Water temperature in estuaries is sensitive to thermal discharges and expansive tidal flats; as such, this parameter is essential in maintaining estuarine ecosystem. Semi-enclosed water bodies with poor water exchange easily accumulate heat. This is especially true for Xiangshan Bay, which contains two power plants and a large area of tidal flats. This bay was used as an example to study water temperature and heat dynamics, considering the thermal discharge and tidal flats. This study developed and validated a three-dimensional hydro-heat flux numerical model using field data on tidal elevation, currents, water temperature, and tidal flat temperature. The Finite Volume Community Ocean Model combines an accurate thermal discharge model with the air-water-tidal flat heat flux model. The findings showed that thermal discharge provides heat to the bay in summer and winter, and increases the water temperature of the entire bay by 0.7°C in summer, while maintaining water temperature at 0.52°C in winter. The atmosphere and open seas had greater impacts on heat in the bay in winter and summer. The atmosphere and tidal flats provided heat to the bay in summer and absorbed heat from the bay during winter; the opposite was true for the open sea. The effect of tidal flats in summer is less than that in winter, and provides 1.31 × 1013 J of heat to the bay in summer, while taking 8.63 × 1013 J of heat from the bay in winter. This study provides a comprehensive understanding of the effects of tidal flats and thermal discharge on water temperature and heat in macro-tidal bays and estuaries; its results are applicable to similar bays around the world.

Influences of Tidal Flat and Thermal Discharge on Heat Dynamics in Xiangshan Bay

Numerical investigation of the transport and accumulation of conservative pollutants off Changjiang Estuary

Objective Phellodendron amurense, a special species in Northeast Asia, is the source of the wild medicinal Phellodendri Amurensis Cortex, the second-level key protection in China. Because of its dual value of medicine and timber, it has been cut in large quantities, resulting in a sharp decline in wild resources. It has been listed as a national first-class rare tree species. Here, we aim to plan protection area and wild tending area for Chinese P. amurense in the context of climate change. Method In this study, based on the Biomod2 model, the main environmental factors affecting the distribution and the potentially suitable areas of the current and future were investigated. Furthermore, the correlation between the main environmental factors and the chemical components was determined by full subset regression. Habitat quality and landscape pattern change were determined by InVEST and Fragstats software based on the land use data in 1980, 2000, and 2020. Then, Zonation software was used to plan the priority protection and wild tending areas. Results The results showed that in the future, the potential habitat of P. amurense will gradually move to the high latitude and high altitude areas in the northwest direction, and the high suitability area will decrease sharply. Since 1980, the habitat quality of P. amurense habitat has gradually deteriorated, and the fragmentation has gradually intensified. In the future, the central part of the Greater Khingan Mountains and the forest area of the Lesser Khingan Mountains will be the long-term stable sanctuary. Fengcheng City and Tonghua County are the most suitable for the wild tending areas. Conclusion The existing nature reserve only contains 6.01% of the priority protection area, so we propose to expand the area to cover a larger proportion of the sanctuary and implement management and restoration plans to increase the suitability and connectivity. These results can support the protection action and provide a reference and scientific basis for further research, rational development, and utilization of P. amurense.

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Optimizing the distribution pattern of species under climate change: the protection and management of Phellodendron amurense in China

