Background and Objective
Drought assessment is crucial for the effective management of water resources and agricultural products, aiming to develop strategies to reduce the loss of these vital resources. Drought assessment indicators are essential tools for evaluating this process, and selecting the appropriate indicator is indispensable for accurate drought assessment. Indicators are quantitative measures that can be used to determine the severity and extent of droughts numerically. According to existing literature, the Standardized Precipitation Index (SPI) and the Standardized Precipitation-Evapotranspiration Index (SPEI) are the most common and widely used drought assessment indicators. Therefore, comparing these two indices is very important to understand their differences and distinctions. On the other hand, one of the limitations of station-based indices is their inadequate spatial coverage. In contrast, satellite-based indices eliminate the limitations of point-based methods. For this purpose, satellite-based drought indices such as NDVI and VHI were developed. The objective of this research is to compare the effectiveness of SPI and SPEI indices in drought assessment based on remote sensing data in areas with natural vegetation and rainfed agriculture. Eliminating the impact of stations located in irrigated agriculture areas in southern Tehran province and its impact on the results of drought indicators is one of the innovations of this research.
Methodology
Tehran province is characterized by diverse topography, varying climatic conditions, and different land uses. The northern and eastern parts of the province feature natural grasslands, while the southern region is used mainly for irrigated agriculture. In this study, meteorological drought indices, SPI and SPEI, were calculated based on precipitation and evapotranspiration data from 31 meteorological stations over a twenty-year period from 2000 to 2019. The choice of this timeframe was due to data availability constraints for precipitation, evapotranspiration, and the MODIS sensor data, which have been accessible since 2000. The relationship between these two indices was generally examined, with a focus on the Southern stations (due to errors caused by irrigation practices affecting vegetation coverage during drought periods). A three-month period ending in June was considered for both indices. To evaluate the accuracy and performance of the SPI and SPEI indices, the Vegetation Health Index (VHI), a satellite-based drought index, was extracted using MODIS products at the station locations and calculated from NDVI and LST values.
Results
The results indicated a stronger statistical correlation between SPI and SPEI after excluding the stations in southern Tehran (where irrigated agriculture is dominant), with the coefficient of determination increasing from 0.65 to 0.86. This suggests that land use significantly influences evapotranspiration values in southern stations, thereby affecting the accuracy of the SPEI index.
Overall, SPI showed a higher correlation with remote sensing drought indicators such as NDVI, VCI, TCI, and VHI. After removing stations in Parndak, Baqerabad, Javadabad, Golkhandan, Ghanjabad, and Hamamk, the relationships between SPI and SPEI and the remote sensing drought indicators were re-evaluated, revealing an increase in model accuracy (coefficients of determination from 0.40 to 0.55 for SPI and from 0.33 to 0.54 for SPEI). The comparison of SPI and SPEI further demonstrated the superior performance of SPEI in areas with dry farming practices.
Conclusion
Results of this study indicated that considering land use types and cultivation methods is essential for improving the effectiveness of SPI and SPEI. Results suggest that SPEI is more effective where higher evapotranspiration occurs, whereas land use diversity reduces the accuracy of relationships between meteorological and satellite-based drought indicators. Developing tailored indices for each land use type could be a promising approach for future research. |
- AghaKouchak, A., Farahmand, A., Melton, F.S.,Teixeira, J., Anderson, M.C., Wardlow, B.D. and Hain, C.R., 2015. Remote sensing of drought: progress, challenges and opportunities. Reviews of Geophysics, 53 (2): 452–480. https://doi.org/10.1002/2014RG000456
- Ba aghideh, M.,Alijani,B. and Ziaeian,P.,2011. Investigating the possibility of using NDVI vegetation index in analyzing droughts in Isfahan province. Arid Regions Geographic Studies, Volume 2, Issue 4, July 2011, Pages 1-16.
- Choi, M., J.M. Jacobs, M.C. Anderson and D.D. Bosch., 2013. Evaluation of drought indices via remotely sensed data with hydrological variables. Journal of Hydrology, 476: 265-273. https://doi.org/10.1016/j.jhydrol.2012.10.042
- Damavandi, A.A., Rahimi, M., Yazdani,M.R. and Norozi,A.A.,2016. Spatial monitoring of agricultural drought through time series of NDVI and LST indices of MODIS data (Case study: Markazi Province). Journal of Geographical Data (SEPEHR), Volume 25, Issue 99(in Persian). https://doi.org/10.22131/sepehr.2016.23200
- Fazel Dehkordi, L., Sohrabi, T.S., Ghanaviz Baf, M.H., and Ghazavi, R., 2016. Drought monitoring using MODIS images in arid regions: A case study of Isfahan province rangelands. Geography and Environmental Planning. https://doi.org/22108/gep.2017.98067
- Ghorbani, K., Salari Jazi, M.,Rezaei Ghaleh.L.,2024. Evaluation of MSDI bivariate drought index based on precipitation and runoff (Arazkouse and Galikesh stations of Golestan province). Journal of the Climate Change Research, Vol. 4, No. 16. https://doi.org/30488/ccr.2023.426470.1179
- Kogan,1995. Application of vegetation index and brightness temperature for drought detection. Adv Space Res 15(11):91–100. https://doi.org/10.1016/0273-1177(95)00079-T
- Luong, V.V.; Tran, T.T.T.,2024. Drought sensitivity analysis of meteorological and vegetation indices in Dak Nong. Vietnam. J. Water Clim. Change, 15, 4968–4988. https://doi.org/ 2166/wcc.2024.661
- McKee, T. B., Doesken, N. J. and Kleist, J., 1993.The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology, 17 (22): 179-183.
- Nosrati, K.,2013. Assessment of Standardized Precipitation Evapotranspiration Index (SPEI) for Drought Identification in Different Climates of Iran. Journal of Environmental Sciences.
- Vicente-Serrano, S.M., Beguería, S. and LópezMoreno, J.I., 2010. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. Journal of climate, 23 (7): 1696–1718.
- Singh, R.P, Sudipa, Roy and Kogan.F,2010. Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India . International Journal of Remote Sensing,Volume 24, 2003 - Issue 22.
- Zand, M., Miri, M., Kousari, M.R., Ghermez cheshmeh,B.,2023. Investigation of Lag Time Correlation between Agricultural and Meteorological Droughts, Water Harvesting Research, Vol. 6, No. 1, Spring & Summer 2023. https://doi.org/22077/jwhr.2023.6602.1094
Zhifang, P., Shibo, F., Lei,W. and Wunian,Y.,2020. Comparative Analysis of Drought Indicated by the SPI and SPEI at Various Timescales in Inner Mongolia, China. Water 2020, 12(7), 1925; https://doi.org/10.3390/w12071925. https://doi.org/ 10.3390/w12071925
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