Investigating the leaf area index changes in response to climate change (case study: Kasilian catchment, Iran)

By Mohammad Reza Ramezani et al.

Abstract

Vegetation cover plays an important role in the hydrologic cycle of Kasilian catchment in Iran. This study aimed to estimate leaf area index (LAI), as an important vegetation factor in hydrologic loses, in response to climate change in the future period (2020–2039) over Kasilian catchment located in the north of Iran. For this purpose, LAI was simulated by gridded BIOME-BGC in 319 pixels within the case study domain over the study period (2004–2013) for three dominant land covers of the Kasilian catchment including deciduous broadleaf forest (DBF), shrubs, and C3 grasses, and BIOME-BGC accuracy has been assessed using MODIS-derived LAI. Then, monthly projections of climate variables obtained from the average of 9 AOGCMs-AR5 in the future period (2020–2039) and annual projection of CO2 level from 2004 to 2039 under RCP2.6 and RCP8.5 scenarios were used to assess the impact of climate change on LAI. Results show that LAI will increase in response to the overall predicted rise in temperature, precipitation, and CO2 level under both scenarios in all pixels. This increase under the RCP8.5 scenario is predicted to be more than RCP2.6 scenario so that the mean LAI in Kasilian catchment will increase by 3.1% and 2.2% under RCP8.5 and RCP2.6 scenarios, respectively. In addition, our analysis showed that DBF land cover will be more sensitive to climate change in this catchment.

Introduction

The leaf area index (LAI) refers to the ratio of the total one-sided green leaf area per unit area of ground [3, 45]. LAI is a consumptive water term in hydrologic processes, which control the amount of water intercepted by leaf area. This vegetation variable plays an important role in hydrologic processes and water accounting in catchments [13, 33, 53, 59, 63, 64]. Regarding the influence of LAI on eco-hydrology processes, investigating its changes in catchment scale is vitally important [48], and it enables hydrologists to estimate accurate water budget under climate change scenarios. Estimating LAI changes in response to climate change (temperature and precipitation changes) and atmospheric variable change (level of CO2) has been less considered in the ecological studies [40, 49].

Climate variables, as a limiting factor, have a significant effect on plant growth [7, 15] so that recent researches have shown that humidity and warming of the climate have led to intensification of the plants’ growth [16, 34]. Also, in areas with water scarcity, changes in plant growth and LAI are particularly dependent on the plant available water held in soil and atmosphere [65]. Besides, another effective factor in plant growth is the carbon dioxide level (CO2) [18]. It has been proven that an increase in carbon dioxide level causes to increase the photosynthesis rate and intensity of the plant growth [23]. Also, a study showed that an increase in carbon dioxide level contributes to LAI increase in grasses land cover [28]. Considering the strong interaction between LAI, climatic variables, and carbon dioxide concentration, it is expected that LAI would change in response to climatic and atmospheric changes in the future [22, 24]. Previous studies showed that an overall increase in temperature, precipitation, and carbon dioxide level in the future would lead to LAI increase in a forested region, for a study case in Montana, USA [40]. In contrast, LAI is expected to decrease in the future, followed by precipitation drop and temperature rise in a catchment, located in southeastern Australia [49].

Modeling the relationships between LAI and environmental factors enables researchers to predict the interactions between them in different conditions, such as climate change [27]. To model these relationships, there is a simple method of making regression models between LAI and climate variables [5, 49]. However, these methods are not able to recognize the interaction between LAI and atmosphere as well as hydrosphere. A more accurate approach is using ecosystem models which simulate the flow of water, carbon, and energy in terrestrial ecosystems [25, 43, 56]. For example, BIOME-BGC is a well-known biogeochemical model. Its capability to simulate LAI has been demonstrated in several studies [20, 37, 39, 55, 66].

Satellite-derived LAI which provides time series of LAI over large areas with the high spatial resolution has been used to assess the ecological model accuracy [6, 26, 60]. There are several satellite sources for estimating LAI such as MODIS (the Moderate Resolution Imaging Spectroradiometer) [31], CYCLOPES [1], and AVHRR (Advanced Very-High-Resolution Radiometer) [10]. MODIS LAI products are one of the most widely used LAI sources in ecological studies, and its reasonable accuracy has been demonstrated [8, 44, 61, 62]. For example, to assess the BIOME-BGC performance in LAI simulation, MODIS annually maximum LAI [66] and MODIS monthly LAI [55] have been used in recent years. Moreover, MODIS LAI images have been used as initial input to different ecosystem models or as observed data for calibration of these models [38, 53].

