Explainable Clustering Applied to the Definition of Terrestrial Biomes - 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:

TreeCover - Vegetation Continuous Fields (VCF) collection 6 fractional tree cover from DiMiceli et al. 2015, regridded as per Kelley et al. 2019

NonTreeCover - VCF fractional herb cover

Urban cover from the History Database of the Global Environment, Version 3.1 (HYDE) Klein Goldewijk et al. 2011

Crop cover (from HYDE)

Pasture Cover (from HYDE)

PopDen (population density from HYDE)

Climate:

MAP_CRU - Mean annual precipitation from version 4.01 of the Climatic Research Unit Time Series high resolution gridded dataset (CRU TS v4.01) (Harris & Jones 2017)

MAT - Mean annual temperature from CRU)

MADD_CRU - Mean annual dry days from CRU - i.e seasonality of rainfall

MTWM - Mean Maximum Temperature of the warmest month from CRU

MTCM - Mean Minumum Temperature of the coldest month from CRU

SW1 - direct downwards SW simulated using the SLASH model using CRU cload cover

SW2 - diffuse downwards SW simulated using the SLASH model using CRU cload cover

BurntArea_GFED_four_s - Burnt area from Global Fire Emissions Database, Version 4.1 (GFEDv4.1) (Van   Der   Werf et al. 2017)

MaxWind (Mean Max Windspeed from CRU-(National Centers for Environmental Prediction (Harris 2019)

Dimiceli,   C.,   Carroll,   M.,   Sohlberg,   R.,   Kim,   D.  H.,Kelly, M., and Townshend, J. R. G. (2015).  Mod44bmodis/terra  vegetation  continuous  fields  yearly  l3global 250m sin grid v006 (v006).

Harris,  I. (2019).   CRU JRA v1. 1:  A forcings dataset ofgridded land surface blend of Climatic Research Unit (CRU)  and  Japanese  reanalysis  (JRA)  data,  January1901–December 2017, University of East Anglia Climatic Research Unit, Centre for Environmental DataAnalysis.

Harris, I. and Jones, P. (2017).  CRU TS4. 01: Climatic Re-search Unit (CRU) Time-Series (TS) version 4.01 ofhigh-resolution gridded data of month-by-month vari-ation  in  climate  (Jan.  1901–Dec.  2016).Centre  forEnvironmental Data Analysis, 25.

Kelley, D. I., Bistinas, I., Whitley, R., Burton, C., Marthews,T. R., and Dong, N. (2019). How contemporary biocli-matic and human controls change global fire regimes

Klein Goldewijk, K., Beusen, A., Van Drecht, G., and DeVos,  M.  (2011).    The  HYDE  3.1  spatially  explicitdatabase  of  human-induced  global  land-use  changeover the past 12,000 years.Global Ecology and Bio-geography, 20(1):73–86.

Van   Der   Werf,   G.   R.,   Randerson,   J.   T.,   Giglio,   L.,Van Leeuwen, T. T., Chen, Y., Rogers, B. M., Mu, M.,Van Marle, M. J., Morton, D. C., Collatz, G. J., et al.(2017).  Global fire emissions estimates during 1997–2016.Earth System Science Data, 9(2):697–720.