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      Detecting forest response to droughts with global observations of vegetation water content

      1 , 2 , 3 , 2 , 4 , 5 , 6 , 3 , 7 , 8 , 9 , 10 , 11 , 12 , 2 , 13 , 14 , 15 , 1 , 16 , 17 , 2 , 18 , 19 , 20 , 21 , 12 , 22 , 18 , 23 , 24 , 11 , 25 , 26 , 27 , 2 , 3 , 28 , 29 , 30 , 31
      Global Change Biology
      Wiley

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          Abstract

          Abstract Droughts in a warming climate have become more common and more extreme, making understanding forest responses to water stress increasingly pressing. Analysis of water stress in trees has long focused on water potential in xylem and leaves, which influences stomatal closure and water flow through the soil‐plant‐atmosphere continuum. At the same time, changes of vegetation water content (VWC) are linked to a range of tree responses, including fluxes of water and carbon, mortality, flammability, and more. Unlike water potential, which requires demanding in situ measurements, VWC can be retrieved from remote sensing measurements, particularly at microwave frequencies using radar and radiometry. Here, we highlight key frontiers through which VWC has the potential to significantly increase our understanding of forest responses to water stress. To validate remote sensing observations of VWC at landscape scale and to better relate them to data assimilation model parameters, we introduce an ecosystem‐scale analog of the pressure–volume curve, the non‐linear relationship between average leaf or branch water potential and water content commonly used in plant hydraulics. The sources of variability in these ecosystem‐scale pressure‐volume curves and their relationship to forest response to water stress are discussed. We further show to what extent diel, seasonal, and decadal dynamics of VWC reflect variations in different processes relating the tree response to water stress. VWC can also be used for inferring belowground conditions—which are difficult to impossible to observe directly. Lastly, we discuss how a dedicated geostationary spaceborne observational system for VWC, when combined with existing datasets, can capture diel and seasonal water dynamics to advance the science and applications of global forest vulnerability to future droughts.

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          Most cited references164

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          Increasing drought under global warming in observations and models

          Aiguo Dai (2013)
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            Global warming and changes in drought

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              On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene

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                Journal
                Global Change Biology
                Glob Change Biol
                Wiley
                1354-1013
                1365-2486
                September 25 2021
                Affiliations
                [1 ]Stanford University Stanford CA USA
                [2 ]Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA
                [3 ]California Institute of Technology Pasadena CA USA
                [4 ]United States Forest Service Washington DC USA
                [5 ]Northern Arizona University Flagstaff AZ USA
                [6 ]University of Utah Salt Lake City UT USA
                [7 ]University of Texas ‐ Austin Austin TX USA
                [8 ]University of California ‐ Santa Barbara Santa Barbara CA USA
                [9 ]University of California ‐ Los Angeles Los Angeles CA USA
                [10 ]Oak Ridge National Laboratory Oak Ridge TN USA
                [11 ]Indiana University‐Bloomington Bloomington IN USA
                [12 ]The Australian National University Canberra ACT Australia
                [13 ]University of Texas – Rio Grande Edinburg TX USA
                [14 ]Massachusetts Institute of Technology Cambridge MA USA
                [15 ]Columbia University New York NY USA
                [16 ]Duke University Durham NC USA
                [17 ]The Ohio State University Columbus OH USA
                [18 ]Centre de Recerca Ecològica i Aplicacions Forestals (CREAF) Barcelona Spain
                [19 ]Universitat Autònoma de Barcelona Barcelona Spain
                [20 ]Pacific Northwest National Laboratory Richland WA USA
                [21 ]Washington State University Pullman WA USA
                [22 ]University of Edinburgh Edinburgh UK
                [23 ]Institució Catalana de Recerca i Estudis Avançats (ICREA) Barcelona Spain
                [24 ]University of California ‐ Irvine Irvine CA USA
                [25 ]University of Campinas Campinas Brazil
                [26 ]University of Massachusetts ‐ Amherst Amherst MA USA
                [27 ]Delft University of Technology Delft The Netherlands
                [28 ]University of Arizona Tucson AZ USA
                [29 ]University of Missouri Columbia MO USA
                [30 ]Cornell University Ithaca NY USA
                [31 ]Wageningen University Wageningen The Netherlands
                Article
                10.1111/gcb.15872
                c4462e80-6e58-44d2-964f-00cea44ed79a
                © 2021

                http://creativecommons.org/licenses/by/4.0/

                http://doi.wiley.com/10.1002/tdm_license_1.1

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