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27 results for “Nutrient loading”
Daily runoff and nutrient loads for the North Sea and the Baltic Sea based on modelling and observations for the period 1961 to 2019 and adapted to NEMO-SCOBI
<p>This dataset consists of daily values of runoff and reconstructed nutrient loads for the period 1961 to 2019 for the North Sea-Baltic Sea system. Both runoff and nutrient loads were obtained from a model simulation performed with the European application of the Hydrological Predictions for the Environment model v.3.1.8 (E-HYPE). This dataset includes a more realistic number of river outlets than those from observational-based datasets, as not all rivers are monitored, and captures well the interannual variability of all parameters. However, the E-HYPE v.3.1.8 was calibrated to represent 2010 and thus cannot simulate all historical changes related to land management (i.e., the increase of fertilizers in the 1960s). Consequently, the observed rise of nutrients from land due to increased fertilizers and the consequent reduction due to nutrient regulation policy in the 1980s is not captured in the outputs from E-HYPE directly. In the North Sea and the Baltic Sea, this is of primary importance for management policy in eutrophication and deoxygenation. Therefore, we have adapted the E-HYPE nutrient loads based on yearly estimates of historical loads that use riverine concentrations, so that the high tempo-spacial resolution is kept, but with a decadal variability that is closer to reality. This dataset is mainly intended as river forcing for biogeochemical-ocean models (i.e. NEMO-SCOBI), but can also provide information on rivers that are not included in monitoring programs. Information on the dataset and the methods used to create it is given as a downloadable PDF file (E-HYPE DecVar documentation.pdf) together with two datasets and the mesh grid file (area_NEMO-Nordic.nc). The datasets are yearly netCDF files one containing daily runoff and nutrient loads for phosphate, nitrate, ammonium, organic nitrogen and organic phosphorus (zip_ehypeDecVar.zip) and the other one provides monthly silica loads (zip_silica.zip). </p>
High-frequency dissolved oxygen, water temperature, wind speed, and radiation data; stream and in-lake nutrient concentration data; and daily metabolism and nutrient loading estimates for 16 lakes in North America and Northern Europe.
In lakes, ecosystem structure and processes are influenced by gross primary production (GPP), ecosystem respiration (R), and net ecosystem production (NEP). The rates of these metabolic processes are often controlled by resource availability, which often reflects catchment loads. Although the relationship between catchment loads and in-lake nutrient concentrations may be well defined in specific lakes, we explored how watershed vs. in-lake predictors of metabolism compare across lake types. To do this, we combined stream loads of carbon (C), nitrogen (N), and phosphorus (P) with high frequency in situ monitoring of lake metabolism and in-lake C, N, and P concentrations from 16 lakes spanning a range of latitudes (39 to 64 degrees N), inflowing stream (0 - 6 streams), and trophic status (oligotrophic to eutrophic). The data package includes high-frequency dissolved oxygen, water temperature, wind speed, and solar radiation data as well as daily estimates of GPP, R, and NEP derived from those data. In addition, the data package includes in-lake and stream concentrations of dissolved organic carbon, total nitrogen, and total phosphorus and stream discharge data. The package also includes estimates of daily carbon, nitrogen and phosphorus loading to each lake derived from the stream concentrations and discharge.
Nutrient Loading and Benthic Macroinvertebrate Assessments in Michigan Streams (2002-2021)
The assembled data package, including both nutrients and benthic macroinvertebrate assessments from stream monitoring sites in Michigan (USA), is part of the project entitled "Long-Term Nitrogen and Phosphorus Dynamics and Macroinvertebrate Assemblages in Temperate Streams." These datasets encompass records from January 1, 2002, until December 31, 2021, that were extracted from the Water Quality Portal (www.waterquality.us). We were aiming to examine the influence of landscape settings on the concentration of N and P constituents in streams and rivers, and determine the effects of land use on benthic macroinvertebrates in the interaction with these nutrients over 20 years. Records were cleaned and analyzed to determine the influence of land use settings on nutrient concentrations and benthic macroinvertebrate composition statewide. All data tables feature monitoring sites with sampling dates, identifiers, and GPS coordinates.
