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Observational constraints of fire, environmental and anthropogenic on pantropical tree cover - Data

Data used for analysis in "Explainable Clustering Applied to the Definition of Terrestrial Biome" - using Decision Tree and Clustering techniques to identify biomes. Land surface properties: <ul> <li>TreeCover - Vegetation Continuous Fields (VCF) collection 6 fractional tree cover from 1, regridded as per 2.</li> <li>urban cover from the History Database of the Global Environment, Version 3.1 (HYDE)  3,4</li> <li>crop cover (from HYDE)</li> </ul> <ul> <li>pas - Pasture Cover (from HYDE)PopDen (population density from HYDE)</li> <li>BurntArea_xxxxx - Burnt area with xxxx denoting different products, provided by fireMIP 5–7: <ul> <li>GFED_four: Global Fire Emissions Database, Version 4 (GFED4)  8</li> <li>GFED_four_s: Global Fire Emissions Database, Version 4.1, including small fires (GFEDv4.1) 9</li> <li>MCD_forty_five: MCD45 10</li> <li>Meris: Fire_CCI4.0 11</li> <li>MODIS: Fire_CCI5.1 12</li> </ul> </li> </ul> Climate: <ul> <li>MAP_xxx - Mean annual precipitation where xxx denotes data source: <ul> <li>CMORPH 13,14</li> <li>CRU from version 4.03 of the Climatic Research Unit Time Series high-resolution gridded dataset (CRU TS v4.01) 15</li> <li>GPCC: 16</li> <li>MSWEP: 17</li> </ul> </li> <li>MAT - Mean annual temperature from CRU)</li> <li>MConc_xxx – Mean annual concentration of rainfall as defined by 18, where xxx denotes precip data source (see “MAP_xxx”)</li> <li>MADD_xxx- Mean annual fractional dry days from CRU - i.e. seasonality of rainfall), where xxx denotes precip data source (see “MAP_xxx”)</li> <li>MDDM_xxx – Mean fractional dry days of the driest month.</li> <li>MADM_xxx – Mean annual precipitation of the driest month.</li> <li>MTWM - Mean Maximum Temperature of the warmest month from CRU</li> <li>MTCM - Mean minimum temperature of the coldest month from CRU</li> <li>SW1 - direct downwards SW simulated using the SLASH model using CRU cloud cover</li> <li>SW2 - diffuse downwards SW simulated using the SLASH model using CRU cloud cover</li> <li>MaxWind (Mean Max Windspeed from CRU-(National Centers for Environmental Prediction 15</li> </ul> ‘output_summary’ contains framework output. There are several directories for different experiments, each containing a netcdf file. Along with standard latitude and longitude,each file contains ‘model_level_number’ dimension, with each layer representing the 1, 5, 10, 25, 50, 75, 90, 95 and 99% quantiles of the model posterior. The folder represents the experiment: <ul> <li>Control – standard full model reconstruction</li> <li>noHumans – without human influence (from crop, pasture, population density or urban influence)</li> <li>noMortality – without disturbance stress (burnt area, wind, heat stress, rainfall seasonality</li> <li>noMAP – without mean annual precip influence.</li> <li>noNoneMAT – without mean annual temperature influence.</li> <li>noFire – tree cover without the influence of fire</li> <li>noDrought – without the influence of rainfall distribution</li> <li>noTasMort – without mortality from heat stress</li> <li>noWind – without influence from max. windspeed</li> <li>noPas – without exclusion from pasture</li> <li>noCrop – without exclusion from crop</li> <li>noPop – without reduction from population density</li> <li>noUrban – without exclusion from urban</li> <li>firePlus1pc – tree cover with burnt area was 1% higher.</li> </ul>   References   1.      Dimiceli, C. & Others. MOD44B MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250m SIN Grid V006 (NASA EOSDIS Land Processes DAAC, 2015). Preprint at (2015). 2.      Kelley, D. I. et al. How contemporary bioclimatic and human controls change global fire regimes. Nat. Clim. Chang. 9, 690–696 (2019). 3.      Klein Goldewijk, K., Goldewijk, K. K., Beusen, A., Van Drecht, G. & De Vos, M. The HYDE 3.1 spatially explicit database of human-induced global land-use change over the past 12,000 years. Glob. Ecol. Biogeogr. 20, 73–86 (2010). 4.      Hurtt, G. C. et al. Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Climatic Change vol. 109 117–161 Preprint at https://doi.org/10.1007/s10584-011-0153-2 (2011). 5.      Hantson, S., Arneth, A., Harrison, S. P. & Kelley, D. I. The status and challenge of global fire modelling. (2016). 6.      Hantson, S. et al. Quantitative assessment of fire and vegetation properties in simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project. Geoscientific Model Development vol. 13 3299–3318 Preprint at https://doi.org/10.5194/gmd-13-3299-2020 (2020). 7.      Rabin, S. S., Melton, J. R. & Lasslop, G. The Fire Modeling Intercomparison Project (FireMIP), phase 1: experimental and analytical protocols with detailed model descriptions. Geoscientific Model (2017). 8.      Giglio, L., Randerson, J. T. & van der Werf, G. R. Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (GFED4). J. Geophys. Res. Biogeosci. 118, 317–328 (2013). 9.      van der Werf, G. R. et al. Global fire emissions estimates during 1997–2016. Earth Syst. Sci. Data 9, 697–720 (2017). 10.    Roy, D. P., Boschetti, L., Justice, C. O. & Ju, J. The collection 5 MODIS burned area product — Global evaluation by comparison with the MODIS active fire product. Remote Sensing of Environment vol. 112 3690–3707 Preprint at https://doi.org/10.1016/j.rse.2008.05.013 (2008). 11.    Alonso-Canas, I. & Chuvieco, E. Global burned area mapping from ENVISAT-MERIS and MODIS active fire data. Remote Sens. Environ. 163, 140–152 (2015). 12.    Chuvieco, E. et al. Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies. Earth System Science Data vol. 10 2015–2031 Preprint at https://doi.org/10.5194/essd-10-2015-2018 (2018). 13.    Joyce, R. J., Janowiak, J. E., Arkin, P. A. & Xie, P. CMORPH: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution. J. Hydrometeorol. 5, 487–503 (2004). 14.    Marthews, T. R., Blyth, E. M., Martínez-de la Torre, A. & Veldkamp, T. I. E. A global-scale evaluation of extreme event uncertainty in the eartH2Observe project. Hydrol. Earth Syst. Sci. 24, 75–92 (2020). 15.    Harris, I. C. & Jones, P. D. CRU TS4.03: Climatic Research Unit (CRU) Time-Series (TS) version 4.03 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2018). (2019) doi:10.5285/10D3E3640F004C578403419AAC167D82. 16.    Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A. & Ziese, M. GPCC Full Data Monthly Product Version 2018 at 0.5◦: Monthly Land-Surface Precipitation from Rain-Gauges Built on GTS-Based and Historical Data. Deutscher Wetterdienst: Offenbach am Main, Germany (2018). 17.    Beck, H. E., Van Dijk, A. & Levizzani, V. MSWEP: 3-hourly 0.25 global gridded precipitation (1979-2015) by merging gauge, satellite, and reanalysis data. Hydrol. Earth Syst. Sci. (2017). 18.    Kelley, D. I., Harrison, S. P., Wang, H. & Simard, M. A comprehensive benchmarking system for evaluating global vegetation models. (2013).

ShareScore

48/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship: 8
Harmonization: 4
Access: 20
Reuse readiness: 8
Engagement: 8

Topics

Tree cover, bioclimate, global, gridded