Chapter 12
Climatic and Geographic Patterns of Spatial
Distribution of Precipitation in Siberia
A. Onuchin and T. Burenina
Abstract The spatial temporal distribution of precipitation is a function of atmos-
pheric circulation and the orography of the terrain. Due to these factors the spatial
temporal distribution of precipitation differs on global, regional and local levels.
Three vast Siberian ecoregions (Western Siberia, Central Siberia and Eastern Siberia)
are differing in their space-temporal patterns of precipitation distribution.
The spatial distribution of precipitation over West Siberia follows a geographical
zonation: precipitation changes from 300 mm in the south to 400 500 mm in the
forest zone. The areas of Central and East Siberia with extreme continental climates
and mountain relief differ in precipitation and moisture characteristics to a great
extent. In Central Siberia precipitation varies between 325 and 525 mm, in East
Siberia between 250 and 330 mm.
Climatic and geographic patterns of the spatial precipitation distribution in
Central Siberia are studied on a regional level. Computer models of spatial precipi-
tation distribution were developed for the Yenisei Mountain Chain, Eastern Sayan,
and the South-eastern Trans-Baikal region.
Owing to irregular spatial distribution of precipitation three groups of landscapes
were defined: (1) slopes of west, north-west and south-west aspect with orographic
precipitation; (2) shadow slopes in mountain regions; (3) plain landscapes. Obtained
equations show correlations between the amount of precipitation and altitude, geo-
graphical latitude, distance from barrier ridge and other parameters.
Keywords Distribution of precipitation
" Ecoregion
" Siberia
" Spatial precipitation
patterns
" Geographical zonality
A. Onuchin (*) and T. Burenina
V.N. Sukachev Institute of Forest,
SB RAS, Akademgorodok, 50, Krasnoyarsk 660036, Russia
e-mail: onuchin@ksc.krasn.ru; burenina@ksc.krasn.ru
H. Balzter (ed.), Environmental Change in Siberia: Earth Observation, 193
Field Studies and Modelling, Advances in Global Change Research 40,
DOI 10.1007/978-90-481-8641-9_12, © Springer Science+Business Media B.V. 2010
194 A. Onuchin and T. Burenina
12.1 Introduction
Atmospheric precipitation is the major moisture circulation component that has a
profound influence on terrestrial ecosystem climate, microclimate, and hydrological
regimes. This explains the numerous studies undertaken to provide a better under-
standing of spatial and temporal precipitation patterns under various vegetation and
climatic conditions (Arkhangelsky 1960; Berg and Shenrok 1925; Bradley 1966;
Burenina et al. 2002; Glebova 1958; Gorec and Younkin 1966; Govsh 1962;
Kolomyts 1975; Kopanev 1966; Matasov 1938; Matveyev 1968, 1984; Mellor and
Smith 1966; Richter and Petrova 1960; Richter 1963, 1984; Shpak and Bulavskaya
1967; Sosedov 1962, 1967; Tikhomirov 1956; Trifonova 1962; Vinogradov 1964).
Spatial precipitation non-uniformity is most readily apparent in mountains, is attribut-
able to trajectories of air masses within the ocean land system, distance from the ocean,
atmospheric processes characteristic of different periods of time, and underlying
surface features. According to the current general precipitation scheme, precipitation
increases with increasing elevation and in steadily low-pressure belts, one of which
lies between 60° and 70° N latitude, and decreases with the distance from the ocean.
However, this general scheme contains almost as many exceptions as strong trends.
Precipitation patterns are extremely complicated in mountainous countries. Air
flows occurring around mountain systems and single (separate) elevations enhance
precipitation on windward slopes, while reducing its amount on downwind slopes
(Beyer 1966; Guralnik et al. 1972). The outermost upwind slopes, acting as natural
moist air mass breaks, receive more precipitation than those located deeper in moun-
tain systems, even if these in-mountain slopes are higher compared to the outermost
ones (Ladeishchikov 1982). In this case, precipitation amount is controlled by a num-
ber of factors, such as air mass water content and movement relatively to mountain
barriers , thermal layering of the atmosphere, and underlying surface characteristics.
Precipitation is known to vary with elevation above sea level (a.s.l.). In mountains,
precipitation usually increases up to a certain elevation called a peak-moisture zone,
beyond which precipitation stops to increase and can even decrease with elevation
due to decreasing water vapour concentration in the upper atmospheric layers.
