Integrated Watershed Management

Integrated Watershed Management

Drought induced vegetation changes in south of Kerman Province

Document Type : Original Article

Authors
saeid barkhori 1
Mojtaba Dolatkordestani 1
Kimia Javaheri 2
1 Department of Ecological Engineering, Faculty of Natural Resources, University of Jiroft, Iran
2 Department of Arid and Mountainous Regions Reclamation, Natural Resources, University of Tehran, Iran
10.22034/iwm.2024.2027447.1150
Abstract
Extended Abstract
Introduction: Drought, as an important climatic condition, has affected extensive areas of the world. Vegetation cover is also affected by low precipitation, high temperatures, and evaporation in dry ecosystems. These impacts can be defined as ecological drought on vegetation cover. Long-term droughts can have dangerous effects on vegetation cover. The SPEI index, which is based on the difference between precipitation and potential evapotranspiration, has been used in various studies to examine the spatiotemporal patterns of drought. Additionally, the use of satellite images with high spatial and temporal resolution is an effective tool for studying vegetation cover changes and the impacts of drought.
 
Materials and methods: To investigate the impact of drought on vegetation cover in the southern Kerman regions, the Enhanced Vegetation Index (EVI) from MODIS satellite imagery and the Standardized Precipitation Evapotranspiration Index (SPEI) were used. In this research, EVI derived from the MOD13Q1 MODIS sensor product with a spatial resolution of 250 meters and a temporal resolution of 16 days was used. First, the monthly average of this index was estimated for the study area from the beginning of 2001 to the end of 2022 on the Google Earth Engine platform, and then the monthly average over the study period was calculated. Based on the results of this section, the two months with the highest EVI values were selected. To investigate the impact of meteorological drought on vegetation cover, the SPEI index with different time scales of 3, 6, 9, and 12 months for the months of March and April over 22 years (2001-2022) was calculated using MATLAB software. The sensitivity of EVI to SPEI in March and April was calculated by the Pearson correlation coefficient in the Trend Analysis Module (ETM) of TerrSet software. Then, the slope of the relationship between the 3, 6, 9, and 12-month SPEI and EVI based on the Chatfield (2016) linear regression equations in the ETM model of TerrSet software was calculated to investigate the impact of SPEI fluctuations at different time scales on vegetation cover.
 
Results and Discussion: According to the results, the months of April and March have the highest EVI values throughout the year, indicating maximum growth and vitality of vegetation during these months. The months of January and December have the lowest average EVI values. The months of April and March, which had the highest EVI values, were selected to investigate the impact of drought on vegetation cover. According to the results obtained in March, the highest correlation of EVI was with the 12-month SPEI, covering 40.4% of the study area. Furthermore, in this month, the lowest correlation of EVI was with the 9-month SPEI, covering around 15.55% of the study area. In April, the highest correlation of EVI was with the 12-month SPEI, followed by the 6-month SPEI, covering 25.53% and 20.57% of the study area, respectively. In most areas of Qalat, Roudbar Jonob, Jiroft, Faryab, Manoojan, Amberabad, and Arzueie, especially from the central regions of the study area towards the south, the 12-month SPEI had the highest correlation with EVI. The lowest correlation of EVI was with the 9-month SPEI, followed by the 3-month SPEI, covering 10.75% and 15.44% of the study area, respectively. In March, the positive high, moderate, and low classes accounted for 29.02%, 19.41%, and 15.03% of the study area, respectively. These percentages for April were 41.89%, 28.1%, and 14.69%. The lowest percentage of area in both months belonged to the very high negative and high negative classes. In March, areas in the north, northeast, and parts of the west of the study area, including most areas of Fahraj, Narmashir, Bam, and Rigan had very low and low sensitivity to drought, while areas in the central regions towards the south, southwest, and southeast, including the south regions of Jiroft, west of Roudbar Jonob, and most areas of Kahnuj, showed the highest sensitivity of EVI to SPEI. In April, EVI in major parts of the western, northwestern, central, east, southeast, south, and southwest regions of the study area showed very high and high sensitivity to drought compared to SPEI. The lowest sensitivity of EVI to drought in this month was related to the northeastern parts.
 
