Ichthyoplankton Species Composition and Assemblages From the Estuary to the Hukou Section of the Changjiang River

Estuary river–lake (ERL) complex is critical for the development of ichthyoplankton, which plays an essential role in the breeding, nursing, and recruitment of freshwater fish species. In this study, different ERL sections were selected to investigate the ichthyoplankton assemblage from the Changjiang River estuary to its Hukou section between May 2018 and August 2020. During the study period, fish larvae of 40, 35, 54, and 46 fish species were sampled in the Rugao (RG), Nanjing (NJ) (nearby estuary), Anqing (AQ), and Hukou (HK) (connected river–lake) sections in the Changjiang River, respectively. Results on ichthyoplankton surveys revealed fluctuating assemblage patterns in the different river sections; larval fish species composition presented ecological habitat-dependent characteristics, which supported the importance of ERL complex for ichthyoplankton development. Furthermore, the density of fish larvae was negatively correlated with water transparency but positively correlated with water temperature, the daily rate of water level increase, and runoff in the limitative range. A generalized additive model analysis indicated that the hydrological factors significantly affecting larval fish abundance are water temperature, transparency, the daily rate of water level increase, and runoff (p < 0.05). These results also indicated that river estuaries and river–lake connected complexes are essential for ichthyoplankton recruitment and migration. In combination with the historical record, ichthyoplankton assemblage’s patterns in the Changjiang River lower reaches were preliminarily elucidated, although additional works are needed such as assessing effects on their survival and recruitment.

Assessment of Extreme Storm Surges over the Changjiang River Estuary from a Wave–Current Coupled Model

Disastrous storm surges and waves caused by typhoons are major marine dynamic disasters affecting the east China coast and the Changjiang River Estuary, especially when they occur coincidentally. In this study, a high-resolution wave–current coupled model consisting of ADCIRC (Advanced Circulation) and SWAN (Simulating Waves Nearshore) was established and validated. The model shows reasonable skills in reproducing the surge levels and waves. The storm surges and associated waves are then simulated for 98 typhoons affecting the Changjiang River Estuary over the past 32 years (1987–2018). Two different wind fields, the ERA reanalysis and the ERA-based synthetic wind with a theoretical typhoon model, were adopted to discern the potential uncertainties associated with winds. Model results forced by the ERA reanalysis show comparative skills with the synthetic winds, but differences may be relatively large in specific stations. The extreme surge levels with a 50-year return period are then presented based on the coupled model results and the Gumbel distribution model. Higher risk is presented in Hangzhou Bay and the nearshore region along the coast of Zhejiang. Comparative runs with and without wave effects were conducted to discern the impact of waves on the extreme surge levels. The wave setup contributes to 2–12.5% of the 50-year extreme surge level. Furthermore, the joint exceedance probabilities of high surge levels and high wave height were evaluated with the Gumbel–logistic statistic model. Given the same joint return period, the nearshore region along the coast of Zhejiang is more vulnerable with high surges and large waves than the Changjiang River Estuary with large waves and moderate surges.

Numerical Study on the Massive Outbreak of the Ulva prolifera Green Tides in the Southwestern Yellow Sea in 2021

The most massive outbreak on record of the Ulva prolifera green tides in the southwestern Yellow Sea occurred in summer of 2021. The environmental factors were investigated based on observations and simulations. The results suggested that the significantly enhanced discharge of the Changjiang River since winter 2020–2021 was crucial for the outbreak of the Ulva prolifera green tides in the southwestern Yellow Sea, which could significantly have contributed to the nutrient enrichment off the Subei coast. Additionally, the southerly wind stress anomaly during winter 2020–2021 favored the upwind transport of Changjiang water. Numerical experiments showed that the remaining winter freshwater coming from the Changjiang River, which persisted in the Subei coast’s upper layer until spring 2021, exceeded the long-term average value by 20%. We demonstrated that these large amount of nutrient inputs, as an effective supplement, were the reason the green tides sharply emerged as an extensive outbreak in 2021. The easterly wind anomaly during spring 2021 contributed to the landing of Ulva prolifera off the Lunan coast.

Influence of Spatial Scale Selection of Environmental Factors on the Prediction of Distribution of Coilia nasus in Changjiang River Estuary

An estuary region is a complex environment with a transition from fresh to brackish to salt water, and in which some environmental factors change dramatically over small ranges. Therefore, it is important to understand the impact of the selection of spatial scale on the prediction of the distribution of estuarine species. As the largest estuary in China, the Changjiang River estuary is the spawning ground, feeding ground, and migration channel for many species. Based on Coilia nasus, an important economic fish species in the Changjiang River estuary, this study uses the two-stage generalized additive model (GAM) to investigate the potential differences in the response of species’ spatial distribution when environmental factors are assessed at different spatial scales (1′ × 1′, 2′ × 2′, 3′ × 3′, 4′ × 4′, 5′ × 5′). The results showed the following: (1) according to the analysis of the variance inflation factor (VIF), the values of all environmental factors were less than three and we found no correlation among the environmental variables selected. (2) The first stage GAM retained six variables, including year, month, latitude (Lat), water depth (Depth, m), bottom salinity (Sal, mg/L), and chemical oxygen demand (COD, mg/L). The second stage GAM retained four variables, including Year, Lat, pH, and chlorophyll a (Chl-a, μg/L). (3) The mean value of the Chla for the 3′×3′ spatial scale was significantly lower than that of the other spatial scales, and the mean value of Sal for the 5′×5′ spatial scale was higher than that of the other spatial scales. (4) In terms of the spatial distribution of abundance, the distribution patterns of C. nasus predicted by all scales were not very similar, and the distribution patterns predicted by the 5′ × 5′ scale, in the autumn of 2012, were significantly different from those at other scales. Therefore, the selection of spatiotemporal scales may affect predictions of the spatial distributions of species. We suggest that potential spatiotemporal scale effects should be evaluated in future studies.