Introduction
Soil loss is highly dependent on soil formation and degradation processes,
and desertification plays a key role in this issue (Xie et al., 2015; Xu et
al., 2014). Land degradation and its manifestation in drylands,
desertification, are still widespread, jeopardizing livelihoods and
sustainable development (Fleskens and Stringer, 2014; Reynolds and Stafford,
2002). Drylands (arid, semiarid and dry subhumid areas) cover approximately
41 % of the Earth's surface and approximately 10 to 20 % of these
regions are experiencing degradation and desertification processes, resulting
in a decline in agricultural productivity, loss of biodiversity and the
breakdown of ecosystems (Keesstra, 2007). Considering the widespread and
threatening aspects of desertification in locations all around the world, many
researchers have tried to analyze it by means of experimental models and
methods (Prince et al., 1988; Ladisa et al., 2011; Liu and Yang, 2003),
remote sensing methods (Symeonakis et al., 2014; Helldén and Tottrup,
2008; Hill et al., 2008; Rasmussen et al., 2001) and modeling (Nicholson et
al., 1998; Salvati and Zitti, 2009; Santini et al., 2010).
Vegetation is an effective way to prevent soil erosion and plays an
important role in soil and water conservation (Gabarrón-Galeote et al.,
2013; Lieskovský and Kenderessy, 2014). The vegetation on the slopes
increases hydraulic roughness and vegetation, and creates the most resistance
compared to other surface roughness (Zhao et al., 2015). Vegetation decreases
wind erosion in arid and semiarid areas in three ways: (a) by reducing the
corrosive power of winds near the earth's surface through breaking wind
direction at several angles; (b) by creating a protective layer against
corrosive air forces; (c) by trapping sediments (Hong et al., 2016). In many
studies conducted on land degradation and desertification, vegetation cover was used
as an important criterion (Helldén and Tottrup, 2008; Hill et al.,
2008). Even in some research, the vegetation cover has been used as the
only criterion for assessing desertification and destruction of land
(Wessels et al., 2008; Rasmussen and Madsen, 2001).
Recently, the net primary production (NPP) index, derived from remote sensing data, is used to assess
the relationship between production and land degradation (Wessels et al.,
2008; Prince et al., 2009). Net primary production is one of the main
components of the carbon cycle and it represents an increase in plant
biomass after deducting the amount used by autotrophs. NPP or the
absorption rate of CO2 through photosynthesis is the basic link between
the atmosphere and biosphere. Human activities release a lot of CO2
into the atmosphere and have direct effects on NPP, evident in changing weather
patterns (Greer et al., 1995; Nakićenović et al., 2000). Furthermore,
humans currently consume almost a quarter of potential NPP (Haberl et al.,
2007). Awareness of the global carbon emissions is necessary for the development
of global policies on climate change (Wofsy, 2002; Piao et al., 2008;
Schulze et al., 2000). It should be noted that changes in environmental factors are different
in areas of natural resources. According to a report presented by FAO in the
1990s, the main factors of forest changes in different continents of the
world are mainly land use changes (Jafari, 2013). In Asia, about 23 % of
changes in forest areas depend on other factors, such as climate change. Climate change such as changes in
temperature and rainfall affect phonology and plant growth timing
(Jafari, 2007). Climate change has been observed in
different parts of Iran and it is also predicted that the changes will also
occur in the future. Climate change causes a biomass production change in
natural ecosystems (Jafari, 2013). Therefore, prediction of NPP in natural
areas, and the understanding of the effect of climate change on global ecosystems, products and
the sustainability of services are fundamental issues that many researchers
have addressed (Fang et al., 2003; Ei-Masri et al., 2013; Hemming et
al., 2013; Piao et al., 2005; Zhao et al., 2010).
