Spatial variability of the multifractal properties of a 36-year-long meteorological time series for Poland coming from the MERRA-2 database was analysed. It constitute a very valuable source of meteorological data, as high similarity with ground data recorded at meteorological stations in the region of interest was stated by the analysis of similarity measures. The highest variability of multifractal spectra parameters was observed for the air temperature, whereas the lowest was for the air pressure. Spatial analysis of multifractal spectra parameters enabled to select the regions vulnerable to the occurrence of extreme events, correlated or uncorrelated processes, etc. Obtained maps of multifractal parameters give a new insight into climate dynamics in the region and are a valuable source of information for comparative analyses. The spatial variation of the parameters of multifractal spectra was also confirmed by geo-statistical analysis, which delivered important information about the range of influence, sill and nugget variance. The paper is the first attempt to use geo-statistical analysis in regard to multifractal properties of meteorological quantities.
The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt-Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.
The aim of the project is the development of a decision support system (DSS) for precision farming. It is based on remote sensing to assess condition of agrocenoses and define requirements for cultivation operations (irrigation, fertilizing, and pest control). The remote sensing method is to be used for monitoring winter wheat and maize as well as for assessing degradation level of meadows on the basis of intensity of carbon dioxide and methane exchange between the ground surface and the atmosphere. The paper discusses application of an ultralight autogiro as an efficient carrier of remote sensing equipment. The project is co-financed by the National Centre for Research and Development, BIOSTRATEG grant no. 298782.
Climate dynamics were assessed using multifractal detrended fluctuation analysis (MF-DFA) for sites in Finland, Germany and Spain across a latitudinal transect. Meteorological time series were divided into the two subsets (1980–2001 and 2002–2010) and respective spectra of these subsets were compared to check whether changes in climate dynamics can be observed using MF-DFA. Additionally, corresponding shuffled and surrogate time series were investigated to evaluate the type of multifractality. All time series indicated underlying multifractal structures with considerable differences in dynamics and development between the studied locations. The source of multifractality of precipitation time series was two-fold, coming from the width of the probability function to a greater extent than for other time series. The multifractality of other analyzed meteorological series was mainly due to long-range correlations for small and large fluctuations. These results may be especially valuable for assessing the change of climate dynamics, as we found that larger changes in asymmetry and width parameters of multifractal spectra for divided datasets were observed for precipitation than for other time series. This suggests that precipitation is the most vulnerable meteorological quantity to change of climate dynamics.
Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (-2 to +9°C) and precipitation (-50 to +50%). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses.The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern.The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description.Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index.Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities.
This work was financially supported by the Spanish National Institute for Agricultural and Food Research and Technology (INIA, MACSUR01-UPM), the Italian Ministry of Agriculture and Forestry and the Finnish Ministry of Agriculture and Forestry (D.M. 24064/7303/15) through FACCE MACSUR − Modelling European Agriculture with Climate Change for Food Security, a FACCE JPI knowledge hub; MULCLIVAR, from the Spanish Ministerio de Economía y Competitividad (MINECO, CGL2012-38923-C02-02); the Academy of Finland (decisions: 277276 and 277403), the EU FP7 IMPRESSIONS project (grant agreement no. 603416), the NORFASYS project (decision nos. 268277 and 292944) and PLUMES project (decision nos. 277403 and 292836); project IGA AF MENDELU no. 7/2015 with the support of the Specific University Research Grant provided by the Ministry of Education, Youth Sports of the Czech Republic; the Ministry of Education, Youth Sports of the Czech Republic within the National Sustainability Programme I (NPU I), grant number LO1415 NAZV QJ1310123 the Polish National Centre for Research and Development in frame of the projects: LCAgri, contract number BIOSTRATEG1/271322/3/NCBR/2015 and GyroScan, contract number BIOSTRATEG2/298782/11/NCBR/2016.
Abstract Scale issues become very important when applying weather time series. We address problems associated with transferring meteorological data across time scales by comparing multifractal properties of hourly and daily meteorological time series. The multifractal detrended fluctuation approach revealed that temporal aggregation of agro-meteorological time series can impact on their multifractal properties. The most apparent evidence of changing the time scale on multifractal properties was found for precipitation. It was the least noticeable for the wind speed time series. The change from hourly to daily time scale had an effect on the long-range correlations and the broadness of the probability density function. The contribution of these two components to series multifractality was smaller than before data aggregation. Our results confirm the loss of unique multifractal features at daily time scale as compared to hourly time series.