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the Creative Commons Attribution 4.0 License.
| 18 Sep 2024
Global Assessment of Socio-Economic Impacts of Subnational Droughts: A Comparative Analysis of Combined Versus Single Drought Indicators
Abstract. The accurate assessment of the propagation of drought hazards to socio-economic impacts poses a significant challenge and is still less explored. To address this, we analyzed a sub-national disaster dataset called the Geocoded Disaster (GDIS) and evaluated the skills of multiple drought indices to pinpoint drought areas identified by GDIS. For the comparative analysis, a widely used Standardized Precipitation Index (SPI), Normalized Difference Vegetation Index (NDVI), Standardized Soil Moisture Index (SSI), and Standardized Temperature Index (STI) were globally computed at the subnational scale. In addition, we developed a novel Combined Drought Indicator (CDI), which was generated by a weighted average of meteorological and agricultural anomalies. Out of 2142 drought events in 2001–2021 recorded by GDIS, NDVI, SSI, SPI, and STI identified 1867, 1770, 1740, and 1680 drought events, respectively. In terms of the skill to cover GDIS-documented drought events, CDI outperformed the other single-input-based drought indices and identified 1885 events. This emphasizes the importance of using CDI to evaluate socio-economic drought risks and prioritize areas of greater concern.
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RC1: 'Comment on hess-2024-245', Jasmin Heilemann, 28 Dec 2024
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Manuscript title: Global Assessment of Socio-Economic Impacts of Subnational Droughts: A Comparative Analysis of Combined Versus Single Drought Indicators
General comments:
In general, it is an interesting paper within the scope of the journal that addresses the highly relevant issue of detecting socio-economic impacts of drought using biophysical indicators on a global scale. The paper proposes a Combined Drought Indicator (CDI), which is constructed by using single-input based drought indices (SPI, NDVI, SSI, STI) and performing a Principal Component Analysis (PCA). The authors show that the CDI outperforms the single-input drought indices in its ability to capture drought events observed in the global GDIS dataset.
While the paper presents a very relevant analysis with noteworthy results, what is currently missing from the paper is a discussion of the benefits of using the PCA method to construct the CDI. This discussion should include details of the benefits of PCA for the CDI, as well as a discussion of the applicability of the CDI for (regionalized) drought impact monitoring and prediction. Including this in the manuscript would significantly enhance the paper and give greater significance to the implications, with potential applications of the CDI beyond this paper. My specific comments are listed below.
Specific comments:
- Title: Global analysis of sub-national droughts: “Sub-national” droughts sound misleading, as droughts are not constrained by national borders. If possible, rephrase as “droughts at sub-national scale”, or similar.
- Abstract: “Out of 2142 drought events in 2001-2021 recorded by GDIS, NDVI, SSI, SPI, and STI identified 1867, 1770, 1740, and 1680 drought events, respectively. […] CDI outperformed the other single-input-based drought indices and identified 1885 events.” Consider adding percentages or otherwise present convincing quantitative results that show the superiority of the CDI more directly.
- Fig. 1: It is a bit confusing to frame the “wet” conditions as drought categories. Would it be possible to find a different notion, e.g. moisture? Or Drought/Wetness category?
- Lines 116-125: The topic of the paper are socio-economic impacts of droughts. However, socioeconomic impacts manifest very differently in different sectors. E.g., in the introduction, you mention urban areas/water shortages in dams (lines 25-36). The drought indices you chose (SPI, NDVI, SSI, STI) are mostly useful for the ag sector. Please elaborate on how this affects the results, and if/how the CDI can capture socio-economic impacts in non-ag sectors, e.g. urban areas.
- Section 3.2: Here, I miss a description of the reasons why the PCA method was chosen to construct the PCA. This is the main innovation of the paper, and should therefore be featured more prominently, also in the introduction. E.g., explain what the added value of the PCA is compared to other techniques to compute a CDI. Why are regression-based approaches not used? (e.g. is it an advantage that the PCA does not have a dependent variable?)
- Lines 227-229: What is the total number of observations for the single-based drought indices used in the PCA? Does this number meet the requirements of the no. of observations usually applied in PCA? Please specify.
- Lines 258: “…has been widely accepted in previous work.” Which previous work? Please provide citations. Please extend this to the other text when you mention previous work without giving references.
- Lines 258-265: The thresholds of the drought indices used for detecting the drought impacts listed in the GDIS dataset are very crucial, though the explanation remains too vague (it’s a simple process, but I had to read over the section several times to understand this). Please make this process more explicit, e.g. via adding a table. Also, I miss a clear explanation of how the spatial scales between the gridded drought indices and the sub-national GDIS events are matched for the detection of drought impacts (is it counted as drought event if more than half of the pixels in the GDIS area show a deviation below the drought threshold? Or do you first calculate the average of the drought indices across all grid points and then compare it with the thresholds?)
- Line 327: Please specify why you chose April as the month for displaying the PCA results. Does it represent the yearly average best? How important are intra-annual fluctuations? April is not a typical drought month in the northern or southern hemisphere.
- Table 2: You show the false-negative (when a GDIS drought event existed, but the drought index did not indicate a drought event) in the table as “not observed”. Likewise, what is the rate of false-positive cases (how often did the drought index indicate a drought event not reported in the GDIS?)? You discuss this in the text (lines 390ff), but it would be beneficial for the reader to understand the magnitude of these cases in numbers.
- Discussion: In the discussion, an important point would be how the CDI could be used/applied for drought impact forecasting and/or policy-making. Could the CDI (computed via PCA) help to improve drought impact forecasting? How does the regionalization of the CDI affect the capacity to be used for that purpose?
- Lines 570ff: You could additionally mention that text mining is a research field potentially providing alternative impact databases for droughts next to the GDIS.
- Line 550: “despite experiencing higher climatic anomalies, developed nations are less likely to be socio-economically affected …”. This statement needs to be specified. It needs to become clear that the higher climatic anomalies relate to the local climate, and are not compared in absolute terms. A small anomaly in an already dry climate can provoke much more negative drought impacts compared to a larger anomaly in a wetter climate. Otherwise, this suggests that climate/drought impacts in developed nations are higher than in developing countries, which is not the case.
- Lines 583-584: “Moreover, there are other methods and techniques that could be used to compute weights in CDI …” Like which methods? Please specify and give a short reason why they could be apt.
Technical corrections:
- Line 121: “combine drought indicator” -> correct to “combined”
- 3a: The legend in this panel is missing.
- 9: Second panel: “CDI vs. TCI”, please correct to “CDI vs. STI”
- Appendix 2: This figure shows four times the same plot. This should be corrected.
Citation: https://doi.org/10.5194/hess-2024-245-RC1
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