11 documents found in 623ms
# 1
Reyer, Christopher • Silveyra Gonzalez, Ramiro • Dolos, Klara • Hartig, Florian • Hauf, Ylva • (et. al.)
Abstract: Current process-based vegetation models are complex scientific tools that require proper evaluation of the different processes included in the models to prove that the models can be used to integrate our understanding of forest ecosystems and project climate change impacts on forests. The PROFOUND database (PROFOUND DB) described here aims to bring together data from a wide range of data sources to evaluate vegetation models and simulate climate impacts at the forest stand scale. It has been designed to fulfill two objectives:- Allow for a thorough evaluation of complex, process-based vegetation models using multiple data streams covering a range of processes at different temporal scales- Allow for climate impact assessments by providing the latest climate scenario data. Therefore, the PROFOUND DB provides general a site description as well as soil, climate, CO2, Nitrogen deposition, tree-level, forest stand-level and remote sensing data for 9 forest stands spread throughout Europe. Moreover, for a subset of 5 sites, also time series of carbon fluxes, energy balances and soil water are available. The climate and nitrogen deposition data contains several datasets for the historic period and a wide range of future climate change scenarios following the Representative Emission Pathways (RCP2.6, RCP4.5, RCP6.0, RCP8.5). In addition, we also provide pre-industrial climate simulations that allow for model runs aimed at disentangling the contribution of climate change to observed forest productivity changes. The PROFOUND Database is available freely but we incite users to respect the data policies of the individual datasets as provided in the metadata of each data file. The database can also be accessed via the PROFOUND R-package, which provides basic functions to explore, plot and extract the data. The data (PROFOUND DB) are provided in two different versions (ProfoundData.sqlite, ProfoundData_ASCII.zip) and documented by the following three documents: (1) PROFOUNDdatabase.pdf: describes the structure, organisation and content of the PROFOUND DB.(2) PROFOUNDsites.pdf: displays the main data of the PROFOUND DB for each of the 9 forest sites in tables and plots.(3) ProfoundData.pdf: explains how to use the PROFOUND R-Package "ProfoundData" to access the PROFOUND DB and provides example scripts on how to apply it.
# 2
Porwollik, Vera • Rolinski, Susanne • Müller, Christoph
Abstract: Tillage is a central element in agricultural soil management and has direct and indirect effects on processes in the biosphere. Effects of agricultural soil management can be assessed by soil, crop, and ecosystem models but global assessments are hampered by lack of information on type and spatial distribution. This dataset is the result of a study on global classification of tillage practices and the spatially explicit mapping of crop-specific tillage systems for around the year 2005. This global gridded tillage system data set is dedicated to modeling communities interested in the quantitative assessment of biophysical and biogeochemical impacts of land use and soil management on cropland. The data set is complemented by the publication of the R- code and can be used for reproducing and build upon for scenarios including the expansion of sustainable soil management practices as Conservation Agriculture (Porwollik et al. 2018, http://doi.org/10.5880/PIK.2018.013). Both, the data set and the R-code are described in detail in Porwollik et al. (2018, ESSD). We present the mapping result of six tillage systems for 42 crop types and potential suitable Conservation Agriculture area as the following variables: We present the mapping result of six tillage systems for 42 crop types and potentially suitable Conservation Agriculture area as variables:1 = conventional annual tillage2 = traditional annual tillage3 = reduced tillage4 = Conservation Agriculture5 = rotational tillage6 = traditional rotational tillage7 = potential suitable Conservation Agriculture area Reference system: WGS84Geographic extent: Longitude (min, max) (-180, 180), Latitude (min, max) (-56, 84)Resolution: 5 arc-minutesTime period covered: around the year 2005Type: NetCDF Dataset sources (with indication of reference): 1. Grid cell allocation key to country: IFPRI/IIASA (2017, cell5m_allockey_xy.dbf.zip)2. Crop-specific physical cropland: IFPRI/IIASA (2017, spam2005v3r1_global_phys_area.geotiff.zip)3. SoilGrids depth to bedrock: Hengl et al. (2014)4. Aridity index: FAO (2015)5. Conservation Agriculture area: FAO (2016)6. Income level: World Bank (2017)7. Field size: Fritz et al. (2015)8. Water erosion: Nachtergaele et al. (2011)
This tillage dataset is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/.
