Long-Term Homogeneity, Trend, and Change-Point Analysis of Rainfall in the Arid District of Ananthapuramu, Andhra Pradesh State, India
Round 1
Reviewer 1 Report
Comments to authors
Review of Title:
Long Term Homogeneity, Trend and Change-Point Analysis of Rainfall in the Arid District of Ananthapuramu, Andhra Pradesh State, India
Summary:
This study assessed the trends of precipitation using the station data in Ananthapuramu district from 1981 to 2016. The authors discovered significant increasing/decreasing trends in different stations. This manuscript is well written and could be considered for publication after minor revision suggested here:
Introduction: Literature reviews could be expanded that discussed the rainfall, hydrological, climatological analysis of the study region (Ananthapuramu district) or Andhra Pradesh state/Southern India if there is limited research on Ananthapuramu. Figure 1, the caption should read “… and rain gauge stations marked as green dots”. A map scale is needed in the figure. Figure 2, please add map scales. Figure 3, I believe the North-east monsoon series at Singanamala station is also significant, it would be useful to include a subplot for Singanamala in Figure 3. Also, isn’t the Confidence Interval a range with lower and upper bounds? I only see one line in the figure?
Author Response
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Author Response File: 
Author Response.docx
Reviewer 2 Report
In this paper, long term homogeneity, trend and change-point analysis of rainfall in India were performed. A Total of 27 rain gauge locations were considered for trend analysis. To obtain serially independent data series, a serial correlation test was applied. Non-Parametric Mann-Kendall test and Spearman’s rank correlation tests were applied to the independent data series. The method of Sen’s slope was applied to identify the magnitude of the trend. Before publishing, the following key questions should be addressed.
The abstract should start either with the overview of the topic or with the aim of the research. It should not start what has been done. Rainfall data has been collected from 1981 to 2016. It was mentioned in the text. As the duration is the same for all the stations, you do not need to mention 1981 – 2016 in the Table 1 again and again. Record period column in Table 1 can be deleted. 63 sub-district rain gauge data were collected. It is mentioned in line 129. From that 63, how does 27 stations were selected? How di check the quality? For what percentage of missing data for a station you have discarded from the analysis? In lines 168-169, it was mentioned that “The null hypothesis is rejected when ?0 is above the critical value, which depends on the sample size”. How does the critical value is determined? In lines 353-354, it was claimed that, “………..26 series out of 459 series found to be serially correlated at a 90% confidence interval”. Based on what value the 26 series are correlated? Please provide scale bar and coordinates (latitude and longitude) of Figure 1 and Figure 2. In Figure 2, increasing rain trends and decreasing rainfall trends should be sub-numbered as a) and b) respectively. How does the rainy days were selected? i.e. in one day, how much minimum rainfall occurrence has been considered as rainy day? Results of SQMK have been shown in Table 5. However, they were not explained in the text. Please explain the SQMK results in the text.
There are also researches on rainfall prediction based on linear and non-linear modelling approaches using the climate indices. These models development require prior statistical correlation analysis and their trend. Some of them shown below should be included in the literature review. 2019. Long‑term seasonal rainfall forecasting using linear and non‑linear modelling approaches: a case study for Western Australia, Meteorology and Atmospheric Physics, DOI: https://doi.org/10.1007/s00703-019-00679-4.
2018. An attempt to use non-linear regression modelling technique in long-term seasonal rainfall forecasting for Australian Capital Territory, Geosciences, 8, pp. 282(1-12).
"Skilful rainfall forecasts from artificial neural networks with long duration series and single-month optimization," Atmospheric Research, vol. 197, pp. 289-299, Nov 2017.
Author Response
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Author Response File: Author Response.docx
