131 documents found in 750ms
# 1
Schaphoff (Ed.), Sibyll • von Bloh, Werner • Thonicke, Kirsten • Biemans, Hester • Forkel, Matthias • (et. al.)
Abstract: LPJmL4 is a process-based model that simulates climate and land-use change impacts on the terrestrial biosphere, the water and carbon cycle and on agricultural production. The LPJmL4 model combines plant physiological relations, generalized empirically established functions and plant trait parameters. The model incorporates dynamic land use at the global scale and is also able to simulate the production of woody and herbaceous short-rotation bio-energy plantations. Grid cells may contain one or several types of natural or agricultural vegetation. A comprehensive description of the model is given by Schaphoff et al. (2017a, http://doi.org/10.5194/gmd-2017-145). We here present the LPJmL4 model code described and used by the publications in GMD: LPJmL4 - a dynamic global vegetation model with managed land: Part I – Model description and Part II – Model evaluation (Schaphoff et al. 2018a and b, http://doi.org/10.5194/gmd-2017-145 and http://doi.org/10.5194/gmd-2017-146). The model code of LPJmL4 is programmed in C and can be run in parallel mode using MPI. Makefiles are provided for different platforms. Further informations on how to run LPJmL4 is given in the INSTALL file. Additionally to the publication a html documentation and man pages are provided. Additionally, LPJmL4 can be download from the Gitlab repository: https://gitlab.pik-potsdam.de/lpjml/LPJmL. Further developments of LPJmL will be published through this Gitlab repository regularly.
# 2
Schaphoff (Ed.), Sibyll • von Bloh, Werner • Rammig, Anja • Thonicke, Kirsten • Biemans, Hester • (et. al.)
Abstract: LPJmL4 is a process-based model that simulates climate and land-use change impacts on the terrestrial biosphere, the water and carbon cycle and on agricultural production. The LPJmL4 model combines plant physiological relations, generalized empirically established functions and plant trait parameters. The model incorporates dynamic land use at the global scale and is also able to simulate the production of woody and herbaceous short-rotation bio-energy plantations. Grid cells may contain one or several types of natural or agricultural vegetation. A comprehensive description of the model is given by Schaphoff et al. (2017a, http://doi.org/10.5194/gmd-2017-145). The data presented here represent some standard LPJmL4 model results for the land surface described in Schaphoff et al. (2017a,). Additionally, these results are evaluated in the companion paper of Schaphoff et al. (2017b, http://doi.org/10.5194/gmd-2017-146). The data collection includes some key output variables made with different model setups described by Schaphoff et al. (2017b). The data cover the entire globe with a spatial resolution of 0.5° and temporal coverage from 1901-2011 on an annual basis for soil, vegetation, aboveground and litter carbon as well as for vegetation distribution, crop yields, sowing dates, maximum thawing depth, and fire carbon emissions. Vegetation distribution is given for each plant functional type (PFT), crop yields, and sowing dates are given for each crop functional type (CFT), respectively. Monthly data are provided for the carbon fluxes (net primary production, gross primary production, soil respiration) and the water fluxes (transpiration, evaporation, interception, runoff, and discharge) and for absorbed photosynthetically active radiation (FAPAR) and albedo. The data are provided in one netcdf file for each variable and experiment described by Schaphoff et al. (2017b). Crop yields and sowing dates are not provided for the LPJmL4-GSI-GlobFIRE-PNV experiment as this represents natural vegetation only. An overview of all variables and the number of bands are given in the file inventory.
# 3
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/.
# 4
Arneth, Almut • Balkovic, Juraj • Ciais, Philippe • de Wit, Allard • Deryng, Delphine • (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. 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 Agricultural Sector models: CGMS-WOFOST, CLM-Crop, EPIC-Boku, EPIC-IIASA, EPIC-TAMU, GEPIC, LPJ-GUESS, LPJmL, ORCHIDEE-CROP, pAPSIM, pDSSAT, PEGASUS, PEPIC, PRYSBI2.
