Dataset Information

The severity of dry and hot climate extremes and their related impacts on vegetation in Madagascar.

ABSTRACT: Madagascar is a low-income country, highly vulnerable to natural disasters affecting the small-scale subsistence farming system. Recently, climate change and environmental degradation have contributed to an intensification of food insecurity. We aim to monitor the link between dry and hot extremes on vegetation conditions, separated or concurrently, using satellite data, such as LST, ET, ET0, and FAPAR products from SEVIRI/MSG disseminated by LSASAF-EUMETSAT. The analysis was made for a long record from 2004 to 2021, focusing on the extreme seasons of 2020 and 2021. Results highlight the higher impact of combined dry and hot events when compared with isolated events, with a strong response of vegetation in the southern part of Madagascar. Results point to the added value of using the recent data records from geostationary satellites with high temporal resolution and updated in near real-time, to early detect, monitor, and characterize the impact of climate extremes on vegetation dynamics.

SUBMITTER: Gouveia CM

PROVIDER: S-EPMC10753072 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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The severity of dry and hot climate extremes and their related impacts on vegetation in Madagascar.

Gouveia Célia M CM Silva Mafalda M Russo Ana A

iScience 20231207 1

Madagascar is a low-income country, highly vulnerable to natural disasters affecting the small-scale subsistence farming system. Recently, climate change and environmental degradation have contributed to an intensification of food insecurity. We aim to monitor the link between dry and hot extremes on vegetation conditions, separated or concurrently, using satellite data, such as LST, ET, ET0, and FAPAR products from SEVIRI/MSG disseminated by LSASAF-EUMETSAT. The analysis was made for a long rec ...[more]

PMID: 38155783

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Project description:The global distribution of vegetation is largely determined by climatic conditions and feeds back into the climate system. To predict future vegetation changes in response to climate change, it is crucial to identify and understand key patterns and processes that couple vegetation and climate. Dynamic global vegetation models (DGVMs) have been widely applied to describe the distribution of vegetation types and their future dynamics in response to climate change. As a process-based approach, it partly relies on hard-coded climate thresholds to constrain the distribution of vegetation. What thresholds to implement in DGVMs and how to replace them with more process-based descriptions remain among the major challenges. In this study, we employ machine learning using decision trees to extract large-scale relationships between the global distribution of vegetation and climatic characteristics from remotely sensed vegetation and climate data. We analyse how the dominant vegetation types are linked to climate extremes as compared to seasonally or annually averaged climatic conditions. The results show that climate extremes allow us to describe the distribution and eco-climatological space of the vegetation types more accurately than the averaged climate variables, especially those types which occupy small territories in a relatively homogeneous ecological space. Future predicted vegetation changes using both climate extremes and averaged climate variables are less prominent than that predicted by averaged climate variables and are in better agreement with those of DGVMs, further indicating the importance of climate extremes in determining geographic distributions of different vegetation types. We found that the temperature thresholds for vegetation types (e.g. grass and open shrubland) in cold environments vary with moisture conditions. The coldest daily maximum temperature (extreme cold day) is particularly important for separating many different vegetation types. These findings highlight the need for a more explicit representation of the impacts of climate extremes on vegetation in DGVMs.

Dataset Information

The severity of dry and hot climate extremes and their related impacts on vegetation in Madagascar.

Publications

The severity of dry and hot climate extremes and their related impacts on vegetation in Madagascar.

Similar Datasets

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