Conclusion: Based on the results of the correlation analysis of EVI with SPEI at different time scales, in both months of March and April, the highest correlation of EVI with SPEI has been with SPEI 12, 6, 3, and 9 months respectively. Therefore, the greatest impact of drought on vegetation cover in southern Kerman is related to SPEI 12 months, with the least impact related to SPEI 9 months. These results are due to different environmental conditions in the study area, which have led to different results in each region. The results of the sensitivity of EVI to SPEI show that the highest sensitivity is allocated primarily to high positive, medium positive, and low positive classes, mainly related to central, western, southwestern, south to southeast and eastern parts of the study area. Additionally, the results of sensitivity of EVI to SPEI show that the lowest sensitivity of vegetation cover to drought is related to the northeastern regions, some parts of the southeastern and northern areas of the study area. These regions mainly include barren lands or pastures with poor vegetation cover. Therefore, due to the lack and scarcity of vegetation cover, the sensitivity of EVI to SPEI is at its lowest level. According to the results of this research, environmental conditions such as climatic characteristics, topography, type of vegetation cover, human management, and so on have a significant influence on determining the relationship between vegetation cover index and meteorological drought index. It is suggested that future research, considering these factors, prioritize predicting this phenomenon and modeling changes in vegetation cover under the influence of drought.
Keywords
Linear regression analysis
Pearson correlation
EVI
SPEI