Liang et al. (2015) investigated the spatial and temporal patterns of
annual, seasonal and monthly changes of the NPP index. They also studied the
climatic factors controlling it at the national biome level from 1982 to
2010 in China. The results showed that the NPP increased under the
influence of precipitation from the north to the south of China; and
temperature was introduced as a control factor of NPP in all biomes except
the dry biome. Raich et al. (1991) monitored the potential of NPP in relation to
climatic variables for different land uses in South America. Their results
showed that seasonal NPP has a positive correlation with the amount of
available moisture in most vegetation cover, but seasonal difference in
cloudiness strongly affects the NPP in tropical evergreen forest. Li et
al. (2015) used NPP, Normalized Difference Vegetation Index (NDVI) and
rain use efficiency (RUE) in order to investigate the dynamics of land
degradation in the Beijing–Tianjin area in the first decade of the twenty-first century.
Their results showed that according to the NPP index, from 2000 to 2010, the Beijing-Tianjin area was extensively degraded at a rate
of 52.7 %, while the reported destruction based on RUE was 65.2 %.
Choosing the appropriate tools that are able to predict the impact of
climate change on NPP has always been a challenge. The most reliable tools
for evaluating the effects of this phenomenon on different systems are climate
variables which are simulated by coupled atmosphere–ocean general circulation
models (GCMs) of the atmosphere (Haghtalab et al., 2013). Along with the
emissions scenarios of greenhouse gas which were codified by the
Intergovernmental Panel on Climate Change (IPCC), the atmospheric general
circulation models have been developed by different emissions assumptions
such as B2, B1, A2 and A1 to determine the climatic conditions in the next
decades (IPCC, 2000). Each of these
different scenarios presents a future climate condition. For example, the A2
scenario is characterized heterogeneously by the continued growth of
population and regional economic growth (Nakićenović et al., 2000).
B2 scenario shows a separate, but more ecologically friendly, world. It considered an average
economic development and a steady increase of population which emphasizes
regional solutions for sustainable development and slower and dissimilar
growth of technologies than A1 and B1 scenarios (Rahmani,
2011). One of the main problems of
current evaluation studies at a regional level is the prediction extent of
variables in these models (the study of areas around 5000 km2). Due to
the topography and climate change in this area, the results cannot be used
directly at the station scale. In other words, the model considers similar
conditions such as surface cover, topography and climate for a grid with
dimensions of several hundred kilometers, while real situations of surface
area can be completely different in the study area. To address this
shortcoming, various methods have been created to generate climate scenarios
at a regional scale, named downscaling (IPCC-TGCIA, 1999).
Various downscaling models and software have been developed. One popularly
used model is the Statistical DownScaling Model (SDSM; Wilby et al.,
2002). For example, Wilby and Perry (2006) combined SDSM with a
conceptual water-balance model and a mass-balance water quality model to
investigate climate change impact assessment and uncertainty in river flow
and water quality.
Reeves et al. (2014) investigate the effects of potential climate change on
NPP by predicting the climate regime under global change scenarios A1, B1, A2
and B2 in the grassland of America, from 2001 to 2100. The results showed
across all three scenarios, that rangeland NPP increased by 0.26 % yr-1
(7 kg C ha-1 yr-1) but increases were not apparent until after
2030, and significant regional variation in NPP was revealed. Bachelet et
al. (2001) tried to model the relationship of vegetation changes under
the influence of temperature and precipitation in the United States,
and then the future vegetation cover of the United States was illustrated
by using emission scenarios.
The methods used for monitoring land degradation and desertification in Iran
have been on the basis of expertise and field measurements such as MEDALUS,
IMDPA, FAO-UNEP and ICD methods (Khosravi and Zehtabian, 2012), which can be
used to evaluate the progress of land degradation (Oldeman et al., 1991;
Stocking, 1995). Although these studies are accurate and appropriate to
determine initial destruction features, some challenges, such as being not
simple for users, high volume data entry and low repetitions in different area
and a loss of accuracy in region with a large surface area, cause a lot of
problems (Omuto, 2008). The aim of this study is to monitor degradation and
desertification in three types of land use, including pasture, forest and
desert, affected by climate change. For this purpose, the data of two
scenarios, A2, B2, provided by the US National Center for Environmental Prediction
(NCEP), have been utilized (Rahmani et al., 2011).