In this study, our overall purpose is to understand how the LAI will change in response to climate and atmospheric changes in the future period in Kasilian catchment. In particular, our main objectives include (1) determining the vegetation conditions of the study area with an ecosystem model during the study period (2004–2013), (2) projecting the changes in climate and atmosphere variables in the future period (2020–2039) relative to baseline period (1986–2005), and (3) determining the LAI changes in response to climate and atmosphere changes under different scenarios of climate change in the future period (2020–2039) relative to the study period (2004–2013).

Case study

The geographic location of Kasilian catchment spans 35.58°–36.07°N in latitude and 53.18°–53.30°E in longitude, with a total catchment area of 68 km2 located in the mountainous lands of Mazandaran Province in the north of Iran (Fig. 1). The catchment elevation ranges from a minimum altitude of 1113 m on the northern part to a maximum altitude of about 3334 m in the southern part. Based on de Martonne’s climate classification, the Kasilian catchment is characterized by the cold climate in southern zones and temperate climate in northern zones. According to a climate data recorded at the Darzikola climate station located in the center of the catchment, the mean annual precipitation and temperature (2004–2013) in the catchment are 700 mm year−1 and 12 °C, respectively. Previous research by agriculture ministry of Iran in Kasilian catchment in 1995 showed that about 60% of Kasilian catchment is covered by dense forest, and the rest of the area is covered by pasture (20%), croplands (15%), and lands with sparse vegetation (5%). Another study showed that about 80% of the trees in this forest consist of two broadleaf tree species including broadleaf beech trees (Fagus Orientalis) and hornbeam trees (Carpinus betulus), and both of them consist of a high interception rate [12]. Also, Kasilian catchment land cover, especially natural forest, plays a fundamental role in the formation of initial hydrologic losses rate [12].

Fig. 1
figure1

Location of case study in Iran and the location of stations in the case study

Moreover, deforestation caused by anthropogenic activities resulted in a rise in the production of potential runoff in Kasilian catchment over four recent decades [21]. Although previous research investigated the land cover change scenarios in the past decades [11], land cover change in response to climate change in the future periods has never been investigated in Kasilian catchment.

Materials and methods

BIOME-BGC model

BIOME-BGC is a biogeochemical model which simulates main physiological processes such as photosynthesis, evapotranspiration, respiration, and decomposition within the terrestrial ecosystems [39, 51]. The latest version of this model is BIOME-BGC version 4.2, which can simulate several vegetation indices such as NPP, GPP, and LAI for 7 vegetation cover types such as the evergreen needle leaf forest (ENF), evergreen broadleaf forest (EBF), deciduous needle leaf forest (DNF), deciduous broadleaf forest (DBF), shrubs, C3 and C4 grasses, based on temporal scales of daily, monthly, and annual. BIOME-BGC model calculates LAI by multiplying carbon allocated to leaves times the specific leaf area.

To simulate LAI using BIOME-BGC model with high spatial resolution, high-resolution land cover map and LAI values are needed. The only available land cover map in Kasilian catchment was related to the year 1995 and has a low spatial resolution of 2 km, while we needed a vegetation map with a higher spatial resolution at the beginning of the study period (2004). Moreover, there were no available field-LAI measurements for the Kasilian catchment. To address these restrictions, MODIS land cover product named as MODIS MCD12Q1 [9] and MODIS LAI product named as MODIS MOD15A2H [32] which have the same spatial resolution (500 m) were used in this study. Using MODIS products enabled us to develop gridded BIOME-BGC with a spatial resolution of 500 m within Kasilian catchment, and consequently, 319 pixels were created over Kasilian catchment area.

The ecosystem process modeling using BIOME-BGC model includes two steps: In the first step, the model was run in a spin-up mode. Spin-up run is a common step for ecosystem models to ensure that it achieves stable ecosystem conditions in the desired site [52]. After achieving an equilibrium state, the model was run in normal mode. At this step, BIOME-BGC simulates carbon, nitrogen, and water cycles in the components of the plant and soil. BIOME-BGC model requires three main categories of information, including climatic data, environmental information, and eco-physiological parameters (Table 1). The climatic data in each pixel were simulated using the weather simulator of BIOME-BGC model called “Mountain Climate Simulator” (MT-CLIM model) [50]. Other environmental information of each pixel, including elevation, slope, slope’s direction, and soil texture information (silt, clay, and sand), was obtained from a digital elevation model (DEM) and basin’s soil map, respectively. These environmental data were re-gridded to a 500 m spatial resolution to enter the BIOME-BGC model.