These relationships exhibit different behaviour depending on specific orographic
and climatic characteristics of mountainous countries. This dependence was sup-
ported by research studies conducted high in the Alps (Berg 1938), Pamir-Altai and
the Pamir highland (Kotlyakov 1968). Among other factors, these differences are
accounted for by foehn, warm and dry wind that occurs in mountains due to down-
draughts found in an atmospheric layer not less than 0.5 1 km deep and promotes
moisture evaporation. The relationship between precipitation amount and topogra-
phy was the focus of a number of studies (Burenina 1998; Hartzman 1971; Korytny
1980; Onuchin 1987; Onuchin and Burenina 2002; Shultz 1972). In closed moun-
tain hollows, precipitation depends on the distance from the surrounding barriers
(Korytny 1980). Precipitation modelling for upwind slopes breaking the prevailing
atmospheric moisture transfer is a sophisticated process, since it has to consider
many more influences (Onuchin and Burenina 2002). Where a moisture transfer
12 Climatic and Geographic Patterns of Spatial Distribution of Precipitation in Siberia 195
trajectory coincides with large valley orientation, atmospheric moisture can be
brought to a fairly small region and discharged there through heavy snow or rain-
falls (Suslov 1954). Although spatial and temporal precipitation patterns are known
to be controlled by numerous landscape-specific factors, precipitation modelling is
usually reduced, with very few exceptions, to interpolating scarce data provided by
a sparse weather station network and assessing the precipitation gradient. The
qualitative trends identified so far for spatial precipitation patterns are still under
considered in development of the quantitative methods adapted to the local condi-
tions of the territory.
Little is known about spatial precipitation distribution in Siberia, with most of
the available publications focusing on spatial precipitation non-uniformity in the
southern Siberian Mountains (Grudinin 1979, 1981; Lebedev 1979, 1982).
Mountain range direction, elevation a.s.l. and location with respect to winds carry-
ing moisture are responsible for this non-uniformity. Orographic diversity was the
reason of dividing mountains of southern Siberia into regions differing in annual
precipitation and its vertical gradient (Chebakova 1986; Grudinin et al. 1975).
A number of scientists (Afanasyev 1976; Ladeishchikov 1982), in their studies of
the precipitation patterns in lake Baikal catchment, noted that the vertical precipita-
tion gradient varied in mountains from 20 to 1,000 mm per 100 m elevation step
depending on the distance from Lake Baikal and the angle between slopes and wet
winds. These factors introduce considerable ambiguity into interpretations of precipi-
tation changes with increasing elevation a.s.l.
Regional and zonal snowfall matches the general macro-scale precipitation
occurrence trends found for Siberia, except that the snow cover distribution is
much more of a mosaic due to numerous influences. Snow cover development
on mountain slopes, under the forest canopy and in open sites, and spatial
snow pack patterns in northern Eurasia were addressed by a number of earlier
studies (Onuchin 1984, 1985, 2001; Onuchin and Burenina 1996a, b). Since
snow cover development requires an extended separate discussion, we leave it
outside the scope of this chapter and focus on general spatial precipitation
trends in Siberia.
12.2 Study Area and Methods
Siberia stretches from the Ural Mountains eastward as far as the Lena river and
includes the West Siberian Plain, Central Siberian Tableland, the watershed of Lena
river, the Altai-Sayan mountain range, and the Lake Baikal region. Our study area
covered the territory from 50° to 70° N latitude and from 60º to 160° E longitude.
Our ground truth data were obtained in the Putoran Plateau, Yenisei Mountain
Chain, north-eastern and central Siberia respectively (Fig. 12.1), south-western
Siberia, Eastern Sayan, and the south-eastern Trans-Baikal region. The study sites
thus covered the entire range of Siberian vegetation zones, from tundra to steppe
and all altitudinal vegetation zones in mountains.
196 A. Onuchin and T. Burenina
Fig. 12.1 Location of points of snow precipitation measurements in Central Siberia. Dots are
points of snow precipitation measurement; the scale is altitude (m)
The precipitation distribution was investigated at ecoregional1 and local levels.
In the latter case, spatial precipitation models were developed for different areas
within ecoregions accounting for local orography and air mass circulation. Macro-
scale (i.e. ecoregional) precipitation distribution was analyzed using weather data
provided by 130 weather stations situated in the study area (USSR Climate Guide
1956, 1969, 1970). Weighted average annual precipitation was calculated by spatially
averaging for each study area (forest provinces or forest districts according to the
current Russian forest regionalization by Korotkov (1994)).
