A climatological divide between wetness dominant and warmth dominant
regimes of the vegetation in Siberia* Rikie SUZUKI, Jianqing XU, and Ken MOTOYA(Frontier Research Center for Global Change, Japan Agency for Marine-Earth Science and Technology)*Frontier Research Center for Global Change,
3173-25 Showamachi, Kanazawa, Yokohama, Kanagawa 236-0001, Japane-mail: rikie@jamstec.go.jp
Abstract
Wetness and warmth are the principal factors that control global vegetation distribution. This paper
examines the normalized difference vegetation index (NDVI), warmth index (WAI), and wetness index(WEI) at a global scale, and investigates climate-vegetation relationships especially over Siberia. The
NDVI was derived from a global, twenty-year AVHRR NDVI dataset with four-minute resolution. The
WEI is defined as a fraction of precipitation to potential evaporation. The WAI is defined as the
cumulative monthly mean temperature that exceeds 5oC annually. Meteorological data from theInternational Satellite Land-Surface Climatology Project, Initiative II (ISLSCP II) dataset were used to
calculate WEI and WAI. All analyses used annual values based on averages from 1986 to 1995 at 1×1
degree resolution over land. Relationships between NDVI, WEI, and WAI values were examined using avegetation-climate diagram with an orthogonal coordinate system using the WEI and the WAI. There are
two major regimes in the vegetation-climate diagram for the NDVI, wetness dominant and warmthdominant. The boundary between the two regimes roughly corresponds to the vegetation boundary
between taiga forest and southern vegetation over Siberia. The boundary occurs in areas where the NDVI
is large and the maximum monthly temperature is around 18oC over Siberia.
Keyword: NDVI, NOAA/AVHRR, ISLSCP, Potential evaporation, Warmth index, Aridity

1. Introduction
The vegetation geographical distribution in terrestrial
area in the world is strongly controlled by the climate (e.g.,
Bonan, 2002). Especially, the wetness and warmth are
the most essential factors that define the limitation of thevegetation distribution. We considers that almost all thevegetation in the world is stressed more or less by thedryness or low-temperature and it is possible to divide theworld vegetation into wetness dominant and warmthdominant.
This study uses remotely-sensed Normalized Difference
Vegetation Index (NDVI) data of the NOAA/AVHRR, andmakes an attempt to divide the vegetation into two regimes,
wetness dominant and warmth dominant, and demonstrates
their distribution. The Wetness Index (WEI) and theWarmth Index (WAI) are calculated and their relation to
the vegetation index is examined using a vegetationclimate diagram. Although the present study considers
these relationships on global scales, the detailcharacteristics over Siberia will be discussed. Study
results contribute to fundamental knowledge on regional
vegetation and climate distributions and produce a baselinethat can be used to predict vegetation change linked tofuture climate change.

2. Data and Method
All analyses occurred on a 1 × 1 degree grid over land
using annual values based on a ten-year average from 1986
to 1995. The study area covers all land between 60oS and
80oN (that is, all land except for Antarctica and the Arctic).

2.1. Normalized Difference Vegetation Index
This study used 10-day NDVI data acquired from theglobal, 20-year, 4-minute-resolution AVHRR NDVI dataset(CEReS, 2001) produced by Chiba University. Monthly
values were created from the 10-day NDVI values in the
CEReS dataset by choosing the maximum NDVI in eachmonth. The original 8-km grid cells were resampled to a1 × 1-degree grid cell. NDVI values in winter are often
meaningless because of low solar angle and snow cover.
Therefore, annual values in this study were computed frommonthly averaged NDVI values during the seven warmest
months: March through September in the Northern
Hemisphere, and September through March in theSouthern Hemisphere.