The ISIMIP2a agriculture outputs are based on simulations from 14 agricultural sector models (see listing) according to the ISIMIP2a protocol (https://www.isimip.org/protocol/#isimip2a). The models simulate cop yields and irrigation water withdrawal (assuming unlimited water supply), based on planting dates, crop variety parameters, approximate maturity dates (to allow for spatially-explicit variety parameterization), as well as fertilizer use (N, P, K). A more detailed description of the models and model-specific amendments of the protocol are available here: https://www.isimip.org/impactmodels/.
# 5
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/.
# 6
Rufin, Philippe • Levers, Christian • Baumann, Matthias • Jägermeyr, Jonas • Krueger, Tobias • (et. al.)
Abstract: The spatial distribution of irrigation dam benefits is poorly understood at the global scale due to a scarcity of spatial information on irrigation dam command areas. Several studies aimed at mapping irrigated lands globally, but the spatially explicit attribution of irrigated lands to dams has rarely been undertaken. First approaches attributing changes in agricultural production to dams were based on aggregated areal units, such as administrative districts (Duflo and Pande, 2007), or watershed boundaries (Strobl and Strobl, 2011). These approaches represent only indirect approximations of command areas, and may be improved by considering spatially explicit dam- and location-specific parameters (e.g. reservoir storage capacity or topography). Such a refined dataset is required for better understanding the spatial distribution and properties of irrigation dam command areas. We approximated irrigation dam command areas for 1,370 dams with irrigation function which were commissioned across 71 countries since 1985. We approximated a) the extent and b) the location of irrigation dam command areas at 500m spatial resolution using global-scale assumptions motivated by existing literature. We first estimated command area extent [ha] based on reservoir storage capacity [m³], while accounting for country-level variations in the ratio of land irrigated with surface water per unit of total national reservoir storage capacity [ha/m³]. We then spatially allocated the estimated command area extent for each dam, accounting for parameters representing: irrigated cropland abundance (P1), topography relative to the dam (P2), watershed boundaries (P3), reservoir size (P4), national borders (P5), and distance to the dam (P6). To understand the sensitivity of the allocation towards the assumptions underlying the selected parameters, we tested 24 different allocation schemes with varying parameter settings. An overview of the datasets used for the command area extent estimation and the spatial allocation procedure, as well as an illustration of an exemplary allocation is included in the download. For a detailed description of the methods used to produce these data, please see Rufin et al. (2018).
The CA1985 dataset includes two raster datasets in geographic coordinates (WGS1984, EPSG: 4326) in GeoTiff format: 1) CA1985_binary.tif: The spatial dataset of irrigation dam command areas commissioned since 1985 used for the analyses in Rufin et al. (under review). Command areas (value 1) are here defined as irrigated areas, within watershed delineations scaled according to the reservoir storage capacity, located topographically below, but at maximum 10 m above the impoundment and within the national borders of the dam location. A detailed description of the parameters and underlying assumptions is provided in Rufin et al. (2018). 2) CA1985_sensitivity.tif: An overlay of 24 allocation schemes with varying parametrizations to inform about the uncertainty associated with each allocated pixel. The data values range between 0 (not identified as a command area in any allocation scheme) and 24 (this pixel was identified as a command area in all 24 allocation schemes). A detailed description of the parameters used in the sensitivity analysis is provided in Rufin et al. (2018).
# 7
Eddy, Tyler D. • Bulman, Cathy M. • Cheung, William W.L. • Coll, Marta • Fulton, Elizabeth A. • (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.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 fisheries & marine ecosystems (regional) models: Atlantis (Southeastern Australia) EwE (Adriatic Sea), EwE (Baltic Sea), EwE (Catalan Sea or NW Mediterranean Sea), EwE (Cook Strait, New Zealand), EwE (East Bass Strait, Australia), EwE (Mediterranean Sea), EwE (North Sea), OSMOSE (Northern Humboldt).