Table 1 Input datasets for BIOME-BGC model

Figure 2a shows the vegetation cover map in the study area in 2004. To use MODIS vegetation cover map information for the BIOME-BGC model, the obtained vegetation cover types were reclassified into available vegetation cover types in the database of BIOME-BGC model (Table 2). Figure 2b shows the average LAI map throughout the Kasilian catchment from 2004 to 2013. It is important to note that the accuracy of MODIS-derived LAI was not validated because there were no field-LAI measurements in Kasilian catchment. Lack of field-LAI measurements was a restriction in this study that might reduce the reliability of results. This restriction did not allow us to use other satellite imageries such as Landsat TM (with 30 m spatial resolution), which has a higher spatial resolution than MODIS images (with 500 m spatial resolution), because derived LAI from Landsat TM needs to be validated against field-LAI measurements. Therefore, we used MODIS LAI product, in which its reasonable accuracy and reliability have been reported by many studies conducted in a different part of the world [8, 44, 61, 62].

Fig. 2
figure2

a MODIS land cover map and b MODIS LAI map in Kasilian catchment

Table 2 Reclassification of MODIS land covers into BIOME-BGC database land cover

Evaluation of BIOME-BGC model

To evaluate the gridded BIOME-BGC model accuracy, BIOME-BGC monthly LAI output was compared against monthly MODIS-derived LAI using three statistical criteria including R2 (coefficient of determination), RMSE (root mean square error), and percent error (PE) shown in Eqs. (1), (2), and (3), respectively, where X: the monthly MODIS LAI, Y: the monthly BIOME-BGC LAI, and N: the total number of monthly MODIS LAI (N = 120). Low quantities of RMSE and PE and high quantities of the coefficient of determination (R2) represent the acceptable accuracy of BIOME-BGC model.

R2=NNi=1YX(Ni=1Y)(Ni=1X)N(Ni=1Y2)(Ni=1Y)2−−−−−−−−−−−−−−−−−−−−√N(Ni=1X2)(Ni=1X)2−−−−−−−−−−−−−−−−−−−−√
(1)
RMSE=Ni=1(YX)2N−−−−−−−−−−−−−√
(2)
PE=(Ni=1|YX|Ni=1Y)×100
(3)

Climate change projections

Climate models

At present, AOGCMs (atmosphere-ocean general circulation models) are the most credible climate simulators that have been used in ecosystem studies [35, 47]. In this study, climate projections from the average of 9 AOGCMs, used in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5), are applied to assess potential climate change impacts on LAI (Table 3). RCP2.6 and RCP8.5 are emission scenarios chosen in this study [30, 54]. These scenarios were used because RCP2.6 and RCP8.5 scenarios cover the minimum and maximum conditions of the emission scenarios in the future, respectively.

Table 3 Description of AOGCMs used in this study

Preparing climate model scenarios

To create a climate change scenario for each AOGCM, the delta method was used (Eqs. 4, 5, and 6).

ΔTmin i=(Tmin¯¯¯¯¯¯¯¯¯¯¯¯GCM,fut,iTmin¯¯¯¯¯¯¯¯¯¯¯¯GCM,base,i)
(4)
ΔTmin i=(Tmax¯¯¯¯¯¯¯¯¯¯¯¯¯GCM,fut,iTmax¯¯¯¯¯¯¯¯¯¯¯¯¯GCM,base,i)
(5)
ΔPi=(P¯GCM,fut,i/P¯GCM,base,i)
(6)

In the above equations, ΔTmini

, ΔTmaxi, and ΔPi represent the changes in the amount of minimum temperature, maximum temperature, and precipitation, respectively, for the future period (2020–2039) relative to the baseline period (1986–2005) in each month (1i12). Also, (Tmin¯¯¯¯¯¯¯¯¯¯¯¯GCM,fut,i), (Tmax¯¯¯¯¯¯¯¯¯¯¯¯¯GCM,fut,i), and (P¯GCM,fut,i) are 20-year average simulated minimum and maximum temperature and precipitation for each month in 2020–2039, respectively. Moreover, (Tmin¯¯¯¯¯¯¯¯¯¯¯¯GCM,base,i), (Tmax¯¯¯¯¯¯¯¯¯¯¯¯¯GCM,base,i), and (P¯GCM,base,i

) are 20-year average observed minimum and maximum temperature and precipitation for each month in the baseline period (1986–2005), respectively [19].

