PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Scaling Low Resolution Regional Climate Model Results to High Resolution for Predicting Rice and Maize Production under Climate Change Disaster Scenario
Version 1
: Received: 8 October 2020 / Approved: 9 October 2020 / Online: 9 October 2020 (14:06:50 CEST)
How to cite:
Amnuaylojaroen, T.; Chanvichit, P. Scaling Low Resolution Regional Climate Model Results to High Resolution for Predicting Rice and Maize Production under Climate Change Disaster Scenario. Preprints2020, 2020100207. https://doi.org/10.20944/preprints202010.0207.v1
Amnuaylojaroen, T.; Chanvichit, P. Scaling Low Resolution Regional Climate Model Results to High Resolution for Predicting Rice and Maize Production under Climate Change Disaster Scenario. Preprints 2020, 2020100207. https://doi.org/10.20944/preprints202010.0207.v1
Amnuaylojaroen, T.; Chanvichit, P. Scaling Low Resolution Regional Climate Model Results to High Resolution for Predicting Rice and Maize Production under Climate Change Disaster Scenario. Preprints2020, 2020100207. https://doi.org/10.20944/preprints202010.0207.v1
APA Style
Amnuaylojaroen, T., & Chanvichit, P. (2020). Scaling Low Resolution Regional Climate Model Results to High Resolution for Predicting Rice and Maize Production under Climate Change Disaster Scenario. Preprints. https://doi.org/10.20944/preprints202010.0207.v1
Chicago/Turabian Style
Amnuaylojaroen, T. and Pavinee Chanvichit. 2020 "Scaling Low Resolution Regional Climate Model Results to High Resolution for Predicting Rice and Maize Production under Climate Change Disaster Scenario" Preprints. https://doi.org/10.20944/preprints202010.0207.v1
Abstract
Climate change effect on human-living in verities of way such as health and food security. This study presents predicting crop yields, and production risk in the near future (2020-2029) in northern Thailand using coupling 1 km resolution of regional climate model which is downscaled using a conservative remapping method and the Decision Support System for the Transfer of Agrotechnology (DSSAT) modeling system. The accuracy of the climate and agricultural model was appropriate compared to the observations with Index of Agreement (IOA) in ranges of 0.65 - 0.89. The DSSAT modeling system predicts that rice, and maize production will decrease by 5% and 4% in northern Thailand. In addition, a short-term risk analysis of rice and maize production has shown that, in the context of climate change, maize production appears to be at a high risk of low production in the near future, while rice cultivation might be a low risk.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
