Evaluation of drought recovery duration in different land uses and climates of Iran using remote sensing

Document Type : Research Paper


1 Department of Water Resources Engineering, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran

2 Department of Water Engineering, College of Agriculture, Islamic Azad University, Lahijan, Iran.


Drought is one of the most harmful natural disasters that affects the plants yield and terrestrial ecosystems and causes significant damage. The severity of drought and duration of drought recovery (the time required for plant to return to normal conditions, after the end of drought) are vital parameters for better drought management. This article assesses and investigates the length of drought recovery period in different land uses and climates of Iran. For this purpose, climate classification was done using the De Martonne method, and agricultural drought was monitored using the Vegetation Health Index (VHI) from 2000 to 2020 for cropland, forest, grassland, and shrubland uses. Years of 2000, 2001, and 2008 were selected as drought periods. Furthermore, using gross primary productivity (GPP), the length of drought recovery period was acquired. The results showed that the average duration of  drought recovery period varies from about 34 days in the forest to 81 days in the shrubland. The rapid recovery of forests after the drought is due to their deep roots. In general, the shrubland and cropland classes had a more prolonged recovery period than the other classes, and the forest class had the shortest recovery period, which indicates the high resilience of the forest and the low resilience of the cropland and shrubland classes. Also, the results revealed that the average length of the recovery period varies from about 20 days in humid climates to 80 days in arid climates, which indicates that the conditions for drought recovery in humid climate are better than that in arid climates. In general, the drought recovery period becomes longer as one moves from a very humid climate to a dry climate. 