Time series of total metal and nutrient loads into Falling Creek Reservoir in southwestern Virginia, USA from 2019-2024
Metal and nutrient loads were calculated from 2019-2024 from the inflow stream to Falling Creek Reservoir (FCR), a drinking water reservoir located in Vinton, Virginia, USA. The reservoir is owned and operated by the Western Virginia Water Authority and is managed as a secondary drinking-water source for the city of Roanoke, VA. Only Fe, Mn, and nutrients (TN and TP) were analyzed and calculated in 2019. The full suite of metals (Li, Na, Mg, Al, K, Ca, Fe, Mn, Cu, Sr, Ba) and nutrients were analyzed from 2020-2024. The loads that were collected using an ISCO automated sampler located at the main inflow tributary to FCR. Sampling frequency was approximately fortnightly from spring to fall (March - November). Load calculations were performed using the calculated cumulative flow over the sampling period from the ISCO and the analyzed total metal and nutrient concentrations. Please note we are publishing this data package before the nutrient samples have been analyzed, but will be included in later versions.
Annual nutrient loading and yield to Plum Island Estuary, as measured at the Ipswich and Parker Dams
Nutrient concentrations for various forms of N, P, C, as well as suspended sediments, are determined from monthly grab samples taken at the Ipswich and Parker dams. These nutrient concentrations are then used in conjunction with USGS discharge data (recorded at gages in the Parker River at Byfield, MA and the Ipswich River at Ipswich, MA) to calculate annual nutrient loading to the Estuary, coming over each dam. Annual yield is also calculated for both dams.
Output files corresponding to "Direct groundwater discharge and vulnerability to hidden nutrient loads along the Great Lakes coast of the United States"
<p>This dataset corresponds to the output files that were produced for the study reported in:</p> <p>Knights, Deon, Kevin C. Parks, Audrey H. Sawyer, Cédric H. David, Trevor N. Browning, Kelsey M. Danner, and Corey D. Wallace, (2017), Direct groundwater discharge and vulnerability to hidden nutrient loads along the Great Lakes coast of the United States, <em>Journal of Hydrology,</em> 554, 331-341</p> <p><strong>Data sources</strong></p> <p>The following sources were used to produce files in this dataset:</p> <ul> <li>The National Hydrography Dataset Plus (NHDPlus) Version 2, obtained from http://www.horizon-systems.com/nhdplus/NHDplusV2_data.php. Region used is: Great Lakes (04)</li> <li>The second phase of the North American Land Data Assimilation System (NLDAS2), obtained from ftp://hydro1.sci.gsfc.nasa.gov/data/s4pa/NLDAS. Model outputs used are: NLDAS_MOS0125_MC.002, NLDAS_NOAH0125_MC.002, and NLDAS_VIC0125_MC.002.</li> <li>The United States 2011 National Land Cover Database (NLCD 2011), obtained from: http://www.mrlc.gov/nlcd2011.php.</li> </ul> <p> </p> <p><strong>Description of files</strong></p> <p>The files in this dataset contain are described below:</p> <ul> <li><em>Flowlines</em>: This folder contains a shapefile (<em>GL_coastcatchment_NHDflowline</em>) with the coastline of the Contiguous United States as described by NHDPlus V2, and was merged from a subsample of all river reaches available in the region used. </li> <li><em>Catchment</em>: This folder contains a shapefile (GL_coastcatchment_polygon) with the contributing catchments of NHDPlus V2 corresponding to the above coastline, and was merged from a subsample of all catchments available in the region used. </li> <li><em>Centroid</em>: This folder contains a shapefile (GL_coastcatchment_centroid) with the centroids of the above catchments. </li> <li><em>DischargeVulnerabilities.csv</em>. This .csv file contains the following data (units are in parentheses): <ul> <li>COMID: Unique feature identifier in NHDPlusV2 ().</li> <li>Length_km: Length of coastline feature (km).</li> <li>Area_sqkm: Area of coastal catchment feature (km<sup>2</sup>).</li> <li>Infiltration_kgsqm: Average annual infiltrating runoff for REACHCODE (kg/m<sup>2</sup>)</li> <li>REACHCODE: Reach identifier in NHDPlusV2; reaches can include multiple features; Submarine Groundwater Discharge (SGD) is computed by reach, not feature ().