The ISIMIP2a Fisheries & Marine Ecosystems (Fish-MIP; regional) outputs are based on simulations from 9 regional fisheries & marine ecosystems models (see listing) according to the ISIMIP2a protocol (https://www.isimip.org/protocol/#isimip2a). The models simulate total ecosystem biomass, total consumer biomass, biomass by different size classes, and fisheries catches based on prescribed input fields information. A more detailed description of the models and model-specific amendments of the protocol are available here: https://www.isimip.org/impactmodels/.
# 8
Tittensor, Derek P. • Lotze, Heike K. • Eddy, Tyler D. • Galbraith, Eric D. • Cheung, William W.L. • (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. 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 six global fisheries & marine ecosystems (global) models: APECOSM, BOATS, DBEM, DBPM, EcoOcean, Macroecological (Jennings) model.
The ISIMIP2a Fisheries & Marine Ecosystems (Fish-MIP; global) outputs are based on simulations from 6 global fisheries & marine ecosystems models (see listing) according to the ISIMIP2a protocol (https://www.isimip.org/protocol/#isimip2a). The models simulate total ecosystem biomass, total consumer biomass, biomass by different size classes, and fisheries catches based on prescribed input fields. A more detailed description of the models and model-specific amendments of the protocol are available here: https://www.isimip.org/impactmodels/.
# 9
von Bloh, Werner • Schaphoff, Sibyll • Müller, Christoph • Rolinski, Susanne • Waha, Katharina • (et. al.)
Abstract: LPJmL5 is a dynamical global vegetation model that simulates climate and land-use change impacts on the terrestrial biosphere, the water, carbon and nitrogen cycle and on agricultural production. In particular, processes of soil nitrogen dynamics, plant uptake, nitrogen allocation, response of photosynthesis and maintenance respiration to varying nitrogen concentrations in plant organs, and agricultural nitrogen management are included into the model. A comprehensive description of the model is given by von Bloh et al. (2017,http://doi.org/10.5194/gmd-2017-228). We here present the LPJmL5 model code described and used by the publications in GMD: Implementing the Nitrogen cycle into the dynamic global vegetation, hydrology and crop growth model LPJmL (version 5) (von Bloh et al., 2017) The model code of LPJmL5 is programmed in C and can be run in parallel mode using MPI. Makefiles are provided for different platforms. Further informations on how to run LPJmL5 is given in the INSTALL file. Additionally to the publication a html documentation and manual pages are provided. The LPJmL5 version is based on LPJmL3.5 that is not publicly available. The LPJmL4 version without nitrogen cycle but with an updated phenology scheme can be found on github (https://github.com/PIK-LPJmL/LPJmL).
# 10
Borg, Erik • Maass, Holger • Renke, Frank • Jahncke, Dirk • Stender, Vivien • (et. al.)
Abstract: The Alt Tellin climate station is part of an agrometeorological test site and aims at supplying environmental data for algorithm development in remote sensing and environmental modelling, with a focus on soil moisture and evapotranspiration.The site is intensively used for practical tests of remote sensing data integration in agricultural land management practices. First measurement infrastructure was installed by DLR in 1999 and instrumentation was intensified in 2011 and later as the site became part of the TERENO-NE observatory.The agrometeorological station Alt Tellin was installed in 2011. It is located on a flat terrain within the field on grassland, surrounded by agricultural used fields. The station is equipped with sensor for measuring the following variables: Temperature, Precipitation, BarometricPressure, RelativeHumidity, WindDirection, WindSpeed, PyranometerCMP3incoming, AC_Soilmoisture010cm, AC_Soilmoisture020cm, AC_Soilmoisture040cm, AC_Soilmoisture060cm, AC_Soilmoisture080cm, AC_Soilmoisture100cm, Soiltemperature000cm, Soiltemperature005cm, Soiltemperature010cm, Soiltemperature020cm, LeafWetness, Soilmoisture10cm, Soilmoisture20cm, Soilmoisture30cm, Soilmoisture40cm, Soilmoisture50cm, Soilmoisture60cm, Soilmoisture70cm, Soilmoisture80cm, Soilmoisture90cm, Soiltemperature15cm, Soiltemperature45cm, Soiltemperature75cm, SoiltemperatureTh3-s100cm, SoiltemperatureTh3-s10cm, SoiltemperatureTh3-s20cm, SoiltemperatureTh3-s30cm, SoiltemperatureTh3-s50cm and SoiltemperatureTh3-s5cm The dataset is also available through the TERENO Data Discovery Portal. The datafile will be extended once per year as more data is acquired at the stations and the metadatafile will be updated. New columns for new variables will be added as necessary. In case of changes in dta processing, which will result in changes of historical data, an new Version of this dataset will be published using a new doi. New data will be added after a delay of several months to allow manual interference with the quality control process.