# 3
Porwollik, Vera • Rolinski, Susanne • Müller, Christoph
Abstract: Tillage is a central element in agricultural soil management and has direct and indirect effects on processes in the biosphere. Effects of agricultural soil management can be assessed by soil, crop, and ecosystem models but global assessments are hampered by lack of information on soil management systems. This study presents a classification of globally relevant tillage practices and a global spatially explicit data set on the distribution of tillage practices for around the year 2005. This source code complements the dataset on the global gridded tillage system mapping described in Porwollik et al. (2018, http://doi.org/10.5880/PIK.2018.012). It shall help interested people in understanding the findings on the global gridded tillage system mapping. The code, programmed in R, can be used for reproducing and build upon for scenarios including the expansion of sustainable soil management practices as CA. Both, the data set and the R-code are described in detail in Porwollik et al. (2018, ESSD). The code is written in the statistical software 'R' using the 'raster', 'fields', and 'ncdf4' packages. We present the mapping result of six tillage systems for 42 crop types and potentially suitable Conservation Agriculture area as variables:1 = conventional annual tillage2 = traditional annual tillage3 = reduced tillage4 = Conservation Agriculture5 = rotational tillage6 = traditional rotational tillage7 = potential suitable Conservation Agriculture area Reference system: WGS84Geographic extent: Longitude (min, max) (-180, 180), Latitude (min, max) (-56, 84)Resolution: 5 arc-minutesTime period covered: around the year 2005Type: NetCDF Dataset sources (with indication of reference):1. Grid cell allocation key to country: IFPRI/IIASA (2017, cell5m_allockey_xy.dbf.zip)2. Crop-specific physical cropland: IFPRI/IIASA (2017, spam2005v3r1_global_phys_area.geotiff.zip)3. SoilGrids depth to bedrock: Hengl et al. (2014)4. Aridity index: FAO (2015)5. Conservation Agriculture area: FAO (2016)6. Income level: World Bank (2017)7. Field size: Fritz et al. (2015)8. Water erosion: Nachtergaele et al. (2011)
# 4
Porwollik, Vera • Rolinski, Susanne • Müller, Christoph
Abstract: Tillage is a central element in agricultural soil management and has direct and indirect effects on processes in the biosphere. Effects of agricultural soil management can be assessed by soil, crop, and ecosystem models but global assessments are hampered by lack of information on soil management systems. This study presents a classification of globally relevant tillage practices and a global spatially explicit data set on the distribution of tillage practices for around the year 2005. This source code complements the dataset on the global gridded tillage system mapping described in Porwollik et al. (2018, http://doi.org/10.5880/PIK.2018.012). It shall help interested people in understanding the findings on the global gridded tillage system mapping. The code, programmed in R, can be used for reproducing and build upon for scenarios including the expansion of sustainable soil management practices as CA. Both, the data set and the R-code are described in detail in Porwollik et al. (2018, ESSD). The code is written in the statistical software 'R' using the 'raster', 'fields', and 'ncdf4' packages. We present the mapping result of six tillage systems for 42 crop types and potentially suitable Conservation Agriculture area as variables:1 = conventional annual tillage2 = traditional annual tillage3 = reduced tillage4 = Conservation Agriculture5 = rotational tillage6 = traditional rotational tillage7 = Scenario Conservation Agriculture area Reference system: WGS84Geographic extent: Longitude (min, max) (-180, 180), Latitude (min, max) (-56, 84)Resolution: 5 arc-minutesTime period covered: around the year 2005Type: NetCDF Dataset sources (with indication of reference):1. Grid cell allocation key to country: IFPRI/IIASA (2017, cell5m_allockey_xy.dbf.zip)2. Crop-specific physical cropland: IFPRI/IIASA (2017, spam2005v3r1_global_phys_area.geotiff.zip)3. SoilGrids depth to bedrock: Hengl et al. (2014)4. Aridity index: FAO (2015)5. Conservation Agriculture area: FAO (2016)6. Income level: World Bank (2017)7. Field size: Fritz et al. (2015)8. GLADIS - Water erosion: Nachtergaele et al. (2011) CHANGELOG for Version 1.1:improved calculation and mapping, for details see README.PDF
# 5
Porwollik, Vera • Rolinski, Susanne • Müller, Christoph