</li> <li>RLength_km: Total length of coastline accumulated by REACHCODE (km).</li> <li>RArea_sqkm: Total area of coastal catchment accumulated by REACHCODE (km<sup>2</sup>).</li> <li>RInfiltration_kgsqm: Average annual infiltrating runoff for REACHCODE (kg/m<sup>2</sup>)</li> <li>DGWD: Average annual direct groundwater discharge for REACHCODE (m<sup>2</sup>/y).</li> <li>Vulnerable_percent: Percentage of reach area with developed or agricultural land use in 2011 (%).</li> <li>Vulnerable: Vulnerability to coastal contamination (0- not vulnerable; 1-vulnerable)</li> </ul> </li> </ul> <p> </p>
The interaction of physical structure and nutrient loading drives ecosystem change in a large tropical lake over 40 years (DATA)
<p><strong><span>Datasets for manuscript entitled "The interaction of physical structure and nutrient loading drives ecosystem change in a large tropical lake over 40 years"</span></strong></p> <p><em><span>Includes data collected by Fadum and Hall as well as unplublished data from Vaux and Goldman 1984.</span></em></p> <p><span><strong>Abstract: </strong>Many lakes across the world are entering novel states and experiencing altered biogeochemical cycling due to local anthropogenic stressors. In the tropics, understanding the drivers of these changes can be difficult due to a lack of documented historic conditions or an absence of continuous monitoring that can distinguish between intra- and inter-annual variation. Over the last forty years (1980–2020), Lake Yojoa (Honduras) has experienced increased watershed development as well as the introduction of a large net-pen Tilapia farm, resulting in a dramatic reduction in seasonal water clarity, increased trophic state and altered nutrient dynamics, shifting Lake Yojoa from an oligotrophic (low productivity) to mesotrophic (moderate productivity) ecosystem. To assess the changes that have occurred in Lake Yojoa as well as putative drivers for those changes, we compared Secchi depth (water clarity), dissolved inorganic nitrogen (DIN), and total phosphorus (TP) concentrations at continuous semi-monthly intervals for the three years between 1979 and 1983 and again at continuous 16-day intervals for 2018–2020. Between those two periods we observed the loss of a clear water phase that previously occurred in the months when the water column was fully mixed. Seasonal peaks in DIN coincident with mixing suggest that an enhanced accumulation of ammonium in the hypolimnion (the bottom layer of a stratified lake) during stratification, and release to the epilimnion (the top layer of a stratified lake) with mixing maintains high algal abundance and subsequently low Secchi depth during what was previously the clear water phase. This interaction of nutrient loading and Lake Yojoa's monomictic stratification regime illustrates a key phenomenon in how physical water column structure and nutrients interact in tropical monomictic lakes. This work highlights the need to consider nutrient dynamics of warm anoxic hypolimnions, not just surface water nutrient concentrations, to understand environmental change in these societally important but understudied ecosystems.</span></p> <p> </p> <p><em><strong><span>(for more recent years of data collection see additional zenodo repositories by Fadum and/or Hall) </span></strong></em></p>
Dataset - Nutrients and biomass dynamics in photo-sequencing batch reactors treating wastewater with high nutrients loadings
<p>The data set attached consists of one excel file where the data from the article “<strong>Nutrients and biomass dynamics in photo-sequencing batch reactors treating wastewater with high nutrients loadings”</strong>, published in Ecological Engineering (vol. 119, 15<sup>th</sup> August 2018, 35-44.), can be found, as well as the different equations and formulae that were used to obtain the published results.</p>
Eutrophication indicators in the Baltic Sea 1970-2100 and nutrient loads to three coastal systems. BALTSEM model simulations and observations.
