model_results.zip].</li> <li>The R scripts for processing the model results for obtaining the information required for each figure [Files: manuscript_figure_#.R].</li> <li>A daily summary of the tree water deficit calculated per PFT, individual tree species, and the whole ecosystem [File: twd.csv].</li> <li>A daily summary of tree transpiration scaled at the catchment level per PFT, individual tree species, and the whole ecosystem [File: et_mm_wei.csv]. This daily summary is based on the hourly data available on: Klaus, J., Fabiani, G., Schoppach, R., Chun, K. P., Iffly, J. F., Penna, D., & Juilleret, J. (2024). Detailed sap flow monitoring data at Weierbach catchment, Luxembourg (Version v01) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.11381618" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11381618</a></li> </ol>
Data archive for journal paper "Assimilation of Sentinel-1 Backscatter into a Land Surface Model with River Routing and Its Impact on Streamflow Simulations in Two Belgian Catchments"
<p>The datasets archived here include data assimilation results presented in the journal paper, "Assimilation of Sentinel-1 Backscatter into a Land Surface Model with River Routing and Its Impact on Streamflow Simulations in Two Belgian Catchments" (https://doi.org/10.1175/JHM-D-22-0198.1). The output was produced by combining land surface modeling (Noah-MP with HYMAP river routing) and Sentinel-1 backscatter data, applying a 1D Ensemble Kalman Filter using the NASA Land Information System. We provide Netcdf daily output files for 6 different experiments</p><p>- OLfd and OLgw: model-only (open-loop, OL) for two different model settings (fd: free drainage and gw: SIMTOP groundwater option) <br>- DASMfd and DASMgw: data assimilation (DA) with soil moisture (SM) updating for two different model settings (fd: free drainage and gw: SIMTOP groundwater option) <br>- DASMLAIfd and DASMLAIgw: data assimilation (DA) with soil moisture (SM) and leaf area index (LAI) updating for two different model settings (fd: free drainage and gw: SIMTOP groundwater option) </p><p>Each experiment directory contains five subdirectories (DAOBS, EnKF, ROUTING, RTM, SURFACEMODEL) with corresponding outputs as described in https://nasa-lis.github.io/LISF/LIS_users_guide/LIS_users_guide.html</p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2001_2005)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2006_2010)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change.</p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model : Model Ouput Data
<p>This upload includes data associated with the manuscript "Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model : )" submitted to Geoscientific Model Development. The dataset includes an output file with the simulated ammonia emissions for the agricultural sector.</p> <p>The emissions (manure management and soil), manure production and soil ammonium concentrations are monthly fields from the simulation for 2007-2015.</p> <p>Additional information is given in the readme file</p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2006_2010)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2011_2015)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2016_2020)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2001_2005)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
A boreal forest model benchmarking dataset for North America: a case study with the Canadian Land Surface Scheme including Biogeochemical Cycles (CLASSIC)
<p>A boreal forest model benchmarking dataset for North America by harmonizing eddy covariance and supporting measurements from black spruce (Picea mariana)-dominated mature forest stands.</p> <p>Dataset glossary and users’ instructions are documented in ‘README.md’. </p>
Realistic afforestation scenarios in Great Britain at a 1 km scale to run with the land surface model JULES
