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23 results for “impoundments”
Chamber level gas fluxes and soil biogeochemical properties from a tidal salt marsh and an impounded brackish wetland in South Carolina, USA
Archived data from a research project assessing the role of plants in driving methane fluxes from coastal wetland systems. Data collection occurred at the inland edge of a salt marsh and a diked, brackish impounded wetland in Georgetown County, South Carolina during 2022 and 2023. The first archived data table includes soil biogeochemical properties for all soil samples (0-10 cm and 10-50 cm mineral soil depth) collected approximately monthly from our two study sites. Specific biogeochemical properties include copy numbers of the mCRA gene, soil C and N concentrations, soil C:N ratios, soil moisture, pH, conductivity, organic matter content and alive and dead root biomass. The second archived data table includes methane and carbon dioxide fluxes from chambers over plants (whole-plant gas fluxes), adjacent to plants (plant-adjacent chambers) and in non-vegetated areas (non-vegetated fluxes) measured approximately monthly at our two study sites. Metadata associated with flux measurements are also included: leaf area, dead stems, oxidation reduction potential at four depths, atmospheric pressure, incoming solar radiation, relative humidity, chamber temperature, windspeed, water salinity, water temperature and water column depth.
Impoundments in the Chesapeake Bay coastal zone, 2016
Migration of salt marshes into adjacent uplands presents an opportunity to maintain ecosystem resilience in the face of sea level rise. However, infrastructure installed in low-lying coastal areas can both intentionally and unintentionally act as barriers to marsh migration. In the Chesapeake Bay, impoundments are commonly installed on salt-impacted agricultural fields to create habitat for waterfowl. Most of these structures are privately built and owned, with no centralized, public record, which makes it difficult to assess ecosystem impacts. We use deep learning tools in ArcGIS Pro 3.2 to identify impoundments installed within the Chesapeake Bay coastal zone (0 - 5 m above sea level). We trained a Mask Region-based Convolutional Neural Network (R-CNN) model to detect impoundments using a dataset of slope generated from the U.S. Geological Survey (USGS) Coastal National Elevation Database (CoNED) high resolution (1 m cell width) Topobathymetric Digital Elevation Model (TBDEM). Training samples were delineated in Somerset County, Maryland because of the extensive and rapid salinization of coastal farmland which has led to the widespread installation of waterfowl impoundments. The final set of training samples contained 3,900 images (512 x 512 m cell chip size), of which 90% were used for training and 10% were set aside for validation. The final model selected for impoundment detection had a precision score of 0.9609, which suggested that it performed well over the training area, and was then applied to the entire study region. We conducted extensive post-processing and visual examination of identified impoundments to account for any errors associated with applying the model to a broader region. The final dataset contained 1,684 impoundments which cover 6.6 km^2 (1,627 acres). The CoNED TBDEM was published in 2016, making these results a conservative estimate of impoundments in the Chesapeake Bay today.
A Spatially Variable Time Series of Sea Level Change Due to Artificial Water Impoundment
<p>This database contains a series of gravitational, rotational, and deformational (GRD) "fingerprints"—the spatial response of sea level—corresponding to redistribution of water mass because of impoundment of water in artificial reservoirs, as reported in Hawley <em>et al</em>. (2020). Fingerprints for the GRanD database (Lehner <em>et al</em>.; 2011) are for individual years, noted in the file name.</p> <p>Three additional files come from the dataset provided by Zarfl <em>et al</em>. (2015), as described in Hawley <em>et al.</em> (2020). "Const" includes the fingerprint for all reservoirs under construction in their database; "Plan" includes the fingerprint for all reservoirs in the planning phase. "Zarfl" includes the fingerprint for all reservoirs in "Const," with 15 years of seepage, as well as all reservoirs for "Plan" with 5 years of seepage, as described in Hawley <em>et al</em>. (2020).</p> <p>Each fingerprint has 525,825 points, which fill out a global grid of 513 x 1025 [lat x lon] points. Each node in latitude and longitude is evenly spaced. The first point represents the northernmost point at 0 [deg] longitude, and increase first to the east, then to the south.</p>
Fig. 2 in Resource use by the facultative lepidophage Roeboides affinis (Günther, 1868): a comparison of size classes, seasons and environment types related to impoundment
Fig. 2. Medians of percentage volume of scales consumed by Roeboides affinis in the upper Tocantins river, from 1995 to 2000, in: (a) different environmental types (Lotic, n = 38, vs. Lentic, n = 52); (b) seasons (Dry, n = 25, vs. Wet, n = 68); (c) size classes 1 (n = 12), 2 (n = 38), 3 (n = 26), 4 (n = 9) and 5 (n = 5) and (d) different post-impoundment phases (Filling, n = 9, vs. Operation, n = 2) in lentic sites.
Fig. 1 in Resource use by the facultative lepidophage Roeboides affinis (Günther, 1868): a comparison of size classes, seasons and environment types related to impoundment
Fig. 1. CA ordination of 155 individuals of Roeboides affinis of two groups formed by five size classes (Group1, n = 33, and Group 2, n = 22) according to the consumption of food items (volumetric proportions) in the upper Tocantins River in two environment types (Lotic, n = 61, vs. Lentic, n = 94) related to its impoundment by the Serra da Mesa Hydroelectric Dam, from 1995 to 2000.
Fig. 3 in Diet and trophic structure of the fish fauna in a subtropical ecosystem: impoundment effects
Fig. 3. Proportion in number and biomass (CPUE) of the trophic guilds along the longitudinal gradient of the Salto Caxias Reservoir, Iguaçu River, before and after the impoundment. (1 = upstream; 2 = middle region; 3 = dam; 4 = downstream) (Alg = algivores; Det = detritivores; Her = herbivores; Ain = aquatic insectivores; Tin = terrestrial insectivores; Inv = invertivores; Omn = omnivores; Pis = piscivores; Pla = planktivores; Car = carcinophages).
