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Reanalysis datasets outperform other gridded climate products in vegetation change analysis in peripheral conservation areas of Central Asia.


ABSTRACT: Global environmental research requires long-term climate data. Yet, meteorological infrastructure is missing in the vast majority of the world's protected areas. Therefore, gridded products are frequently used as the only available climate data source in peripheral regions. However, associated evaluations are commonly biased towards well observed areas and consequently, station-based datasets. As evaluations on vegetation monitoring abilities are lacking for regions with poor data availability, we analyzed the potential of several state-of-the-art climate datasets (CHIRPS, CRU, ERA5-Land, GPCC-Monitoring-Product, IMERG-GPM, MERRA-2, MODIS-MOD10A1) for assessing NDVI anomalies (MODIS-MOD13Q1) in two particularly suitable remote conservation areas. We calculated anomalies of 156 climate variables and seasonal periods during 2001-2018, correlated these with vegetation anomalies while taking the multiple comparison problem into consideration, and computed their spatial performance to derive suitable parameters. Our results showed that four datasets (MERRA-2, ERA5-Land, MOD10A1, CRU) were suitable for vegetation analysis in both regions, by showing significant correlations controlled at a false discovery rate?

SUBMITTER: Zandler H 

PROVIDER: S-EPMC7775429 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Reanalysis datasets outperform other gridded climate products in vegetation change analysis in peripheral conservation areas of Central Asia.

Zandler Harald H   Senftl Thomas T   Vanselow Kim André KA  

Scientific reports 20201231 1


Global environmental research requires long-term climate data. Yet, meteorological infrastructure is missing in the vast majority of the world's protected areas. Therefore, gridded products are frequently used as the only available climate data source in peripheral regions. However, associated evaluations are commonly biased towards well observed areas and consequently, station-based datasets. As evaluations on vegetation monitoring abilities are lacking for regions with poor data availability,  ...[more]

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