2.2. Wetness Index (WEI)
The wetness index (WEI) is defined as the ratio of total
precipitation to total potential evaporation during thewarmest seven months. Potential evaporation, Ep, isdefined in the present study as the evaporation expectedfrom a continuously saturated surface (Kondo and Xu,
1997; Xu and Haginoya, 2001) for which surface
roughness is assumed to be 0.005 m, albedo ref = 0.06
(water surface), surface emissivity ε = 0.98, andevaporation efficiency β* = 1.0. Ground heat flux isneglected. According to the definition,R↓ = εσTSE 4 + H + lE ,H = cpρCHU (TSE − TA) ,lE = lρβ* CHU (h qSAT(TSE ) − qA)

where,R↓ = (1− ref) S↓ +εL↓ .
↓Here, R is the input radiation at the ground surface. σ is
the Stefan-Boltzmann constant (5.670 × 10-8 Wm-2K-4),
and TSE is the calculated surface temperature that satisfies
the heat balance equations. Sensible heat flux H andlatent heat flux lE are computed for the same time; cp is the
specific heat of air, and ρ is the air density. TA is the airtemperature, l is the latent heat of vaporization, h is therelative humidity (%), qSAT(TSE) is the specific humidity atsaturation of TSE, and qA is the specific humidity of air.
The evaporation rate E under these conditions is defined as
the potential evaporation Ep. CHU is the exchange speed,
CHU = Max[a + b × 0.7U, c × (TSE – TA )1/3], where a= 0.0027 ms-1, b = 0.0031, and c = 0.0036 ms-1K-3. The
ratio Pa/Epa is the wetness index (WEI), where Pa andEpa are the total P and Ep during the warmest sevenmonths.
A climatological value of the WEI for each pixel was
calculated by averaging the WEI for 10 years in each 1 ×
1-degree grid cell. The International Satellite Land
Surface Climatology Project (ISLSCP) II (Hall et al.,2004) dataset was used to calculate Pa and Epa.
Precipitation values were proportional to the values
obtained every 6 hours from the National Centers forEnvironmental Prediction (NCEP) reanalysis.
These
NCEP values were adjusted so that the monthly totals
corresponded to the monthly precipitation derived fromprecipitation gauge observations at the Global Precipitation
Climatology Centre (GPCC).
Moreover, systematic
precipitation measurement errors linked to wind-induced
under-catchment at rain gauges were compensated forusing a method similar to empirical WMO formulas(Goodison et al., 1989; Sevruk, 1982).

the WEI is small, then the NDVI also has low values,
corresponding to sparse vegetation in arid areas.
Similarly, if the WAI is small, then the NDVI also has low
values, corresponding to sparse vegetation in low
temperature areas.
Plotted NDVI points fall into two main domains on the
diagram. The first domain extends from the upper left
corner to the upper right part of the diagram, and thesecond domain stretches from the lower right to the upper
right. Vertically-drawn contours of the NDVI in thedomain from the upper left to the upper right imply that the
NDVI changes mainly with the WEI. In contrast,
horizontally-drawn contours of the NDVI in the domainfrom the lower right to the upper right suggest that the
NDVI changes mainly with the WAI.

4. Discussion
4.1. Vegetation regimes where wetness andwarmth dominate
This section examines the implications of the twodomains aforementioned. Figure 2 shows a smoothed
slope of the NDVI against the WEI (GIE) and WAI (GIA) by
a vector (declining direction of NDVI slope) at eachplotted point in Fig. 1 About 1/10 points in Fig. 1 areselected to construct Fig. 2 for a clear picture. Those
vectors were determined by a plain fitting (least square
method) using NDVI, WEI, and WAI values which are in asmoothing circle.
Vectors in the upper part of the domain have a
WEI-directed slope and vectors in the lower-right part ofthe domain have a WAI-directed slope.
Wetness
principally influences the NDVI in the upper domain;
warmth dominates the NDVI in the lower-right domain.
Global vegetation can therefore be partitioned into
wetness- dominant and warmth-dominant regimes from a

2.3. Warmth index (WAI)

0.5

100

2

0.5

0.4

50
0.3

20

0.4

0.1

0.3
10
0.2

Warmth Index (˚C) (log)

0.6
1

Figure 1 shows a vegetation-climate diagram that
includes the NDVI, WEI, and WAI in each 1 × 1-degree
grid cell at global scale. Contours of the NDVI weresuperimposed on the figure after smoothing the NDVI
distribution.
Large NDVI values match the regions where the WEI
and WAI have large values in the diagram. For example,
almost all NDVI values exceeding 0.4 occur where the
WEI exceeds 0.3, and where the WAI exceeds 20oC. If

2000.

