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مرتع 2023, 17(1): 15-31 |
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Bahreini F, Panahi F, Malekian A, Tahmoures M. Evaluation of rangeland gross primary productivity sensitivity potential to drought using ecosystem modelling. مرتع 2023; 17 (1) :15-31
URL: http://rangelandsrm.ir/article-1-588-en.html
URL: http://rangelandsrm.ir/article-1-588-en.html
Department of Combating Desertification, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan
Abstract: (2274 Views)
Background and objectives: Primary Gross Productivity (GPP) is a crucial factor in the carbon cycle of ecosystems. With the increasing occurrence of droughts due to global warming and the unique response of plant cover ages to these changes, it is necessary to identify and quantify the relationship between climate data and ecosystem's primary gross productivity. This research aims to determine the plant cover's response to drought by analyzing the region's primary gross productivity in the Bordekhun region in southwest Iran. This study introduces a new ecosystem model, focusing on two objectives: (1) simulating primary gross productivity through ecosystem modeling and (2) examining the response of primary gross productivity to drought during the growth season, evaluating the effectiveness of this model in an arid region.
Methodology: The research was conducted in three stages, including determining meteorological drought conditions over a 16-year period using the Standardized Precipitation Index (SPI), simulating primary gross productivity (GPP) using the new ecosystem model BGC-MAN (Bio-Geo-Chemistry Management Model), and validating it with MODIS satellite images. The quantification of the ecosystem's response to drought during the vegetation growth season was performed for the period from 2000 to 2015. The relationship between GPP and SPI was analyzed using the Pearson correlation coefficient, and the model was validated using the MODIS GPP product. The evaluation focused on the response of various grass and shrub vegetation forms to drought, with the percentage of coverage for each vegetation form utilized in 29 sampling points.
Results: The statistical analysis revealed a significant correlation coefficient (p < 0.05, R2 = 0.14) between the modeled GPP and the GPP estimated from MODIS satellite images on a monthly scale. The temporal analysis of drought conditions over the 16-year period indicated consecutive droughts with varying severity, and the spatial distribution of drought severity showed that 48.79% and 51.21% of the study area were in normal and moderate drought states, respectively. The temporal analysis of changes in primary gross productivity during the growth season demonstrated a decreasing trend in most months except for December, with notable variations. The correlation analysis between primary gross productivity and drought showed a significant positive correlation in grass forms during the months of January, February, and March, while for shrub forms, a significant positive correlation was observed only in the month of March.
Conclusion: The results indicated that grass showed the highest correlation in the middle of the growing season (December and January) (R = 0.29), while shrubs showed the highest correlation at the end of the growing season (February and March) (R = 0.28), indicating their different sensitivity to water scarcity during the growth season. The study also revealed that ecosystem modeling could be an alternative approach to estimating GPP with the potential to simulate spatial GPP in arid regions. However, the proposed model's performance in other regions still requires further verification.
Methodology: The research was conducted in three stages, including determining meteorological drought conditions over a 16-year period using the Standardized Precipitation Index (SPI), simulating primary gross productivity (GPP) using the new ecosystem model BGC-MAN (Bio-Geo-Chemistry Management Model), and validating it with MODIS satellite images. The quantification of the ecosystem's response to drought during the vegetation growth season was performed for the period from 2000 to 2015. The relationship between GPP and SPI was analyzed using the Pearson correlation coefficient, and the model was validated using the MODIS GPP product. The evaluation focused on the response of various grass and shrub vegetation forms to drought, with the percentage of coverage for each vegetation form utilized in 29 sampling points.
Results: The statistical analysis revealed a significant correlation coefficient (p < 0.05, R2 = 0.14) between the modeled GPP and the GPP estimated from MODIS satellite images on a monthly scale. The temporal analysis of drought conditions over the 16-year period indicated consecutive droughts with varying severity, and the spatial distribution of drought severity showed that 48.79% and 51.21% of the study area were in normal and moderate drought states, respectively. The temporal analysis of changes in primary gross productivity during the growth season demonstrated a decreasing trend in most months except for December, with notable variations. The correlation analysis between primary gross productivity and drought showed a significant positive correlation in grass forms during the months of January, February, and March, while for shrub forms, a significant positive correlation was observed only in the month of March.
Conclusion: The results indicated that grass showed the highest correlation in the middle of the growing season (December and January) (R = 0.29), while shrubs showed the highest correlation at the end of the growing season (February and March) (R = 0.28), indicating their different sensitivity to water scarcity during the growth season. The study also revealed that ecosystem modeling could be an alternative approach to estimating GPP with the potential to simulate spatial GPP in arid regions. However, the proposed model's performance in other regions still requires further verification.
Type of Study: Research |
Subject:
General
Received: 2018/07/5 | Accepted: 2018/12/29 | Published: 2023/05/31
Received: 2018/07/5 | Accepted: 2018/12/29 | Published: 2023/05/31
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