<p>Afforestation scenarios created in the work of Buechel et al. (XXXX) which cover Great Britain at a 1 km spatial resolution and attempt to represent potential realistic broadleaf afforestation. The datasets represent a 50% and 100% afforestation scenario. The netCDF files are designed so that may be run with the Joint UK Land Environment Simulator (JULES), a community land surface model. The dataset is structured similar to the CHESS-land dataset (Martinez-De La Torre, 2018) where each grid contains information on the fractional coverage of eight different land cover types: Broadleaf woodland, needleleaf woodland, grassland, shrubland, crops, bare soil, urban areas and inland water. </p> <p> </p> <p>This dataset was created as part of the NERC doctoral training partnerships (grant number NE/L002612/1).</p> <p> </p> <p>Martinez-de la Torre, A.., Blyth, E.M.. M. and Robinson, E.L.. L. (2018) ‘Water, carbon and energy fluxes simulation for Great Britain using the JULES Land Surface Model and the Climate Hydrology and Ecology research Support System meteorology dataset (1961-2015) [CHESS-land]’. NERC Environmental Information Data Centre. doi:10.5285/c76096d6-45d4-4a69-a310-4c67f8dcf096.</p> <p> </p>
Improving nitrogen cycling in a land surface model (CLM5) to quantify soil N2O, NO and NH3 emissions from enhanced rock weathering with croplands
<p>This repository contains CLM5 model soil nitrogen (N2O, NO and NH3) output and metadata, and soil N<sub>2</sub>O fluxes from the Energy Farm ERW field trials used in the paper "Improving nitrogen cycling in a land surface model (CLM5) to quantify soil N2O, NO and NH3 emissions from enhanced rock weathering with croplands" (https://gmd.copernicus.org/preprints/gmd-2023-47/).</p>
PALM Model System v 6.0 input and configuration files for coupled large eddy simulations of land surface heterogeneity effects and diurnal evolution of late summer and early autumn atmospheric boundary layers during the CHEESEHEAD19 field campaign
<p>Namelist, configuration and forcing files for the PALM Model System 6.0 revision number 21.10-rc.2 used for the numerical simulations Coupled Large Eddy Simulations of land surface heterogeneity induced atmospheric boundary layer response during the CHEESEHEAD19 field campaign.</p>
Output of the Land Surface Model ORCHIDEE over river catchments in Europe, run with GSWP3 and synthetic forcings where the precipitation is modified
<p># Description of the data file</p> <p>This dataset contains the main outputs used for the results of the article: "Budyko framework based analysis of the effect of climate change on watershed evaporation efficiency and its impact on discharge over Europe", by Julie Collignan, Jan Polcher, Sophie Bastin, Pere Quintana-Segui, accepted by the journal *Water Resources Research*.</p> <p>This study uses the outputs of a land surface model (LSM), forced with differentan atmospheric datasets from 1901 to 2010. The atmospheric dataset are based on GSWP3 (Hyungjun, K. (2017), doi: 10.20783/DIAS.501) and was modified to create synthetic forcings with different precipitation characteristics (annual average, intra-annual distribution). The LSM was run with all synthetic forcings, and the outputs (precipitation, evapotranspiration, potential evapotranspiration, discharge) were integrated at the level of each river basin which are sampled by gauging stationss. These outputs are used to fit a parametric equation of the Budyko framework to decompose the partial trends in discharge and the relative weight of the different climatic components.</p> <p># Details of the variables and attribute of the file</p> <p>This study was led over 2196 river basins over Europe. </p> <p>For each catchment used in the study, it gathers:<br> - name of the station at the outlet *name*<br> - name of the river associated *rivers*<br> - lat/lon of the station *Localisation*<br> - upstream area of the catchment *upstream*<br> - Observed discharge (data not used in the article) *DisObs*<br> These data come from three different sources:<br> * *Global Runoff Data Centre (GRDC)*, https://www.bafg.de/GRDC/EN/02_srvcs/21_tmsrs/riverdischarge_node.html ;<br> * *Ministere de lenergie* (February 2021), https://www.hydro.eaufrance.fr/" ;<br> * *Geoportal of Spain Ministerio (Ministerio de Agricultura, pesca y alimentacion, Ministerio para la transicion ecologica y el reto demografico*, 2020</p> <p>Each catchment was projected on the grid of the LSM ORCHIDEE (*IPSL, https://orchidee.ipsl.fr/*) during the construction of its river routing system. More details are given in the associated article.