3. Relationship between NDVI, WEI, and WAI

0.7

0.

To assess the climatological parameter of warmth, thisstudy used a cumulative monthly temperature similar to thewarmth index (WAI) of Kira's (1948) study on climatic
zones and vegetation. The WAI is an annual, cumulative
temperature of monthly mean temperatures exceeding 5oC.
The WAI represents an effective temperature for vegetationand has been used in past studies of vegetation andtemperature distributions. The WAI in each 1 × 1 degree
grid cell was calculated using temperatures from the
ISLSCP II dataset.

0.2

5
0.1
0.01 0.02

0.05 0.1 0.2

0.5

1

2

5

Wetness Index (log)Fig. 1: The relationship between the NDVI, wetness index(WEI), and warmth index (WAI). The NDVI value (grayscale) at each 1 × 1-degree grid cell is plotted in the
WEI-WAI orthogonal coordinate. The NDVI contours are
drawn according to the smoothed NDVI distribution.

At low- to mid-latitudes, most grid cells are dominated bywetness (horizontally-directed vectors), while at highlatitudes in the Northern Hemisphere, warmth dominates(vertically-directed vectors). The boundary is along about
60oN line in Eurasia, corresponding to the boundary
between boreal forest and cool forest/field.

warmth index (C)

200
100
50

4.3. Vegetation and climate in Siberia
Northern Asia clearly shows the boundary between
wetness- and warmth-dominant regimes, as demonstrated
in Fig. 3. Suzuki et al. (2000) discussed the meridional(south-north) distribution of vegetation in this area,
considering two meridional transects from arid-to-taiga andtaiga-to-tundra. NDVI values in the arid-to-taiga transect
closely relate to the precipitation. NDVI values in thetaiga-to-tundra transect closely relate to warmth. A
subsequent paper, Suzuki et al. (2001), found maximum
NDVI values over the zonal region where the maximum
monthly temperature is around 18oC. The results of theseprevious studies suggest that the zone with maximum
monthly temperatures around 18oC corresponds with thedivide between wetness-dominant and warmth-dominant
regimes.
Figure 4 shows the grid cells that have maximum
monthly NDVI values (mean from 1986 to 1995)
exceeding 0.7. Grid cells that have comparable NDVI
slopes for the WEI and the WAI (the ratio of GIE to GIA isfrom 0.5 to 1.5) are superimposed on Fig. 4. Note that thezone of grid cells with maximum NDVI values over 0.7
agrees with the zone with comparable slopes of the WEI
and the WAI. This demonstrates that the zone with high
NDVI values, or the zone with temperatures around 18oC
(Suzuki et al., 2001), corresponds to the climatologicaldivide between wetness-dominant and warmth-dominant
vegetation regimes. Vegetation south of this zone is

20
GIE

GIA

10
5
0.01 0.02

0.05 0.1 0.2

0.5

1

2

5

wetness index
Fig. 2: Smoothed NDVI slope against the wetness index (WEI)
and the warmth index (WAI). The gray line denotes theboundary between the wetness-dominant regime and thewarmth-dominant regime.

macroscopic viewpoint. The grey line in Fig. 2 represents
visually interpreted boundary between the two domains.
4.2. Geographical distribution of two regimes
Figure 3 shows the geographical distribution of thevectors which are the same in Fig. 2 but the direction was
inverted for convenience sake. Most desert areas areabsence from the vectors, because grid cells there have
NDVI values of less than 0.1 and are therefore excluded
from the slope calculation.
The two domains are clearly mapped in separate areas.