Fig. 1 in Diet and trophic structure of the fish fauna in a subtropical ecosystem: impoundment effects
Fig. 1. Location of sampling sites along the longitudinal gradient of the Iguaçu River, in the area influenced by the Salto Caxias Reservoir, Paraná State. a) before the impoundment; b) after the impoundment. (site 1 = upstream; site 2 = middle region; site 3 = dam; site 4 = downstream).
Fig. 2 in Diet and trophic structure of the fish fauna in a subtropical ecosystem: impoundment effects
Fig. 2. Graphical representation of the first two axes of the Nonmetric multidimensional scaling (nNMDS), demonstrating the food resources used by the fish fauna in the different sites and phases, in the area influenced by the Salto Caxias Reservoir, Iguaçu River. FR = Food resources (AI = aquatic insects; TI = terrestrial insects; DE = decapods; MC = microcrustaceans; MA = macroinvertebrates; MI = microinvertebrates; FI = fish; FS = fish scales; AP = aquatic plants; TP = terrestrial plants; AL = algae; DS = detrit/sediment); B = before impoundment, A = after impoundment; 1 to 4 = sampling sites.
Fig. 2 in Alterations on piscivorous diet following change in abundance of prey after impoundment in a Neotropical river
Fig. 2. Abundance of Moenkhausia dichroura and "other species" in the period I (From March 2000 to February 2001) and II (From March 2003 to February 2004), after impoundment of Manso River, Mato Grosso State, Brazil. Vertical bars represents the mean ± S.D.
Fig. 5 in Alterations on piscivorous diet following change in abundance of prey after impoundment in a Neotropical river
Fig. 5. Regression analysis between predator length (Acestrorhynchus pantaneiro) and prey length for sampling periods I (a) and II (b) at Manso Reservoir, Mato Grosso State, Brazil, followed by their respective equations fitted by the model (n = 379; 255-period I and 124- period II).
Predicting the density of zooplankton subsidy to a stream with multiple impoundments using water quality parameters
<p>Damming a stream inserts a lentic system (an impoundment or reservoir) into a lotic system, changing downstream hydrological, biogeochemical, and ecological processes. One such ecological effect of damming is to create a resource subsidy of easily captured and consumed zooplankton, which are preyed upon by filter-feeders and visual predators. The data included here were used to predict the density of lentic zooplankton subsidizing downstream habitats with water quality parameters as an alternative to microscopy. We also used this data to detect three different water quality regimes (high conductivity, high-CDOM, and a remainder) that are associated with differences in the density of zooplankton. This dataset is contained in two parts, both of which are focused on zooplankton density in the effluent of a series of tributary-impoundment reservoirs: 1) zooplankton density for a single summer season with water quality parameters and 2) zooplankton density for a series of three summers without water quality parameters.</p>
Predicting the density of zooplankton subsidy to a stream with multiple impoundments using water quality parameters
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Dataset for the manuscript "Potential Seismicity by Impoundment of the Baihetan Reservoir, Xiaojiang Fault Zone, Southwest China"
<p>Files corresponding to submitted manuscript "Potential Seismicity by Impoundment of the Baihetan Reservoir, Xiaojiang Fault Zone, Southwest China". Dataset used for the numerical calculation of Baihetan Reservoir. Model parameters, mesh file and the main simulation results are included. </p>
FIG. 2 in Trends in River Discharge and Water Temperature Cue Spawning Movements of Blue Sucker, Cycleptus elongatus, in an Impounded Great Plains River
FIG. 2. Delineation of seasons (vertical lines) and average discharge and water temperature with 95% confidence bands (2006–2014 and averaged among all monitoring stations) on the Missouri River in Montana within our study area when environmental covariates were measured (1 April–24 October) for movement distance and probability modeling.
FIG. 5 in Trends in River Discharge and Water Temperature Cue Spawning Movements of Blue Sucker, Cycleptus elongatus, in an Impounded Great Plains River
FIG. 5. Log-transformed and model averaged movement rate (river kilometer per week, rkm/wk; with 95% confidence bands) and predicted probability of movement (with 95% confidence bands) with increasing water temperature (8C; 'A' and 'C,' respectively) and discharge (cubic meters per second, cms; 'B' and 'D,' respectively) for transmittered Cycleptus elongatus in the Missouri River in Montana from 2006–2014. For 'A' and 'B,' the y-axis has been back-transformed for clarity.
FIG. 1 in Trends in River Discharge and Water Temperature Cue Spawning Movements of Blue Sucker, Cycleptus elongatus, in an Impounded Great Plains River
FIG. 1. Study area on the Missouri River in Montana ranging from upstream of the confluence with the Marias River to upstream of the headwaters of Fort Peck Reservoir. Locations of remote stations are denoted with a double-crossed vertical line and labeled with general location names. Locations of select dams are represented with stars on the inset map.
Supplement scripts for the paper " The importance of impoundment interception in simulating riverine dissolved organic carbon"
<p>The supplement scripts used in the paper, "The importance of impoundment interception in simulating riverine dissolved organic carbon".</p>
Data from: 100-year time-series reveal little morphological change following impoundment and predator invasion in two Neotropical characids
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Comparative aquatic greenhouse gas emission rates across multiple land-use types and along impounded river systems
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Fig. 3 in Alterations on piscivorous diet following change in abundance of prey after impoundment in a Neotropical river
Fig. 3. Dominance curve of prey items in the diet of Acestrorhynchus pantaneiro, based on Index of Relative Importance (IRI) in each sampling period at Manso Reservoir, Mato Grosso State, Brazil (n = 314; 187-period I and 127- period II). B = trophic niche breadth.
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