Next Article in Journal
Three-Dimensional Numerical Method for Simulating Large-Scale Free Water Surface by Massive Parallel Computing on a GPU
Next Article in Special Issue
Simulation of Non-Gaussian Correlated Random Variables, Stochastic Processes and Random Fields: Introducing the anySim R-Package for Environmental Applications and Beyond
Previous Article in Journal
Investigation into Groundwater Resources in Southern Part of the Red River’s Delta Plain, Vietnam by the Use of Isotopic Techniques
Previous Article in Special Issue
Development of a Non-Parametric Stationary Synthetic Rainfall Generator for Use in Hourly Water Resource Simulations
 
 
Article
Peer-Review Record

Innovative Variance Corrected Sen’s Trend Test on Persistent Hydrometeorological Data

Water 2019, 11(10), 2119; https://doi.org/10.3390/w11102119
by Wenpeng Wang 1,2,*, Yuelong Zhu 1, Bo Liu 3, Yuanfang Chen 3 and Xu Zhao 4
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Reviewer 3: Anonymous
Reviewer 4: Anonymous
Water 2019, 11(10), 2119; https://doi.org/10.3390/w11102119
Submission received: 29 August 2019 / Revised: 7 October 2019 / Accepted: 9 October 2019 / Published: 12 October 2019

Round 1

Reviewer 1 Report

The article addresses some of the challenges in the trend analysis of hydrological data by explaining and applying the modified Sen’s trend test on some of the hydrological time series variables in China. The manuscript is well-written, provides a comprehensive and detailed explanation of methods, and fits within the scopes of the Water journal. However, there is less emphasis on the application of methods in hydrological modeling, in particular, methods have been applied to a limited real world case and results of the statistical methods were not well interpreted with respect to the physical variables and/or dynamics of the system (please see major comments). Such efforts will provide more evidence for the readers of the manuscript regarding the effectiveness and applicability of the proposed methods for hydrological time series data.

 

Major Comments:

Lines 439-443: What is the physical correspondence of these different datasets to each other? That relationship could be used for interpretation of trend and other statistical analyses. Your conclusion and result sections lack such discussions.

Also, according to the title you are focused on hydrological data, however, the selected variables are known to be meteorological variables rather than hydrological variables. Figure 10: Please discuss the results of autocorrelation with respect to the trend analysis. In this part of manuscript, you can emphasize on how these techniques may support each other and be used for interpretation of physical data/systems? Also, the quality of Figure 10 should be improved before publication (e.g., increase marker and text size).

 

Table 4 (and also Figure 10): What if you select monthly data for your analyses rather than annual averages (of the selected variables, if available, or any other real-world hydrometeorological data)? There is more seasonality in monthly data that can affect the trend analysis.

 

Minor Comments:

Title: trend test Line 16: What do you mean by “this study”? Your current study? The sentence is vague. Line 284: acronyms starting with F should precede with an: an FGN Figure 10a: Are those trends significant? Please show the statistics.

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Reviewer 2 Report

Dear Authors,

The authors made an important attempt to improve the existing Sen test by introducing variance correction statistics trend test.
They show on the basis of their own analyzes that the current Sen test strengthens and overstates the importance of trends. A detailed analysis of the authors indicates the places where appropriate corrections were made.
After improving the Sen test and introducing variance correction, the authors analyze new tests using real data and indicate that the new Sen test allows you to remove the exaggeration of the importance of trends.

 

There is no clearly marked section in the article regarding literature.


On lines 380 and 381, literature is incorrectly cited.

Author Response

Dear reviewer,

Appreciate for reviewing the manuscript and pointing out deficiencies. We have corrected the mentioned citation problem. The reference list have been rechecked.

Best regards.

 

Reviewer 3 Report

Paper presents novel method for testing of the hydrological time series. Methodology is explained with all details, mathematical tool is well prepared. I suggest small changes of the paper, i.e. minor revision.

Presented method gives graphical representation of the possible sub-series within original time serie. Rescaled Adjusted Partial Sums (RAPS) method is one of the most used methods for mentioned analysis. Authors should give comments on this, especially about advantages or disadvantages of their method and RAPS method.

Figure 8 is not clear; authors should different perspective of 3D view, due to better and clearly view of the plane (x, y) axis.

 

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 4 Report

The article is interesting and provides substantial progress in estimating hydro-meteorological data trends. But unlike the Mann-Kendall method which is very well documented, the original Sen’s Trend Test is not widely used and should be better explained as a method. It is a required condition before going further.
After this additional information I will review the paper again with interest.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 4 Report

The paper is of great interest because it warns of early conclusions about trend analysis in the hydrometeorological series. The analysis of results based on actual observations has been much improved since the original version.
I think the paper meets the standards of Water.

Author Response

Appreciate for your time and efforts. We will further improve the manuscript according to editor's comments.

Back to TopTop