Context Numerous dams have been built in China to develop hydropower, a sustainable and clean energy source. In recent years, the impact of dam construction on the regional climate has gradually attracted the attention of researchers. Aims This study has evaluated the impact of large-scale dam construction on regional precipitation. Methods This paper used the precipitation data of more than 2400 national stations of the China National Meteorological Information Center from 1990 to 2012. The regional precipitation data before and after the construction of the Xiaolangdi Dam and the Three Gorges Dam on the Yangtze River were analysed using geostatistical tools. Wavelet transform and Yamamoto signal-to-noise ratio analysis were further adopted. Key results Analysis of the variation points of precipitation characteristics confirmed the correlation between dam construction and regional precipitation; the precipitation values in the two dam study areas had an increasing trend after the completion of the dams, and the fitting trend line showed an obvious increasing trend. Conclusions According to the analysis of precipitation variation points, it can be concluded that the establishment of the dam affected the precipitation in the area of ∼200 km upstream and increased the precipitation value in this area. The study showed that dam construction has a strong correlation with regional precipitation. Implications It is speculated that the construction of super dams will have a greater impact on precipitation. 

Impact of dam construction on precipitation: a regional perspective

Three decades of depth-dependent groundwater response to climate variability and human regime in the transboundary Indus-Ganges-Brahmaputra-Meghna mega river basin aquifers

Quantile-based Bayesian Model Averaging approach towards merging of precipitation products

In this study, various wavelet analysis methods are used to investigate possible influences of large-scale climate patterns, such as El Nino-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), and Indian Ocean Dipole (IOD), on precipitation extremes over Beijing-Tianjin-Hebei region in China at different time scales. Firstly, the temporal patterns of precipitation extremes are detected by wavelet transform. Significant annual or inter-annual oscillations for the precipitation extremes during 1958–2017, with periodicities of around 0.5–1 year, 1–2 years, and 2–5 years were being found for monthly, seasonal, and annual time series, respectively. Subsequently, wavelet coherence method is used to identify the dominant driving factors of precipitation extremes, with ENSO, IOD, and NAO showing stronger correlations with monthly, seasonal, and annual precipitation extremes, respectively. Meanwhile, partial wavelet coherence analyses indicate that the standalone influences of climate factors may be weak, and the influences seem to be stronger because of their interdependences on other climate indices. Finally, multiple wavelet coherences reveal that variations of precipitation extremes could be better explained by combinations of two or more factors, although the additional explanatory variable may have not a significant increase in percent number of significant coherence.

Potential linkages of precipitation extremes in Beijing-Tianjin-Hebei region, China, with large-scale climate patterns using wavelet-based approaches

Precipitation is a fundamental input for many hydrological and water management studies. Nowadays, a number of satellite precipitation products are easily accessible online at free of cost. Despite so, the utility of such products is still limited owing to their lack of accuracy in capturing the ground truth. To improve the reliability of the satellite precipitation products, we have developed a quantile based Bayesian model averaging (QBMA) approach to merge the satellite precipitation products. QBMA approach was compared with traditional methods, namely, simple model averaging and one outlier removed. We have considered three SPPs (TRMM, PERSIANN-CDR, CMORPH) for QBMA merging during the monsoon season over India's coastal Vamsadhara river basin. QBMA optimal weights were trained using 2001 to 2013 daily monsoon precipitation data and validated for 2014 to 2018. Results indicated that the bias-corrected QBMA outperformed the other methods. On monthly evaluation, it is observed that all the products perform better during July and September than that in June and August. The QBMA approaches do not have any significant improvement over the SMA approach in terms of POD. However, the bias-corrected QBMA products have lower FAR. The developed QBMA approach with bias-corrected inputs outperforms the IMERG product in terms of RMSE. 

Precipitation patterns and extremes are significantly influenced by various climatic factors and large-scale atmospheric circulation patterns. This study uses wavelet coherence analysis to detect significant interannual and interdecadal oscillations in monthly precipitation extremes across India and their teleconnections to three prominent climate indices, namely, Nino 3.4, Pacific Decadal Oscillation, and Indian Ocean Dipole (IOD). Further, partial wavelet coherence analysis is used to estimate the standalone relationship between the climate indices and precipitation after removing the effect of interdependency. The wavelet analysis of monthly precipitation extremes at 30 different locations across India reveals that (a) interannual (2–8 years) and interdecadal (8–32 years) oscillations are statistically significant, and (b) the oscillations vary in both time and space. The results from the partial wavelet coherence analysis reveal that Nino 3.4 and IOD are the significant drivers of Indian precipitation at interannual and interdecadal scales. Intriguingly, the study also confirms that the strength of influence of large-scale atmospheric circulation patterns on Indian precipitation extremes varies with spatial physiography of the region.

Maheswaran Rathinasamy

Papers

Wavelet analysis of precipitation extremes over India and teleconnections to climate indices