The Influence of Land Use in the Catchment Area of Small Waterbodies on the Quality of Water and Plant Species Composition

Land use significantly affects the function of waterbodies in landscape. Although there have been numerous studies on the influence of the catchment area on the trophic and ecological status of waterbodies, still is not reached an agreement on the width of the buffer zone that is necessary for effective protection of waterbodies. The aim of the study was to show whether small waterbodies are predominantly influenced by land use in the entire catchment area or only in the zone extending 100 or 200 m away from the shoreline. For two years the waters in six small waterbodies located in the Wielkopolska region (Poland) were analysed. The canonical correspondence analysis (CCA) showed that the influence of land use, especially agricultural land, was much greater within the zone extending 100 m from the shore line of the waterbodies than in the total catchment area. Therefore, it would be advisable to move the border of intensive farming areas beyond the entire 100-metre-wide buffer zone, or at least to reduce the intensity of agriculture and to introduce diversified and perenial vegetation creating effective biogeochemical barriers.

Species diversity and community structure of crustacean zooplankton in the highland small waterbodies in Northwest Yunnan, China

Small waterbodies are a unique aquatic ecosystem with an increasing recognition for their important role in maintaining regional biodiversity and delivering ecosystem services. However, small waterbodies in Northwest Yunnan, one of the most concerned global biodiversity hot-spots, remain largely unknown. Here, we investigated the community structure of crustacean zooplankton and their relationships with limnological, morphometric and spatial variables in the highland small waterbodies in Northwest Yunnan in both the dry (October 2015) and rainy (June 2016) seasons. A total of 38 species of crustacean zooplankton were identified in our study, which is significantly higher than many other reported waterbodies in the Yunnan–Guizhou plateau as well as in the Yangtze River basin. This suggests that the highland small waterbodies are critical in maintaining regional zooplankton diversity in Northwest Yunnan. Meanwhile, we found limnological variables could explain most variation of crustacean zooplankton community, comparing to the morphometric and spatial variables in both the rainy and dry seasons. Our study revealed the diversity and community structure of crustacean zooplankton in the highland small waterbodies in Northwest Yunnan and highlighted the importance of small waterbodies in maintaining regional biodiversity.

Small waterbodies reduce the carbon sink of a polygonal tundra landscape

Arctic permafrost landscapes have functioned as a global carbon sink for millennia. These landscapes are very heterogeneous, and the omnipresent waterbodies are a carbon source within them. Yet, only a few studies focus on the impact of these waterbodies on the landscape carbon budget. We compare carbon dioxide and methane fluxes from small waterbodies to fluxes from the surrounding tundra using eddy covariance measurements from a tower located between a large pond and semi-terrestrial vegetated tundra. When taking the open-water areas of small waterbodies into account, the carbon dioxide sink strength of the landscape was reduced by 11 %. While open-water methane emissions were similar to the tundra emissions, some parts of the studied pond's shoreline exhibited much higher emissions, underlining the high spatial variability of methane emissions. We conclude that gas fluxes from small waterbodies can contribute significantly to the carbon budget of arctic tundra landscapes. Consequently, changes in arctic hydrology and the concomitant changes in the waterbody distribution may substantially impact the overall carbon budget of the Arctic.

Microbial drama in four acts - Extreme rain events cause cyclic succession in plankton communities

Highly abundant, small waterbodies contribute substantially to global freshwater shoreline and surface area. They are strongly interlinked with the terrestrial surrounding, thus controlling the flow of energy, nutrients and organisms through the landscape. Disturbance events can have severe consequences for these ecosystems and the entire downstream freshwater network and require more attention in the context of global change-induced increases in weather extremes. Here we show that extreme rain events (floods) cause cyclic successions in microbial communities and the planktonic food web of a small forest pond. We analyzed the dynamics of nutrients and the entire plankton community during two flood events and subsequent quasi-stable conditions. Floods induced a repeated washout of resident organisms and hundred-fold increases in nutrient load. However, within two weeks, the microbial community recovered to a pre-disturbance state through four well-defined succession phases. Reassembly of phyto- and especially zooplankton took considerably longer and displayed both repetitive and adaptive patterns. Release of dissolved nutrients from the pond was associated with inflow rates and state of community recovery, and it returned to pre-disturbance levels earlier than microbial composition. Our study exemplifies extraordinary compositional and functional resilience of small waterbodies and presents the detailed mechanism of the underlying processes.

Retrieval of Non-Optically Active Parameters for Small Scale Urban Waterbodies by a Machine Learning-Based Strategy

Water quality retrieval for small urban waterbodies by remote sensing get used to be difficult due to coarse spatial resolution of the remote sensing imagery. The recently launched Sentinel-2 produces imagery with a spatial resolution of 10 m. It provides an opportunity to solve the problem of retrieving water quality for small waterbodies. Additionally, many water management issues also require fine resolution of imagery, e.g. illegal discharge to an urban waterbody. Since illegal discharges are an important issue for urban water management, chemical oxygen demand (COD), total phosphorous (TP), and total nitrogen (TN) were chosen as the target parameters for water quality retrieval in this study. COD, TP and TN, however, are non-optically active parameters. There were limited studies in the past to retrieve these parameters in comparison with optically active parameters, e.g. Chlorophyll-A etc. This study compared three machine learning models, namely Random Forest (RF), Support Vector Regression (SVR), and Neural Networks (NN), to investigate the opportunity to retrieve the above non-optically active parameters. Results showed that R2 of TP, TN, and COD by NN, RF and SVR were 0.94, 0.88, and 0.86, respectively. The performances of water quality retrieval for these non-optically active parameters were significantly improved by the optimized machine learning models. These models hence solved the problem to use remote sensing data to retrieve these non-optically active water quality parameters and provided a new monitoring strategy for small waterbodies. Water quality mapping obtained by Sentinel-2 imagery provided a full spatial coverage of the water quality characterization for the entire water surface. Compared with water samples collecting and testing, it greatly reduced labor cost, reagents cost, and waste treatment cost. It also may help identify illegal discharges to urban waterbodies. The method developed in this research provides a new practical and efficient water quality monitoring strategy in managing water with consideration of environmental sustainability.