Then, the ensemble average climate change scenarios of 9 AOGCMs were calculated for minimum temperature, maximum temperature, and precipitation in the future period (2020–2039) relative to the baseline period (1986–2005) (Eqs. 7, 8, and 9).

ΔTmin(ensemble)i=j=1n(ΔTmini)/n
(7)
ΔTmax(ensemble)i=j=1n(ΔTmaxi)/n
(8)
ΔP(ensemble)i=j=1n(ΔPi)/n
(9)

In the above equations, ΔTmin(ensemble)i,

ΔTmax(ensemble)i, and ΔP(ensemble)i represent the ensemble average climate change scenarios for minimum temperature, maximum temperature, and precipitation in each month (12i1

). Also, (n) is the number of AOGCMs.

LARS-WG model

To introduce daily climate data to the BIOME-BGC model in the future period (2020–2039) under the climate change scenarios, a weather generator model called LARS-WG was used [2]. LARS-WG model (version 5.5) is a stochastic weather generator which is useful for generating the daily time series of maximum and minimum temperatures (degrees Celsius) and precipitation (millimeters) in a climate station under current and future climate conditions [36, 41].

The modeling processes using LARS-WG model are composed of three main steps, including calibration, evaluation, and generation of meteorological data for the future. In the calibration step, the probabilistic distribution parameters of the observed daily climate variable (1986–2005), including daily maximum and minimum temperatures and precipitation, were analyzed and computed. In the second step, the ability of the LARS-WG model to simulate daily climate data during the baseline period (1986–2005) was evaluated by comparing observed and simulated climate data. In the third step, by introducing the ensemble average climate change scenario under RCP2.6 and RCP8.5 emission scenarios created by Eqs. (7), (8), and (9), daily climate data for 2020–2039 were generated under future climate conditions.

Results and discussion

Evaluation results of BIOME-BGC model

The monthly LAI values were simulated by BIOME-BGC in 319 pixels separately over the Kasilian catchment. The amount of calculated R2 is greater than or equal to 0.7 (R2 ≥ 0.7) in all pixels which demonstrate that monthly MODIS LAI and BIOME-BGC LAI were in good agreement. As an example, the scatterplot of the monthly MODIS LAI and BIOME-BGC LAI for three pixels including deciduous broadleaf forest (DBF), shrubs, and C3 grasses is shown in Fig. 3a, b, c, respectively. Although the R2 values indicate an acceptable correlation between monthly MODIS LAI and BIOME-BGC LAI, the values of percentage error (PE) and RMSE show a small amount of error between these two LAI time series. The PE and RMSE values in all pixels were less than or equal to 30% and 0.9, respectively. This error is resulted from the inability of BIOME-BGC model to simulate the rapid growth of plants in the early part of the growing season. Furthermore, the comparison of the annual mean values of the MODIS LAI and BIOME-BGC LAI showed a slight discrepancy from 2004 to 2013 (Fig. 4). Figure 4 also demonstrates that BIOME-BGC model simulated the annual mean LAI values with high accuracy in all years.

Fig. 3
figure3

Scatterplots of monthly MODIS LAI and BIOM-BGC LAI at three different pixels (a DBF, b shrub and c C3 grasses)

Fig. 4
figure4

Comparison of annual mean LAI derived from MODIS and BIOME-BGC model over the Kasilian catchment

Changes in climate variables and CO2 level

Monthly changes of minimum temperature, maximum temperature, and precipitation in the future period (2020–2039) relative to the baseline period (1986–2005), calculated based on Eqs. (7), (8), and (9), are shown in Fig. 5a, b, c, respectively. The results showed that minimum and maximum temperature would increase under both scenarios (RCP2.6 and RCP8.5) in all months. Under both scenarios, minimum and maximum temperatures are expected to increase more in May, June, July, August, and September compared to other months in the future period. It is also projected that minimum and maximum temperatures under the RCP8.5 scenario will see a higher increase compared to the RCP2.6 scenario in all months, with an exception in February and March.

