Search

Aquatic macrophytes in inland lakes provide a wide range of ecosystem services. Management efforts to restore aquatic macrophytes for ecosystem recovery need comprehensive understanding of the interaction between growth and nutrient removal efficiency. In contrasting hydrodynamic environments, we examined the growth and phosphorus accrual of two common submerged (Vallisneria natans) and emergent (Acorus calamus) macrophytes. Both biomass and nutrient storage of two macrophytes were observed with significant increases during the incubation period, suggesting the great potential of selected macrophytes in suppressing eutrophication in shallow lakes. Experimental results showed very opposite impacts of flowing water on two macrophyte types. The flowing water was observed to restrain submerged macrophyte's growth and nutrient accrual, but oppositely to stimulate the emergent type. Specifically, our study demonstrated an average reduction of 33.7% in individual plant biomass and 11.4% in nutrient inventory of V. natans, but an overall increase of 27.2% in biomass and 50.0% in nutrient inventory for A. calamus in flowing-water environments. We estimated a total removal of 470-ton phosphorus assuming the complete comeback of aquatic macrophytes in Lake Taihu, as much as 22% of the annual external loading. The results from this study could inform management of aquatic macrophytes at local and lake-wide scales. [ABSTRACT FROM AUTHOR]

Waterbodies (natural lakes and reservoirs) are a critical part of a watershed's ecological and hydrological balance, and in many cases dictate the downstream river flows either through natural attenuation or through managed controls. Investigating waterbody dynamics relies primarily on understanding their morphology and geophysical characteristics that are primarily defined by bathymetry. Bathymetric conditions define stage-storage relationships and circulation/transport processes in waterbodies. Yet many studies oversimplify these mechanisms due to unavailability of the bathymetric data. We developed a novel GLObal Bathymetric (GLOBathy) dataset of 1.4+ million waterbodies to align with the well-established global dataset, HydroLAKES. GLOBathy uses a GIS-based framework to generate bathymetric maps based on the waterbody maximum depth estimates and HydroLAKES geometric/geophysical attributes of the waterbodies. The maximum depth estimates are validated at 1,503 waterbodies, making use of several observed data sources. We also provide estimations for head-Area-Volume (h-A-V) relationships of the HydroLAKES waterbodies, driven from the bathymetric maps of the GLOBathy dataset. The h-A-V relationships provide essential information for water balance and hydrological studies of global waterbody systems. Measurement(s) lake depth • reservoir depth • bathymetry • Head-Area-Volume relationship Technology Type(s) machine learning • Geographic Information System • bathymetry data processing Sample Characteristic - Environment water body Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.16695070 [ABSTRACT FROM AUTHOR]

Fox River is the main source of land-based pollutants that flows into the southern Green Bay of Lake Michigan. Evaluation of water quality is normally based on time consuming and expensive in situ measurements. Remotely sensed data is an efficient alternative for field monitoring because of its spatial and temporal coverage. In this study, remote sensing imagery combined with in situ measurements of water quality were used to estimate an empirical relationship between water surface reflectance and water quality parameters including water turbidity and Total Suspended Sediment (TSS). Surface reflectance values is obtained from MODerate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua satellite. The empirical equations were derived from data over summers 2011-13 and show high correlation coefficients of equal to 0.83 and 0.87 for TSS and turbidity respectively. The validity of the proposed equations was tested for summer 2014 data. The NRMSE for prediction of measured data by the proposed equations are 0.36 and 0.3 for TSS and turbidity. Remotely sensed data was also used to produce water quality maps to improve our understanding of the spatiotemporal variations of Fox River turbid plume. The proposed approach can be extended to other coastal regions of Great Lakes and provide a framework to study pollution transportation in coastal areas. [ABSTRACT FROM AUTHOR]