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A new drought index and its application based on geographically weighted regression (GWR) model and multi-source remote sensing data

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Abstract

Drought is the most widespread natural disaster in the world. How to monitor regional drought scientifically and accurately has become a hot topic for many scholars. In this paper, Geographically Integrated Dryness Index (GIDI) was integrated from an assortment source including Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI), Vegetation Condition Index (VCI), and Standardized Precipitation Evapotranspiration Index (SPEI) (as the dependent variable) based on geographically weighted regression method. Besides, the comprehensive drought situation and changing trends in China from 2001 to 2019 were also examined. The results showed that (1) GIDI has excellent performance in monitoring medium- and long-term droughts and the monitoring results shows 2003, 2016, and 2019 were relatively wet years, while 2007, 2009, and 2011 were major drought years, and spring and March were the most frequent droughts season and month, respectively, and (2) except for the middle and upper reaches of the Yellow River and Northeastern China, which have a tendency to become wet, other places have a tendency to fluctuating dry. This study took advantage of simple and efficient methods to integrate existing indices to obtain a new index for monitoring a wider range of droughts, taking into account the physical mechanism of drought formation and the time scale of drought development, so it can scientifically evaluate the spatial and temporal distribution characteristics of drought and changes.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This research was supported by the National Natural Science Foundation of China (No. 41861040).

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Authors and Affiliations

  1. College of Geography and Environmental Science, Northwest Normal University, Lanzhou, 730070, Gansu, China

    Wei Wei, **ng Zhang, Junju Zhou & Haoyan Zhang

  2. College of Social Development and Public Administration, Northwest Normal University, Lanzhou, 730070, Gansu, China

    Chunfang Liu

  3. Gan Su Engineering Research Center of Land Utilization and Comprehension Consolidation, Lanzhou, 730070, Gansu, China

    Chunfang Liu

  4. School of Urban Management, Lanzhou City University, Lanzhou, 730070, Gansu, China

    Binbin **e

Authors
  1. Wei Wei
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  2. **ng Zhang
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  3. Chunfang Liu
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  4. Binbin **e
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  5. Junju Zhou
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  6. Haoyan Zhang
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Contributions

Wei Wei: conceptualization, methodology, and software.

**ng Zhang: data processing, research framework, and paper writing.

Chunfang Liu: supervision and software.

Binbin **e: visualization and investigation.

Junju Zhou: data processing and programing.

Haoyan Zhang: writing including reviewing and editing.

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Correspondence to **ng Zhang.

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Highlights

• A new drought index calculated by satellite data was proposed based on geographically weighted regression model.

• Drought character was monitored on large scale based on remote sensing technology.

• Drought conditions in China were valuated both on month scale and annual scale.

• Drought clusters display was analyzed using Gettis-Ord Gi*.

Appendix

Appendix

In this paper, when exploring the dry and wet conditions of different agricultural areas, the duration of the dry and wet condition events and the maximum dry and wet condition ranks can be extracted by a simple run-length identification method. The detailed dry and wet event results are shown in Appendix Table 5, which can supplement the dry and wet conditions of each region and summarize historical dry and wet events for the policy makers and meteorological departments in the relevant regions.

Table 5 Major wet and dry events in each agricultural region
Full size table

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Wei, W., Zhang, X., Liu, C. et al. A new drought index and its application based on geographically weighted regression (GWR) model and multi-source remote sensing data. Environ Sci Pollut Res 30, 17865–17887 (2023). https://doi.org/10.1007/s11356-022-23200-8

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  • DOI: https://doi.org/10.1007/s11356-022-23200-8

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