Ontogenetic Structure and Temporal Patterns of Summer Ichthyoplankton in Upper Course of the Xijiang River, SW China

The summer ichthyoplankton characteristics in the Laibin section of the Xijiang River were analyzed based on a survey during summer 2017. The ontogenetic structure and temporal patterns of ichthyoplankton and the correlation between environmental parameters and the temporal patterns were investigated. A total of 10,665 eggs and 447 larval belonging to four orders, ten families, and 28 species were collected. According to the flood regime, summer is divided into three periods (pre-flood, flood period, and post-flood). Ichthyoplankton proved to be heterogeneous between periods in summer with differences in the composition and abundance. The assemblages were distinguished by multiple analytical tools, and presented a chronological pattern of marked variability in composition of the species between the periods, and under the strong influence of flood. The assemblages were mainly represented by eggs of S. wui Fang and S. robusta in the pre-flood period, S. argentatus and S. macrops in the flood period, and H. leucisculus and S. curriculus in the post-flood period, while, the larval occurred mainly in the flood period. Understanding these temporal patterns of the upper course of the Xijiang River is useful for the recruitment of fish resources and conservation of fish community diversity.

Long-Term Rainfall Trends and Future Projections over Xijiang River Basin, China

Precipitation trend detection is vital for water resources development and decision support systems. This study predicts the climate change impacts on long-term precipitation trends. It deals with the analysis of observed historical (1960–2010) and arithmetic mean method in assembling precipitation from CMIP5 Global Climate Models (GCMs) datasets for a future period (2020–2099) under four emission scenarios. Daily precipitation data of 32 weather stations in the Xijiang River Basin were provided by National Meteorological Information Centre (NMIC) of the China Meteorological Administration (CMA) and Global Climate Models (GCMs) with all four emission scenarios statistically downscaled using Bias Correction Special Disaggregation (BCSD) and applied for bias correction via Climate Change Toolkit (CCT). Nonparametric Mann–Kendall test was applied for statistical significance trend analysis while the magnitude of the trends was determined by nonparametric Sen’s estimator method on a monthly scale to detect monotonic trends in annual and seasonal precipitation time series. The results showed a declined trend was observed for the past 50 years over the basin with negative values of MK test (Z) and Sen’s slope Q. Historical GCMs precipitation detected decreasing trends except for NoerESM1-M which observed slightly increasing trends. The results are further validated by historical precipitation recorded by the Climate Research Unit (CRU-TS-3.1). The future scenarios will likely be positive trends for annual rainfall. Significant positive trends were observed in monsoon and winter seasons while premonsoon and postmonsoon seasons will likely be slightly downward trends. The 2040s will likely observe the lowest increase of 6.6% while the 2050s will observe the highest increase of 11.5% over the 21st century under future scenarios. However, due to the uncertainties in CMIP5, the future precipitation projections should be interpreted with caution. Thus, it could be concluded that the trend of change in precipitation around the Xijiang River Basin is on the increase under future scenarios. The results can be valuable to water resources and agriculture management policies, as well as the approach for managing floods and droughts under the perspective of global climate change.