<p>Dataset and model code accompanying manuscript: </p> <p>Ehrnsten, E. Humborg C., Gustafsson, E. and Gustafsson B. G. 2024. Disaster avoided: current state of the Baltic Sea without human intervention to reduce nutrient loads. Resubmitted to Limnology & Oceanography Letters 2024-09-13. </p> <p> </p> <p>This repository contains the following files:</p> <p> </p> <p>1_Data_description.pdf</p> <p>Description of data sets and details on model forcing and data collection methods.</p> <p> </p> <p>Eutrophication_indicators1970-2021_BALTSEM_and_observations.xlsx</p> <p>Eutrophication indicators in the Baltic Sea: BALTSEM model simulation output from real load and no reduction scenarios as well as observations 1970-2021.</p> <p> </p> <p>BALTSEM_output_future_1970-2100.xlsx</p> <p>BALTSEM model simulation output 1970-2021 with observed nutrient loads (Real loads scenario) and statistics of 100 model runs 2022-2100 with present (2021) nutrient loads. The 100 runs represent statistical variations in forcing and boundary conditions to account for uncertainty in future weather and sea level conditions.</p> <p> </p> <p>NPloads_BS_M_C.xlsx</p> <p>Nitrogen and phosphorus loads from the Baltic Sea, Mississippi and Changjiang catchments 1950-2021 collected from several published sources.</p> <p> </p> <p>baltsem9.5_carbon.tar.gz</p> <p>Copressed folder with model code for BALTSEM 9.5 as well as forcing data used in the simulations. Information on folder contents and a user guide to run the model simuations can be found in the file BALTSEMGettingStartedCarbon.pdf</p>
Rates of greenhouse gas (carbon dioxide, methane and nitrous oxide) fluxes, denitrification-derived N2O and N2 fluxes and nitrification-derived N2O fluxes from salt marsh soils in Quebec, Canada and Louisiana, U.S. under ambient and elevated temperature and nutrient loading.
<p>Dataset used in <a href="https://link.springer.com/article/10.1007/s10533-023-01104-0?utm_source=rct_congratemailt&utm_medium=email&utm_campaign=oa_20231214&utm_content=10.1007/s10533-023-01104-0#citeas">Elevated temperature and nutrients lead to increased N<sub>2</sub>O emissions from salt marsh soils from cold and warm climates</a>.</p> <p>The dataset contains fluxes calculated from headspace gas samples taken over a 24 hour period from intact soil cores, as well as corresponding environmental data. Intact soil cores (0-15 cm depth, 2.5 cm diameter) were taken at five sampling locations along a 20 m transect using a soil auger or piston corer. Samples were collected along a transect in four marsh sites in Quebec, Canada (La Pocatière: 47°22'24.7"N 70°03'26.3"W) and Louisiana, U.S. (Barataria Basin: 29°33'47.3"N 90°04'22.8"W and 29°29'52.2"N 89°55'00.2"W) from two vegetation types (<em>Sporobolus alterniflorus</em> formerly known as <em>Spartina alterniflora </em>and<em> Sporobolus pumilus</em> formerly known as<em> Spartina patens</em>). In Quebec, the two vegetation zones were in the same marsh whereas in Louisiana two separate marshes, dominated by the relevant vegetation, were chosen. Soil samples were collected on the 20-21<sup>st</sup> July 2021 from Louisiana and the 9-10<sup>th</sup> August 2021 from Quebec. Environmental data was collected including <em>in-situ</em> soil temperature and salinity, and gravimetric soil moisture, extractable soil dissolved organic carbon (DOC), extractable soil total dissolved nitrogen (TDN), extractable soil nitrate, extractable soil ammonium, extractable soil soluble reactive phosphate, soil total carbon, soil total nitrogen, soil carbon to nitrogen ratio, soil d<sup>13</sup>C and soil d<sup>15</sup>N determined from additional 0-15 cm core samples. This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement no. 838296, a NSERC Discovery Grant and a Natural Environment Research Council grant number (NE/T012323/1).