The role of spatiotemporal plant trait variability in model predictions of ecohydrological responses to climate change in a desert shrubland

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Abstract

Although spatial heterogeneity of soil properties, topography, and climate is commonly incorporated into ecohydrological models, the spatial and temporal variability in plant functional traits is typically overlooked. The objective of our study is to evaluate the impact of trait parameter variability on modeled ecohydrological processes. We implemented a model-data fusion approach to constrain spatiotemporally dynamic parameters in plant functional traits at two desert shrubland sites located along a topographic and climate gradient in the Mojave Desert. Our results showed that the estimates for specific leaf area and rooting depth for the broadleaf-evergreen-shrub plant-functional-type showed spatial variability, with lower specific leaf area and deeper rooting depth found at the low elevation site. We also found that the specific leaf area estimates changed over time at both sites in response to water stress, but with different sensitivities, possibly depending on species and/or climate. The spatial variability in trait parameter estimates was greater than temporal variability and played a more important role in accurately simulating ecohydrological processes, but including the temporal variability in specific leaf area further improved seasonal predictions. In simulations forced by future climate projections under the Representative Concentration Pathway 4.5 (RCP 4.5) and 8.5 (RCP 8.5) greenhouse gas emissions scenarios, spatial variability in trait parameters impacted predictions of both carbon and water fluxes, while temporal variability in trait parameters resulted in predictions of higher ecological function and water use efficiency. The higher water use efficiency led to improved ecohydrological function in simulations under RCP 4.5, but it showed little capacity for buffering intensive water stresses under the more pessimistic RCP 8.5 scenario, indicating that with spatiotemporally variable trait parameters, the impact on predicted ecohydrological processes depends on the climate projections. Overall, our modeling results prompt further field-based examination of temporal and belowground trait variability in desert shrublands, and they raise the question of how combined spatiotemporal variabilities of multiple traits may support ecohydrological function under water stress.

Original languageEnglish (US)
Article number125088
JournalJournal of Hydrology
Volume588
DOIs
StatePublished - Sep 2020

Bibliographical note

Funding Information:
This study was supported by funding from NSF (NSF-1724781). Minnesota Supercomputing Institute (MSI) at University of Minnesota-Twin Cities and the Cheyenne cluster at NCAR provided the supercomputing resources. We acknowledge the data from the TRY initiative on plant traits (http://www.try-db.org). The TRY initiative and database is hosted, developed and maintained by J. Kattge and G. Bönisch (Max Planck Institute for Biogeochemistry, Jena, Germany). The dataset MACAv2-METDATA was produced with funding from the Regional Approaches to Climate Change (REACCH) project and the SouthEast Climate Science Center (SECSC). We acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output. For CMIP, the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. The authors thank Dr. James M. Andre (Granite Mountains Desert Research Center and UC Riverside Department of Biology) for helping to identify plant species at the Globe site in the Mojave Desert. The authors thank Dr. David R. Bedford (posthumously), Dr. David M. Miller, and Dr. Andrew Cyr (U.S. Geological Survey, Menlo Park, California, USA) for their work to collect and compile the meteorological forcing data and soil moisture measurements in Kelso Valley; Dr. David M. Miller also provided helpful comments on the manuscript. We acknowledge the National Parks Service (NPS) for access to the Kelso Valley study sites within the Mojave National Preserve. The data used in the study is provided in the supplement information. This work is dedicated to the memory of Dr. David R. Bedford, whose passion for understanding the geology and ecohydrology of the Mojave Desert provided the foundation of this study.

Funding Information:
This study was supported by funding from NSF (NSF-1724781). Minnesota Supercomputing Institute (MSI) at University of Minnesota-Twin Cities and the Cheyenne cluster at NCAR provided the supercomputing resources. We acknowledge the data from the TRY initiative on plant traits (http://www.try-db.org). The TRY initiative and database is hosted, developed and maintained by J. Kattge and G. B?nisch (Max Planck Institute for Biogeochemistry, Jena, Germany). The dataset MACAv2-METDATA was produced with funding from the Regional Approaches to Climate Change (REACCH) project and the SouthEast Climate Science Center (SECSC). We acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output. For CMIP, the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. The authors thank Dr. James M. Andre (Granite Mountains Desert Research Center and UC Riverside Department of Biology) for helping to identify plant species at the Globe site in the Mojave Desert. The authors thank Dr. David R. Bedford (posthumously), Dr. David M. Miller, and Dr. Andrew Cyr (U.S. Geological Survey, Menlo Park, California, USA) for their work to collect and compile the meteorological forcing data and soil moisture measurements in Kelso Valley; Dr. David M. Miller also provided helpful comments on the manuscript. We acknowledge the National Parks Service (NPS) for access to the Kelso Valley study sites within the Mojave National Preserve. The data used in the study is provided in the supplement information. This work is dedicated to the memory of Dr. David R. Bedford, whose passion for understanding the geology and ecohydrology of the Mojave Desert provided the foundation of this study.

Publisher Copyright:
© 2020 Elsevier B.V.

Keywords

  • Ecohydrological model
  • Model-data fusion
  • Plant functional trait

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