Siberia is remarkable for contrasting natural conditions; its weather station
network is highly irregular, and particularly sparse in the north. Therefore, one of
the most reliable methods to obtain average precipitation for an area involves build-
ing average precipitation isoclines and measuring areas between pairs of adjacent
isoclines by planometric techniques (Schwer 1984). Average precipitation values
were calculated for the ecoregions of interest by the following equation:
Sk m Fk + Fk +1
(12.1)
F = ×
s "
S 2
1
where Sk is area between a pair of isolines corresponding to Fk
1
Ecoregion refers to a big area identified based on a landscape approach. Ecoregion examples in
Siberia are West-Siberian Plain, Central Siberian table land, and Altai-Sayan mountain system.
Ecoregional boundaries coincide with those of forest oblasts as identified by Korotkov (1994).
12 Climatic and Geographic Patterns of Spatial Distribution of Precipitation in Siberia 197
and Fk + 1 average precipitation values, m is the number of these areas, and S is the
entire area for which averaging is done.
Local precipitation distribution was calculated based on weather station infor-
mation combined with our in situ precipitation measurements. The data obtained
were subjected to multiple regression analysis (Lvovsky 1988).
12.3 Results
12.3.1 Spatial Precipitation Patterns in Siberian Ecoregions
The interactions of the atmosphere with the underlying land surface, with the
former being dynamic in time and the latter having noticeable spatial variabil-
ity, are responsible for precipitation development and, hence, its spatial and
temporal patterns.
Atmospheric precipitation is a key moisture cycling component which controls
hydrological regimes of terrestrial ecosystems to a great extent. Precipitation pat-
tern is a major factor accounting for landscape differentiation. The precipitation
amount varies, in turn, depending on regional atmospheric circulation and underlying
surface characteristics.
Geographical contrasts of Siberia are reflected in both latitudinal and meridian
precipitation occurrence in this region. For this reason, it is most appropriate to
analyze macro-scale precipitation distribution based on large ecoregions. According
to the current Russian forest regionalization (Korotkov 1994), eight forest regions
(defined as ecoregions ) found in Siberia contain 32 forest provinces, which are
divided into subprovinces (or forest districts) (Fig. 12.2). This forest regionaliza-
tion considers precipitation amount by latitude and continentality. Table 12.1 shows
the spatial distribution of precipitation for forest provinces and forest districts inside
of large Siberian ecoregions. The numbers of forest provinces and districts in
Table 12.1 are given according to Fig. 12.2.
A latitudinal precipitation change characteristic of the West Siberian Plain is
manifest in the decreasing precipitation amounts proceeding south and northward
from the taiga forest provinces. Precipitation is fairly high (505 508 mm) in the
northern and southern taiga, while it decreases further to the north and averages only
378 mm in the north of the Trans-Ural-Yenisei province, which is actually forest-
tundra. The lowest precipitation (296 mm) was found for the Kulunda province
situated in the steppe zone.
Precipitation tends to decrease west-eastward in eastern Siberia. The western part
of the Central Siberian tableland can be considered as a transition to the dry continental
climate of eastern Siberia. While the maximum annual precipitation (617 675 mm)
was found to occur in the Putoran and Yenisei forest provinces, the Anabar province
and Kotuy-Olenek forest district received the lowest amount of precipitation
(325 342 mm). The non-uniform precipitation distribution found within the western
part of the Central Siberian tableland is most probably caused by its orography.
198 A. Onuchin and T. Burenina
Fig. 12.2 Scheme of forest regionalization of Russia by Korotkov (1994)
The Lena-Veluy and Aldan forest provinces situated in central Yakutia appeared
to be fairly dry, with annual precipitation ranging from 298 335 mm. Precipitation
exhibited a decrease moving southward, down to the Trans-Baikal region. All study
areas were determined to receive precipitation mainly through rainfall, with snow-
fall accounting for only 25 30% of the annual total.
Since mountains are known to enhance atmospheric processes responsible for
precipitation development, precipitation is much heavier in mountains compared to
the plains surrounding them. Precipitation changes with elevation depend on the
atmospheric circulation over a particular mountain massif location. For any moun-
tain system, there exists a certain upper precipitation threshold depending on eleva-
tion controlled by rising air. Besides global air mass transfer, precipitation in
mountains is considerably impacted by large-scale thermal circulation between a
mountain system and the adjacent plain.