Abstract: Tillage is a central element in agricultural soil management and has direct and indirect effects on processes in the biosphere. Effects of agricultural soil management can be assessed by soil, crop, and ecosystem models but global assessments are hampered by lack of information on type and spatial distribution. This dataset is the result of a study on global classification of tillage practices and the spatially explicit mapping of crop-specific tillage systems for around the year 2005. This global gridded tillage system data set is dedicated to modeling communities interested in the quantitative assessment of biophysical and biogeochemical impacts of land use and soil management on cropland. The data set is complemented by the publication of the R- code and can be used for reproducing and build upon for scenarios including the expansion of sustainable soil management practices as Conservation Agriculture (Porwollik et al. 2018, http://doi.org/10.5880/PIK.2018.013). Both, the data set and the R-code are described in detail in Porwollik et al. (2018, ESSD). We present the mapping result of six tillage systems for 42 crop types and potential suitable Conservation Agriculture area as the following variables: We present the mapping result of six tillage systems for 42 crop types and potentially suitable Conservation Agriculture area as variables:1 = conventional annual tillage2 = traditional annual tillage3 = reduced tillage4 = Conservation Agriculture5 = rotational tillage6 = traditional rotational tillage7 = Scenario Conservation Agriculture area Reference system: WGS84Geographic extent: Longitude (min, max) (-180, 180), Latitude (min, max) (-56, 84)Resolution: 5 arc-minutesTime period covered: around the year 2005Type: NetCDF Dataset sources (with indication of reference): 1. Grid cell allocation key to country: IFPRI/IIASA (2017, cell5m_allockey_xy.dbf.zip)2. Crop-specific physical cropland: IFPRI/IIASA (2017, spam2005v3r1_global_phys_area.geotiff.zip)3. SoilGrids depth to bedrock: Hengl et al. (2014)4. Aridity index: FAO (2015)5. Conservation Agriculture area: FAO (2016)6. Income level: World Bank (2017)7. Field size: Fritz et al. (2015)8. GLADIS - Water erosion: Nachtergaele et al. (2011) CHANGELOG for Version 1.1improved calculation and mapping, for details see README.PDF
This tillage dataset is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/.
# 6
Arzhanov, Maxim • Betts, Richard • Eliseev, Alexey • Morfopoulos, Catherine • Schaphoff, Sibyll • (et. al.)
Abstract: Description of changes in the new version:- On October 18, 2018 we republished all simulation data for all impact models to get the data sets into the new search facet structure. There were no changes to the simulation data.- Files for JULES-B1 (formerly JULES_UoE) were not available since the date of issuing the DOI until March 13, 2019. Until that date, these files were only available in the ISIMIP DKRZ server. ---------------------------------------------------------------------The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) simulation data is under continuous review and improvement, and updates are thus likely to happen. All changes and caveats are documented under https://www.isimip.org/outputdata/output-data-changelog/. For accessing the data set as in http://doi.org/10.5880/PIK.2018.006 before March 13, 2019 please write to the ISIMIP Data Management Team: isimip-data[at]pik-potsdam.de--------------------------------------------------------------------- The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a set of consistent, multi-sector, multi-scale climate-impact simulations, based on scientifically and politically-relevant historical and future scenarios. This framework serves as a basis for robust projections of climate impacts, as well as facilitating model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. It also provides a unique opportunity to consider interactions between climate change impacts across sectors. ISIMIP2a is the second ISIMIP simulation round, focusing on historical simulations (1971-2010) of climate impacts on agriculture, fisheries, permafrost, biomes, regional and global water and forests. This may serve as a basis for model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. The focus topic for ISIMIP2a is model evaluation and validation, in particular with respect to the representation of impacts of extreme weather events and climate variability. During this phase, four common global observational climate data sets were provided across all impact models and sectors. In addition, appropriate observational data sets of impacts for each sector were collected, against which the models can be benchmarked. Access to the input data for the impact models is provided through a central ISIMIP archive (see ISIMIP 2a Input Data & Bias Correction at https://www.isimip.org/gettingstarted/#input-data-bias-correction). This entry refers to the ISIMIP2a simulation data from permafrost models: JULES-B1 (formerly JULES_UoE), LPJmL, IAPRAS-DSS.
The ISIMIP2a Permafrost outputs are based on simulations from 3 permafrost models (see listing) according to the ISIMIP2a Simulation Protocol (https://www.isimip.org/protocol/#isimip2a). The models simulate coupled water and carbon processes, like the soil carbon storage on permafrost soils, non-linear effects in changing vegetation and fire, and the physical state of the permafrost based on soil, climate and physio-geographical information. A more detailed description of the models and model-specific amendments of the protocol are available here: https://www.isimip.org/impactmodels/.
# 7
Arzhanov, Maxim • Betts, Richard • Eliseev, Alexey • Morfopoulos, Catherine • Schaphoff, Sibyll • (et. al.)