Climate change, especially climate extremes, can increase the uncertainty of locust outbreaks. The Italian locust (Calliptamus italicus (Linnaeus, 1758)), Asian migratory locust (Locusta migratoria migratoria Linnaeus, 1758), and Siberian locust (Gomphocerus sibiricus (Linnaeus, 1767)) are common pests widely distributed in the semidesert grasslands of Central Asia and its surrounding regions. Predicting the geographic distribution changes and future habitats of locusts in the context of climate warming is essential to effectively prevent large and sudden locust outbreaks. In this study, the optimized maximum entropy (MaxEnt) model, employing a combination of climatic, soil, and topographic factors, was used to predict the potential fitness areas of typical locusts in the 2030s and 2050s, assuming four shared socioeconomic pathways (SSP126, SSP245, SSP370, and SSP585) in the CMIP6 model. Modeling results showed that the mean area under the curve (AUC) and true statistical skill (TSS) of the MaxEnt model reached 0.933 and 0.7651, respectively, indicating that the model exhibited good prediction performance. Our results showed that soil surface sand content, slope, mean precipitation during the hottest season, and precipitation seasonality were the key environmental variables affecting locust distribution in the region. The three locust species were mainly distributed in the upstream region of the Irtysh River, the Alatao Mountain region, the northern slopes of the Tianshan Mountains, around Sayram Lake, the eastern part of the Alakol Lake region, the Tekes River region, the western part of Ulungur Lake, the Ili River, and the upstream region of the Tarim River. According to several climate projections, the area of potential habitat for the three most common locust species will decrease by 3.9 × 104-4.6 × 104 km2 by the 2030s and by 6.4 × 104-10.6 × 104 km2 by the 2050s. As the climate becomes more extreme, the suitable area will shrink, but the highly suitable area will expand; thus, the risk of infestation should be taken seriously. Our study present a timely investigation to add to extensive literature currently appearing regarding the myriad ways climate change may affect species. While this naturally details a limited range of taxa, methods and potential impacts may be more broadly applicable to other locust species. 

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Predictions Based on Different Climate Change Scenarios: The Habitat of Typical Locust Species Is Shrinking in Kazakhstan and Xinjiang, China

In order to accurately predict noctiluca density, a new prediction model PSO-GRNN was constructed according to the relationship between noctiluca density and environmental factors. Particle swarm optimization (PSO) algorithm was used to obtain the optimal smoothing factor of generalized regression neural network (GRNN) in this model, that could reduce human subjective influence and improve the generalization ability and performance of generalized regression neural network. The dissolved oxygen, water temperature, total nitrogen, salinity, phytoplankton density and soluble inorganic phosphorus were taken as the model input, and the noctiluca density was taken as the model output. Finally, the new model and other traditional models were both tested, and the prediction results of the improved model were compared with other traditional models. The experimental results showed that the improved new model had high accuracy in the prediction of noctiluca density, and could indirectly realize the early prediction of red tide.

An improved GRNN model and prediction of noctiluca density

Distribution pattern and change prediction of Saposhnikovia divaricata suitable area in China under climate change

Analysis of the inter-annual variability and southward expansion of red tides in the Zhejiang coastal waters from 1981 to 2018

It is a key problem that when the temperature rise in seawater reaches balance under plants' thermal discharge. In the process of numerical simulation of Xiangshan Bay's temperature rise from plants' thermal discharge, TATR (Total Amount of Temperature Rise) was introduced as an index to help find the balance time. TATR shows its advantages of stability and reliability when compared with other indices such as volume. With TATR, it was found that the temperature rise reaches balance after 80 days' thermal discharge in winter while it takes 70 days in summer. Under thermal discharge of two plants, TATR at balance time is 1.77 (109m3°C) in winter and 2.82 in summer. Under discharge of only Guohua Plant, it is 0.60 in winter and 1.56 in summer. Under discharge of only Wushashan Plant, it is 1.16 and 1.37 respectively. Based on the correct balance time, the temperature rise in nearby waters due to two major plants' thermal discharge was calculated, which provided useful data for researches such as water-environmental value loss.

The Use of TATR (Total Amount of Temperature Rise) Index in Numerical Simulation of Xiangshan Bay's Temperature Rise

Distribution change and protected area planning of Tilia amurensis in China: A study of integrating the climate change and present habitat landscape pattern

Bing Rui Chen

Papers

Distribution pattern and change prediction of Saposhnikovia divaricata suitable area in China under climate change

Analysis of the inter-annual variability and southward expansion of red tides in the Zhejiang coastal waters from 1981 to 2018

The Use of TATR (Total Amount of Temperature Rise) Index in Numerical Simulation of Xiangshan Bay's Temperature Rise

Distribution change and protected area planning of Tilia amurensis in China: A study of integrating the climate change and present habitat landscape pattern

Optimizing the distribution pattern of species under climate change: the protection and management of Phellodendron amurense in China