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ORIGINAL SUBMISSION: Dataset for "Mechanisms underlying the vulnerability of seasonally dry ecosystems to drought"
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| Type: | Resource | |
| Storage: | The size of this resource is 495.9 MB | |
| Created: | May 11, 2022 at 11:26 p.m. | |
| Last updated: | Jan 18, 2023 at 4:44 p.m. | |
| Citation: | See how to cite this resource |
| Sharing Status: | Public |
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Abstract
CODE AVAILABLE: https://github.com/erica-mccormick/storage-dynamics
MANUSCRIPT ABSTRACT: Large-scale plant mortality has far-reaching consequences for the water and carbon cycles. The role of belowground root-zone water storage (RWS) on extreme water stress remains uncertain. It has been proposed that the RWS capacity, Smax, can determine ecosystem vulnerability to drought, however, incorporating information about RWS into prediction of vegetation dynamics and mortality has been limited due to the challenge of quantifying RWS at large scales. Here, we present a mass-balance framework for assessing forest resilience to year-to-year variability in precipitation, including megadroughts, by quantifying RWS. We use the relationship between RWS and annual precipitation to evaluate the sensitivity of woody ecosystems to precipitation variability by classifying them as either capacity-limited, where RWS is nearly constant annually and set by Smax, or precipitation-limited, where RWS varies annually based on precipitation amount. We applied this framework to seasonally dry forests and savannas in California and found that approximately 16-23% of the state's total biomass is found in precipitation-limited locations where plants commonly rely on carryover of moisture from one year to the next. These precipitation limited areas experienced disproportionately high rates of mortality in recent drought. In contrast, approximately 51-58% of the state's biomass is found in capacity-limited locations and thus experiences annually reliable moisture supply. Using precipitation projections for the next century, the model framework reveals a tipping point by which 5,163 km2 (27 Tg aboveground carbon) of forest and savanna could transition from stable to unstable moisture supply. An additional 11,950 km2 (55 Tg aboveground carbon), where moisture supply is already annually unstable, is projected to experience increased water stress, due to additional years where precipitation is not sufficient to refill moisture deficits generated in dry years. This framework provides a novel approach for assessing vulnerability of RWS, and thus woody ecosystems, to climate change.
Subject Keywords
Coverage
Spatial
Temporal
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Content
readme.md
Code for: "Resilience of woody ecosystems to precipitation variability" (Submitted: PNAS, May 2022)
Daniella M. Rempe1, Erica L. McCormick1, W. Jesse Hahm, Geeta Persada, Cameron Cummins, Dana A. Lapides, K. Dana Chadwick, and David N. Dralle
1 co-first authors
Last updated May 11, 2022 by Erica McCormick
CODE AVAILABILITY
FILE STRUCTURE SUMMARY
Descriptions and metadata of all files are available in rasters_main_metadata.xlsx.
All .tifs at 500 m resolution unless otherwise noted.
├── future_percentile_data_and_code
│ ├── arrays
│ │ └── rcp45
│ │ ├── rcp45_percentile_ACCESS1-0_rcp45.csv
│ │ ├── rcp45_percentile_CCSM4_rcp45.csv
│ │ ├── rcp45_percentile_CESM1-BGC_rcp45.csv
│ │ ├── rcp45_percentile_CMCC-CMS_rcp45.csv
│ │ ├── rcp45_percentile_CNRM-CM5_rcp45.csv
│ │ ├── rcp45_percentile_CanESM2_rcp45.csv
│ │ ├── rcp45_percentile_GFDL-CM3_rcp45.csv
│ │ ├── rcp45_percentile_HadGEM2-CC_rcp45.csv
│ │ ├── rcp45_percentile_HadGEM2-ES_rcp45.csv
│ │ └── rcp45_percentile_MIROC5_rcp45.csv
│ ├── future_precipitation.py
│ ├── starting_data
│ │ ├── Sr_2003_2020.tif
│ │ ├── percentile_historical.tif
│ │ ├── rcp45_models.nc
│ │ └── rcp85_models.nc
│ └── tifs
│ ├── agreement_rcp45.tif
│ └── rcp45
│ ├── percentile_ACCESS1-0_rcp45.tif
│ ├── percentile_CCSM4_rcp45.tif
│ ├── percentile_CESM1-BGC_rcp45.tif
│ ├── percentile_CMCC-CMS_rcp45.tif
│ ├── percentile_CNRM-CM5_rcp45.tif
│ ├── percentile_CanESM2_rcp45.tif
│ ├── percentile_GFDL-CM3_rcp45.tif
│ ├── percentile_HadGEM2-CC_rcp45.tif
│ ├── percentile_HadGEM2-ES_rcp45.tif
│ └── percentile_MIROC5_rcp45.tif
├── future_percentile_data_and_code.zip
├── qgis_styles
│ ├── SI
│ │ ├── S_2003_2020.qml
│ │ ├── Smax.qml
│ │ ├── agreement.qml
│ │ ├── carryover_mag.qml
│ │ ├── cat_agreement.qml
│ │ ├── deltaP_perc.qml
│ │ └── spearman_pval.qml
│ ├── fig1
│ │ ├── carryover.qml
│ │ ├── percentile.qml
│ │ ├── spearman.qml
│ │ └── state_outline_thick.qml
│ └── fig3
│ ├── agreement_blue.qml
│ └── agreement_brown.qml
├── qgis_styles.zip
├── rasters_main
│ ├── boxplots
│ │ ├── carryover_with_vars_biomass.tif
│ │ ├── map_wy.tif
│ │ ├── percentile_with_vars_biomass.tif
│ │ └── spearman_with_vars_biomass.tif
│ ├── carryover
│ │ ├── S_2020.tif
│ │ ├── carryover_mag.tif
│ │ └── carryover_perc.tif
│ ├── future
│ │ ├── agreement_rcp45_plim.tif
│ │ ├── agreement_rcp45_storlim.tif
│ │ ├── deltaP_mag.tif
│ │ └── deltaP_perc.tif
│ ├── interim
│ │ ├── Smax_lastimage_2006.tif
│ │ ├── Smax_lastimage_2010.tif
│ │ ├── percentile_p_s_stack.tif
│ │ ├── spearman_et_summer.tif
│ │ └── spearman_p_wy.tif
│ ├── masks
│ │ ├── cat_agreement.tif
│ │ ├── et_gt_p.tif
│ │ ├── mask.tif
│ │ ├── mask_landcover.tif
│ │ └── selected_landcover.tif
│ ├── percentile
│ │ └── percentile_historical.tif
│ └── spearman
│ ├── spearman_corr.tif
│ └── spearman_pval.tif
├── rasters_main.zip
├── rasters_main_metadata.xlsx
└── readme.md
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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