Αποτίμηση των επιπτώσεων των αποκλίσεων που προκύπτουν από τη χρήση πλεγματικών δεδομένων με το μοντέλο CERES-Wheat = Estimating the errors in the output of CERES-Wheat crop model by using reanalysis data.
Περίληψη
Τα Agri4cast και Eobs-0.1 ήταν τα καλύτερα προϊόντα όσον αναφορά την κλιματική ταξινόμηση κατά Köppen. Κατά την εποχιακή σύγκριση, όλα τα πλεγματικά προϊόντα συστηματικά υποεκτίμησαν τις κεντρικές τιμές των παρατηρήσεων των θερμοκρασιών και της βροχόπτωσης. Καλύτερες επιλογές αναδείχθηκαν τα Eobs-0.1 και Agri4cast για την μέγιστη και ελάχιστη θερμοκρασία και τα Eobs-0.1 και Eobs-0.25 για την βροχόπτωση. Όσον αφορά την ηλιακή ακτινοβολία, τα πλεγματικά προϊόντα υπερεκτίμησαν τις κεντρικές τιμές, με τα Eobs-0.1 και Eobs-0.25 να αποτελούν τις ιδανικότερες επιλογές. Τους θερμοκρασιακούς δείκτες προσεγγίζουν καλύτερα τα Eobs-0.1 και Agri4cast, ενώ τους υγρομετρικούς τα Eobs-0.1 και Eobs-0.25.
Συνολικά τα Eobs-0.1 ήταν τα καλύτερα πλεγματικά προϊόντα παρουσιάζοντας (α) για τις θερμοκρασίες, τις μικρότερες διαφορές (εκφρασμένες ως RMSE/mean *100), το καλοκαίρι (2%, 5%, για Tmax και Tmin, αντιστοίχως) και το φθινόπωρο (3%, 8%, για Tmax και Tmin, αντιστοίχως) και τις μεγαλύτερες την άνοιξη (4%, 10%, για Tmax και Tmin, αντιστοίχως) και τον χειμώνα (6%, 30%, για Tmax και Tmin, αντιστοίχως), (β) για την βροχόπτωση, τα μικρότερα λάθη την άνοιξη, το φθινόπωρο και τον χειμώνα (14%, 17%, 15%, αντιστοίχως) και τα μεγαλύτερα το καλοκαίρι (46%) και (γ) για την ηλιακή ακτινοβολία, παρόμοιες διαφορές (6-7%).
Τα Eobs-0.1 εμφάνισαν τις μικρότερες αποκλίσεις για τα στάδια ανάπτυξης και τη συγκομιδή για την περίοδο αναφοράς (κατά μέσο όρο 5 ημέρες για την άνθηση, 6 ημέρες για την ωρίμανση και 10% για ην συγκομιδή) και κατά την εφαρμογή των κλιματικών σεναρίων (κατά μέσο όρο 4 ημέρες για την άνθηση, 3 ημέρες για την ωρίμανση και 8% για την συγκομιδή), με τα Agri4Cast να ακολουθούν σε μικρή απόσταση. Τα Agri4Cast και Eobs-0.1 αναδείχθηκαν καλύτερες επιλογές και κατά την απεικόνιση των αποκλίσεων των σταδίων ανάπτυξης και συγκομιδής σε επιφάνειες.
Gridded datasets are widely used to study weather and climate, so it is important investigators consider their strengths and weaknesses. Maximum and minimum temperature (Tmax and Tmin, respectively), precipitation (Prec), and solar radiation data (QQ) from three gridded datasets (E-OBS in 2 spatial resolutions (10km (Eobs-0.1)) and 25km (Eobs-0.25) and Agri4cast) and 13 Mediterranean stations (Obs) (selected for their proximity to wheat crops), during 1980-2019, were compared. The comparison was made a) by determining the climate type based on the Köppen classification, b) on seasonal basis for all and each station separately, using a variety of statistical measures (mean, Std, Q25, Q50 και Q75), statistical indices (RMSE, EF1 και r) and temperature (frequency of days with Tmax>25ºC, Tmax<0ºC, Tmin>20ºC και Tmin<0ºC)- and precipitation (95th and 99th percentiles and frequency of days with Prec≥0.1mm and Prec≥1mm)- based indices. Additional comparisons, for the reference and climate change scenarios (constructed by perturbing the historical Tmax and Tmin time series), were made between development stages (anthesis and maturity) and yield, when the CERES-Wheat model was run on potential mode with the gridded and observations datasets.
With regards to Köppen climate classification, Agri4cast and Eobs-0.1 were the best products. All gridded products systematically underestimated the central trends of temperature and precipitation observations on seasonal basis. The best choices were Eobs-0.1 and Agri4cast for temperature and Eobs-0.1 and Eobs-0.25 for precipitation. The gridded products also overestimated the central trends of solar radiation, with Eobs-0.1 and Eobs-0.25 being the best choices. Regarding the temperature- based indices, Eobs-0.1 and Agri4cast exhibited the lower deviations from observations, while the Eobs gridded products approximated better the precipitation-based indices.
Overall, Eobs-0.1 was the best gridded product presenting (a) for temperatures, the lower discrepancies (expressed as RMSE/mean*100) in summer (2%, 5%, for Tmax and Tmin, respectively) and autumn (3%, 8%, for Tmax and Tmin, respectively) and the larger in spring (4%, 10%, for Tmax and Tmin, respectively) and winter (6%, 30%, for Tmax and Tmin, respectively) (b) for precipitation, the lowest errors in spring, autumn and winter (14%, 17%, 15%, respectively) and the larger in summer (46%) and (c) for solar radiation, similar seasonal discrepancies (6-7%).
Eobs-0.1 also showed the smallest discrepancies for both developmental stages and yield production estimated with CERES-Wheat, during the reference period (5 days for anthesis, 6 for maturity and 10% for yield production, on average) and climate scenarios (4 days for anthesis, 3 days for maturity and 8% for yield production, on average), followed by Agri4cast. Additionally, Agri4cast and Eobs-0.1 were the best choices after depicting the stages of growth and yield production on surfaces.
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