Climatic Variability and Periodicity for Upstream Sub-Basins of the Yangtze River, China

Abstract

The headwaters of the Yangtze River are located on the Qinghai Tibetan Plateau, which is affected by climate change. Here, treamflow trends for Tuotuohe and Zhimenda sub-basins and relations to temperature and precipitation trends during 1961–2015 were investigated. The modified Mann–Kendall trend test, Pettitt test, wavelet analysis, and multivariate correlation analysis was deployed for this purpose. The temperature and precipitation significantly increased for each sub-basin, and the temperature increase was more significant in Tuotuohe sub-basin as compared to the Zhimenda sub-basin. A statistically significant periodicity of 2–4 years was observed for both sub-basins in different time spans. Higher flow periodicities for Tuotuohe and Zhimenda sub-basin were found after 1991 and 2004, respectively, which indicates that these are the change years of trends in streamflows. The influence of temperature on streamflow is more substantial in Tuotuohe sub-basin, which will ultimately impact the melting of glaciers and snowmelt runoff in this sub-basin. Precipitation plays a more critical role in the Zhimenda streamflow. Precipitation and temperature changes in the headwaters of the Yangtze River will change the streamflow variability, which will ultimately impact the hydropower supply and water resources of the Yangtze Basin. This study contributes to the understanding of the dynamics of the hydrological cycle and may lead to better hydrologic system modeling for downstream water resource developments.

Soft Periodic Convolutional Recurrent Network for Spatiotemporal Climate Forecast

“…The study of El Niño-Southern Oscillation (ENSO) [21] on South African rainfall showed that the periodic cycle may vary in many ranges of years. The study of upstream sub-basins of the Yangtze River [22] also shows statistically significant 2-to 4-year periodicities for mean areal precipitation. To tackle the problem of phase shift and non-stationarity of periodicity patterns, the model should consider the nearby time (months) and multiple cycles.…”

“…In the study of Lanchang-Mekong [18], the sub-region periodicity pattern of temperature and precipitation significantly increased. Looking deeper into the periodic patterns, there are two periodic patterns that we consider in this research; (1) the phase shift, such as the late summer and early autumn [19], and (2) non-stationarity of periodicity [20][21][22]. Climate seasonal shifts in terms of spatial and intensity may not be the same every year [23].…”

Possible Linkages of Hydrological Variables to Ocean–Atmosphere Signals and Sunspot Activity in the Upstream Yangtze River Basin

“…With a total length of more than 6300 km [46], the Yangtze River is the longest river in China and the third-longest river in the world, second merely to the Nile and the Amazon river [5,43]. The upper Yangtze River is 4511 km in length, originating in the hinterland of Qinghai-Tibetan Plateau and ending at Yichang, Hubei Province.…”

“…The effect of large-scale climate effects in modulating water cycle events provides the key for anticipating variation of hydrological factors [2]. Comprehending the variability of hydrological variables such as precipitation and streamflow is fundamental to understanding water cycle dynamics [3][4][5], while such variability has been indicated to be associated with climate change effects and large-scale climate anomalies [3,6]. Hence, understanding the telecorrelated relationship between different climate indices and local hydrological variables can provide new guidance for improving water system models and optimizing water resources management in specific regions [7].…”

Irrigation Supply and Demand, Land Use/Cover Change and Future Projections of Climate, in Indus Basin Irrigation System, Pakistan

“…Currently, climate change is a reality and has received the attention of researchers across the globe in the past two decades [30][31][32]. Globally, climate change threatens agricultural productivity [33,34].…”