the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Global High-Resolution Drought Indices for 1981–2022
Solomon H. Gebrechorkos
Jian Peng
Ellen Dyer
Diego G. Miralles
Sergio M. Vicente-Serrano
Chris Funk
Hylke E. Beck
Dagmawi T. Asfaw
Michael B. Singer
Simon J. Dadson
Abstract. Droughts are among the most complex and devastating natural hazards globally. High-resolution datasets of drought metrics are essential for monitoring and quantifying the severity, duration, frequency and spatial extent of droughts at regional and particularly local scales. However, current global drought indices are available only at a coarser spatial resolution (>50 km). To fill this gap, we developed five high-resolution (5 km) gridded drought records based on the Standardized Precipitation Evaporation Index (SPEI) covering the period 1981–2022. These multi-scale (1–48 months) SPEI indices are computed based on monthly precipitation (P) from the Climate Hazards group InfraRed Precipitation with Station data (CHIRPS, version 2) and Multi-Source Weighted-Ensemble Precipitation (MSWEP, version 2.8) and potential evapotranspiration (PET) from the Global Land Evaporation Amsterdam Model (GLEAM, version 3.7a) and Bristol Potential Evapotranspiration (hPET). We generated four SPEI records based on all possible combinations of P and PET datasets: CHIRPS-GLEAM, CHIRPS-hPET, MSWEP-GLEAM, and MSWEP-hPET. These drought records were evaluated globally and exhibited excellent agreement with observation-based estimates of SPEI, root zone soil moisture, and vegetation health indices. The newly developed high-resolution datasets provide more detailed local information and be used to assess drought severity for particular periods and regions and to determine global, regional, and local trends, thereby supporting the development of site-specific adaptation measures. These datasets are publicly available at the Centre for Environmental Data Analysis (CEDA, https://dx.doi.org/10.5285/ac43da11867243a1bb414e1637802dec) (Gebrechorkos et al., 2023).
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Solomon H. Gebrechorkos et al.
Status: final response (author comments only)
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RC1: 'Comment on essd-2023-276', Anonymous Referee #1, 04 Aug 2023
This manuscript released the global 5km drought indices for 1981-2022. This is a very meaningful work because it is a high-resolution dataset of drought. However, some other problems in the manuscript are still concerned in the following:
- In Figure 5, I suggest the authors to replace (e) with North America.
- A flow chart of generating this dataset could help the future readers.
- More details on the method should be exposed.
- One advantage of this dataset is high resolution. Therefore, the examples of spatial details could be shown and compared with other datasets.
- The organization of this manuscript should be added to the end of the introduction.
Citation: https://doi.org/10.5194/essd-2023-276-RC1 - AC3: 'Reply on RC1', Solomon Hailu Gebrechorkos, 01 Oct 2023
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RC2: 'Comment on essd-2023-276', Anonymous Referee #2, 27 Aug 2023
The manuscript tries to develop five high-resolution (5 km) gridded drought records based on the Standardized Precipitation Evaporation Index (SPEI), which is essential and exciting for related fields. The structure of the paper is clear and the research questions are clear. However, I think the introduction part needs to improve, and more details about the development of such a field need to be described. The result and discussion should be separated and focus on the main result of the study. Besides, the structure of the result needs to improve significantly, such as in sections 3.1 to 3.3. A significant polish is needed. Others, such as line 86-87 is it correct?
Citation: https://doi.org/10.5194/essd-2023-276-RC2 - AC2: 'Reply on RC2', Solomon Hailu Gebrechorkos, 01 Oct 2023
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RC3: 'Comment on essd-2023-276', A. D. Mehr, 28 Aug 2023
The manuscript produced global high-resolution (0.05°) and long-term (1981-2022) SPEI datasets. It is suitable for publication after a minor correction.
Line 191: Replace Table 1 with Table 2.
Line 192: Please justify the selection of a log-logistic probability distribution among others. How you attained the model parameters?
Remove the underlined sign from the values given in Table 2.
Citation: https://doi.org/10.5194/essd-2023-276-RC3 - AC1: 'Reply on RC3', Solomon Hailu Gebrechorkos, 01 Oct 2023
Solomon H. Gebrechorkos et al.
Data sets
Hydro-JULES: Global high-resolution drought datasets from 1981-2022 S. Gebrechorkos, J. Peng, E. Dyer, D. G. Miralles, S. M. Vicente-Serrano, C. Funk, H. Beck, D. Asfaw, M. Singer, and S. Dadson https://catalogue.ceda.ac.uk/uuid/ac43da11867243a1bb414e1637802dec
Solomon H. Gebrechorkos et al.
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