The most extreme rainfall erosivity ever recorded in China: The "7.20" rainstorm in Henan Province
Abstract. Severe water erosion occurs during extreme storm events. Such an extreme storm occurred in Zhengzhou in central China on 20 July 2021 (the “7.20” rainstorm). The magnitude and frequency of occurrence of this storm event were examined in terms of its erosivity values. To contextualize this extreme event, hourly rainfall data from 2420 automatic meteorological stations in China from 1951 to 2021 were analyzed to: (1) characterize the spatial and temporal distribution of rainfall and rainfall erosivity of the “7.20” rainstorm, (2) evaluate the average recurrence interval of the maximum daily and event rainfall erosivity, and (3) establish the geographical distribution of the maximum daily and event rainfall erosivity in China. The center of the “7.20” rainstorm moved from southeast to northwest in Henan province, and the most intense period of rainfall occurred in the middle and late stages of the storm. Zhengzhou meteorological station happened to be aligned with the center of the storm, with a maximum daily rainfall of 552.5 mm and a maximum hourly rainfall intensity of 201.9 mm"∙" h-1. The average recurrence interval of the maximum daily rainfall erosivity (43,354 MJ·mm·ha-1· h-1) and the maximum event rainfall erosivity (58 874 MJ·mm·ha-1·h-1) was estimated to be 109 079 and 154 154 years, respectively, assuming the generalized extreme value distribution, and these were the maximum rainfall erosivity ever recorded among 2420 meteorological stations in mainland China. The “7.20” rainstorm suggests that the most erosive of storms does not necessarily occur in the wettest places in southern China, and it can occur in mid-latitude around 35 °N with a moderate mean annual precipitation of 549.2 mm in Zhengzhou meteorological station.
Yuanyuan Xiao et al.
Status: final response (author comments only)
RC1: 'Comment on hess-2022-351', Anonymous Referee #1, 11 Oct 2022
- AC1: 'Reply on RC1', Shuiqing Yin, 01 Feb 2023
RC2: 'Comment on hess-2022-351', Anonymous Referee #2, 25 Nov 2022
- AC2: 'Reply on RC2', Shuiqing Yin, 01 Feb 2023
RC3: 'Comment on hess-2022-351', Anonymous Referee #3, 12 Dec 2022
- AC3: 'Reply on RC3', Shuiqing Yin, 01 Feb 2023
Yuanyuan Xiao et al.
Yuanyuan Xiao et al.
Viewed (geographical distribution)
In the submitted paper authors investigate the characteristics of one extreme rainfall event (20th July, last year) in comparison to spatial and temporal rainfall erosivity characteristics in China. The paper is interesting and within the scope of the HESS journal. However, there are several points that could be improved.
Firstly, the reported rainfall amounts are relatively extreme. Hence, more details about the measuring equipment used to measure rainfall (and accuracy of these instruments) should be reported since this could have an effect on the measured rainfall amounts (can at least for this extreme event uncertainty be estimated).
Secondly, the reported results are sensitive to the selected empirical equation used to calculate the energy. Hence, are there any other data available (e.g., optical disdrometer) measurements that could be used to validate these calculations in order to make less uncertain rainfall erosivity estimates?
Thirdly, the reported frequency analysis is missing uncertainty estimation (confidence intervals).
Fourthly, I am not sure what is the purpose of envelope curves (see also specific comment below). I would suggest to add some specific details about the impact of this extreme event on soil erosion (some measurements perhaps, if available) or at least on the sediment concentrations in rivers (some measurements) or something similar. Hence, could you say that extreme erosive events (with return period over 100,000 years) also leads to soil erosion rates with similar recurrence interval (the same for sediment transport rates).
Finally, some specific comments are provided below.
Figure 1: Maybe you could more clearly indicate Henan province in this figure.
Equation 1: You should cite the original source of this equation. Additionally, what is the sensitivity of results with respect to the selection of equations (1) and (4).
Lines 132-135: Please provide more details about interpolation method used.
Equation 5: Please provide the original reference.
Line 147: Shape, scale and location parameters and not position parameter.
Equations (6)-(7): Please double check it, I am not sure if these are correctly written.
Equations (8)-(17): I am not sure if these need to be reported in a paper about rainfall erosivity. More details about the rainfall erosivity calculation procedure and measurements could be provided instead.
Line 186: “It showed”. Is this referring to Want et al. (2016) study?
Figure 3: It is not clear how was Figure 3a created, is this station-based data interpolated or this is from other source (radar)?
Figure 4: The same as for Figure 3.
Figure 5d: Here these results are probably quite sensitive to the selection of the empirical equations used to calculate the rainfall erosivity. It would be nice to elaborate a bit about this issue.
Table 1: Why ha? I suggest to use either km2 or 1000*km2 or something similar. Also in this table you are comparing areal rainfall erosivity with station-based (gauge, probably 200 cm2 or something similar).
Figure 6: Here you clearly need the conference intervals. I am not sure if you could just say that the return period of this event is exactly 154,154 years. Additionally. You should note that in (flood) frequency analysis there are usually some specific rules about the longest return period that could be estimated based on specific data length (sample size). Different rules can be found in the literature. At least some discussion about this should be added.
Section 3.2.2: I am sorry but I do not completely understand the purpose of defining these envelope curves? How could these be used? It is clear that the shape of the “curve” is defined by the extreme events (as authors also indicate in the last sentences of this paragraph) that are a result of stochastic process.