The DEMMIN (Durable Environmental Multidisciplinary Monitoring Information Network; upper left corner: 54°20N, 12°520E, lower right corner: 53°450N, 13°270E) test area was designed and established by the DLR in cooperation with farmers in the Demmin region in 2000. The site was used as a calibration and validation test site for national and international remote sensing missions. In 2011, the test site was integrated into the TERENO initiative. The DEMMIN test site is located within the central monitoring sites of the TERENO Northeastern German Lowland Observatory. It covers 900 km² and exhibits mostly glacial formed lowlands with terminal moraines in the southern part, containing the highest elevation of 83m a.s.l. The region between the rivers Tollense and Peene consists of flat ground moraines, whereas undulation ground moraines determine the landscape character north of the river Peene. The lowest elevation is located near the town Loitz with 0.5m a.s.l. The region is characterized by intense agricultural use and the three rivers Tollense and Trebel which confluence into the Peene River at the Hanseatic city Demmin. The present climate is characterized by a long-term (1981–2010) mean temperature of 8.7 °C and mean precipitation of 584 mm/year, measured at the Teterow weather station by Deutscher Wetterdienst (DWD). The Northeastern German Lowland Observatory is situated in a region shaped by recurring glacial and periglacial processes since at least half a million years. Within this period, three major glaciations covered the entire region, the last time this happened approximately 25 15 k ago (Weichselian glaciation).Since that time, a young morainic landscape developed characterized by many lakes and river systems that are connected to the shallow ground water table. The test site is instrumented with more than 40 environmental measurement stations (DLR, GFZ). Additionally, 63 soil moisture stations were installed by GFZ, a lysimeter-hexagon (DLR, FZJ) near to the village Rustow and is part of the SOILCan project. A crane completes the measurement technique currently available in the test site installed by GFZ/DLR in 2011. Data is automatically collected via a telemetry network by DLR. The quality control of all environmental data transferred via Telemetry network of DLR is carried out by DLR by visual control and, since 2012, by automatic processing by GFZ. The delivered dataset contains the measured data and quality flags indicating the validity of each measured value and detected reasons for exclusion. The TERENO (TERrestrial ENvironmental Observatories) is an initiative of the Helmholtz Centers (Forschungszentrum Jülich – FZJ, Helmholtz Centre for Environmental Research – UFZ, Karlsruhe Institute of Technology – KIT, Helmholtz Zentrum München - German Center for Environmental Health – HMGU, German Research Centre for Geosciences - GFZ, and German Aerospace Center – DLR) (http://www.tereno.net/overview-de). TERENO Northeastern German Lowland Observatory.TERENO (TERrestrial ENvironmental Observatories) spans an Earth observation network across Germany that extends from the North German lowlands to the Bavarian Alps. This unique large-scale project aims to catalogue the longterm ecological, social and economic impact of global change at regional level. Further specific goals of the TERENO remote sensing research group at GFZ are (1) supplying environmental data for algorithm development in remote sensing and environmental modelling, with a focus on soil moisture and evapotranspiration, and (2) practical tests of remote sensing data integration in agricultural land management practices.
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