Material and methods
Study area
Tehran province is located between 35∘14′ and 36∘17′ N
latitude and 50∘14′ and 53∘6′ E longitude. It covers an
area of 18 909 km2 and is located to the north of the central
plateau of Iran (Fig. 1). The province of Tehran has over 12 million
inhabitants and is Iran's most densely populated region. Approximately
86.5 % reside in urban areas and 13.5 % in rural areas of the
province. Environmentally, the climate of Tehran province in the southern
areas is warm and dry, but in the mountain areas, it is cold and semi-humid,
and in the higher regions, it is cold with long winters. The hottest months of
the year are from mid-July to mid-September, when temperatures range from 28
to 30 ∘C and the coldest months reach 1 ∘C around
December–January, but at certain times in winter, it can reach
-15 ∘C. The city of Tehran has moderate winters and hot summers. Average
annual rainfall is approximately 200 mm, the maximum being during the winter
season. On the whole, the province has a semiarid, steppe climate in the
south and an alpine climate in the north.
Location of Tehran and its different land uses.
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Climate data and SDSM
The data that have been used in this study include the average, minimum and maximum
of rainfall, and temperatures at the Mehrabad synoptic station, from 1961 to 2005.
The Hadcm3 model, under emission scenarios A2 and B2, was used to determine
GCMs and appropriate scenarios according to the region. In the study area
the scenario which had the highest accuracy under the Hadcm3 model was
selected as a scenario that has more similarities with the basin. Finally,
downscaled model data and observational data were analyzed in order to
select the suitable general circulation climate models and scenarios. R2 (correlation coefficient),
root mean square error (RMSE) and percent bias (PBIAS) criteria were used to evaluate the model. Equations (1)
to (4) show how these criteria were calculated.
The correlation coefficient (R2) is calculated as follows:
R2=∑i=1N(yi-yi^)2∑i=1N(yi-y¯)2,
where yi is the observed value and y^ is the estimated value.
The root mean square error (RMSE) is calculated as follows:
RMSE=SSEN,
where SSE is the sum of squared errors and N is the number of samples used.
The SSE is calculated as follows:
SSE=∑i=1N(yi-yi^)2,
where yi is the observed value and y^ is the estimated value.
Bias
Bias=E(H)-θ
The bias of an estimator H is the expected value of the estimator less the estimated
value of θ.
NCEP and GCM predictive variables are calibrated and analyzed by the SDSM. SDSM is a two-phase sampling and conditional method. In this
method, at the first predicator variables of temperature and precipitation are
downscaled by using regression methods and by generating a random
meteorological method. Precipitation was therefore produced at the station
again. The SDSM is a combination of a statistical weather generating method
and composed functions
(Taei Semiromi et al., 2014). The statistical downscaling processes of the
climate variables are done by SDSM Software as follows:
quality control and transformation of data,
selection of the best predictor variables,
calibration of the model,
analysis of climate models,
statistical analysis,
selection of graphical output model,
production of climate scenarios (by using the predictor model).
The climate change trend
A Mann–Kendall test and Sen's estimator slope method were used to assess the
climate change trend under emissions scenarios. This test was presented
firstly by Mann in 1945 and then developed by Kendall in 1975. One of the
advantages of this method is the suitability of its application for time
series that do not follow a specific statistical distribution. This method is
less affected by the limit values observed in some time series (Salmi et
al., 2002).
The Sen's slope estimator method is a nonparametric technique
for estimating a linear trend. The procedure was computed in MAKESENS1.0
macros (Salmi et al., 2002).
Net primary production (NPP)
Satellite images used in this study are from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on the Terra
satellite. This sensor has been receiving and sending images to the terrestrial
receivers every day since 2000. In all Earth observing system satellites, the MODIS radiometer is a
key tool. This sensor has continuous and broad spectral and spatial
coverage. Two bands have a resolution of 250 m, five bands have a
resolution of 500 m and 29 other bands have a spatial resolution of 1000 m.
