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Evaluation of two satellite-based products against ground-based observation for drought analysis in the southern part of Iran

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  • Seyedeh Mahboobeh Jafari

    (University of Northern British Columbia)

  • Mohammad Reza Nikoo

    (Shiraz University)

  • Maryam Dehghani

    (Shiraz University)

  • Mohammadali Alijanian

    (University of Isfahan)

Abstract

Water stress or more specifically drought assessment plays a key role in water management, especially in extreme climate conditions. Basically, globally gridded satellite-based precipitation products are potential sources of data as alternatives for ground-based measurements. However, for a reliable application, they should be evaluated in different regions. In this paper, two satellite-based rainfall products, namely Modern-Era Retrospective Analysis for Research and Applications (MERRA)-Land and Global Land Data Assimilation System-2 (GLDAS-2), have been evaluated against ground-based observations in terms of precipitation and their application for drought analysis. At first, the coarse-resolution MERRA-Land is downscaled to the finer resolution of interest for better comparison. After comparison of these datasets against ground-based observations in terms of precipitation, it is concluded that MERRA-Land can better estimate precipitation. Then, the nonparametric SPIs at various timescales are derived to analyze how well MERRA-Land performs in drought monitoring. Different categorical and statistical error indices are used to assess the efficiency of MERRA-Land in capturing drought events. The results revealed that the downscaled MERRA-Land data can properly detect short-term and mid-term drought events known as agricultural and meteorological droughts throughout the study area, respectively. In addition, drought maps show that the majority of lands experience mid-term scale drought which are in agreement with ground-based observations. The methodology adopted in this study can be applied in areas lacking in rain-gauge stations which significantly extend current capabilities for drought monitoring and early warning systems.

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  • Seyedeh Mahboobeh Jafari & Mohammad Reza Nikoo & Maryam Dehghani & Mohammadali Alijanian, 2020. "Evaluation of two satellite-based products against ground-based observation for drought analysis in the southern part of Iran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 102(3), pages 1249-1267, July.
    • Handle: RePEc:spr:nathaz:v:102:y:2020:i:3:d:10.1007_s11069-020-03965-2
    DOI: 10.1007/s11069-020-03965-2
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    References listed on IDEAS

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    1. Tayeb Raziei & Isabella Bordi & Luis Pereira, 2011. "An Application of GPCC and NCEP/NCAR Datasets for Drought Variability Analysis in Iran," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(4), pages 1075-1086, March.
    2. Tayeb Raziei & Bahram Saghafian & Ana Paulo & Luis Pereira & Isabella Bordi, 2009. "Spatial Patterns and Temporal Variability of Drought in Western Iran," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(3), pages 439-455, February.
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    Cited by:

    1. Soheila Pouyan & Mojgan Bordbar & Venkatesh Ravichandran & John P. Tiefenbacher & Mehrzad Kherad & Hamid Reza Pourghasemi, 2023. "Spatiotemporal monitoring of droughts in Iran using remote-sensing indices," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(1), pages 1-24, May.
    2. Mehmet Dikici, 2022. "Drought Analysis for the Seyhan Basin with Vegetation Indices and Comparison with Meteorological Different Indices," Sustainability, MDPI, vol. 14(8), pages 1-17, April.

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