Main Subjects

Abbasi, F., Bazgeer, S., Kalehbasti, P.R. et al. New climatic zones in Iran: a comparative study of different empirical methods and clustering technique. Theor Appl Climatol 147, 47–61 (2022). https://doi.org/10.1007/s00704-021-03785-9
AghaKouchak, A., Farahmand, A., Melton, F., Teixeira, J., Anderson, M., Wardlow, B. D., & Hain, C. (2015). Remote sensing of drought: Progress, challenges and opportunities. Reviews of Geophysics, 53(2), 452-480.
Ahmadi, B., Ahmadalipour, A., Tootle, G., & Moradkhani, H. (2019). Remote sensing of water use efficiency and terrestrial drought recovery across the contiguous united states. Remote Sensing, 11(6), 731.
Attrill, M. J., & Power, M. (2000). Modelling the effect of drought on estuarine water quality. Water Research, 34(5), 1584-1594.
Beresford, A. E., Sanderson, F. J., Donald, P. F., Burfield, I. J., Butler, A., Vickery, J. A., & Buchanan, G. M. (2019). Phenology and climate change in Africa and the decline of Afro‐Palearctic migratory bird populations. Remote Sensing in Ecology and Conservation, 5(1), 55-69.
Chen, J., Cao, X., Peng, S., & Ren, H. (2017). Analysis and applications of GlobeLand30: a review. ISPRS International Journal of Geo-Information, 6(8), 230.
Cohen, W. B., Maiersperger, T. K., Turner, D. P., Ritts, W. D., Pflugmacher, D., Kennedy, R. E., Kirschbaum, A., Running, S. W., Costa, M., & Gower, S. T. (2006). MODIS land cover and LAI collection 4 product quality across nine sites in the western hemisphere. IEEE Transactions on Geoscience and Remote Sensing, 44(7), 1843-1857.
de Martonne, E. (1926). Une nouvelle function climatologique: L'indice d'aridité. Meteorologie, 2, 449-459.
Didan, K. (2015). MOD13A2 MODIS/Terra Vegetation Indices 16-Day L3 Global 1km SIN Grid V006 [Data set]. NASA EOSDIS LP DAAC. In.
Dinpashoh, Y., Fakheri-Fard, A., Moghaddam, M., Jahanbakhsh, S., & Mirnia, M. (2004). Selection of variables for the purpose of regionalization of Iran's precipitation climate using multivariate methods. Journal of hydrology, 297(1-4), 109-123.
Gatis, N., Anderson, K., Grand‐Clement, E., Luscombe, D. J., Hartley, I. P., Smith, D., & Brazier, R. E. (2017). Evaluating MODIS vegetation products using digital images for quantifying local peatland CO 2 gas fluxes. Remote Sensing in Ecology and Conservation, 3(4), 217-231.
Gittins, J. R., Hemingway, J. R., & Dajka, J. C. (2021). How a water-resources crisis highlights social-ecological disconnects. Water Research, 194, 116937.
Godfray, H. C. J., Beddington, J. R., Crute, I. R., Haddad, L., Lawrence, D., Muir, J. F., Pretty, J., Robinson, S., Thomas, S. M., & Toulmin, C. (2010). Food security: the challenge of feeding 9 billion people. science, 327(5967), 812-818.
Golian, S., Mazdiyasni, O., & AghaKouchak, A. (2015). Trends in meteorological and agricultural droughts in Iran. Theoretical and applied climatology, 119(3), 679-688.
 He, B., Liu, J., Guo, L., Wu, X., Xie, X., Zhang, Y., ... & Chen, Z. (2018). Recovery of ecosystem carbon and energy fluxes from the 2003 drought in Europe and the 2012 drought in the United States. Geophysical Research Letters, 45(10), 4879-4888.
Huang, L., Zhou, P., Cheng, L., & Liu, Z. (2021). Dynamic drought recovery patterns over the Yangtze River Basin. Catena, 201, 105194.
Javed, T., Li, Y., Rashid, S., Li, F., Hu, Q., Feng, H., Chen, X., Ahmad, S., Liu, F., & Pulatov, B. (2021). Performance and relationship of four different agricultural drought indices for drought monitoring in China's mainland using remote sensing data. Science of The Total Environment, 759, 143530.
Jiao, W., Wang, L., & McCabe, M. F. (2021). Multi-sensor remote sensing for drought characterization: current status, opportunities and a roadmap for the future. Remote Sensing of Environment, 256, 112313.
Kogan, F. N. (1995a). Application of vegetation index and brightness temperature for drought detection. Advances in space research, 15(11), 91-100.
Kogan, F. N. (1995b). Droughts of the late 1980s in the United States as derived from NOAA polar-orbiting satellite data. Bulletin of the American Meteorological Society, 76(5), 655-668.
Kogan, F. N. (1997). Global drought watch from space. Bulletin of the American Meteorological Society, 78(4), 621-636.
Kogan, F., Stark, R., Gitelson, A., Jargalsaikhan, L., Dugrajav, C., & Tsooj, S. (2004). Derivation of pasture biomass in Mongolia from AVHRR-based vegetation health indices. International journal of remote sensing, 25(14), 2889-2896.
Liu, L., Gudmundsson, L., Hauser, M., Qin, D., Li, S., & Seneviratne, S. I. (2019). Revisiting assessments of ecosystem drought recovery. Environmental Research Letters, 14(11), 114028.
Martorell, S., DIAZ‐ESPEJO, A., Medrano, H., Ball, M. C., & Choat, B. (2014). Rapid hydraulic recovery in E ucalyptus pauciflora after drought: linkages between stem hydraulics and leaf gas exchange. Plant, Cell & Environment, 37(3), 617-626.