</p> <p>Stable <sup>15</sup>N tracers were added to the intact soil cores so that at each location, at each treatment level (ambient and elevated, described below), there was one core receiving no tracer for greenhouse gas fluxes, one core receiving <sup>15</sup>N-NO<sub>3</sub><sup>‑ </sup>for denitrification rates and one core receiving <sup>15</sup>N-NH<sub>4</sub><sup>+</sup> for nitrification rates. The cores were incubated at ambient temperature (16 ℃ and 28.1 ℃ for Quebec and Louisiana, respectively) and nutrient concentrations (3.2 NO<sub>3</sub><sup>-</sup>, 2.0 NH<sub>4</sub><sup>+</sup>; 2.9 NO<sub>3</sub><sup>-</sup>, 2.5 NH<sub>4</sub><sup>+</sup>; 0.5 NO<sub>3</sub><sup>-</sup>, 7.3 NH<sub>4</sub><sup>+ </sup>and 5.7 NO<sub>3</sub><sup>-</sup>, 2.8 NH<sub>4</sub><sup>+</sup> mg g wet soil<sup>-1</sup> for Quebec <em>S. alterniflorus</em>, Quebec <em>S. pumilus</em>, Louisiana <em>S. alterniflorus</em> and Louisiana <em>S. pumilus</em>, respectively), and elevated temperature (ambient temperature +5 ℃) and nutrient concentration (double ambient concentration). Gas samples were collected from the headspace of 0-15 cm intact cores in a 20 cm high PVC pipe, capped at the top and bottom to create a 5 cm headspace. Gas samples were analysed for greenhouse gases (GHGs: N<sub>2</sub>O, CH<sub>4</sub>, CO<sub>2</sub>) and <sup>15</sup>N in denitrification-derived N<sub>2</sub>O, denitrification-derived N<sub>2</sub> and nitrification-derived N­<sub>2</sub>O.</p> <p>Soil temperature (YSI 30, Baton Rouge, USA or DeltaTrak 11050, Pleasanton, USA) and porewater salinity (YSI 30, Baton Rouge, USA or portable ATC refractometer) were measured in-situ or in the laboratory using the portable refactometer. Additional soil samples were used for multiple analyses; one subsample was extracted with ultrapure water (18.2 MΩ) for DOC and TDN analysis, one subsample was extracted with 2M KCl for NO<sub>3</sub><sup>-</sup> and NH<sub>4</sub><sup>+</sup>, one subsample was extracted with Olsen-P solution (0.5 M NaHCO<sub>3</sub>, pH 8.5), for soluble reactive phosphate analysis and one subsample was weighed and dried for soil moisture and then finely ground and analysed for total carbon, total nitrogen, d<sup>13</sup>C and d<sup>15</sup>N.</p> <p>N<sub>2</sub>O, CH<sub>4</sub> and CO<sub>2</sub> concentrations were measured in the gas samples using a gas chromatograph interfaced with a PAL3 autosampler (Agilent 7890A, Agilent Technologies Ltd, USA) fitted with a flame ionisation detector (FID) for CH<sub>4</sub> analysis and a micro electron capture detector (mECD) for N<sub>2</sub>O analysis. CO<sub>2</sub> was methanised to CH<sub>4</sub> before analysis on the FID. The instrument precision as the relative standard deviation was < 5 % for all of the gases, while the minimum detectable concentration difference (MDCD) was 9 ppb N<sub>2</sub>O, 72 ppb CH<sub>4 </sub>and 31 ppm CO<sub>2</sub>. Potential GHG fluxes were calculated from the linear portion or where the highest production was observed in the concentration-time series ( https://doi.org/10.2134/jeq2003.2436). If fluxes were below the MDCD they were set to zero see (https://doi.org/10.1002/2017JG003783). The <sup>15</sup>N content of the N<sub>2</sub> and N<sub>2</sub>O was determined using a continuous flow isotope ratio mass spectrometer (Elementar Isoprime PrecisION; Elementar Analysensysteme GmbH, Hanau, Germany) coupled with a trace-gas pre-concentrator inlet with autosampler (isoFLOW GHG; Elementar Analysensysteme GmbH, Hanau, Germany), with a standard deviation of d<sup>15</sup>N < 0.05 %. Extractable dissolved organic carbon and total dissolved nitrogen were analysed in soil extractant (ultrapure water 18.2 MΩ, 7:1 of extractant to soil) on a TOC/TDN analyser (TOC VCSn + TMN-1, Shimadzu, Kyoto, Japan), with 50 mg C l<sup>-1</sup> and 10 mg l<sup>-1</sup> standards resulting in accuracy and precision of 0.3 and ±0.3 mg C l<sup>-1</sup>, and 0.5 and ±0.3 mg N l<sup>-1</sup>, respectively. Extractable nitrate+nitrite (assumed to be nitrate) and ammonium were analysed in soil extractant (2M KCl, 5:1 