Abstract: The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a set of consistent, multi-sector, multi-scale climate-impact simulations, based on scientifically and politically-relevant historical and future scenarios. This framework serves as a basis for robust projections of climate impacts, as well as facilitating model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. It also provides a unique opportunity to consider interactions between climate change impacts across sectors. ISIMIP2a is the second ISIMIP simulation round, focusing on historical simulations (1971-2010) of climate impacts on agriculture, fisheries, permafrost, biomes, regional and global water and forests. This may serve as a basis for model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. The focus topic for ISIMIP2a is model evaluation and validation, in particular with respect to the representation of impacts of extreme weather events and climate variability. During this phase, four common global observational climate data sets were provided across all impact models and sectors. In addition, appropriate observational data sets of impacts for each sector were collected, against which the models can be benchmarked. Access to the input data for the impact models is provided through a central ISIMIP archive (see ISIMIP 2a Input Data & Bias Correction at https://www.isimip.org/gettingstarted/#input-data-bias-correction). This entry refers to the ISIMIP2a simulation data from permafrost models: JULES-B1 (formerly JULES_UoE), LPJmL, IAPRAS-DSS.
The ISIMIP2a Permafrost outputs are based on simulations from 3 permafrost models (see listing) according to the ISIMIP2a Simulation Protocol (https://www.isimip.org/protocol/#isimip2a). The models simulate coupled water and carbon processes, like the soil carbon storage on permafrost soils, non-linear effects in changing vegetation and fire, and the physical state of the permafrost based on soil, climate and physio-geographical information. A more detailed description of the models and model-specific amendments of the protocol are available here: https://www.isimip.org/impactmodels/.
# 8
Gosling, Simon • Müller Schmied, Hannes • Betts, Richard • Chang, Jinfeng • Ciais, Philippe • (et. al.)
Abstract: VERSION HISTORY:-On October 18, 2018 we republished all simulation data for all water (global) sector impact models to get the data sets into the new ESGF search facet structure. There were no changes to the simulation data.- On November 27, 2018 we republished simulation data for monthly variables swe, soilmoist and rootmoist for impact model PCR-GLOBWB due to an error in the units. Instead of reporting mass per area (kg/m2), values corresponded to mass flux rate (kg/m2/s). Values were thus multiplied by 86400 in order to obtain the correct values in kg/m2. This data caveat was documented in the ISIMIP website (ISIMIP2a: PCR-GLOBWB reported three variables in wrong unit). ----------------------------------------------------------------------------The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) simulation data is under continuous review and improvement, and updates are thus likely to happen. All changes and caveats are documented under https://www.isimip.org/outputdata/output-data-changelog/. For accessing the data set as in http://doi.org/10.5880/PIK.2017.010 before November 27, 2018 please write to the ISIMIP Data Management Team: isimip-data[at]pik-potsdam.de.---------------------------------------------------------------------------- DATA DESCRIPTION: The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a set of consistent, multi-sector, multi-scale climate-impact simulations, based on scientifically and politically-relevant historical and future scenarios. This framework serves as a basis for robust projections of climate impacts, as well as facilitating model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. It also provides a unique opportunity to consider interactions between climate change impacts across sectors. ISIMIP2a is the second ISIMIP simulation round, focusing on historical simulations (1971-2010 approx.) of climate impacts on agriculture, fisheries, permafrost, biomes, regional and global water and forests. This may serve as a basis for model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. The focus topic for ISIMIP2a is model evaluation and validation, in particular with respect to the representation of impacts of extreme weather events and climate variability. During this phase, four common global observational climate data sets were provided across all impact models and sectors. In addition, appropriate observational data sets of impacts for each sector were collected, against which the models can be benchmarked. Access to the input data for the impact models is provided through a central ISIMIP archive (see https://www.isimip.org/gettingstarted/#input-data-bias-correction). This entry refers to the ISIMIP2a simulation data from global hydrology models: CLM4, DBH, H08, JULES_W1, JULES_B1, LPJmL, MATSIRO, MPI-HM, ORCHIDEE, PCR-GLOBWB, SWBM, VIC, WaterGAP2
The ISIMIP2a water (global) outputs are based on simulations from 13 global hydrology models (see listing) according to the ISIMIP2a protocol (https://www.isimip.org/protocol/#isimip2a). The models simulate hydrological processes and dynamics (part of the models also considering human water abstractions and reservoir regulation) based on climate and physio-geographical information. A more detailed description of the models and model-specific amendments of the protocol are available here: https://www.isimip.org/impactmodels/.
# 9
Gosling, Simon • Müller Schmied, Hannes • Betts, Richard • Chang, Jinfeng • Ciais, Philippe • (et. al.)