Therefore, studying and evaluating short-term and long-term changes in the sea,
land and atmosphere is possible by MODIS. The spectral range of these 36
bands is between 0.4 and 14.4 µm. Mod17A3 is a set of net land data and
net primary production with a resolution of 1 km obtained from Terra
spacecraft and MODIS. Net photosynthesis is defined in Eq. (5):
PsnNet=GPP-Rml-Rmr,
where Rml is respiring leaves and Rmr is respiring roots. The
annual NPP is calculated by Eq. (6):
NPP=∑i=1365PsnNet-(Rmo+Rg),
where Rmo is respiration by other organisms except leaves and Rg
is breath growth.
Gross primary production (GPP) is the storage capacity and carbon and energy
absorption during photosynthesis (Heinsch et al., 2003; Running et al.,
2004). GPP is derived from the estimation of net ecosystem exchange (NEED) and
ecosystem respiration (Reco). NPP is the net stored carbon after
subtraction plant respiration of autotrophs from GPP. A part of the annual
NPP in ecosystems may be lost by events such as strong winds and fire. There is
a need for ecosystem services such as fuel, food, feed, fiber and materials
for the purposes of metabolism.
In this study, annual NPP was acquired from global MODIS data (MOD17A3) with
a resolution of 1 km in the period of 2001–2010. NPP obtained from MODIS
(MOD17A3) based on the light useable model and annual NPP provides the
evaluation of the temporal and spatial variations in production and land
behavior in annual scale. For obtaining the NPP and determining land use in the
study area, the ENVI 4.9 software was used.
In order to evaluate the effect of climatic factors on NPP in Tehran
province, first, a logical relationship was calculated between the decrease and
increase of NPP and both variations of temperature and precipitation in
2001–2010. Then, the decreasing or increasing changes of NPP were calculated
in the 2030s per unit changes in rainfall and temperature portrayed by
emission scenarios in the Hadcm3 model (A2 and B2).
Result and discussion
Prediction of climate trends
Table 1 shows the results of the annual temperature variations in both
emission scenarios A2 and B2 for Tehran synoptic station. The results of
temperature average using the Mann–Kendall and Sen's slope estimator methods
showed a significant increasing trend in both scenarios. Emission scenario A2
showed 0.004 % reduction, and 0.05 and 0.15 % increase for the
decades of 2030, 2060 and 2090 respectively (Fig. 2). These values for
emission scenarios B2 were 0.02, 0.001 and 0.1 % increase respectively.
The temperature reduction in the A2 scenario happened in the 2030s; the rate
of change in the increasing temperature was greater than B2 scenario in
2060. The maximum temperature rise was in A2 emission scenario, which will
happen in early 2090. To evaluate the significant difference between the
results of two scenarios for imaging the average temperature, a t test of
two samples was used. According to the significance of 0.02 in the test which was lower than
the intended significance (0.05), it is concluded that there is a
significant difference between the results of the two emission scenarios. The
results of the trend test for the imagined amount of the rainfall average by two
emission scenarios, A2 and B2, are shown in Table 2. The results of both
the Mann–Kendall and Sen's slope estimator represent a significant decreasing
trend in rainfall amounts between 2006 and 2099. Additionally, the percentage of rainfall change
was studied for 3 decades of 2030, 2060 and 2090 compared to the base decade of 2000 based on two scenarios, A2 and B2. The results showed
that the rainfall average in A2 scenario increased 0.27 and 0.32 % in
the decades of 2030 and 2060, respectively. However, in the 2090s, the rate of rainfall
average decreased by 0.15 %, but in general, from 2006 onwards, average
annual rainfall had a decreasing slope of -1.12. Under this scenario, the
rainfall annual average will vary between 362.4 and 412 mm in the coming
8 decades. These amounts in scenarios B2 have increased 0.16, 0.07 and
0.09 for the 3 decades of 2030, 2060 and 2090, respectively. Additionally, based on
the results of this scenario, the trend line slope of -0.06 has been seen
from 2006. The rainfall annual average will vary between 423.9 and
382.4 mm at Tehran synoptic station in the next 8 decades (Fig. 3).
