Data-Driven Models to Forecast the Impact of Temperature Anomalies on Rice Production in Southeast Asia

Models are a core element in performing local estimation of the climate change input. In this work, a novel approach to perform a fast downscaling of global temperature anomalies on a regional level is presented. The approach is based on a set of data-driven models linking global temperature anomali...

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Bibliographic Details
Published in:	Forecasting 2024-01, Vol.6 (1), p.100-114
Main Authors:	De Nardi, Sabrina, Carnevale, Claudio, Raccagni, Sara, Sangiorgi, Lucia
Format:	Article
Language:	eng
Subjects:	Agricultural production Agriculture Anomalies ARX models Book publishing Carbon dioxide Case studies Climate change climate change impacts East Asia Emissions Food security Geopolitics Global temperature changes Impact analysis information downscaling International economic relations Natural resources Regions Rice rice production Southeast Asia Temperature United Kingdom Weather forecasting
Online Access:	Get full text
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Summary:	Models are a core element in performing local estimation of the climate change input. In this work, a novel approach to perform a fast downscaling of global temperature anomalies on a regional level is presented. The approach is based on a set of data-driven models linking global temperature anomalies and regional and global emissions to regional temperature anomalies. In particular, due to the limited number of available data, a linear autoregressive structure with exogenous input (ARX) has been considered. To demonstrate their relevance to the existing literature and context, the proposed ARX models have been employed to evaluate the impact of temperature anomalies on rice production in a socially, economically, and climatologically fragile area like Southeast Asia. The results show a significant impact on this region, with estimations strongly in accordance with information presented in the literature from different sources and scientific fields. The work represents a first step towards the development of a fast, data-driven, holistic approach to the climate change impact evaluation problem. The proposed ARX data-driven models reveal a novel and feasible way to downscale global temperature anomalies to regional levels, showing their importance in comprehending global temperature anomalies, emissions, and regional climatic conditions.
ISSN:	2571-9394 2571-9394