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Integration of multiple drought indices using a triple collocation approach

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Abstract

Three drought indices (the Standardized Precipitation Index [SPI], Evaporative Stress Index [ESI], and Soil Moisture Anomaly Index [SMAI]) were integrated using triple collocation (TC) to produce the merged drought index (MDI). The new index was then compared with the Gravity recovery and climate experiment (GRACE)–Drought severity index (DSI), a comprehensive drought index reflecting storage variation in surface, sub-surface, and groundwater levels across East Asia and Australia, from 2003 to 2014. Before merging the three drought indices, their performance was analyzed. The mean correlation between the three drought indices and the GRACE–DSI indicated that the performance of the ESI was superior to the SMAI and SPI over the study areas. In terms of average weight results using the merging approach, the ESI was associated with larger weights (0.372 and 0.359) and contributions (43% and 38%), followed by the SMAI and SPI for East Asia and Australia, respectively. The SMAI achieved a similar weight (0.360) and contribution (39%) as the ESI across Australia. To determine the robustness of the MDI as estimated by TC weights, we evaluated the MDI and the reference GRACE-DSI with respect to documented drought records in the study areas. The MDI produced trends similar to those of the GRACE-DSI in Australia, while MDI and GRACE-DSI trends were not similar in East Asia. The correlation between the MDI and GRACE-DSI in Australia (0.41–0.62) was also higher than in East Asia (0.24–0.32) during the study periods. This discrepancy was due to the conceptual difference in that MDI reflects the near-surface water storage variation while GRACE-DSI reflects the variation of deeper water. Nevertheless, our results showed that the MDI out-performed single drought indices and was able to capture documented drought events across the study regions. This suggests that merging different drought indices into a single tool can better represent droughts, and may be a valuable approach for water resource management.

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Abbreviations

SPI:

Standardized Precipitation Index

ESI:

Evaporative Stress Index

SMAI:

Soil Moisture Anomaly Index

MDI:

Merged Drought Index

TWS:

Total Water Storage

DSI:

Drought Severity Index

TC:

Triple Collocation

GRACE:

Gravity Recovery and Climate Experiment

P:

Precipitation

ET:

Evapotranspiration

SM:

Soil Moisture

GLEAM:

Global Land Evaporation Amsterdam Model

ESA CCI:

The European Space Agency’s Climate Change Initiative

PERSIANN:

Remotely Sensed Information using Artificial Neural Networks

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Acknowledgements

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (NRF-2018R1D1A1B07049029). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2019R1A2B5B01070196). This research was supported by a grant (2021-MOIS37-002) of “Intelligent Technology Development Program on Disaster Response and Emergency Management” funded by Ministry of Interior and Safety (MOIS, Korea). This research was supported by a grant (2021-MOIS37-002) of “Intelligent Technology Development Program on Disaster Response and Emergency Management” funded by Ministry of Interior and Safety (MOIS, Korea). The authors thank the teams from GRACE (http://www2.csr.utexas.edu/grace) and GLEAM (https://www.gleam.eu/) products for making their datasets publicly available. The authors would like to express special thanks to the Center for Hydrometeorology and Remote Sensing (CHRS; https://chrsdata.eng.uci.edu/) at the University of California, Irvine (UCI), European Space Agency (ESA) Climate Change Initiative (CCI) (https://www.esa-soilmoisture-cci.org/) for providing the PERSIANN and ESA CCI SM products, respectively.

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  1. Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea

    Jongjin Baik

  2. University Space Research Association, Columbia, MD, USA

    Jongmin Park

  3. Department of Water Resources, Graduate School of Water Resources, Sungkyunkwan University, 2066, Seobu-ro, Suwon, Gyeonggi-do, Republic of Korea

    Yuefeng Hao

  4. School of Civil, Architectural Engineering and Landscape Architecture, Sungkyunkwan University, 2066, Seobu-ro, Suwon, Gyeonggi-do, Republic of Korea

    Minha Choi

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Baik, Jongjin: Conceptualization, Methodology, Writing-Original draft preparation; Park, Jongmin: Conceptualization, Software; Hao, Yuefeng: Visualization, Writing-Original draft preparation; and Choi, Minha: Supervision Writing- Reviewing and Editing,

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Baik, J., Park, J., Hao, Y. et al. Integration of multiple drought indices using a triple collocation approach. Stoch Environ Res Risk Assess 36, 1177–1195 (2022). https://doi.org/10.1007/s00477-021-02044-7

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