Using two sample t tests showed that there were no significant differences between the
results of two time series of rainfall obtained by Hadcm3 model (A2 and B2).
The results showed that in both scenarios, the rainfall annual average will
decrease from the 2030s, while the results of temperature changes are in
contrast and incremental changes in temperature will happen from the 2030s.
The amount of Z test and trend slope of the temperature average at Tehran
synoptic stations during 2006–2099.
Mann–Kendall trend
Sen's slope
estimate
Time series
First year
Last year
n
Test Z
Significant
Slope
level
trend
Temperature A2 projection
2006
2099
94
10.23
0.01
0.05
Temperature B2 projection
2006
2099
94
8.61
0.01
0.03
The amount of Z and trend slope of the rainfall average at Tehran
synoptic stations during 2006–2099.
Mann–Kendall trend
Sen's slope
estimate
Time series
First year
Last year
n
Test Z
Significant
Slope
level
trend
Precipitation A2 projections
2006
2099
94
-5.23
0.01
-1.12
Precipitation B2 projections
2006
2099
94
-3.11
0.01
-0.67
The average temperature in A2 and B2 scenarios for the synoptic
station in Tehran.
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NPP trend
Given that the Mann–Kendall test and Sen's slope estimator are not suitable to
determine the trend of short time series (Sheng and Pilon, 2004), in this study the map of the deviation from the
average of NPP was used to evaluate the changes in NPP. In other words, the
10-year average of NPP for three types of land use, forest, grassland and
desert, was estimated separately and then the annual changes of every pixel to
the 10-year average of that pixel were evaluated. For this aim, changes were
classified into eight classes: 0–0.04 C, 0.04–0.08 D, 0.08–0.12 E,
0.12–0.16 F and 0.16 < (kg cm2 yr-1).
These refer to positive amount and increase of NPP related to the long-term average.
Classes of -0.04–0 B and < -0.04 A are in relation to the
amounts of decrease to the long-term average. Figure 3 shows the percentage
of each class area in the three mentioned land use types for the period 2001–2010.
The results of NPP changes from annual long-term average for rangeland
showed that in 2007, over 60 % of rangeland area was in the C class (0 to -0.04). This means that production had declined in
these years. However, from 2008, these changes were more balanced, and the
percentage of area with production more than 0 to 0.04 has become equal
with the B class (-0.04 to 0). It is necessary to note that in 2008,
the whole areas of Tehran rangeland had 0 to 0.04 NPP growth compared
to the annual long-term average.
The average precipitation in A2 and B2 scenarios for the synoptic
station in Tehran.
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In forest land use, the percentage of changes of NPP area compared to long-term
average was variable in each year (Fig. 4); in 2001, more than
90 % of forest land had a production rate in the B class (-0.04 to 0). In
other words, a decline in the production of most forestland occurred.
However, in 2002, the percentage of land in this class was 0. Instead, the percentage of
the classes that had a greater rate of production than the average
increased. After that, more than 35 % of forests were less productive
than the average by 2005; and in areas where production increased, the
production amount was in the class of 0.00–0.04. However, in 2006, approximately
80 % of the forest area had a greater production rate than the long-term NPP.
There was a dramatic change in these fluctuations in 2007 and 2008 and
over 80 % of forest land had a decline in NPP. In 2009 and 2010 the
forest production increased compared to the average.
Percentage of changes in rangeland area or the deviation from the
long-term average of NPP. 0–40 C, 40–80 D, 80–120 E, 120–160 F
and 160 <are related to a positive amount; classes of -40–0 B
and < -40 A are related to the amounts of decrease compared to the
long-term average.
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The trend of net primary production changes in deserts in Tehran province is
quite different to forests and rangelands; in the 10-year studied
period, except in 2003, the production changes compared to long-term average decreased. In the 2000s, more than 80 % of desert area
had a reduction in the production to -0.04
(kg cm2 yr-1) compared to the
long-term average (Figs. 5 and 6). Although, the net primary production is
low in areas with rainfall less than 100 mm per year, in many semiarid
ecosystems, net production on land may also reach approximately the same
production of temperate forests (Whitford, 2002). Therefore, a decrease of change in
initial production of the desert areas in Tehran province cannot just be
related to the land use type and the reasons for this reduction should be
determined.
