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Predicting vegetation phenology in response to climate change using bioclimatic indices in IraqAuthor(s): Afrah Daham; Dawei Han; W. Matt Jolly; Miguel Rico-Ramirez; Anke Marsh
Source: Journal of Water and Climate Change. jwc2018142.
Publication Series: Scientific Journal (JRNL)
Station: Rocky Mountain Research Station
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DescriptionAlthough most phenology models can predict vegetation response to climatic variations, these models often perform poorly in precipitation-limited regions. In this study, we modified a phenology model, called the Growing Season Index (GSI), to better quantify relationships between weather and vegetation canopy dynamics across various semi-arid regions of Iraq. A modified GSI was created by adding a cumulative precipitation control to the existing GSI framework. Both unmodified and modified GSI values were calculated daily from 2001 to 2010 for three locations in Eastern Iraq: Sulaymaniyah (north), Wasit (central) and Basrah (south) and a countrywide mean and compared to the Normalized Difference Vegetation Index (NDVI) from MODerate-resolution Imaging Spectroradiometer (MODIS) for the same time period. Countrywide median inter-annual correlations between GSI and NDVI more than doubled with the addition of the precipitation control and within-site correlations also show substantial improvements. The modified model has huge potential to be used to predict future phenological responses to changing climatic conditions, as well as to reconstruct historical vegetation conditions. This study improves our understanding of potential vegetation responses to climatic changes across Iraq, but it should improve phenological predictions across other semi-arid worldwide, particularly in the face of rapid climate change and environmental deterioration.
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CitationDaham, Afrah; Han, Dawei; Jolly, W. Matt; Rico-Ramirez, Miguel; Marsh, Anke. 2018. Predicting vegetation phenology in response to climate change using bioclimatic indices in Iraq. Journal of Water and Climate Change. jwc2018142.
Keywordsclimate change, GSI phenological model, minimum temperature, photoperiod, precipitation, vapour pressure deficit
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