The mountains of southern Siberia have considerable differences in their
precipitation distribution associated with mountain range height, morphometric
characteristics, and position relative to the prevailing moisture transfer. An annual
precipitation of 600 1,048 mm was found to be common in the Altai-Sayan ecore-
gion, whereas the northern and southern Trans-Baikal ecoregions were determined
to receive 450 527 and 373 465 mm per year, respectively. Precipitation amount
12 Climatic and Geographic Patterns of Spatial Distribution of Precipitation in Siberia 199
Table 12.1 Precipitation patterns in Siberian ecoregions
Precipitation (mm)
Warm period Cold period
(April (November
No Forest provinces and districts October) March) Annual
West Siberian Plain forest oblast (ecoregion)
22. Trans-ural forest province:
near-tundra forests and open
woodlands
22.1. Forest-tundra district 378 256 122
22.2. District of northern taiga open 466 330 136
stands and open woodlands
23. Trans-Ural-Yenisei taiga forest
province
23.1. Northern taiga forest district 508 375 133
forest district
23.2. Central taiga forest district 505 378 127
23.3. Southern taiga and subtaiga forest 471 369 102
district
24. Irtish-Ob forest-steppe province 414 368 46
64. Kulunda steppe province 296 254 42
Altai-Sayan Mountain forest oblast (ecoregion)
26. Northern Altai-Sayan forest 781 651 130
province
27. Eastern Sayan forest province 663 542 121
28. Central Altai forest province 772 430 342
29. Western Altai forest province 986 594 392
30. Eastern Tuva (Tojin) forest 660 514 146
province
31. Khakasia-Minusinsk forest 463 387 76
province
32. Salair-Kuznetsk forest province 1,078 668 410
Central Siberian Tableland forest oblast (ecoregion)
33. Putoran mountain forest province 617 422 195
34. Anabar forest province 325 250 75
35. Yenisei forest province 675 461 214
36. Kotuy-Olenek forest province 342 268 74
37. Angara-Tunguska taiga forest
province
37.1. Lower Tunguska forest district 548 377 171
37.2. Podkamennaya Tunguska forest 489 358 131
district
37.3. Fore-Angara forest district 465 355 110
38. Kansk forest-steppe province 435 382 53
39. Upper Angara forest province 425 360 65
40. Upper Lena forest province 411 356 55
Central Yakutian plain forest Oblast (ecoregion)
41. Lena-Viluy forest province 298 204 94
42. Aldan forest province 335 230 105
(continued)
200 A. Onuchin and T. Burenina
Table 12.1 (continued)
Precipitation (mm)
Warm period Cold period
(April (November
October) March) Annual
Yan-Kolyma mountain forest Oblast (ecoregion)
43. Lower Kolyma forest province 256 171 85
44. Yan-Indigirka forest province 278 181 97
45. Kolyma forest province 302 170 132
Northern Trans-Baikal Forest Oblast (ecoregion)
46. Baikal-Stanovaya forest province 527 307 220
47. Upper Vitim-Olekin tableland 496 289 207
forest province
48. Uchura-Maya forest province 450 308 142
Southern Trans-Baikal mountain forest oblast (ecoregion)
49. Jidin forest province 465 362 103
50. Selenga forest province 373 317 56
51. Chikoy-Ingodin forest province 446 336 110
52. Dahur forest province 391 304 87
Fore-Baikal mountain forest oblast (ecoregion)
53. Fore-Baikal forest province 596 405 191
appeared to vary in southern mountain forests found in each of the ecoregions
under study. This difference was determined to be 100 150 mm for the forest pro-
vinces of the Southern and Northern Trans-Baikal ecoregions, while it exceeded
500 mm for those in the Altai-Sayan ecoregion Table 12.1). Furthermore, the amount
of precipitation presumably varies within provinces with elevation and slope aspect.
Annual precipitation exhibited a considerable variability in the Altai Mountains.
West-facing slopes were found to receive over 1,600 mm, while the value appeared
to be as low as 300 mm for interior downwind slopes, narrow valleys, and hollows,
with Altai and Tarbogatai south-facing slopes looking at Zaisan lake being espe-
cially dry (about 250 mm of precipitation). Annual precipitation was found to
range from 1,000 1,300 mm on western slopes in the Mountainous Shoria. Places
of the Salair mountain range and Kuznetsk Alatau that are exposed to moist western
wind appeared to receive twice as much precipitation as the adjacent valley. The
Sayan Mountains also showed precipitation contrasts. The boundaries of the
Western Sayan regions identified as differing in precipitation (Chebakova 1986;
Lebedev 1982) appeared to coincide with those of the forest vegetation regions,
which differ in vegetation belt. Precipitation changes with elevation are presented
in Table 12.2.