of extractant to soil) using a microplate reader and methods in Sims et al., 1995 (<a href="https://doi.org/10.1080/00103629509369298">https://doi.org/10.1080/00103629509369298</a>) with a limit of detection of 0.1 ppm and accuracy of ±5 %. Extractable phosphate was analysed in soil extractant (Olsen-P solution 0.5M NaHCO­<sub>3</sub>, pH 8.5, 10:1 of extractant to dry soil) using a microplate reader and methods in Jeannotte et al., 2004 (https://doi.org/10.1007/s00374-004-0760-4) with a limit of detection of 1 mg P l<sup>-1</sup> and accuracy of ±6 %. Soil total carbon, total nitrogen, d<sup>13</sup>C and d<sup>15</sup>N analysis was performed using a continuous flow isotope ratio mass spectrometer (Elementar Isoprime PrecisION; Elementar Analysensysteme GmbH, Hanau, Germany) coupled with an elemental analyser (EA) inlet (vario PYRO cube; Elementar Analysensysteme GmbH, Hanau, Germany). The precision was < 5 % for both C and N and the precision as a standard deviation was < 0.06 % for both d<sup>13</sup>C and d<sup>15</sup>N. Results from the experiments were entered into an Excel spreadsheet for ingestion into the Zenodo data repository.</p>
Data for: Interactive effects of light and snail herbivory rather than nutrient loading determine early establishment of submerged macrophytes
<p><span>Submerged macrophytes play a key role in maintaining a clear-water phase and promoting biodiversity in shallow aquatic ecosystems. Since their abundance has declined globally due to anthropogenic activities, it is important to include them in aquatic ecosystem restoration programs. Macrophytes establishment in early spring is crucial for the subsequent growth of other warm-adapted macrophytes. However, factors affecting this early establishment of submerged macrophytes have not been fully explored yet. Here, we conducted an outdoor experiment from winter to early spring using the submerged macrophytes <em>Potamogeton crispus</em> and <em>Vallisneria spinulosa</em> to study the effects of shading, nutrient loading, snail herbivory (<em>Radix swinhoei</em>) and their interactions on the early growth and stoichiometric characteristics of macrophytes. The results show that the effects strongly depend on macrophyte species. Biomass and number of shoots of <em>P. crispus</em> decreased, and internode length increased during low light conditions, but were not affected by nutrient loading. <em>P. crispus</em> shoot biomass and number showed hump-shaped responses to increased snail biomass under full light. In contrast, the biomass of the plant linearly decreased with snail biomass under low light.</span> <span>This indicates an interaction of light with snail herbivory. Since snails prefer grazing on periphyton over macrophytes, a low density of snails promoted growth of <em>P. crispus</em> by removing periphyton competition, while herbivory on the macrophyte increased during a high density of snails.</span> <span>The growth of <em>V. spinulosa</em> was not affected by any of the factors, probably because of growth limitation by low temperature. Our study demonstrates that the interaction of light with snail herbivory may affect establishment and growth of submerged macrophytes in early spring. Macrophyte restoration projects may thus benefit from lowering water levels to increase light availability and making smart use of cold-adapted herbivores to reduce light competition with periphyton.</span></p>
Data for: Interactive effects of light and snail herbivory rather than nutrient loading determine early establishment of submerged macrophytes
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Nutrient concentrations, loading, and N:P stoichiometry (1983 - 2020) and impacts in Flathead Lake (Montana, USA)