Subjects

Alamdarloo, E. H., Abolhasani, A., Manesh, M. B., & Khosravi, H. (2024). Application of remote sensing techniques for evaluating land surface vegetation. In Remote Sensing of Soil and Land Surface Processes (pp. 199-216). Elsevier. https://doi.org/10.1016/B978-0-443-15341-9.00006-X
Ahmad, M. I., Sinclair, C. D., & Werritty, A. (1988). Log-logistic flood frequency analysis. Journal of Hydrology, 98(3-4), 205-224. https://doi.org/10.1016/0022-1694(88)90015-7.
Bagheri, S., Heydari Alamdarloo, E., Khosravi, H., Abolhasani, A. (2021). The effect of meteorological drought on vegetation dynamics in Iran. Journal of Rangeland, 15(4) :622-637. (In Persian)
Bazarafshan, C., Hejabi, S. (2016). Drought and its monitoring methods (along with applications in MATLAB programming environment). Tehran University Press, 224 pages. (In Persian)
Behrang Manesh, M., Khosravi, H., Heydari Alamdarloo, E., Saadi Alekasir, M., Gholami, A., & Singh, V. P. (2019). Linkage of agricultural drought with meteorological drought in different climates of Iran. Theoretical and Applied Climatology, 138, 1025-1033. http://doi.org/10.1007/s00704-019-02878-w.
Boori, M. S., Choudhary, K., & Kupriyanov, A. (2022). Detecting vegetation drought dynamics in European Russia. Geocarto International, 37(9), 2490-2505. http://doi.org/10.1080/10106049.2020.1750063.
Chatfield, C. (2016). The analysis of time series: an introduction. CRC press. Pp 352. https://doi.org/10.4324/9780203491683
Ding, Y., Xu, J., Wang, X., Peng, X., & Cai, H. (2020). Spatial and temporal effects of drought on Chinese vegetation under different coverage levels. Science of The Total Environment, 716, 137166. https://doi.org/10.1016/j.scitotenv.2020.137166.
Eskandari Damaneh, H., Eskandari Damaneh, H., Khosravi, H., Gilevari, A., & Adeli Sardooei, M. (2021a). A survey on the effect of drought on environmental indices derived from the MODIS data over the 2001-2019 period (Case study: Rangelands of Isfahan province). Rangeland, 15(3), 460-476. (In Persian). 20.1001.1.20080891.1400.15.3.8.7
Eskandari Damaneh, H., Eskandari Damaneh, H., Sayadi, Z., & Khoorani, A. (2021b). Evaluation of spatiotemporal changes and correclations of aerosol optical depth, NDVI and climatic data over Iran. Iranian Journal of Range and Desert Research, 28(4), 772-786. http://doi.org/10.22092/ijrdr.2021.125252  (In Persian)
Eskandari Damaneh, H., Gholami, H., Mahdavi, R., Khoorani, A., & Li, J. (2022a). Evaluation of land degradation trend using satellite imagery and climatic data (Case study: Fars province). Desert Ecosystem Engineering, 8(24), 49-64. https://doi.org/10.22052/deej.2018.7.24.35. (In Persian)
Eskandari Damaneh, H., Zehtabian, G., Khosravi, H., Azarnivan, H., & Barati, A. (2022b). Investigating the Influence of Drought on Trend of Vegetation Changes in Arid and Semiarid Regions, Using Remote Sensing Technique: A Case Study of Hormozgan province). Desert Ecosystem Engineering, 9(28), 13-28. http://doi.org/10.22052/deej.2020.9.28.11 (In Persian)
Evans, J. D. (1996). Straightforward statistics for the behavioral sciences. Thomson Brooks/Cole Publishing Co.
Greve, P., Orlowsky, B., Mueller, B., Sheffield, J., Reichstein, M., & Seneviratne, S. I. (2014). Global assessment of trends in wetting and drying over land. Nature geoscience, 7(10), 716-721. http://doi.org/10.1038/ngeo2247
Heydari Alamdarloo, E., Moradi, E., Abdolshahnejad, M., Fatahi, Y., Khosravi, H., & da Silva, A. M. (2021). Analyzing WSTP trend: a new method for global warming assessment. Environmental Monitoring and Assessment, 193, 1-15. https://doi.org/10.1007/s10661-021-09600-2
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L.G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1-2), 195-213. https://doi.org/10.1016/S0034-4257(02)00096-2
Javed, T., Li, Y., Feng, K., Ayantobo, O.O., Ahmad, S., Chen, X., & Suon, S. (2021). Monitoring responses of vegetation phenology and productivity to extreme climatic conditions using remote sensing across different sub-regions of China. Environmental Science and Pollution Research, 28, 3644-3659. https://doi.org/10.1007/s11356-020-10769-1
Khosravi, H., Eskandari Dermaneh, H., Eskandari Damaneh, H., Borji, M., & Nakhaee Nejadfard, S. (2018). Drought Trend Assessment in Riverheads of Karkheh and Dez Basins based on Streamflow Drought Index (SDI). Desert Ecosystem Engineering, 7(2), 45-54. https://doi.org/10.22052/jdee.2018.101087.1019 (In Persian)
