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Atmosphere 2018, 9(8), 290;

Effects of Different Spatial Precipitation Input Data on Crop Model Outputs under a Central European Climate

Institute of Meteorology, University of Natural Resources and Life Sciences (BOKU), Gregor-Mendel-Straße 33, 1180 Vienna, Austria
CzechGlobe—Global Change Research Institute CAS, Belidla 986, 4a, 603 00 Brno, Czech Republic
Research Institute for Geo-Hydrological Protection, National Research Council, Via della Madonna Alta 126, 06128 Perugia, Italy
Department of Geodesy and Geoinformation, Vienna University of Technology (TU Wien), Gußhausstraße 27–29, 1040 Vienna, Austria
Author to whom correspondence should be addressed.
Received: 30 May 2018 / Revised: 16 July 2018 / Accepted: 23 July 2018 / Published: 26 July 2018
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Crop simulation models, which are mainly being utilized as tools to assess the consequences of a changing climate and different management strategies on crop production at the field scale, are increasingly being used in a distributed model at the regional scale. Spatial data analysis and modelling in combination with geographic information systems (GIS) integrates information from soil, climate, and topography data into a larger area, providing a basis for spatial and temporal analysis. In the current study, the crop growth model Decision Support System for Agrotechnology Transfer (DSSAT) was used to evaluate five gridded precipitation input data at three locations in Austria. The precipitation data sets consist of the INtegrated Calibration and Application Tool (INCA) from the Meteorological Service Austria, two satellite precipitation data sources—Multisatellite Precipitation Analysis (TMPA) and Climate Prediction Center MORPHing (CMORPH)—and two rainfall estimates based on satellite soil moisture data. The latter were obtained through the application of the SM2RAIN algorithm (SM2RASC) and a regression analysis (RAASC) applied to the Metop-A/B Advanced SCATtermonter (ASCAT) soil moisture product during a 9-year period from 2007–2015. For the evaluation, the effect on winter wheat and spring barley yield, caused by different precipitation inputs, at a spatial resolution of around 25 km was used. The highest variance was obtained for the driest area with light-textured soils; TMPA and two soil moisture-based products show very good results in the more humid areas. The poorest performances at all three locations and for both crops were found with the CMORPH input data. View Full-Text
Keywords: DSSAT; INCA; ASCAT soil moisture; SM2RAIN; satellite precipitation data DSSAT; INCA; ASCAT soil moisture; SM2RAIN; satellite precipitation data

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Thaler, S.; Brocca, L.; Ciabatta, L.; Eitzinger, J.; Hahn, S.; Wagner, W. Effects of Different Spatial Precipitation Input Data on Crop Model Outputs under a Central European Climate. Atmosphere 2018, 9, 290.

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