As is clear from Table 12.2, the northern upwind macro slope of Western Sayan
receives the greatest amounts of precipitation yearly: up to 1,700 mm for the exces-
sively humid Jebash and Amyl ranges and 1,200 1,450 mm for lower ranges with
high precipitation. The Western Sayan foothills and Minusinsk hollow are fairly dry
12 Climatic and Geographic Patterns of Spatial Distribution of Precipitation in Siberia 201
Table 12.2 Precipitation distribution depending on elevation in Western Sayan (Chebakova 1986)
Altitudinal vegetation belt Elevation a.s.l. (m) Annual precipitation (mm)
Excessively humid regions
Forest-steppe 300 350 550 580
Light-needled forest 350 400 580 950
Dark-needled forest:
Mixed fir/Siberian pine chern 400 900 950 1,400
stands
Mountain taiga mixed Siberian 800 1,300 1,400 1,500
pine/fir stands
Subalpine belt (mixed Siberian 1,300 1,800 1,500 1,650
pine/fir open stands)
Mountain tundra 1,800 2,100 165 1,700
Highly humid regions
Light-needled forest 700 1,000 760 950
Dark-needled forest:
Mountain taiga Siberian pine stands 700 1,500 750 1,200
Subgolets-taiga Siberian pine 1,500 1,800 1,200 1,350
standsa
Mountain tundra 1,800 2,200 135 1,450
Moderately humid regions
Steppe 2,509 400 300 350
Forest-steppe (mountain hollows) 400 800 350 550
Light-needled forest:
Mixed subtaiga larch/Scots pine 500 1,200 400 750
stands
Mountain taiga larch stands 800 1,500 500 800
Dark-needled forest:
Mountain taiga Siberian pine stands 1,100 1,600 700 850
Subgolets-taiga Siberian pine 1,600 1,900 850 950
stands
Dry regions
Steppe 800 1,800 250 450
Light-needled forest:
Mountain taiga larch stands 1,200 2,000 350 500
Subgolets-taiga larch stands 2,000 2,200 500 600
Dark-needled forest:
Subgolets-taiga Siberian pine 1,800 2,200 480 600
and mixed Siberian pine/larch
stands
Mountain tundra 2,200 3,000 600
a
Subalpine belt for more continental climate conditions
(350 400 mm), since western and south-western air masses precipitate abundantly
when going through the high parts of Kuznetsk Alatau, western Sayan, and the
Tanu-Ola mountain range. The Alash plateau has a markedly dry climate, with
annual precipitation totalling 250 300 mm and steppe being the most widespread
vegetation type.
202 A. Onuchin and T. Burenina
Considerable elevation-induced precipitation changes were observed in each of
the above regions. The greatest precipitation gradient (100 200 mm/100 m) was
found for low and middle mountains of excessively humid regions (Chebakova 1986).
This gradient drops drastically down to 20 mm/100 m in high mountains, since air
masses precipitate intensively while moving upslope and thus loose much of their
water before they reach high-mountain vegetation. An average annual precipitation
gradient of 70 mm/100 m determined for highly humid regions exhibited a decrease
in highland. This gradient appeared to vary from 50 to 35 mm per 100 m elevation
step. The lowest gradient was calculated for Alash plateau.
The Eastern Sayan watershed mountain range stretching from southeast to northwest
is a well pronounced climatic boundary between a highly humid (>1,200 mm of
precipitation) south-southwest areas and the dry, highly continental (annual precipi-
tation less than 400 mm) Sayan highland situated east-northeast of this range.
12.3.2 Spatial and Temporal Precipitation Pattern Models
Weather data used in the current methodologies of building generalized precipitation
maps are provided by a sparse weather station network. This drawback is aggra-
vated by the fact that these data are not always representative of the area of interest.
Atmospheric circulation and underlying surface are the two factors controlling
spatial and temporal precipitation patterns. The former factor has temporal dynamics,
whereas the latter varies in space.
While atmospheric moisture content and thermal layering change in time and
cyclone and anticyclone trajectories often differ from that of the general air mass
transfer, there are seasonal location-specific characteristics of the atmospheric
circulation that occur every year. For this reason, regional average multi-year
precipitation distribution is mainly a function of topography. Our studies of spatial
precipitation patterns conducted in the Fore-Baikal region support this.
12.3.2.1 The Central Part of the Krasnoyarsk Region
The relationship between precipitation and topography varies depending on
landscape characteristics. We identified three groups of landscapes in the central
part of the Krasnoyarsk region. These groups differ in spatial precipitation patterns
and in landscape characteristics controlling the precipitation amount. The first
group, so-called barrier landscapes , can be divided into two subgroups of the
upwind western and south-western slopes of Yenisei mountain range and the east-
ern Sayan foothills. Southwest-facing slopes of the Yenisei mountain range
located at a right angle to the prevailing wind direction fall into the first subgroup.
