the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Flash drought characteristics based on three identification methods in the North China Plain, China
Abstract. Flash drought (FD) is an onset and intensify rapidly type of drought that can harm the terrestrial ecosystem, and cause economic and agricultural losses. The North China Plain (NCP) is an important agricultural region in China where sustainable development is restricted by the frequent droughts and insufficient water resources. Coping with FD requires an understanding of the FD onset and identification in the NCP. Based on root zone soil moisture (RZSM), standardized evaporative stress ratio (SESR) and multiples of mean evaporative stress ratio (MESR), this study identified the FD events in the NCP from 1981 to 2022, revealed the FD characteristics such as frequency, duration, severity and intensity, explored the temporal and spatial trend, determined the FD hotspots, and demonstrated the impact of FD identification thresholds on the FD identification. The frequency distributions of FD events identified by RZSM, SESR, and MESR are all high in the central and northern NCP and low in the southern, whereas the total duration is high in the southern and eastern NCP and low in the northern. As the FD intensity increases, the onset stage lengthens, the recovery stage shortens, the total duration reduces, and the severity declines. The FD affected areas from various FD identification methods exhibit significant and similar seasonal variations, primarily occurring from May to August. Besides, NCP is prone to extreme and exceptional FDs. The NCP has a decreasing tendency of the FD characteristics, and three hotspots with frequent and serious FD events are identified in the northwestern, eastern and southwestern NCP. The FD frequency is also significantly influenced by the thresholds in the identification methods. This study provides insights into the FD characteristics in the NCP, and clarifies its trend and hotspots, which may be valuable for FD understanding and adaptation.
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RC1: 'Comment on hess-2024-185', Anonymous Referee #1, 15 Nov 2024
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The following is a review of the manuscript titled "Flash drought characteristics based on three identification methods in the North China Plain, China," submitted to the Journal of Hydrology and Earth System Sciences. In this manuscript, the authors developed a new flash drought identification method, a modified version of SESR, called MESR. Additionally, the authors identified flash droughts (FDs) in the NCP from 1981 to 2022 using RZSM, SESR, and MESR, analyzing their frequency, duration, severity, intensity, spatial trends, and seasonal patterns. FD hotspots were found in the northwestern, eastern, and southwestern NCP, with most events occurring between May and August. The findings highlight the influence of identification thresholds on FD frequency and offer insights to support FD understanding and adaptation strategies.
Overall, this paper is well-written, well-structured, and fits within the aims and scope of this journal. However, I have some major and minor concerns that need to be addressed before it is publishable. Below are my comments:
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### Major Comments
1. Uncertainty in Data Sources:
  The study implemented reanalysis ET, PET, and SM data. Due to the uncertainty inherent in these datasets, it is challenging to confirm whether the proposed MESR methodology accurately captures flash drought events. While the authors evaluated the methodology using three historical drought events (1981, 1983, and 1989), the nature of these events (flash or conventional drought) remains unclear, and their characteristics are not provided. Comparing MESR performance with recent, well-documented flash drought events would strengthen the reliability of the findings.
2. Spatial Heterogeneity and Climate Regimes:Â
  The study area is semi-humid, and identifying flash droughts requires consideration of background aridity and land cover impacts. One concern is the spatial heterogeneity in FD frequency detected by MESR, with significant differences between adjacent pixels scattered across the area, and such patterns are not evident in the other two methods. Evaluating MESR’s performance in different climate regimes, such as semi-arid or sub-humid regions, using the Aridity Index (AI), would improve the robustness and generalizability of this research.
3. Justification of Methodology:
  The paper lacks a clear explanation for multiplying ESR values by their mean (climatological or long-term) to create MESR. This difference appears to be a primary factor distinguishing MESR from SESR in terms of frequency. The authors should further clarify and justify this decision in the main text for better understanding and transparency.
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**Minor Comments**
Lines 26-27: Replace "becomes" with "become" to align with the plural subject "droughts."
Line 40: Remove "respectively" for clarity.
Lines 84-85: SERS is a method for identifying flash droughts, so it is better to use the word 'using' in this sentence. Here is the revised version:
"However, the SESR application had some problems. When identifying FD using SESR, both SESR and the change in SESR''
Line 96: It would be better to rephrase this sentence for better clarification, and start with 'In this study, ... ' Here is the revised version:
In this study, a new method based on the ......
Line 111: One of the most important factors in characterizing droughts is considering background aridity. One of my concerns regarding this paper is that the study area is mainly a semi-humid region. The baselines of SM percentile or SESR vary across different climate regimes.
Lines 120-127: Soil moisture, ET, and PET datasets used in this study are reanalysis and there is uncertainty in these data sets. Additionally, different reanalysis datasets have great differences in their values, so this study can benefit from using different SM, ET, and PET datasets. GLEAM, or MERRA2?
Lines 182-183: what is the main reason for dividing ESR value by its mean?
Although there are some benefits to doing this, I am concerned that normalizing ESR by the mean could reduce the sensitivity of the method in detecting flash droughts, especially in periods or regions with naturally higher evapotranspiration stress. The underlying issue with normalizing ESR by its mean is that it reduces the relative magnitude of MESRâ‚€ values when the baseline ESR is high, effectively making it harder to detect rapid changes. If the authors have any explanation for this, it would be helpful to include it in the main text.8- Line 184: It is climatological mean or long-term mean?
Line 184-191: Specify whether the mean is climatological or long-term.
Line 191: Again, climatological mean or long-term mean?
Line 205: Likely a typo; change FD (SESR) to FD (MESR).
