Robust clustering for assessing the spatiotemporal variability of groundwater quantity and quality

The Pearl River Estuary (PRE) is one of the major fishing grounds for the squid Uroteuthis chinensis. Taking that into consideration, this study analyzes the environmental effects on the spatiotemporal variability of U. chinensis in the PRE, on the basis of the Generalized Additive Model (GAM) and Clustering Fishing Tactics (CFT), using satellite and in situ observations. Results show that 63.1% of the total variation in U. chinensis Catch Per Unit Effort (CPUE) in the PRE could be explained by looking into outside factors. The most important one was the interaction of sea surface temperature (SST) and month, with a contribution of 26.7%, followed by the interaction effect of depth and month, fishermen’s fishing tactics, sea surface salinity (SSS), chlorophyll a concentration (Chl a), and year, with contributions of 12.8%, 8.5%, 7.7%, 4.0%, and 3.1%, respectively. In summary, U. chinensis in the PRE was mainly distributed over areas with an SST of 22–29 °C, SSS of 32.5–34‰, Chl a of 0–0.3 mg × m−3, and water depth of 40–140 m. The distribution of U. chinensis in the PRE was affected by the western Guangdong coastal current, distribution of marine primary productivity, and variation of habitat conditions. Lower stock of U. chinensis in the PRE was connected with La Niña in 2008.

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Drivers of Macrophyte and Diatom Diversity in a Shallow Hypertrophic Lake

Water ◽

10.3390/w13111569 ◽

2021 ◽

Vol 13 (11) ◽

pp. 1569

Author(s):

Kateřina Šumberová ◽

Ondřej Vild ◽

Michal Ducháček ◽

Martina Fabšičová ◽

Jan Potužák ◽

...

Keyword(s):

Fish Farming ◽

Spatial Diversity ◽

Spatiotemporal Variability ◽

Water Level Fluctuations ◽

Nutrient Requirements ◽

Diatom Assemblages ◽

Climatic Extremes ◽

Growing Seasons ◽

High Species Richness ◽

Different Parts

We studied macrophyte and diatom assemblages and a range of environmental factors in the large hypertrophic Dehtář fishpond (Southern Bohemia, Czech Republic) over the course of several growing seasons. The spatial diversity of the environment was considered when collecting diatoms and water samples in three distinct parts of the fishpond, where automatic sensor stations continually measuring basic factors were established. Macrophytes were mapped in 30 segments of the fishpond littoral altogether. High species richness and spatiotemporal variability were found in assemblages of these groups of autotrophs. Water level fluctuations, caused by the interaction of fish farming management and climatic extremes, were identified as one of the most important factors shaping the structure and species composition of diatom and macrophyte assemblages. The distance of the sampling sites from large inflows reflected well the spatial variability within the fishpond, with important differences in duration of bottom drainage and exposure to disturbances in different parts of the fishpond. Disturbances caused by intensive wave action are most probably a crucial factor allowing the coexistence of species with different nutrient requirements under the hypertrophic conditions of the Dehtář fishpond. Due to a range of variables tested and climatic extremes encountered, our study may be considered as a basis for predictive model constructions in similar hypertrophic water bodies under a progressing climate change.

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Spatiotemporal Variability of Surface Phytoplankton Carbon and Carbon-to-Chlorophyll a Ratio in the South China Sea Based on Satellite Data

Remote Sensing ◽

10.3390/rs13010030 ◽

2020 ◽

Vol 13 (1) ◽

pp. 30

Author(s):

Wenlong Xu ◽

Guifen Wang ◽

Long Jiang ◽

Xuhua Cheng ◽

Wen Zhou ◽

...

Keyword(s):

South China Sea ◽

Chlorophyll A ◽

South China ◽

Satellite Data ◽

Phytoplankton Biomass ◽

The South China Sea ◽

Spatiotemporal Variability ◽

The South ◽

Chl A ◽

China Sea

The spatiotemporal variability of phytoplankton biomass has been widely studied because of its importance in biogeochemical cycles. Chlorophyll a (Chl-a)—an essential pigment present in photoautotrophic organisms—is widely used as an indicator for oceanic phytoplankton biomass because it could be easily measured with calibrated optical sensors. However, the intracellular Chl-a content varies with light, nutrient levels, and temperature and could misrepresent phytoplankton biomass. In this study, we estimated the concentration of phytoplankton carbon—a more suitable indicator for phytoplankton biomass—using a regionally adjusted bio-optical algorithm with satellite data in the South China Sea (SCS). Phytoplankton carbon and the carbon-to-Chl-a ratio (θ) exhibited considerable variability spatially and seasonally. Generally, phytoplankton carbon in the northern SCS was higher than that in the western and central parts. The regional monthly mean phytoplankton carbon in the northern SCS showed a prominent peak during December and January. A similar pattern was shown in the central part of SCS, but its peak was weaker. Besides the winter peak, the western part of SCS had a secondary maximum of phytoplankton carbon during summer. θ exhibited significant seasonal variability in the northern SCS, but a relatively weak seasonal change in the western and central parts. θ had a peak in September and a trough in January in the northern and central parts of SCS, whereas in the western SCS the minimum and maximum θ was found in August and during October–April of the following year, respectively. Overall, θ ranged from 26.06 to 123.99 in the SCS, which implies that the carbon content could vary up to four times given a specific Chl-a value. The variations in θ were found to be related to changing phytoplankton community composition, as well as dynamic phytoplankton physiological activities in response to environmental influences; which also exhibit much spatial differences in the SCS. Our results imply that the spatiotemporal variability of θ should be considered, rather than simply used a single value when converting Chl-a to phytoplankton carbon biomass in the SCS, especially, when verifying the simulation results of biogeochemical models.

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