Simulated sensitivity of African terrestrial ecosystem photosynthesis to rainfall frequency, intensity, and rainy season length

Kaiyu Guan; Stephen P Good; Kelly K Caylor; David Medvigy; Ming Pan; Eric F Wood; Hisashi Sato; Michela Biasutti; Min Chen; Anders Ahlström; Xiangtao Xu

doi:10.1088/1748-9326/aa9f30

Environmental Research Letters (Jan 2018)

Simulated sensitivity of African terrestrial ecosystem photosynthesis to rainfall frequency, intensity, and rainy season length

Kaiyu Guan,
Stephen P Good,
Kelly K Caylor,
David Medvigy,
Ming Pan,
Eric F Wood,
Hisashi Sato,
Michela Biasutti,
Min Chen,
Anders Ahlström,
Xiangtao Xu

Affiliations

Kaiyu Guan: ORCiD; Department of Natural Resources and Environmental Sciences and National Center for Supercomputing Applications , University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States of America; Author to whom any correspondence should be addressed.
Stephen P Good: Department of Biological and Ecological Engineering , Oregon State University, Corvallis, OR 97331, United States of America
Kelly K Caylor: Department of Geography , Bren School of Environmental Science & Management, University of California - Santa Barbara, Santa Barbara, CA 93106, United States of America
David Medvigy: Department of Biological Sciences , University of Notre Dame, Notre Dame, IN 46556, United States of America
Ming Pan: Department of Civil and Environmental Engineering , Princeton University, Princeton, NJ 08544, United States of America
Eric F Wood: Department of Civil and Environmental Engineering , Princeton University, Princeton, NJ 08544, United States of America
Hisashi Sato: Institute of Arctic Climate and Environment Research , Japan Agency for Marine-Earth Science and Technology (JAMSTEC), 3173-25 Showamachi, Kanazawa-ku, Yokohama, 236-0001, Japan
Michela Biasutti: Lamont-Doherty Earth Observatory of Columbia University , 61 Route 9 W, Palisades, NY, 10964-8000, United States of America
Min Chen: Joint Global Change Research Institute , Pacific Northwest National Laboratory, College Park, MD, United States of America
Anders Ahlström: Department of Earth System Science , Stanford University, Stanford, CA 94025, United States of America; Department of Physical Geography and Ecosystem Science, Lund University , 223 62 Lund, Sweden
Xiangtao Xu: Department of Organismic and Evolutionary Biology , Harvard University, Cambridge, MA 02138, United States of America

DOI: https://doi.org/10.1088/1748-9326/aa9f30
Journal volume & issue: Vol. 13, no. 2
p. 025013

Abstract

Read online

There is growing evidence of ongoing changes in the statistics of intra-seasonal rainfall variability over large parts of the world. Changes in annual total rainfall may arise from shifts, either singly or in a combination, of distinctive intra-seasonal characteristics –i.e. rainfall frequency, rainfall intensity, and rainfall seasonality. Understanding how various ecosystems respond to the changes in intra-seasonal rainfall characteristics is critical for predictions of future biome shifts and ecosystem services under climate change, especially for arid and semi-arid ecosystems. Here, we use an advanced dynamic vegetation model (SEIB-DGVM) coupled with a stochastic rainfall/weather simulator to answer the following question: how does the productivity of ecosystems respond to a given percentage change in the total seasonal rainfall that is realized by varying only one of the three rainfall characteristics (rainfall frequency, intensity, and rainy season length)? We conducted ensemble simulations for continental Africa for a realistic range of changes (−20% ~ +20%) in total rainfall amount. We find that the simulated ecosystem productivity (measured by gross primary production, GPP) shows distinctive responses to the intra-seasonal rainfall characteristics. Specifically, increase in rainfall frequency can lead to 28% more GPP increase than the same percentage increase in rainfall intensity; in tropical woodlands, GPP sensitivity to changes in rainy season length is ~4 times larger than to the same percentage changes in rainfall frequency or intensity. In contrast, shifts in the simulated biome distribution are much less sensitive to intra-seasonal rainfall characteristics than they are to total rainfall amount. Our results reveal three major distinctive productivity responses to seasonal rainfall variability—‘chronic water stress’, ‘acute water stress’ and ‘minimum water stress’ - which are respectively associated with three broad spatial patterns of African ecosystem physiognomy, i.e. savannas, woodlands, and tropical forests.

Published in Environmental Research Letters

ISSN: 1748-9326 (Online)
Publisher: IOP Publishing
Country of publisher: United Kingdom
LCC subjects: Technology: Environmental technology. Sanitary engineering; Geography. Anthropology. Recreation: Environmental sciences; Science: Physics
Website: https://iopscience.iop.org/journal/1748-9326

About the journal

Abstract

Keywords