Line 213: This sentence seems to be uncompleted!
Line 242: Clarify that "mean value" refers to a single point, and specify whether it is climatological or long-term.
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Lines 259-264: Although the discussion in this paragraph is statistically correct, I believe an important point has been overlooked: the most critical aspect of flash droughts is their rapid onset and intensification. As long as the SESR method can detect this characteristic of flash droughts, the exact value of the 50th percentile is less significant, particularly in humid or hyper-humid climate regimes where the ESR baseline might be higher than in arid or semi-arid regions. In regions with lower background aridity, the 50th percentile of SESR might be higher than zero, primarily due to dense vegetation. In such cases, it is crucial to capture the rapid reduction in SM/ESR that leads to flash drought.
Mukherjee and Mishra (2022) demonstrated that using different indicators, such as ESR and soil moisture, to identify flash droughts results in varying frequencies across different climate regimes. This is an important factor to consider, especially in this study.
Mukherjee, S., & Mishra, A. K. (2022). Global Flash Drought Analysis: Uncertainties From Indicators and Datasets. Earth's Future, 10(6), e2022EF002660. https://doi.org/10.1029/2022EF002660
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Figure 4, first row: As shown in this figure, the frequency of events detected by MESR is significantly lower than that detected by SESR. It would be helpful if the authors could show how many events detected by MESR are similar to those detected by SESR. Since both methods use ESR, it would be beneficial to compare these methods. One possible approach would be to compare binary time series of drought events detected by these methods and calculate their overlap.
Line 305: Is 5 pentads for the flash drought onset stage not too long? Flash droughts are characterized by their rapid onset, and 5 pentads is not particularly rapid. I would suggest setting a limitation on the duration of the flash drought onset stage in this research, similar to the RZSM method, which has an onset duration limitation. Additionally, are Figures 4 and S1 showing the average duration? If so, this implies that in some events, the onset stage of the flash drought is longer than 5 pentads!
Line 321: The maps of severity, duration, and intensity of flash droughts detected by MESR are almost evenly distributed, especially compared to the other two methods. Although there are some concentrated areas, overall, these maps appear evenly distributed, suggesting that this method does not respond significantly to regional characteristics, unlike the RZSM and SESR methods. Are there any reasons for this? If so, it would be helpful to discuss this further in the main text.
Lines 325-326: Are there any specific characteristics in the northern NCP that cause this change (e.g., land cover or background aridity)? It would be helpful if the authors could justify the spatial differences in the characteristics of droughts using this method.
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Line 361: A considerable number of flash droughts detected by MESR are categorized as grade 4, but they are not confirmed or detected by the other two methods. Moreover, this study relies on reanalysis ET, PET, and SM data, which contain uncertainties. Therefore, it is unclear whether the FD4 events detected by this method are actual flash drought events.
Line 366: "Decrease trend" should be changed to "decreasing trend" for grammatical correctness.
Line 387: "Increases" should be changed to "increase" to match the plural subject ("duration and severity").
Line 389: "Increases" should be changed to "increase" to match the plural subject ("duration and severity").
Line 399: Are these drought events categorized as flash drought or they are conventional drought events?
Lines 395-495: I am not sure if this section can provide any reliable results, mainly because it is unclear whether those events are flash droughts or not, and if they are, how they were detected. Moreover, this study utilizes reanalysis data, which have inherent uncertainties. Therefore, this study could benefit from a comparison between these three methods and some real flash drought events in the study area that occurred recently and have reliable information on their characteristics.
Figure 9: What is the meaning of 'The color bands represent the pentads with FD events from June to August of that year.' For example, in 1989 event, what does it mean to have 44 (max) FD events from June to August in each pixel?
Lines 418-419: Do you have any reference for this? What are the main characteristics of these regions, mainly in term of land cover?
Line 448: It would be better if the authors could start this section with a sentence stating that they developed MESR method in this study.
Line 476: change 'decrease' to 'decreasing trend'
Line 477: change 'slowly' to 'slow'
Lines 489-490: The 50th percentile of SESR is not greater than zero in all regions. As shown in Figure S7, in the vast majority of regions, it is around zero or even lower. Moreover, in regions with a higher evaporation baseline, the 50th percentile is slightly higher than zero, but this is not a disadvantage of the SESR method. Perhaps one reason your method shows spatially heterogeneous frequencies is this, which could lead to missing some rapidly developing events, particularly in wetter climate regimes.
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Lines 498-499: Actually, there are three differences. In addition to what you have mentioned (PDF fitting and threshold), your method multiplies ESR by its mean, whereas in the original SESR, ESR anomalies are standardized. Please include this in the main text.
Line 528: Again, these historical events are not reliable, mainly because as the authors mentioned that there is no detailed information on these events, and it is unclear whether they were flash droughts or not. The combination of uncertainty in the reanalysis datasets used in this study and the lack of adequate information on these events cannot lead to a reliable conclusion.
Lines 447-448: Please rephrase this sentence as follows: "There are notable differences in the spatial distribution of severity among the three FD methods."
Lines 545-549: This study could benefit from a deeper discussion on the main reasons for changes in the frequencies and characteristics of FD events. Such a discussion could incorporate land cover or the background aridity of the study area.
Lines 561-565: Isn't it obvious? As the depth of soil moisture increases, the impact of flash droughts on SM can become less pronounced. Moreover, this study used reanalysis SM data, so finding these trends in reanalysis datasets is not surprising!
Citation: https://doi.org/10.5194/hess-2024-185-RC1
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