5 documents found in 386ms
# 1
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/.
# 2
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/.
# 3
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/.
# 4
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).
# 5
Jarmer, Thomas • Siegmann, Bastian
Abstract: The dataset is composed of hyperspectral imagery acquired during airplane overflights on May 10th, 2011, June 27th, 2011 and May 24th, 2012 consisting of 367 and 368 spectral bands, respective-ly, ranging from VIS to SWIR (400 - 2500 nm) wavelength regions. The hyperspectral image data was acquired in the framework of the EnMAP preparation project HyLand (Hyperspectral remote sensing for the assessment of crop and soil parameters in precision farming and yield estimation). Within the project, innovative techniques were developed to derive crop and soil parameters from hyper-spectral remote sensing and terrestrial laser scanning data, which served as input parameters for novel yield estimation models.
The Environmental Mapping and Analysis Program (EnMAP) is a German hyperspectral satellite mission that aims at monitoring and characterizing the Earth’s environment on a global scale. EnMAP serves to measure and model key dynamic processes of the Earth’s ecosystems by extract-ing geochemical, biochemical and biophysical parameters, which provide information on the status and evolution of various terrestrial and aquatic ecosystems. In the frame of the EnMAP preparatory phase, pre-flight campaigns including airborne and in-situ measurements in different environments and for several application fields are being conducted. The main purpose of these campaigns is to support the development of scientific applications for EnMAP. In addition, the acquired data are input in the EnMAP end-to-end simulation tool (EeteS) and are employed to test data pre-processing and calibration-validation methods. The campaign data are made freely available to the scientific community under a Creative Commons Attribution-ShareAlike 4.0 International License. An overview of all available data is provided in in the EnMAP Flight Campaigns Metadata Portal (http://www.enmap.org/?q=flightbeta).
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