Fig. 5
figure5

Changes in MIN temperature a and maximum temperature b and precipitation c resulted from nine ensembles AOGCMs AR5 in (2020–2039) relative to (1986–2005)

The percentage of precipitation will increase in half of the months and decrease in the rest of the months. Precipitation changes will be negative from February to August. For example, the highest precipitation fall in both scenarios is related to May and July, when precipitation will see a reduction of more than 10%. In contrast, the highest precipitation increase is expected to occur in June (more than 30%) and in September (more than 50%) under the RCP2.6. It is projected that the average temperature over the Kasilian catchment will be increased by 1.1 °C and 1.3 °C under RCP2.6 and RCP8.5 scenarios, respectively. Moreover, average precipitation in Kasilian catchment will rise by 5.5% and 1.3% under RCP2.6 and RCP8.5 scenarios. In addition, based on annual time series of carbon dioxide derived from IPCC website (www.IPCC-data.org), carbon dioxide concentration under RCP2.6 and RCP8.5 scenarios will increase by + 18 ppm and + 23 ppm in Iran from 2020 to 2039, respectively (Fig. 6).

Fig. 6
figure6

Changes of CO2 level from 2004 to 2039

Evaluation of the LARS-WG model

Figure 7a, b, c shows scatterplots between monthly mean values of minimum temperature, maximum temperature, and precipitation simulated by the LARS-WG model and observed data at “Darzikola” climatology station located in the Kasilian catchment during the baseline period (1986–2005). The high value of R2 between observed and simulated monthly mean minimum temperature, maximum temperature, and precipitation indicates that LARS-WG model regenerates climate variables with high accuracy at “Darzikola” climatology station. R2 value for monthly mean minimum and the maximum temperature is more than 0.99, and for monthly mean precipitation it is about 0.86. It represents that simulated monthly mean precipitation values have a relatively lower accuracy compared to simulated monthly mean minimum and maximum temperature.

Fig. 7
figure7

LARS-WG monthly mean climate variables against observed monthly mean climate variables in the baseline period (1986–2005). a Precipitation, b maximum temperature and c minimum temperature

Table 4 shows probability values (P value) calculated by T test between the observed and simulated monthly mean minimum and maximum temperature and precipitation. It also shows P values calculated by Kolmogorov–Smirnov test (KS) between probabilistic distributions of observed and simulated daily minimum and maximum temperature and precipitation. Calculated p values by the T test and KS test are above 0.1 in all months. Therefore, the null hypothesis for the T test and KS test is significant at 10% level for minimum and maximum temperatures and precipitation in all the months. This means that daily and monthly observed and simulated values had a similar statistical distribution. Results mentioned in this section show that the LARS-WG model has a high capability to simulate daily and monthly minimum temperature, maximum temperature, and precipitation at “Darzikola” climatology station. After evaluating the LARS-WG model, daily climate variables in the future period (2020–2039) were generated under climate scenarios mentioned in Sect. 4.3.

Table 4 The evaluation result of LARS-WG ability to simulate climate variables in “Darzikola” climatology station located in Kasilian catchment

LAI changes under climate change

By introducing daily climate series generated by the LARS-WG model and annual level of CO2 derived from IPCC website (www.IPCC-data.org) to BIOME-BGC model, daily LAI series were estimated under RCP2.6 and RCP8.5 in the future period (2020–2039) in all pixels. Figure 8 shows percentage changes in monthly LAI in all pixels in response to changes in climate variables of temperature, precipitation and carbon dioxide concentration (ppm) under the RCP2.6 and RCP8.5 scenarios during the future period (2020–2039) compared to the study period (2004–2013).

Fig. 8
figure8

Percentage of LAI change in the future period (2020–2039) relative to the study period (2004–2013) under RCP2.6 and RCP8.5, in all pixels within Kasilian catchment domain

Based on Fig. 8, LAI is expected to increase in all pixels under both scenarios. Also, it is demonstrated that LAI under RCP8.5 will rise more than under RCP2.6 in all pixels. The highest rise of LAI under RCP2.6 and RCP8.5 will be about 2.8 and 4%, and the least rise of LAI under RCP2.6 and RCP8.5 is expected to be 0.3% and 1.1%, respectively. Under both scenarios, the highest increase in LAI will be related to pixels with DBF located in center and north of the catchment, and also pixels with shrubs and C3 grasses located in the south of the basin. LAI in these pixels will increase between 2 and 2.8% under RCP2.6 and between 3 and 4% under RCP8. In contrast, the least increase in LAI, less than 1%, will occur in pixels with DBF located in the southern parts of the catchment under both scenarios.