<p><span>Considerable attention is given to absolute nutrient levels in lakes, rivers, and oceans but less is paid to their relative concentrations, their N:P stoichiometry, and to the consequences of imbalanced stoichiometry. Here we report 38 years of nutrient dynamics in Flathead Lake, a large oligotrophic lake in Montana (USA), and its inflows. While nutrient levels were low, the lake had sustained high total N : total P ratios (TN:TP: 60-90:1 molar) throughout the observation period. N and P loading to the lake as well as loading N:P ratio varied considerably among years but showed no systematic long-term trend. Surprisingly, TN:TP ratios in river inflows were consistently lower than in the lake, suggesting that forms of P in riverine loading are removed preferentially to N. In-lake processes, such as differential sedimentation of P relative to N or accumulation of fixed N in excess of denitrification, likely also operate to maintain the lake's high TN:TP ratios. Regardless of causes, the lake's stoichiometric imbalance is manifested in P limitation of phytoplankton growth during early and mid-summer, resulting in high C:P and N:P ratios in suspended particulate matter that propagate P limitation to zooplankton. Finally, the lake's imbalanced N:P stoichiometry appears to raise the potential for aerobic methane production via metabolism of phosphonate compounds by P-limited microbes. These data highlight the importance of not only absolute N and P levels in aquatic ecosystems but also their stoichiometric balance and call attention to potential management implications of high N:P ratios.</span></p>
Livestock as vectors of organic matter and nutrient loading in aquatic ecosystems in African savannas
<p>Populations of large wildlife have declined in many landscapes around the world, and have been replaced or displaced by livestock. The consequences of these changes on the transfer of organic matter (OM) and nutrients from terrestrial to aquatic ecosystems are not well understood. We used behavioural data, excretion and egestion rates and C: N: P stoichiometry of dung and urine of zebu cattle, to develop a metabolism-based estimate of loading rates of OM (dung), C, N and P into the Mara River, Kenya. We also directly measured the deposition of OM and urine by cattle into the river during watering. Per head, zebu cattle excrete and/or egest 25.6 g dry matter (DM, 99.6 g wet mass; metabolism) - 27.7 g DM (direct input) of OM, 16.0-21.8 g C, 5.9-9.6 g N, and 0.3-0.5 g P per day into the river. To replace loading rates OM of an individual hippopotamus by cattle, around 100 individuals will be needed, but much less for different elements. In parts of the investigated sub-catchments loading rates by cattle were equivalent to or higher than that of the hippopotamus. Changing these patterns of OM and nutrients transport and cycling are having significant effects on the structure and functioning of both terrestrial and aquatic ecosystems.</p>
Synergistic effects of warming and internal nutrient loading interfere with the long-term stability of lake restoration and induce sudden re-eutrophication
<p><strong>This repository contains the dataset linked to the following publication:</strong></p> <p><strong>Article title: </strong>Synergistic effects of warming and internal nutrient loading interfere with the long-term stability of lake restoration and induce sudden re-eutrophication</p> <p><strong>Journal: </strong><em>Environmental Science & Technology</em></p> <p><strong>DOI</strong>: 10.1021/acs.est.2c07181</p> <p><strong>Abstract:</strong> Phosphorus (P) precipitation is among the most effective treatments to mitigate lake eutrophication. However, after a period of high effectiveness, studies have shown possible re-eutrophication and the return of harmful algal blooms. While such abrupt ecological changes were attributed to the internal P loading, the role of lake warming and its potential synergistic effects with internal loading, thus far, has been understudied. Here, in a eutrophic lake in central Germany, we quantified the driving mechanisms of the abrupt re-eutrophication and cyanobacterial blooms in 2016 (30 years after the first P