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A framework for standardized calculation of weather indices in Germany

GND
1173645446
Zugehörigkeit
Julius Kühn-Institute (JKI), Federal Research Centre for Cultivated Plants,Institute for Strategies and Technology Assessment, Kleinmachnow, Germany
Möller, Markus;
Zugehörigkeit
Martin Luther University Halle-Wittenberg, Institute of Agricultural and Nutritional Sciences, Agribusiness Management Group, Karl-Freiherr-von-Fritsch-Str. 4, 06120 Halle (Saale), Germany
Doms, Juliane;
Zugehörigkeit
Martin Luther University Halle-Wittenberg, Institute of Geosciences and Geography, Von-Seckendorff-Platz 4, 06120 Halle (Saale), Germany
Gerstmann, Henning;
GND
143656902
Zugehörigkeit
Julius Kühn-Institute (JKI), Federal Research Centre for Cultivated Plants,Institute for Strategies and Technology Assessment, Kleinmachnow, Germany
Feike, Til

Climate change has been recognized as a main driver in the increasing occurrence of extreme weather. Weather indices (WIs) are used to assess extreme weather conditions regarding its impact on crop yields. Designing WIs is challenging, since complex and dynamic crop-climate relationships have to be considered. As a consequence, geodata for WI calculations have to represent both the spatio-temporal dynamic of crop development and corresponding weather conditions. In this study, we introduce a WI design framework for Germany, which is based on public and open raster data of long-term spatio-temporal availability. The operational process chain enables the dynamic and automatic definition of relevant phenological phases for the main cultivated crops in Germany. Within the temporal bounds, WIs can be calculated for any year and test site in Germany in a reproducible and transparent manner. The workflow is demonstrated on the example of a simple cumulative rainfall index for the phenological phase shooting of winter wheat using 16 test sites and the period between 1994 and 2014. Compared to station-based approaches, the major advantage of our approach is the possibility to design spatial WIs based on raster data characterized by accuracy metrics. Raster data and WIs, which fulfill data quality standards, can contribute to an increased acceptance and farmers’ trust in WI products for crop yield modeling or weather index-based insurances (WIIs).

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Rechteinhaber: Springer-Verlag GmbH Austria, part of Springer Nature 2018

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