Phytoplankton and Eutrophication Degree Assessment of Baiyangdian Lake Wetland, China

Eight typical sampling sites were chosen to investigate the phytoplankton community structure and to assess the eutrophication degree of Baiyangdian Lake in 2009. Our results showed that among the total 133 species identified, Cyanophyta, Chlorophyta, and Bacillariophyta dominated the phytoplankton community. In spring, Chlorophyta and Bacillariophyta were the dominant phyla, and the dominant species included Chlorella sp., Chroomonas acuta Uterm., and Microcystis incerta Lemm.; the density of the phytoplankton ranged from 496×104 to 6256×104 cells/L with an average of 2384×104 cells/L. However, Chlorophyta and Cyanophyta became the dominant phyla in summer, and the dominant species were Chlorella sp., Leptolyngbya valderiana Anagn., and Nephrocytium agardhianum Nageli.; the density of the phytoplankton varied from 318×104 to 4630×104 cells/L with an average of 1785×104 cells/L. The density of the phytoplankton has increased significantly compared to the previous investigations in 2005. The index of Carlson nutritional status (TSIM) and the dominant genus assessment indicated that the majority of Baiyangdian Lake was in eutrophic state

A WRF/WRF-Hydro Coupled Forecasting System with Real-Time Precipitation–Runoff Updating Based on 3Dvar Data Assimilation and Deep Learning

This study established a WRF/WRF-Hydro coupled forecasting system for precipitation–runoff forecasting in the Daqing River basin in northern China. To fully enhance the forecasting skill of the coupled system, real-time updating was performed for both the WRF precipitation forecast and WRF-Hydro forecasted runoff. Three-dimensional variational (3Dvar) multi-source data assimilation was implemented using the WRF model by incorporating hourly weather radar reflectivity and conventional meteorological observations to improve the accuracy of the forecasted precipitation. A deep learning approach, i.e., long short-term memory (LSTM) networks, was adopted to improve the accuracy of the WRF-Hydro forecasted flow. The results showed that hourly data assimilation had a positive impact on the range and trends of the WRF precipitation forecasts. The quality of the WRF precipitation outputs had a significant impact on the performance of WRF-Hydro in forecasting the flow at the catchment outlet. With the runoff driven by precipitation forecasts being updated by 3Dvar data assimilation, the error of flood peak flow was decreased by 3.02–57.42%, the error of flood volume was decreased by 6.34–39.30%, and the Nash efficiency coefficient was increased by 0.15–0.52. The implementation of LSTM can effectively reduce the forecasting errors of the coupled system, particularly those of the time-to-peak and peak flow volumes

Development and evaluation of the Lake Multi-biotic Integrity Index for Dongting Lake, China

A Lake Multi-biotic Integrity Index (LMII) for the China’s second largest interior lake (Dongting Lake) was developed to assess the water quality status using algal and macroinvertebrate metrics. Algae and benthic macroinvertebrate assemblages were sampled at 10 sections across 3 subregions of Dongting Lake. We used a stepwise process to evaluate properties of candidate metrics and selected ten for the LMII: Pampean diatom index, diatom quotient, trophic diatom index, relative abundance diatoms, Margalef index of algae, percent sensitive diatoms, % facultative individuals, % Chironomidae individuals, % predators individuals, and total number of macroinvertebrate taxa. We then tested the accuracy and feasibility of the LMII by comparing the correlation with physical-chemical parameters. Evaluation of the LMII showed that it discriminated well between reference and impaired sections and was strongly related to the major chemical and physical stressors (r = 0.766, P&lt;0.001). The re-scored results from the 10 sections showed that the water quality of western Dongting Lake was good, while that of southern Dongting Lake was relatively good and whereas that of eastern Dongting Lake was poor. The discriminatory biocriteria of the LMII are suitable for the assessment of the water quality of Dongting Lake. Additionally, more metrics belonging to habitat, hydrology, physics and chemistry should be considered into the LMII, so as to establish comprehensive assessment system which can reflect the community structure of aquatic organisms, physical and chemical characteristics of water environment, human activities, and so on.</p

Assessing benthic ecological status in coastal area near Changjiang River estuary using AMBI and M-AMBI

The Changjiang (Yangtze) River estuary has been subject to a variety of anthropogenic pressures in recent decades. To assess the ecological health of the coastal benthic ecosystem adjacent to the estuary, three surveys were conducted in 2005, 2009, and 2010. The AZTI's Marine Biotic Index (AMBI) and multivariate-AMBI (M-AMBI) were used to analyse the benthic ecological status of this coast. The AMBI indicate that the ecological status of the coast adjacent to the Changjiang River estuary was only slightly degraded in all 3 years. In contrast, the M-AMBI indicated that the ecological status was seriously degraded, a result that is most likely due to pollution and eutrophication induced by human activities. The assessment of the coast's ecological status by the AMBI was not in agreement with that of the M-AMBI at some stations because of lower biodiversity values at those sites. The analysis of the two indices integrated with abiotic parameters showed that the M-AMBI could be used as a suitable bio-indicator index to assess the benthic ecological status of the coast adjacent to the Changjiang River estuary. The reference conditions proposed for the coast of the Changjiang River estuary should be further evaluated in future studies. Designation of local species could also provide an important reference for Chinese waters. To improve the reliability of AMBI and M-AMBI, further research into the ecology of local species is required to understand their arrangement in ecological groups.The Changjiang (Yangtze) River estuary has been subject to a variety of anthropogenic pressures in recent decades. To assess the ecological health of the coastal benthic ecosystem adjacent to the estuary, three surveys were conducted in 2005, 2009, and 2010. The AZTI's Marine Biotic Index (AMBI) and multivariate-AMBI (M-AMBI) were used to analyse the benthic ecological status of this coast. The AMBI indicate that the ecological status of the coast adjacent to the Changjiang River estuary was only slightly degraded in all 3 years. In contrast, the M-AMBI indicated that the ecological status was seriously degraded, a result that is most likely due to pollution and eutrophication induced by human activities. The assessment of the coast's ecological status by the AMBI was not in agreement with that of the M-AMBI at some stations because of lower biodiversity values at those sites. The analysis of the two indices integrated with abiotic parameters showed that the M-AMBI could be used as a suitable bio-indicator index to assess the benthic ecological status of the coast adjacent to the Changjiang River estuary. The reference conditions proposed for the coast of the Changjiang River estuary should be further evaluated in future studies. Designation of local species could also provide an important reference for Chinese waters. To improve the reliability of AMBI and M-AMBI, further research into the ecology of local species is required to understand their arrangement in ecological groups