Kong, D., Zhang, Q., Singh, V.P., & Shi, P. (2017). Seasonal vegetation response to climate change in the Northern Hemisphere (1982–2013). Global and Planetary Change, 148, 1-8. https://doi.org/10.1016/j.gloplacha.2016.10.020
Lau, W.K.M., Wu, H.T., & Kim, K.M. (2013). A canonical response of precipitation characteristics to global warming from CMIP5 models. Geophysical Research Letters, 40(12), 3163-3169. http://doi.org/10.1002/grl.50420
Lee, S., Moriasi, D.N., Mehr, A.D., & Mirchi, A. (2024). Sensitivity of Standardized Precipitation and Evapotranspiration Index (SPEI) to the choice of SPEI probability distribution and evapotranspiration method. Journal of Hydrology: Regional Studies, 53, 101761. https://doi.org/10.1016/j.ejrh.2024.101761
Ma, J., Zhang, C., Li, S., Yang, C., Chen, C., & Yun, W. (2023). Changes in Vegetation Resistance and Resilience under Different Drought Disturbances Based on NDVI and SPEI Time Series Data in Jilin Province, China. Remote Sensing, 15(13), 3280. https://doi.org/10.3390/rs15133280
Mazidi, A., Mohammadi Ravari, F., & Behzadi Shahrebabak, Z. (2023). Assessment of the Drought Situation in Kerman Using Drought Indicators and its Relationship with the State of Vegetation Change in the Region. Nivar, 47(120-121), 166-180. https://doi.org/10.30467/nivar.2023.3949001244
Moradi, E., Darabi, H., Alamdarloo, E.H., Karimi, M., & Kløve, B. (2023). Vegetation vulnerability to hydrometeorological stresses in water-scarce areas using machine learning and remote sensing techniques. Ecological Informatics, 73, 101838. https://doi.org/10.1016/j.ecoinf.2022.101838
Nasabpour, S., Khosravi, H., & Heydari Alamdarloo, E. (2017). National assessment of climate resources for tourism seasonality in Iran using the tourism climate index. Desert, 22(2), 175-186.
Nejadrekabi, M., Eslamian, S., & Zareian, M. J. (2022). Spatial statistics techniques for SPEI and NDVI drought indices: A case study of Khuzestan Province. International Journal of Environmental Science and Technology, 19(7), 6573-6594. https://doi.org/10.1007/s13762-021-03852-8
Novick, K. A., Miniat, C. F., & Vose, J. M. (2016). Drought limitations to leaf‐level gas exchange: results from a model linking stomatal optimization and cohesion–tension theory. Plant, cell & environment, 39(3), 583-596. https://doi.org/10.1111/pce.12657
Savari, M., Damaneh, H.E., & Damaneh, H.E. (2024). Managing the effects of drought through the use of risk reduction strategy in the agricultural sector of Iran. Climate Risk Management, 100619. https://doi.org/10.1016/j.crm.2024.100619
Sheffield, J., & Wood, E.F. (2008). Projected changes in drought occurrence under future global warming from multi-model, multi-scenario, IPCC AR4 simulations. Climate dynamics, 31, 79-105. http://doi.org/10.1007/s00382-007-0340-z
Singh, V.P., Guo, H., & Yu, F.X. (1993). Parameter estimation for 3-parameter log-logistic distribution (LLD3) by Pome. Stochastic Hydrology and Hydraulics, 7, 163-177. https://doi.org/10.1007/BF01585596
Stagge, J.H., Tallaksen, L.M., Xu, C.Y., & Van Lanen, H. A. (2014). Standardized precipitation-evapotranspiration index (SPEI): Sensitivity to potential evapo-transpiration model and parameters. In Hydrology in a changing world, 363, 367-373.
Thornthwaite, C.W. (1948). An approach towarda rational classification of climate. Geographical Review, 38, 55–94. https://doi.org/10.2307/210739.
Vicente-Serrano, S. M., Cabello, D., Tomás-Burguera, M., Martín-Hernández, N., Beguería, S., Azorin-Molina, C., & El Kenawy, A. (2015). Drought variability and land degradation in semiarid regions: Assessment using remote sensing data and drought indices (1982–2011). Remote Sensing, 7(4), 4391-4423. https://doi.org/10.3390/rs70404391.
Wang, Q., Wu, J., Lei, T., He, B., Wu, Z., Liu, M., & Liu, D. (2014). Temporal-spatial characteristics of severe drought events and their impact on agriculture on a global scale. Quaternary International, 349, 10-21. http://doi.org/10.1016/j.quaint.2014.06.021.
Won, J., & Kim, S. (2023). Ecological Drought Condition Index to Monitor Vegetation Response to Meteorological Drought in Korean Peninsula. Remote Sensing, 15(2), 337. https://doi.org/10.3390/rs15020337.
Zhao, H., Huang, Y., Wang, X., Li, X., & Lei, T. (2023). The performance of SPEI integrated remote sensing data for monitoring agricultural drought in the North China Plain. Field Crops Research, 302, 109041. https://doi.org/10.1016/j.fcr.2023.109041
 

  • Receive Date 27 April 2024
  • Revise Date 03 June 2024
  • Accept Date 10 June 2024