Investigating the leaf area index changes in response to climate change (case study: Kasilian catchment, Iran)
Materials and methods
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  and MODIS LAI product named as MODIS MOD15A2H  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 . 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) . 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.
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.
Climate change projections
Climate modelsAt 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.
Preparing climate model scenariosTo create a climate change scenario for each AOGCM, the delta method was used (Eqs. 4, 5, and 6).
LARS-WG modelTo 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 . 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 modelThe 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.
Changes in climate variables and CO2 levelMonthly 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.
Evaluation of the LARS-WG modelFigure 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.
LAI changes under climate changeBy 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).
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