Percentage of changes in forest area or the deviation from the
long-term average of NPP.
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Percentage of changes in pasture land area or the deviation from the
long-term average of NPP.
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The long-term average of NPP in deserts (a), forests
(b) and ranges (c).
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Figure 7 shows the average of NPP changes in the range, forests and deserts
land in Tehran province. The variation range of average NPP in rangeland is
variable from 0 to 0.37 (kg cm2 yr-1) This range for forest and desert
changed between 0 and 0.21 and 0 and 0.39 (kg cm2 yr-1), respectively.
However, it should be noted that except for some microclimate zone considering as spots,
production of forest land use was higher than range and desert land use because of the vegetation amount (Barnes et al., 1998). On the other hand, given the
definition of land degradation include reduction of productivity and
ecosystem functioning in a long-term period (Bai et al., 2008), this is
often related to the reduction in plant cover and biomass (Wessels et al.,
2007; Salvati and Zitti, 2009). With these descriptions and based on the map
of the different land uses average NPP (Fig. 7), the fact that the process of
desertification is undeniable for forest and less intense for the rangeland is undeniable
in the 2000s; more than 50 % of rangeland has 0 production
(kg cm2 yr-1) in Tehran province (Fig. 7). Despite the small
forest area, some areas of forest have 0 production, and also the maximum
amount of NPP average of forest is 0.21 (kg cm2 yr-1). This
maximum production is around that of the desert NPP (Fig. 7).
Average NPP changes of each type of land use regarding the annual average
temperature changes.
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The relationship between climate change and NPP
One of the important impacts of climate change on forest ecosystems, rangelands and desert is the effects of changes in temperature and precipitation
on the NPP of vegetation. The impacts of changes in precipitation (per mm
decreasing or increasing) or temperature (for each degree of increase or
decrease) on NPP were separately determined in rangeland, forest and desert
ecosystems in Tehran province.
The results showed that the range of temperature changes varied
between 18.1 and 19.6 ∘C in the 2000s at Tehran synoptic station.
Figure 8 shows the relationship between NPP and the average annual
temperature for range, forest and desert land uses. In rangeland use in most
years, every time the temperature has increased or decreased, the NPP decreased or increased, respectively (Figs. 8 and 9). Only in
2002–2003, while the temperature decreased 0.7 ∘C, the NPP
amount decreased 0.0026 (kg cm2 yr-1) per unit
decrease in temperature. However, in other years, per unit reduction in
temperature (1 ∘C) net rangeland production increased
0.0118 (kg cm2 yr-1). Per unit increase in average
temperatures in rangelands and the NPP of the rangelands declined
0.0015 (kg cm2 yr-1). Therefore, based on this relationship and the
portrayed values of emission scenarios A2 and B2, changes in NPP in the 2030s
were estimated based on the annual average temperature variable. The
rainfall range also varied between 174 and 311.7 mm in the 2000s. NPP changes
were aligned with rainfall changes unlike temperature. In other words,
whenever precipitation increased or decreased, the amount of NPP
increased or decreased, respectively. Obtained patterns showed that with a one
unit increase or decrease in rainfall (1 mm), the NPP increases or decreases
0.00016 (kg cm2 yr-1) in rangelands. Such as temperature,
based on the relationship between changes in precipitation and NPP in the
2000s, NPP changes in the 2030s were estimated in different emission
scenarios, A2 and B2.
Average NPP changes of each type of land use regarding the annual average
rainfall changes.
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The projected results of the NPP of the range ecosystem, under the A2 emission
scenario, showed that the highest amount will be reached in the 2030s
(Fig. 10a). Under this scenario, changes in precipitation and temperature
will increase the NPP in the 2030s to the 2000s. However, assuming the
existence of the B2 scenario, production changes of rangelands will
decline in the 2030s (Fig. 10a). The changes occur in such a way that
the production average of rangelands (in both precipitation and temperature
patterns) will decrease under B2 scenario in 2038 and 2039 compared to 2000s. It
should be noted that based on the rainfall in scenarios B2, the lowest NPP
will occur in rangelands in Tehran province. Under these conditions, the
average annual of NPP in 2030s will reach the same production rate in the
desert as during the 2000s.