precipitation). A process-based lake ecosystem model (GOTM-WET) was established using a high-frequency monitoring dataset covering contrasting trophic states. Model analyses suggested that the internal P release accounted for 68% of the cyanobacterial biomass proliferation, while lake warming contributed to 32%, including direct effects via promoting growth (18%) and synergistic effects via intensifying internal P loading (14%). The model further showed that the synergy was attributed to prolonged lake hypolimnion warming and oxygen depletion. Our study unravels the substantial role of lake warming in promoting cyanobacterial blooms in re-eutrophicated lakes. The warming effects on cyanobacteria via promoting internal loading need more attention in lake management, particularly for urban lakes.</p> <p><strong>SYNOPSIS: </strong>Warming synergistically promotes re-eutrophication with internal nutrient loading and exacerbates cyanobacterial blooms in urban lakes 30 years after phosphorus mitigation.</p> <p> </p> <p><strong>Data description </strong>by Xiangzhen Kong (<a href="mailto:xzkong@niglas.ac.cn">xzkong@niglas.ac.cn</a>), 2023-02-20</p> <p>---Wet chemical analysis on water samples taken at five depths (0.5, 2.5, 5.0, 7.0 and 9.0 m) from the deepest point in the lake (BA1) at biweekly intervals from 2018.5-2021.8.</p> <p> File name:</p> <ul> <li>BAB_BA1_TN_mgL.obs (total nitrogen concentration)</li> <li>BAB_BA1_NH4_mgL.obs (ammonium nitrogen concentration)</li> <li>BAB_BA1_NO3_mgL.obs (nitrate nitrogen concentration)</li> <li>BAB_BA1_TP_mgL.obs (total phosphorus concentration)</li> <li>BAB_BA1_SRP_mgL.obs (Soluble reactive phosphorus concentration)</li> <li>BAB_BA1_DP_mgL.obs (dissolved P concentration)</li> <li>BAB_BA1_DOC_mgL.obs (Dissolved organic carbon concentration)</li> <li>BAB_BA1_Si_mgL.obs (dissolved silicon concentration)</li> <li>BAB_BA1_Chla_HPLC_DIN_mgL.obs (Chl-a concentration)</li> </ul> <p> </p> <p>---CTD probe profile data from the deepest point in the lake (BA1) from 2017.8 to 2021.8 at biweekly basis with approximately 0.1 m vertical resolution</p> <p> File name:</p> <ul> <li>t_prof_file_barleber_ctm644.obs (water temperature)</li> <li>oxy_prof_file_barleber_ctm644 (Dissolved oxygen)</li> <li>turb_prof_file_barleber_ctm644.obs (Turbidity)</li> <li>chla_prof_file_barleber_ctm644.obs (Chl-a concentration)</li> </ul> <p> </p> <p>---BBE probe profile data from the deepest point in the lake (BA1) from 2017.8 to 2021.8 at biweekly basis with approximately 0.1 m vertical resolution</p> <p> File name:</p> <ul> <li>totalChla_prof_file_barleber_FP2101.obs (Chl-a concentration)</li> <li>bluegreen_prof_file_barleber_FP2101.obs (Blue-green algae Chl-a concentration)</li> <li>green_prof_file_barleber_FP2101.obs (Green algae Chl-a concentration)</li> <li>diatom_prof_file_barleber_FP2101.obs (Diatom Chl-a concentration)</li> </ul> <p> </p> <p> </p>
Mississippi River watershed nutrient loading 1967 to 2016
<p>Archived water quality data collected between 1901 and 2019 were used to reconstruct annual averages of various forms of C, N, P and silicate concentrations and alkalinity in the lower Mississippi River. The data in this spreadsheet is by the United States Geological Survey which no longer posts the data on their website. They were used in the data analysis for this paper.</p>
Data from: Changes in the location of biodiversity–ecosystem function hot spots across the seafloor landscape with increasing sediment nutrient loading
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Livestock as vectors of organic matter and nutrient loading in aquatic ecosystems in African savannas
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Nutrient concentrations, loading, and N:P stoichiometry (1983 - 2020) and impacts in Flathead Lake (Montana, USA)
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Mississippi River watershed nutrient loading 1967 to 2016
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