The second subgroup covers west- and northwest-facing slopes of the Yenisei
mountain range and the eastern Sayan foothills located at an acute angle to the
prevalent south-westerly winds. Landscapes of the second group are found in
12 Climatic and Geographic Patterns of Spatial Distribution of Precipitation in Siberia 203
West-Siberian Plain (Ket and Kas riverheads). Intermountain valleys of the above
mountain systems that fall within the wind shade and, thus, within precipitation
shade under the prevailing regional moisture transfer make up the third landscape
group ( shaded landscapes).
The data obtained were analyzed for the relationship of precipitation distribution
with geographical locality characteristics (elevation above sea level, a.s.l., the distance
down to the barrier foot, location coordinates, and distance from the locality to the
highest barrier point and relief features such as terrain roughness, i.e. the ratio between
elevation difference total and a profile length, and a screening coefficient that shows
how much of a location is shaded by a barrier). As a result of this analysis, precipita-
tion models were obtained fore the two barrier landscape subgroups (Eqs. 12.2 and
12.3), the plain landscape group (Eq. 12.4), and shaded landscapes (Eq. 12.5).
2
X = -309 + 227·lnH + 83·J -149·lnH·L + 801·L - 898· h - Lb /100 (12.2)
( )
p p
R2= 0.61
à = 93
F = 15.4
where X is the average multiyear precipitation (mm); H is elevation a.s.l. (m); J is
the relief form coefficient taken equal to 1, 0, or -1 for mountain peaks, slopes, and
valleys, respectively; L is the distance to the barrier foot (km); h is the height of
p
the first barrier occurring in the way of the prevailing moisture transfer (m); Lb is
the distance to the highest point of the first barrier occurring in the way of the pre-
vailing moisture transfer (km); R2 is the multiple coefficient of determination; s is
the standard error; and F is the Fisher criterion.
X = 484 + 246.6·lnH·ln(10·L +1) - 228.4·ln + (10L +1) (12.3)
pp
R2 = 0.72 Ã = 53.9 F = 26.9
X = -4125 + 0.52·H + 80.4·S -173· U
(12.4)
R2 = 0.49 Ã = 40 F = 3.9
Where S is latitude (°N); U is relief roughness within a 20 km orographic profile
section beginning from the barrier foot along the prevailing moisture transfer axis.
X =-746.4 + 0.23·H + 20.2·S
(12.5)
R2 = 0.62 Ã = 23.7 F = 26.1
Equation 12.2 indicates that the elevation of a locality, the distance from it to
the barrier foot, and screening of its parts by barriers are the main factors responsible
for precipitation amount on the upwind slopes of the Yenisei mountain range.
204 A. Onuchin and T. Burenina
Fig. 12.3 Correlation of precipitation with
altitude and distance from foot barrier ridge
for the Yenisei chain of hills. X mean
annual precipitation (mm); H altitude (m);
Lp distance from foot barrier ridge (km)
Fig. 12.4 Correlation of precipitation with
elevation of the first barrier ridge and dis-
tance from first barrier ridge for Yenisei
chain of hills. X mean annual precipita-
tion (mm); h elevation of the first barrier
ridge (m); Lb distance from first barrier
ridge (km)
While precipitation decreases with increasing distance to a barrier foot, is shows an
increase with increasing elevation. However, the latter trend is observed only within
two longitude degrees away from the barrier foot, beyond which distance the trend
is reversed (Fig. 12.3). Single (separate) elevations and orographic barriers lower
than the main watershed, but similarly oriented, appeared to reduce precipitation
received by barrier-shaded sites. Immediately behind a barrier, precipitation
decreases depending on the barrier height. Precipitation was observed to increase
with increasing distance from the precipitation sample point to the barrier, at non-
shaded precipitation sample points (Fig. 12.4).
Equation 12.3 shows a monotonous, non-linear increase in precipitation with
increasing elevation and distance from the barrier foot for west- and northwest-
facing slopes of the Eastern Sayan and Yenisei mountain range foothills located at
acute angles to the prevailing south-westerly winds (Fig. 12.5).
In plain (flat) landscapes, precipitation amount appeared to depend mainly on
latitude, elevation, and relief roughness. As is clear from Eq. 12.4, precipitation
increases from south to north and with increasing elevation. Precipitation was
found to decrease with increasing relief roughness within a 20 km distance from a
precipitation sample point in the direction opposite to that of the prevailing
moisture transfer.