The results of NPP in forest had significant changes in temperature
patterns of two emission scenarios, A2 and B2, while the NPP amount showed no
significant changes in rainfall patterns of two scenarios, and the results
were very similar (Fig. 10b). In scenario A2 and under the temperature
changes, the production amount will increase from
0.118 (kg cm2 yr-1) in 2030 to
0.191 (kg cm2 yr-1) in 2039, and the average of production will
have a rising trend in the entire decade (Fig. 10b). However, projected results of
the NPP under temperature change of scenario B2 show the relative
decreasing trend for NPP amount in forest land use (Fig. 10b). It is important
to note that if there are conditions of scenario B2 and even under rainfall
pattern of the A2 scenario, the annual average of NPP for forest land use in the
2030s will be in the range of the lowest levels of production (desert
production in the 2000s).
The NPP annual average in range lands (a), forests
(b) and deserts (c) in 2030. NPP B2T is the NPP under the
thermal pattern on B2 emission scenario, NPP A2T is the NPP of thermal under A2
emissions scenario, NPP A2P is the NPP of thermal under A2 emissions
scenario and NPP B2P is the NPP model under thermal emission B2 scenario.
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The forecasting results of production changes in the desert land use showed
assumptions of the temperature conditions of the B2 scenario, NPP amount decreased
sharply and it will decrease from 0.014 (kg cm2 yr-1) in 2031
to 0 in 2033 (Fig. 10c). However, due to the amount of average annual
rainfall generated in this scenario (between 316 and 520 mm), occurrence of such
a mode of production is not reasonable in desert land use and it would be more
correct to consider production changes under precipitation conditions of this
scenario. The study of Seely (1987) on Namib Desert production indicated that
the annual rainfall changes between 12.5 and 95 mm caused the production
to increase from 0.00075 to 0.50 (kg cm2 yr-1). He attributed this amount
of production in the driest desert of the world to the perennial grasses with
developing root systems that allow them to respond quickly to soil moisture,
and thus they are better able to take advantage of rainfall. Therefore, judgment
about the occurrence of such cases depends on precise knowledge about the
desert flora. Production changes fluctuated between 0.01 and 0.025 (kg cm2 yr-1) under the temperature of A2 scenario. In
terms of rainfall under both scenarios, the desert production was
0.017 (kg cm2 yr-1), which is less than the average annual production of
the 2000s (Fig. 10c).
Discussion
Understanding the complex relationships between the effects of environmental factors
on NPP and recoverable biomass is essential to avoid overharvesting (leading
to desertification) (Whitford, 2002). In this study, the effects of climate
change on NPP of range, forest and desert in Tehran province, Iran,
were studied. For this purpose, the NPP index derived from MODIS satellite
images was used to monitor degradation over the period of 2001 to 2010 for
each land use. The scenarios of Hadcm3 model were also used to investigate
the climate scenarios. Climate assessment results showed that both A2 and B2
scenarios have the most similarity to the actual amount of climate parameters.
The changes in trends of temperature and precipitation variables were evaluated
under each scenario by using the Mann–Kendall and Sen's slope estimator
methods. The results showed that the rising trend of temperature and reducing
trend of rainfall were significant for both emission scenarios in the period
of 2006 to 2099. The results are consistent with the findings of Haghtalab et al. (2013)
in Tehran and Mazandaran province in Iran. The annual production
deviation compared to the long-term average was used to study the NPP changes trend
for each type of land use. The results showed that more than 60 % of range lands in
Tehran province had a production rate less than average by 2007. More than 80 %
of the forests had a production rate less than the long-term average in half of the
2000s. More than 80 % of desert lands had an NPP less than the
average amount in the 2000s except in 2003. The results showed that NPP values in
some areas of forest are equal or even less than the production in desert regions
in this decade. In some desert areas, there are some microclimate
that cause an increase in the NPP, but certainly in most desert areas, the
production is at the lowest level in comparison with other land uses (Tietjen
et al., 2010). According to the desertification definition (desertification
is defined as the impairment or destruction of the biological potential of
land) by Whitford (2002) and by comparing the results of the long-term
average NPP in three types of land use, range, forest and desert (Fig. 7), the
desertification trend of forest and range can be studied in the 2000s in
Tehran province. This result is consistent with the results of Haghtalab et al. (2013).