Nemani, R., Hashimoto, H., Votava, P., Melton, F., Wang, W., Michaelis, A., Mutch, L., Milesi, C., Hiatt, S., & White, M. (2009). Monitoring and forecasting ecosystem dynamics using the Terrestrial Observation and Prediction System (TOPS). Remote Sensing of Environment, 113(7), 1497-1509.
Palumbo, I., Rose, R. A., Headley, R. M., Nackoney, J., Vodacek, A., & Wegmann, M. (2017). Building capacity in remote sensing for conservation: present and future challenges. Remote Sensing in Ecology and Conservation, 3(1), 21-29.
Pan, M., Yuan, X., & Wood, E. F. (2013). A probabilistic framework for assessing drought recovery. Geophysical Research Letters, 40(14), 3637-3642.
Parry, S., Prudhomme, C., Wilby, R. L., & Wood, P. J. (2016). Drought termination: Concept and characterisation. Progress in Physical Geography, 40(6), 743-767.
Rhee, J., Im, J., & Carbone, G. J. (2010). Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data. Remote Sensing of environment, 114(12), 2875-2887.
Running, S., Mu, Q., & Zhao, M. (2015). MOD17A2H MODIS/terra gross primary productivity 8-day L4 global 500m SIN grid V006. NASA EOSDIS Land Processes DAAC.
Schwalm, C. R., Anderegg, W. R., Michalak, A. M., Fisher, J. B., Biondi, F., Koch, G., Litvak, M., Ogle, K., Shaw, J. D., & Wolf, A. (2017). Global patterns of drought recovery. Nature, 548(7666), 202-205.
Shahabfar, A., Ghulam, A., & Eitzinger, J. (2012). Drought monitoring in Iran using the perpendicular drought indices. International Journal of Applied Earth Observation and Geoinformation, 18, 119-127.
Tabari, H., Talaee, P. H., Nadoushani, S. M., Willems, P., & Marchetto, A. (2014). A survey of temperature and precipitation based aridity indices in Iran. Quaternary International, 345, 158-166.
Tran, Q. K., Jassby, D., & Schwabe, K. A. (2017). The implications of drought and water conservation on the reuse of municipal wastewater: Recognizing impacts and identifying mitigation possibilities. Water research, 124, 472-481.
Van Loon, A. F., & Laaha, G. (2015). Hydrological drought severity explained by climate and catchment characteristics. Journal of hydrology, 526, 3-14.
Wan, Z., Hook, S., Hulley, G., 2015. MODIS/Terra Land Surface Temperature/Emissivity 8-Day L3 Global 1km SIN Grid V061. NASA EOSDIS Land Processes DAAC.
West, H., Quinn, N., & Horswell, M. (2019). Remote sensing for drought monitoring & impact assessment: Progress, past challenges and future opportunities. Remote Sensing of Environment, 232, 111291.
Wu, B., Ma, Z., & Yan, N. (2020). Agricultural drought mitigating indices derived from the changes in drought characteristics. Remote Sensing of Environment, 244, 111813.
Wu, J., Zhou, L., Liu, M., Zhang, J., Leng, S., & Diao, C. (2013). Establishing and assessing the Integrated Surface Drought Index (ISDI) for agricultural drought monitoring in mid-eastern China. International Journal of Applied Earth Observation and Geoinformation, 23, 397-410.
Xue, B.-L., Guo, Q., Otto, A., Xiao, J., Tao, S., & Li, L. (2015). Global patterns, trends, and drivers of water use efficiency from 2000 to 2013. Ecosphere, 6(10), 1-18. 
Yao, Y., Fu, B., Liu, Y., Li, Y., Wang, S., Zhan, T., Wang, Y., & Gao, D. (2022). Evaluation of ecosystem resilience to drought based on drought intensity and recovery time. Agricultural and Forest Meteorology, 314, 108809.
Yu, P., Zhou, T., Luo, H., Liu, X., Shi, P., Zhao, X., Xiao, Z., Zhang, Y., & Zhou, P. (2022). Interannual variation of gross primary production detected from optimal convolutional neural network at multi‐timescale water stress. Remote Sensing in Ecology and Conservation.
Yu, Z., Wang, J., Liu, S., Rentch, J. S., Sun, P., & Lu, C. (2017). Global gross primary productivity and water use efficiency changes under drought stress. Environmental Research Letters, 12(1), 014016.
Zarei, A. R., Shabani, A., & Mahmoudi, M. R. (2019). Comparison of the climate indices based on the relationship between yield loss of rain-fed winter wheat and changes of climate indices using GEE model. Science of The Total Environment, 661, 711-722.
Zeng, J., Zhang, R., Qu, Y., Bento, V. A., Zhou, T., Lin, Y., Wu, X., Qi, J., Shui, W., & Wang, Q. (2022). Improving the drought monitoring capability of VHI at the global scale via ensemble indices for various vegetation types from 2001 to 2018. Weather and Climate Extremes, 35, 100412.
Zhang, Z., Ju, W., Zhou, Y., & Li, X. (2022). Revisiting the cumulative effects of drought on global gross primary productivity based on new long-term series data (1982–2018). Global Change Biology, 00, 1– 16.
Zhu, Q., Luo, Y., Zhou, D., Xu, Y. P., Wang, G., & Gao, H. (2019). Drought monitoring utility using satellite-based precipitation products over the Xiang River Basin in China. Remote Sensing, 11(12), 1483.