</p> <p>The results for four different run of the LSM are included in this dataset. This dataset gathers for each run:<br> * Precipitation *P*<br> * Evapotranspiration *E*<br> * Potential evapotranspiration *PET*<br> * Discharge *DisMod*</p> <p>The different synthetic forcings are:<br> - the reference forcing with the un-modified atmospheric dataset: *the Global Soil Wetness Project Phase 3 (GSWP3)*, Hyungjun, K. (2017), doi: 10.20783/DIAS.501<br> - *f2000*: A forcing where all 3h values of $P$ are set to the values of the year 2000 (September 1999 to September 2000) for each year. Therefore, all components of $P$ (average and intra-annual variations) are set constant.<br> - *cstmean*: A forcing for which we keep the relative intra-annual distribution of $P$ of each year, but where the average $P$ of each year is set constant. The 3h values of $P$ are scaled so the hydrological year average is set to the one of the year 2000 (September 1999 to September 2000).<br> - *cstintravar*: A forcing for which we keep the annual average of $P$ for each year, but where the relative intra-annual distribution of $P$ is set constant. The 3h values of $P$ are set to the values of the year 2000 (September 1999 to September 2000) for each year and then scaled over each hydrological year so the yearly average is set to the one of the corresponding years in the reference forcing.<br> \end{itemize}</p> <p>## ncdump -h Filename.nc</p> <p>```<br> dimensions:<br> basins = 2196 ;<br> years = UNLIMITED ; // (110 currently)<br> loc = 2 ;<br> lenstr = 57 ;<br> variables:<br> short years(years) ;<br> years:long_name = "Years" ;<br> years:units = "year" ;<br> char names(basins, lenstr) ;<br> names:long_name = "Name of the station at the catchment output" ;<br> char rivers(basins, lenstr) ;<br> rivers:long_name = "Name of the river where the station is positioned" ;<br> float upstream(basins) ;<br> upstream:long_name = "Upstream area of the catchment" ;<br> upstream:units = "km^2" ;<br> float Localisation(basins, loc) ;<br> Localisation:long_name = "Position of each catchment: (Lon, Lat)" ;<br> Localisation:units = "degrees_east, degrees_north" ;<br> float DisObs(years, basins) ;<br> DisObs:long_name = "Discharge observation at the outlet of the catchment" ;<br> DisObs:units = "m3/s" ;<br> float E_ref(years, basins) ;<br> E_ref:long_name = "Modeled average annual evaporation with forcing ref" ;<br> E_ref:units = "m3/s" ;<br> float P_ref(years, basins) ;<br> P_ref:long_name = "Average annual precipitation for forcing ref" ;<br> P_ref:units = "m3/s" ;<br> float PET_ref(years, basins) ;<br> PET_ref:long_name = "Modeled average annual potential evaporation with forcing ref" ;<br> PET_ref:units = "m3/s" ;<br> float DisMod_ref(years, basins) ;<br> DisMod_ref:long_name = "Modeled Discharge with forcing ref" ;<br> DisMod_ref:units = "m3/s" ;<br> float E_f2000(years, basins) ;<br> E_f2000:long_name = "Modeled average annual evaporation with forcing f2000" ;<br> E_f2000:units = "m3/s" ;<br> float P_f2000(years, basins) ;<br> P_f2000:long_name = "Average annual precipitation for forcing f2000" ;<br> P_f2000:units = "m3/s" ;<br> float PET_f2000(years, basins) ;<br> PET_f2000:long_name = "Modeled average annual potential evaporation with forcing f2000" ;<br> PET_f2000:units = "m3/s" ;<br> float DisMod_f2000(years, basins) ;<br> DisMod_f2000:long_name = "Modeled Discharge with forcing f2000" ;<br> DisMod_f2000:units = "m3/s" ;<br> float E_cstmean(years, basins) ;<br> E_cstmean:long_name = "Modeled average annual evaporation with forcing cstmean" ;<br> E_cstmean:units = "m3/s" ;<br> float P_cstmean(years, basins) ;<br> P_cstmean:long_name = "Average annual precipitation for forcing cstmean" ;<br> P_cstmean:units = "m3/s" ;<br> float PET_cstmean(years, basins) ;<br> PET_cstmean:long_name = "Modeled average annual potential evaporation with forcing cstmean" ;<br> PET_cstmean:units = "m3/s" ;<br> float DisMod_cstmean(years, basins) ;<br> DisMod_cstmean:long_name = "Modeled Discharge with forcing cstmean" ;<br> DisMod_cstmean:units = "m3/s" ;<br> float E_cstintravar(years, basins) ;<br> E_cstintravar:long_name = "Modeled average annual evaporation with forcing cstintravar" ;<br> E_cstintravar:units = "m3/s" ;<br> float P_cstintravar(years, basins) ;<br> P_cstintravar:long_name = "Average annual precipitation for forcing cstintravar" ;<br> P_cstintravar:units = "m3/s" ;<br> float PET_cstintravar(years, basins) ;<br> PET_cstintravar:long_name = "Modeled average annual potential evaporation with forcing cstintravar" ;<br> PET_cstintravar:units = "m3/s" ;<br> float DisMod_cstintravar(years, basins) ;<br> DisMod_cstintravar:long_name = "Modeled Discharge with forcing cstintravar" ;<br> DisMod_cstintravar:units = "m3/s" ;</p> <p>// global attributes:<br> :author = "Julie Collignan, julie.collignan@lmd.ipsl.fr" ;<br> :model = "ORCHIDEE, IPSL, https://orchidee.ipsl.fr/" ;<br> :source_stations1 = "Global Runoff Data Centre (GRDC), https://www.bafg.de/GRDC/EN/02_srvcs/21_tmsrs/riverdischarge_node.html" ;<br> :source_stations2 = "Ministere de lenergie (February 2021), https://www.hydro.eaufrance.fr/" ;<br> :source_stations3 = "Geoportal of Spain Ministerio (Ministerio de Agricultura, pesca y alimentacion, Ministerio para la transicion ecologica y el reto demografico, 2020" ;<br> :atmospheric_dataset = "GSWP3, Hyungjun, K. (2017), doi: 10.20783/DIAS.501" ;<br> :date = "28/07/2023";<br> }<br> ```</p>
Modeling archive of How does humidity data impact land surface modeling of hydrothermal regimes at a permafrost site in Utqiaġvik, Alaska?
<p>Modeling archive contains the meteorological forcings, model input files, and Jupyter notebooks used to generate model meshes and figures for the paper entitled "How does humidity data impact land surface modeling of hydrothermal regimes at a permafrost site in Utqiaġvik, Alaska?"</p>
Flux data kit, a comprehensive data set of ecosystem fluxes for land surface modelling
<blockquote> <p>Newer versions (>= v3.0) are released by Benjamin Stocker and can be found at his Zenodo account at: https://zenodo.org/records/10885934</p> </blockquote> <p>The Flux data kit is an effort to expand upon the existing work by Ukolla et a. (2022) to synthesize various sources of ecosystem flux data (i.e. the PLUMBER2 data set, gathered from all major networks). We further expand upon the original data set by integrating data which was either expanded upon (temporally) or where sites were added (e.g. the integration of ICOS data).</p> <p>The effort uses the FluxnetLSM package by the above mentioned authors, as well as their general workflow. In contrast to the PLUMBER2 data set we do not apply stringent quality control, and all quality control on the availability of variables and/or their duration <em>should be done by the user</em>. Furthermore, we include both leaf area index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) in the netcdf output, where PLUMBER2 only provided LAI. On all other parts the formatting and naming conventions as well as quality control specifications remain the same as in PLUMBER2. We therefore refer to Ukolla et al. (2022) for details.</p> <p><strong>Data included</strong></p> <p>The data included consists of three files, containing different versions of the same data. The FLUXDATAKIT_LSM.tar.gz file contains compressed netCDF files compatible with the ALMA scheme for land surface modelling. The FLUXDATAKIT_FLUXNET.tar.gz file contains data in a CSV format according to the FLUXNET specifications. And finally the rsofun_driver_data_clean.rds file is a compressed serialized R file containing data formatted for use with the `rsofun` R package.</p> <p><strong>Data generation</strong></p> <p>Data is generated using the FluxDataKit project. Although this project is not meant for continuous releases, and no support is provided in using this code with data provided AS IS, it might still be useful to some:</p> <p><a href="https://github.com/geco-bern/FluxDataKit">https://github.com/geco-bern/FluxDataKit</a></p> <p>The data can be further complimented using the FluxnetEO dataset, which is accessible through the package with the same name as found here:</p> <p><a href="https://github.com/geco-bern/FluxnetEO">https://github.com/geco-bern/FluxnetEO</a></p> <p><strong>Acknowledgements</strong></p> <p>The flux data kit is part of the LEMONTREE project and funded by Schmidt Futures and under the umbrella of the Virtual Earth System Research Institute (VESRI).</p> <p><strong>References:</strong></p> <ul> <li>Ukkola, Anna M., Gab Abramowitz, and Martin G. De Kauwe. "A flux tower dataset tailored for land model evaluation." Earth System Science Data 14.2 (2022): 449-461.</li> </ul>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.