In this study, the average annual rainfall and temperature variables were
used as the most effective factors on NPP to find a suitable relation between
climate change and production in different types of land use. The projection of NPP was
done in range, forest and desert land uses in Tehran province for the 2030s.
The results showed that the amount of production increased in the range
lands under the rainfall and temperature pattern in the A2 scenario that is
consistent with the results of Reeves et al. (2014). However, under the B2 scenario,
the production level of rangelands declined. The temperature pattern in the A2
emission scenario has the greatest influence on the variability of NPP of
forest land use. The NPP changes in the desert are pretty low under the
rainfall pattern in both scenarios, and under the temperature pattern in the
A2 scenario also change sinuously in the range of 0.01–0.025 (kg cm2 yr-1). The notable point will happen for
desert NPP in the 2030s; the NPP level will reach 0 from 2033
onwards. This situation would be logical only if there were no vegetation in
desert areas and it had also been completely destroyed in previous years for
some reason. It is recommended that NPP projection should not be done by only considering the
influence of one climate pattern such as temperature.
This paper explored the relative contributions of climate change to NPP
variations. However, this study only addresses the IPCC SRES (Special Report on Emissions Scenarios) B2 and A2
emission scenarios concerning climate change. Future work will involve
extending the study to a wide range of emission scenarios, while representing
the probable climate change in future decades. This includes some other
scenarios investigated in IPCC AR4 (e.g., SRES A1, A2, B1 and B2).
Conclusions
In this paper, the probable impacts of climate change on NPP in future
decades were explored by using a combination of downscaled GCMs and remote-sensing-based carbon models. For modeling future forest, range and desert
land NPP, an NPP proportion method was proposed, assuming the ratio of real
NPP in this areas to their potential NPP will be constant in future decades.
The relative contributions of climate change to the NPP were investigated by
using scenario analysis.
The results showed that the rising trend of temperature and reducing trend of
rainfall were significant for both emission scenarios in the period of
2006–2099. In this study, temperature was detected as the most
significant driving force of change in NPP in range, forest and desert
ecosystems. The rainfall variable demonstrated fewer changes than temperature in
these ecosystems.
The results indicate that the NPP index can be used as the key criterion for
monitoring the environmental features. This index shows the stress magnitude
logged to the
environmental characteristics, the degree of stress to which an
ecosystem can be exposed or the degree of ecological response to the stress. The
index of NPP has reduced in all natural areas in Tehran province. In
addition, an increasing trend in production and a reduction in land degradation cannot be imagined
under the terms of the climate emissions scenarios, A2 and B2, in the region.
It is worth noting that all models are a simplified form of reality and the interpretation of the results depends on uncertainty, inputs and
model assumptions. In this study, the issues related to climate scenarios
were verified properly on the basis of actual data. However, due to a lack of field
measurement of NPP in Iran, the validation of NPP amounts has not been done
for the base period (2001–2010). Given the continuing destruction in arid and
semiarid areas of Iran, determination of the destruction process and early
warning systems is very important. This purpose can be obtained by
monitoring and evaluating the systems with reasonable accuracy. Given that
the monitoring function is a time-consuming process, it is recommended
that terrestrial sampling of NPP must be done in different types of land use in Tehran
province and generally in the whole country, and adequate data should be
provided for the status of each ecosystem in its geographical scope. Therefore, in
the future, they can be used to check the accuracy of satellite images
results. This will cause costs to be reduced and time to be saved in the monitoring
of the desertification trend in ecosystems through remote sensing methods.