Based on the data shown in Table 5, it is clear that Kasilian catchment average LAI in pixels with DBF will have higher increase rather than that of pixels with shrubs and C3 grasses under both scenarios. It indicates that DBF will be more sensitive to changes in climate variables and the CO2 level compared to shrubs and C3 grasses in the case study. The behavior of shrubs and C3 grasses will be almost similar in response to climate and CO2 changes.

Table 5 The percentage changes in the average LAI under the two RCPs in the future period (2020–2039) compared to the study period (2004–2013) in DBF, shrubs, and C3 grasses pixels

Figure 9 shows the changes in monthly average LAI over the Kasilian catchment. As shown in Fig. 9, LAI will increase in response to climate and carbon dioxide level change in all months, especially in spring and summer. Also, LAI will experience a higher rise under the RCP8.5 compared to RCP2.6 in all months. The highest increase in LAI will occur in May under both scenarios.

Fig. 9
figure9

Monthly Kasilian catchment average LAI changes in the future period (2020–2039) relative to the study period (2004–2013)

Overall, our results showed that average LAI in the Kasilian catchment will increase by about 2.2% and 3.1% under RCP2.6 and RCP8.5 in the future period (2020–2039) relative to the study period (2004–2013). These results demonstrate that the projected increase in both temperature and precipitation (mentioned in Sect. 4.2) is likely to have a positive impact on LAI growth in the Kasilian catchment, while another study conducted in Australia showed that LAI is expected to decrease for three land covers (crop, pasture, and tree) in response to increase in mean monthly temperature and a general decrease in precipitation under four scenarios (RCP2.6, RCP4.5, RCP6, and RCP8) [49]. This comparison shows that LAI behavior in response to climate change varies from region to region, and it depends on the condition of climate variables in the future. Therefore, investigating LAI behavior in each particular region would be necessary, and the approach applied here can be used in other studies.

To calculate the accurate impact of LAI increase (2.2% and 3.1% under RCP2.6 and RCP8.5) on the amount of runoff in the Kasilian catchment, projected LAI data should be coupled with hydrologic models. A slight increase in LAI may not have a great impact on the particular amount of runoff, but the seasonal or annual amount of runoff would change a lot [48, 59]. Therefore, our approach can be used by hydrologists to assess the impact of dynamic LAI on the amount of water balance in other regions.

Conclusion

In this study, a new framework is presented to investigate future LAI changes over Kasilian catchment located in the north of Iran in response to climate change using an ecological model named as BIOME-BGC model. AOGCMs projections show an overall increase in mean temperature (+ 1.1 °C under RCP2.6 and + 1.3 °C under RCP8.5) and precipitation (+ 5.5% under RCP2.6 and + 1.3% under RCP8.5) during the future period (2020–2039) compared to the baseline period (1986–2005). These results indicate that climate conditions are expected to be warmer under RCP8.5 and more humid under RCP2.6 across the region. In addition, the concentration of CO2 will rise by + 18 ppm and + 23 ppm under RCP2.6 and RCP8.5 between 2004 and 2039, respectively. Model outputs illustrate that the catchment mean LAI will increase by 2.2% and 3.1% under RCP2.6 and RCP8.5 during the future period (2020–2039), respectively. The study shows the vegetation feedback is more sensitive in response to temperature change compared to precipitation change. Therefore, RCP8.5 will experience higher LAI than RCP2.6.

There are two sources of uncertainty in this paper. We used satellite-derived LAI (MODIS LAI) for assessing the accuracy of BIOME-BGC LAI outputs, while estimated LAI derived from satellite imagery is not quite accurate. Moreover, due to the uncertainty of AOGCMs in the simulation of climate variables in the future, forecasted LAI has also an uncertainty. However, overall, these predictions can provide an appropriate pattern from changes in vegetation cover conditions in the future in catchments. The results of this study could be associated with hydrologic models to investigate the vegetation dynamic behavior and climate change on the hydrologic behavior of the Kasilian catchment.

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Acknowledgements

We are grateful to the Numerical Terradynamic Simulation Group (NTSG) at the University of Montana, USA, for freely sharing the BIOME-BGC model at https://www.ntsg.umt.edu/data.

Funding

Ali Reza Massah Bavani received research grant from the University of Tehran.

This article appeared on the SN Applied Sciences website at https://link.springer.com/article/10.1007/s42452-020-2290-6