In shaded landscapes (Eq. 12.5), precipitation amount changes mainly with
latitude and elevation, with other factors having much less influence. The vertical
precipitation gradients obtained for these landscapes indicate a complicated
12 Climatic and Geographic Patterns of Spatial Distribution of Precipitation in Siberia 205
Fig. 12.5 Correlation of precipitation
with altitude and distance from foot
barrier ridge for East Sayan. X mean
annual precipitation (mm); H altitude (m);
L distance from foot barrier
p
ridge (km)
distribution of its amount. While the model-based annual precipitation gradient
appeared to be 23 and 52 mm/100 m for shaded and plain landscapes, respec-
tively, it showed a drop from 60 mm/100 m at an elevation of 100 200 m down to
20 mm/100 m at 600 700 m elevation for barrier landscapes, where air flows
strike on barriers at acute angles. In case of perpendicular barrier location to the
prevailing moisture transfer, the precipitation gradient was found to decrease from
80 mm/100 m near a barrier foot (100 200 m a.s.l.) down to as low as zero near the
watershed part of the Yenisei mountain range (800 900 m a.s.l.).
The windward slopes of the Yenisei mountain range found in the central part of
the Krasnoyarsk region were determined to enjoy the highest precipitation (ca. 800
mm), while 600 mm is precipitated on the upwind slopes of the northern foothills
of Eastern Sayan and southern foothills of the Yenisei mountain range. Plain and
shaded landscapes were calculated to receive about 550 and 450 mm of precipita-
tion, respectively.
Precipitation appeared to have the highest spatial non-uniformity in barrier land-
scapes, whereas it exhibited a relatively uniform occurrence for shaded land-
scapes. Upwind slopes of the Yenisei mountain range were found to have the most
complicated spatial pattern of precipitation (six factors accounted for 61% of the
total precipitation variability). Conversely, spatial precipitation pattern appeared to
be fairly simple for shaded landscapes (two factors accounted for 62% of the total
precipitation variability).
12.3.2.2 South-Eastern Fore-Baikal Region
The orographic diversity of this region makes it virtually impossible to build any
universal model describing the relationship between precipitation and relief. For
this reason, our modelling efforts were initially limited to the northern Khamar-
Daban macro-slope. Apart from elevation a.s.l., the distance from a barrier foot to
an in-mountain locality, and slope, this model considers the shortest distance to the
206 A. Onuchin and T. Burenina
regional axial line going through Angara Gate 2. This latter parameter was incor-
porated, because our preliminary analysis of multi-year precipitation data provided
by Baikal weather stations revealed a distinct precipitation trend to decrease west
and east of Tankhoy weather station situated opposite Angara Gate .
The precipitation models obtained for the Khamar-Daban regions found west
(Eq. 12.6) and east (Eq. 12.7) of the axial line of Angara Gate appeared to be
qualitatively and quantitatively different:
InX = 4.89 + 0.32 ln H - 0.027L1 + 0.0147L2 - 0.35ln L2 + 0.037ln H·lmL1 (12.6)
R2 = 0.42, Ã = 206, F = 82,
L1 e" 0.5 km, L2 e" 0.5 km
ln X = 1,1+ 0.7 ln H - 0,0067 lnL2 + 0,001Xt / ln L1
(12.7)
+ 0,0004Xt / ln L2 + 0,0008· ln H ·ln L1·ln L2
R2 = 0.75, Ã = 114, F = 220,
L1 e" 0.5 km,
L2 e" 0.5 km
where H is elevation a.s.l. (m); L1 is the distance from a barrier foot to an
in-mountain locality down the prevailing wind (km); L2 is the shortest distance to
the axial line going through Angara Gate (km); Xt is the annual precipitation at
Tankhoy (representative) weather station (mm); R2 is the multiple coefficient of
determination; s is the precipitation mean-square error (mm); and F is the Fisher
criterion (factor).
As is clear from Eq. 12.6 derived for western Khamar-Daban, there exists a
distinct negative correlation between precipitation amount and the distance from a
barrier foot to an in-mountain locality situated down the prevailing wind (L1), while
precipitation shows a positive correlation with elevation a.s.l. The model built for
eastern Khamar-Daban (Eq. 12.7) is structurally different from Eq. 12.6.
Equation 12.7 reveals a more complicated relationship of annual precipitation (X )
t
occurring on the eastern Khamar-Daban slope with the distance from a barrier foot to
an in-mountain locality situated down the prevailing wind (L1) and the distance to the
Angara Gate axial line (L2) as compared to the western Khamar-Daban model.