Abstract: The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a set of consistent, multi-sector, multi-scale climate-impact simulations, based on scientifically and politically-relevant historical and future scenarios. This framework serves as a basis for robust projections of climate impacts, as well as facilitating model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. It also provides a unique opportunity to consider interactions between climate change impacts across sectors. ISIMIP2a is the second ISIMIP simulation round, focusing on historical simulations (1971-2010 approx.) of climate impacts on agriculture, fisheries, permafrost, biomes, regional and global water and forests. This may serve as a basis for model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. The focus topic for ISIMIP2a is model evaluation and validation, in particular with respect to the representation of impacts of extreme weather events and climate variability. During this phase, four common global observational climate data sets were provided across all impact models and sectors. In addition, appropriate observational data sets of impacts for each sector were collected, against which the models can be benchmarked. Access to the input data for the impact models is provided through a central ISIMIP archive (see https://www.isimip.org/gettingstarted/#input-data-bias-correction). This entry refers to the ISIMIP2a simulation data from global hydrology models: CLM4, DBH, H08, JULES_W1, JULES_B1, LPJmL, MATSIRO, MPI-HM, ORCHIDEE, PCR-GLOBWB, SWBM, VIC, WaterGAP2.
The ISIMIP2a water (global) outputs are based on simulations from 13 global hydrology models (see listing) according to the ISIMIP2a protocol (https://www.isimip.org/protocol/#isimip2a). The models simulate hydrological processes and dynamics (part of the models also considering human water abstractions and reservoir regulation) based on climate and physio-geographical information. A more detailed description of the models and model-specific amendments of the protocol are available here: https://www.isimip.org/impactmodels/.
# 10
Krysanova, Valentina • Hattermann, Fred • Aich, Valentin • Alemayehu, Tadesse • Arheimer, Berit • (et. al.)
Abstract: The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a set of consistent, multi-sector, multi-scale climate-impact simulations, based on scientifically and politically-relevant historical and future scenarios. This framework serves as a basis for robust projections of climate impacts, as well as facilitating model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. It also provides a unique opportunity to consider interactions between climate change impacts across sectors. ISIMIP2a is the second ISIMIP simulation round, focusing on historical simulations (1971-2010) of climate impacts on agriculture, fisheries, permafrost, biomes, regional and global water and forests. This will serve as a basis for model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. In the regional water sector, future simulations of climate-change impacts were also carried out, using climate data from five global climate models (GCMs: HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, GFDL-ESM2M and NorESM1-M) for the four Representative Concentration Pathways (RCPs: RCP2.6, RCP4.5, RCP6.0 and RCP8.5). The focus topic for ISIMIP2a is model evaluation and validation, in particular with respect to the representation of impacts of extreme weather events and climate variability. During this phase, four common global observational climate data sets were provided across all impact models and sectors. In addition, appropriate observational data sets of impacts for each sector were collected, against which the models can be benchmarked. Access to the input data for the impact models is provided through a central ISIMIP archive (see https://www.isimip.org/gettingstarted/#input-data-bias-correction). This entry refers to the ISIMIP2a simulation data from regional hydrology models (river basins in brackets):HBV-CMA (Yangtze)HBV-IWW (Tagus)HBV-JLU (Rhine, Ganges, Mississippi)HBV-PIK (Rhine, Niger, Yellow, Blue Nile, Amazon)HYMOD-JLU (Rhine, Ganges, Mississippi)HYMOD-UFZ (Rhine, Niger, Blue Nile, Ganges, Yellow, Darling, Mississippi, Amazon)HYPE (Rhine, Tagus, Niger, Ganges, Lena, Mackenzie)mHM (Rhine, Niger, Blue Nile, Ganges, Yellow, Darling, Mississippi, Amazon)SWAP (Rhine, Tagus, Niger, Ganges, Yellow, Yangtze; Lena, Darling, MacKenzie, Mississippi, Amazon)SWAT (Yangtze; Darling; Blue Nile; Amazon; Mississippi; Niger)SWIM (Rhine, Yellow, Mississippi; Niger; Lena; Tagus; Blue Nile; Yangtze; Ganges, Amazon)VIC (Tagus, Blue Nile, Yellow, Lena, Darling, Amazon, MacKenzie; Rhine, Niger, Mississippi; Ganges; Yangtze)VIP (Yellow)WaterGAP3 (Rhine, Tagus, Niger, Blue Nile, Ganges, Yellow, Lena, Mississippi)ECOMAG (Lena, MacKenzie)
The ISIMIP2a water (regional) outputs are based on simulations from 15 regional hydrology models (see listing) according to the ISIMIP2a protocol (https://www.isimip.org/protocol/#isimip2a). The models simulate hydrological processes and dynamics (part of the models also considering human water abstractions and reservoir regulation) based on climate and physio-geographical information. A more detailed description of the models and model-specific amendments of the protocol are available here: https://www.isimip.org/impactmodels/.
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