180˚

240˚

300˚

0˚

60˚

120˚

180˚

60˚

GIA

0˚

GIE
-60˚
Fig. 3: The distribution of the slope of the NDVI against WEI (GIE) and WAI (GIA). The vector
directions are inverted from those in Fig. 1. Vertical and horizontal directions of vectors correspond to
the slope for WAI and WEI, respectively. Areas with NDVI values less than 0.1 are blank.

17

0˚
16 0˚
60
˚
15
0˚

50˚

100˚

90˚

80˚

70˚

60˚

˚
140
130˚

120˚

110˚

40˚

Fig. 4: The distribution of grid cells in which the maximum monthly NDVI exceeds 0.7 (gray circle), and the grid cells in which the
NDVI slopes against the wetness index (WEI) and the warmth index (WAI) are comparable (the ratio of GIE to GIA is from 0.5 to 1.5)
(small open circle).

constrained by aridity with higher temperatures than 18oC
of maximum monthly temperature and small amounts ofprecipitation. Vegetation to the north of the zone isconstrained by temperatures lower than 18oC.
5.

Summary

Global NDVI distributions are related to two important
climatological factors, wetness and warmth. The 10-year
mean NDVI for the warmest seven months from 1986 to
1995 was calculated at 1 × 1-degree resolution using the
CEReS NDVI dataset. The wetness index (WEI) and thewarmth index (WAI) were defined and the 10-year mean
WEI over the warmest seven months and the 10-year mean
WAI were calculated using the ISLSCP II data.
Geographic distributions of the NDVI, WEI, and WAI weretransformed onto a vegetation-climate diagram, which uses
the WEI and the WAI as orthogonal coordinates, tofacilitate a discussion of the relationships among them.
There are two major vegetation regimes in thevegetation-climate diagram: wetness-dominant andwarmth-dominant.
The boundary between the two
regimes roughly corresponds to the vegetation boundary
between taiga forest and other southern vegetation. The
boundary between the two regimes over northern Eurasia
coincides with the high NDVI zone discussed by Suzuki etal. (2001). These results contribute to fundamental
knowledge on regional vegetation and climate distributions
and produce a baseline that can be used to predictvegetation change linked to future climate change.

References
Bonan G., Ecological Climatology – Concepts andapplications.
Cambridge
University
Press,
Cambridge, 2002..
CEReS, Chiba University, Twenty-year global 4-minute
AVHRR NDVI Dataset. Chiba University, Chiba,
Japan, 2001.
Goodison, B.E., P.Y.T. Louie, and D. Yang, WMO solid
precipitation measurement intercomparison, final report.
WMO-872, 212pp, World Meteorological Organization,
Geneva, 1989.
Hall, F.G., B. Meeson, S. Los, L. Steyaert, E. Brown de Colstoun,and D. Landis, eds. ISLSCP Initiative II. NASA.
DVD/CD-ROM. NASA, 2004.
Kira, T., On the altitudinal arrangement of climatic zones in Japan−A contribution to the rational land utilization in coolhighlands. Agricultural Science of the North Temperate
Region, 2, 143—173, 1948. (in Japanese)
Kondo, J, and J. Xu, Seasonal variations in the heat and waterbalances for non-vegetated surfaces. J. Appl. Meteor.,
36,.1676—1695, 1997.
Sevruk, B., Method of correction for systematic error in point
precipitation measurement for operational use. WMO-589,
91 pp, World Meteorological Organization, Geneva,
1982..
Suzuki, R., S. Tanaka, and T. Yasunari. Relationships between
meridional profiles of satellite-derived vegetation index(NDVI) and climate over Siberia. Int. J. Climatol., 20,955—967, 2000.
Suzuki, R., T. Nomaki, and T. Yasunari. Spatial distribution andits seasonality of satellite-derived vegetation index(NDVI) and climate in Siberia. Int. J. Climatol., 21,
1321—1335, 2001.
Xu, J., and S. Haginoya. An estimation of heat and water balancesin the Tibetan Plateau. J. Meteor. Soc. Japan, 71,
485—504, 2001.