According to Eq. 12.7, precipitation decreases with increasing distance from the
axial line, presumably due to increasing climate continentality west-eastward.
Also, these two equations indicate that L1 and L2 are not as important regarding
2
Angara Gate is the Angara river valley where this river flows out of Lake Baikal. This valley
stretches from south-east to north west and is the only place on the northern Baikal bank where
northwestern moist air masses can pass.
12 Climatic and Geographic Patterns of Spatial Distribution of Precipitation in Siberia 207
precipitation as elevation above sea level. Variables L1 and L2 appear to be
positively correlated with annual precipitation (Tankhoy weather station), however,
the influence of these two factors decreases proceeding from the barrier foot
into the mountain system down the prevailing wind, and with increasing distance
from the Angara Gate axial line. Our precipitation distribution models obtained for
the south-eastern Fore-Baikal region thus describe general dependences of precipi-
tation on locality elevation and situation regarding the prevailing wind direction, as
well as its relationship with a representative weather station data.
12.4 Discussion
Our analysis of precipitation patterns in Siberia revealed that the precipitation dis-
tribution found for the Siberian plain landscapes exhibits the highest agreement (the
best fit) with the global precipitation pattern, according to which precipitation
increases with elevation and in a steadily low-pressure zone between 60° and 70°
N latitudes and decreases proceeding into continents.
The precipitation pattern found for upwind mountain slopes appeared to differ
considerably from that for plains. This difference can be attributed to a complicated
transformation of water-laden air flows where they encounter mountain systems. In
this case, both snow and rainfall patterns are controlled by a much greater number
of factors than in plains, particularly where barriers are located at right angle to the
prevailing moisture transfer and induce drastic changes of air mass speed, direction,
moisture and heat exchange with the environment (e.g. the Yenisei and Khamar-
Daban mountain ranges). Moisture-carrying air flow changes were determined to
be less distinct where they meet with barriers at acute angles (e.g. northern and
southern foothills of the Eastern Sayan and southern Yenisei mountain range,
respectively). Air masses coming to east-facing macro-slopes of the Yenisei moun-
tain range and the eastern Sayan foothills, as well as to inter-mountain hollows, and
big river valleys occurring at the right angle to the prevailing air mass transfer
direction were determined to have little water. Any influence on precipitation pat-
tern is minimal here and, therefore, precipitation occurs uniformly.
These precipitation trends appeared to be most pronounced at a macro-slope
scale, while the impact of the slope aspect on precipitation was found to decrease at
meso- and micro-scale. Sosedov (1967), in a precipitation study in Trans-Ily Alatau,
found that, with the same moisture availability and slope screening effect, snowfall
amount did not depend on either meso-, or micro-slope aspects. In this case, slope
aspect had obvious influence only on the precipitation measurement error, since little
snow fell into measuring buckets due to high wind speed. Our study showed no
significant snowfall variability among slope aspects at meso- or micro-scale.
Therefore, it makes sense to discuss only the influence of the macro-slope aspect on
the amount of winter precipitation, namely, when precipitation sample plots cover a
range of orography and vegetation zone-specific moisture availabilities.
208 A. Onuchin and T. Burenina
The most complicated precipitation pattern was observed where mountain
massifs were dissected by big winding rivers (Kureyka and Khantaika rivers)
making the terrain extremely rough. It appeared to be very hard to quantify the
influence of any factor on precipitation patterns in these areas.
12.5 Conclusion
Our data analysis showed that spatial precipitation non-uniformity found for
Siberia can be attributable to the location of this region in inland Eurasia. In the
West-Siberian Plain, precipitation occurrence was determined to depend on latitude
and reach its maximum in the central taiga forest zone. A general trend of decreas-
ing precipitation proceeding from southwest to northeast identified for eastern
Siberia was often found to be broken due to the orographic diversity of this region.
This diversity results in a highly variable precipitation pattern: the upwind slopes
of even fairly low elevations were found to receive more precipitation than valley,
plateaus, and hollows.
Precipitation appeared to differ greatly between north- and south-facing slopes
in the southern Siberian Mountains located right in the centre of Eurasia. Because
of orographic variability, southern Siberian Mountains were divided into regions
differing in annual precipitation and precipitation gradient.
The regional and local precipitation patterns revealed by our study for Siberia
were based upon in developing regional precipitation models that consider relief
parameters and orographic barrier location with respect to the prevailing direction
of moisture transfer.
Acknowledgment This study was supported by the Russian Foundation for Basic Sciences
No.07-05-00016.
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