Climate change impact evaluation in various regions in Europe on the base of ensemble modelling

Abstract

<p>Climate change uncertainty largely complicates adaptation and risk management evaluation at the regional level, therefore new approaches for managing this uncertainty are still being developed. In this study three crop models (DNDC, WOFOST and DSSAT) were used to explore the utility of impact response surfaces (IRS) and adaptation response surfaces (ARS) methodologies (Pirttioja et al., 2015; Ruiz-Ramos et al., 2018).</p><p>To build IRS, the sensitivity of modelled yield to systematic increments of changes in temperature (-1 to +6°C) and precipitation (-30 to +50%) was tested by modifying values of baseline (1981 to 2010) daily weather. Four levels of CO2 (360, 447, 522 and 601 ppm) representing future conditions until 2070 were considered. In turn, to build ARS, adaptation options were: shortening or extending the crop cycle of the standard cultivar, sowing earlier or later than the standard date and additional irrigation. Preliminary data indicate that yields are declining with higher temperatures and decreased precipitation. Yield is more sensitive to changes in baseline temperature values and much less sensitive to changes in baseline precipitation values for arable fields in Finland, while for arable fields in Germany, ARS indicates yield sensitivity at a similar level for both variables. Also, our data suggests that some adaptation options provides increase of the yield up to 1500 kg/ha, which suggest that ARSs may be valuable tool for planning an effective adaptation treatments. This research shows how to analyze and assess the impact of adaptation strategies in the context of the high level of regional uncertainty in relation to future climate conditions. Developed methodology can be applied to other climatic zones to help in planning adaptation and mitigation strategies.</p><p>This study has been partly financed from the funds of the Polish National Centre for Research and Development in frame of the project: MSINiN, contract number: BIOSTRATEG3/343547/8/NCBR/2017</p>