Hydroclimate variability and its statistical links to the large-scale climate indices for the Upper Chao Phraya River Basin, Thailand

The local hydroclimates get impacts from the large-scale atmospheric variables via atmospheric circulation. The developing of their relationships could enhance the understanding of hydroclimate variability. This study focuses on the Upper Chao Phraya River Basin in Thailand in which rainfall is influenced by the Indian Ocean and tropical 5 Pacific Ocean atmospheric circulation. The Southwest monsoon from the Indian Ocean to Thailand is strengthened by the temperature gradient between land and ocean. Thus, the anomalous sea surface temperature (SST) is systematically correlated with the monthly rainfall and identified as the best predictor based on the significant relationships revealed by cross-correlation analysis. It is found that rainfall, especially during 10 the monsoon season in the different zones of study basin, corresponds to the different SST indices. This suggests that the region over the ocean which develops the temperature gradient plays a role in strengthening the monsoon. The enhanced gradient with the SST over the South China Sea is related to rainfall in High Rainfall Zone (HRZ); however, the anomalous SST over the Indian Ocean and the equatorial Pacific Ocean 15 are associated with rainfall in Normal and Low Rainfall Zone (NRZ and LRZ) in the study area. Moreover, the identified predictors are related to the rainfall with lead periods of 1–4 months for the pre-monsoon rainfall and 6–12 months for the monsoon and dry season rainfall. The study results are very useful in developing rainfall forecasting models and consequently in the management of water resources and extreme events. 20

Comment: 6667, 19: Please specify exactly what is meant by "±0.26".-Fig.5 is stretched horizontally so that the displayed correspondence of variables is misleading.Response: The +0.26 and -0.26 are the upper and lower bound of the 95% confidence levels of correlation.Fig. 5 is presented in the same range of x-axis (rainfall) to compare the amount of dry season rainfall among three zones.Revision: The explanation of "±0.26" is included in section 5.1.2in the revision and reads as "…Figure 5 shows a strong inverse relationship between the dry season rainfall and MAM temperature with significance at 95% confidence level which the upper and lower bound of significance is +0.26 and -0.26, respectively…".No revision is made in Fig. 5.
Comment: 6667, 23: Why do you show a moving window here instead of total correlations?Response: The moving window correlations can show the variability and development of relationships between temperature and rainfall over decades, whereas the total correlations obtained from the entire time series cannot show the changes of their interdecadal relationships.

Response:
The meaning of "vice versa" is …if the air temperature during MAM is low, the MJJ and ASO rainfall are expected to decrease.This is clearly described in the revised manuscript.The legends in Fig. 6 are changed to make it more distinguishable.Revision: The "vice versa" is deleted to avoid ambiguity and it has been clearly described.The revised text now reads "…For the NRZ and LRZ, as the positive correlations indicate, the higher MAM air temperature increases the MJJ and ASO rainfall, whereas the lower MAM air temperature decreases the MJJ and ASO rainfall…".The legends in Fig. 6 are changed to make the curves distinguishable as shown below.Comment: 6668, 9: The statement "The negative correlations ..." is unclear.Response: The negative correlations between ASO rainfall in HRZ and surface temperature over the study basin shown in Fig. 7a are consistent with the negative correlations from 20-year moving window analysis shown in Fig. 6.In the revision, authors make it clearer for readers.Revision: The revised manuscript describes clearly the statement as above.The relevant text in section 5.1.2 of the revised manuscript reads as "…The negative correlations between ASO rainfall in HRZ and surface temperature over the study basin shown in Fig. 7(a) are consistent with the negative correlations from 20-year moving window correlations shown in Fig. 6, which verify the quality of data…".

Figure 6 .
Figure 6.Correlations of 20-year moving window between MAM temperature and (a) MJJ rainfall and (b) ASO rainfall.
: 6668, 14: If correlations are better or more consistent with the SST then why isn't SST considered in the first place?Response: This study considered the SST in terms of the standard SST indices which are the large-scale anomalous SST over different regions of the Pacific Ocean and Indian Ocean -i.e.NINO1+2, NINO3, NINO4, NINO3.4 and ION.The study did not consider other variables.Authors are currently analyzing other large-scale variables to develop a statistical forecasting model.Revision: No revision is made.Comment: 6668, 27: The trend analysis here starts somewhat unmotivated from the context.(b)