Publications

Faiz, MA; Liu, D; Fu, Q; Qamar, MU; Dong, S; Khan, MI; Li, T (2018). Complexity and trends analysis of hydrometeorological time series for a river streamflow: A case study of Songhua River Basin, China. RIVER RESEARCH AND APPLICATIONS, 34(2), 101-111.

Abstract
In China's national economic growth, an important role is being played by the Songhua River because of the river's abundant resources and natural conditions. Therefore, study of hydrometeorological time series is very important to understand the basin behaviour. This research uses the snow cover data derived from MODIS, streamflow, and meteorological records in the Songhua River Basin to evaluate similarity, complexity, and trends in the snow cover, temperature, precipitation, and streamflow. In this paper, we suggest a new method of ranking the statistics symbolic sequences to examine the degree of similarity (distance measurement) between meteorological stations and compare it with non-parametric correlation methods and also investigate the deviations in the complexity of a hydrometeorological time series. Information-based similarity index and multiscale entropy confirm that the hydrometeorological time series of different stations have self-similarity and abundant complexity. Wavelet entropy is also used to investigate the basin behaviour by taking streamflow records and population. It is found that with the increase in population and urbanization, the complexity values are increased. The results also exhibit that due to increase in urbanization, it affects the hydrological process and nature of environment resulting in complex catchment behaviour. Furthermore, the streamflow trend results displayed significant decline (22.21m(3)/sxyear(-1)) in the Songhua River. The results also indicated that the seasonal snow cover trend has no impact on changes of the streamflow. However, the decline of the streamflow may be influenced by the significant human activity upstream of the Songhua River.

DOI:
10.1002/rra.3236

ISSN:
1535-1459