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  • 1
    ISSN: 1435-0629
    Keywords: Key words: CDOM photobleaching; solar wavebands; ultraviolet radiation; lake ecosystems; dissolved organic carbon.
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology
    Notes: Abstract Dissolved organic matter (DOM) contains molecules that absorb light at various wavelengths. This chromophoric DOM (CDOM) influences the transmission of both visible and ultraviolet energy through water. The absorption of light by CDOM often causes structural changes that reduce its capacity to further absorb light, a process termed ‘photobleaching‘. A model was designed to assess photobleaching through the entire water column of lake ecosystems. The model uses lake morphometry and dissolved organic carbon (DOC) concentration in conjunction with a defined solar spectrum and experimentally measured photobleaching rates to compute the total water columm photobleaching. The model was initially applied to a theoretical ‘average‘ lake using solar spectra for both the north (N) and south (S) temperate western hemispheres and variable DOC from 0.3 to 30 mg L−1. The consequences of varying waveband-specific photobleaching coefficients and lake morphometry were explored in a second set of simulations. Finally, the model was also applied to four temperate northern lakes for which we had prior measurements of CDOM photobleaching rates. The model demonstrates that all three wavebands of solar radiation (UVB, UVA, and PAR) contribute significantly to total water column photobleaching, with UVA being most important. The relative contributions of the three wavebands were invariant for DOC more than 3 mg L−1. Total water column photobleaching at 440 nm was three to five times faster under the UV-enriched solar spectrum of the southern hemisphere. Increasing the lake’s mean depth (from 0.37 to 9.39 m) resulted in five- or 15-fold slower rates of total water column photobleaching for DOC concentrations of 1 or 10 mg L−1, respectively. Varying the waveband-specific photobleaching coefficients by 10-fold resulted in a similar change in total water column photobleaching rates. Applying the model to four specific lakes revealed that photobleaching for the entire water column would reduce CDOM light absorption by 50% in 18–44 days under summer conditions.
    Type of Medium: Electronic Resource
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  • 2
    ISSN: 1573-515X
    Keywords: alkalinity ; color ; dissolved organic matter ; ecosystems ; lakes ; photobleaching
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology , Geosciences
    Notes: Abstract Dissolved organic matter (DOM) is a major light-absorbing substance, responsible for much of the color in water bodies. When sunlight energy is absorbed by DOM, some color can be lost by the process of photobleaching. We measured rates of DOM photobleaching in thirty lakes that varied greatly in color, trophic status and ionic composition. Loss of color (measured as absorbance at 440 nm and expressed as absorption coefficients) was a first order function of sunlight dose, and rates were nearly identical for 0.2 μm- and GF/F-filtered samples suggesting that the process was predominantly abiotic. Photobleaching rates were rapid (color loss of 1–19% d−1) and varied about seven-fold among lakes. Our method underestimated the actual rate by 15–20% based on comparisons between the glass bottles we used in the survey and quartz containers. The large variation in photobleaching rates was examined in relation to lake trophy and chemical conditions. The best predictor of this variability was acid- neutralizing capacity (ANC) (r2 = 0.94; p 〈 0.001) such that photobleaching was most rapid in the most alkaline lakes. The relationship between ANC and photobleaching suggests that differences in ionic conditions among lakes may influence the solubility and configuration of humic and fulvic acids and hence their susceptibility to photobleaching.
    Type of Medium: Electronic Resource
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  • 3
    ISSN: 1573-515X
    Keywords: alkalinity ; color ; dissolved organic matter ; ecosystems ; lakes ; photobleaching
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology , Geosciences
    Notes: Abstract Dissolved organic matter (DOM) is a major light-absorbing substance, responsible for much of the color in water bodies. When sunlight energy is absorbed by DOM, some color can be lost by the process of photobleaching. We measured rates of DOM photobleaching in thirty lakes that varied greatly in color, trophic status and ionic composition. Loss of color (measured as absorbance at 440 nm and expressed as absorption coefficients) was a first order function of sunlight dose, and rates were nearly identical for 0.2μm- and GF/F-filtered samples suggesting that the process was predominantly abiotic. Photobleaching rates were rapid (color loss of 1–19% d−1) and varied about seven-fold among lakes. Our method under-estimated the actual rate by 15–20% based on comparisons between the glass bottles we used in the survey and quartz containers. The large variation in photobleaching rates was examined in relation to lake trophy and chemical conditions. The best predictor of this variability was acid-neutralizing capacity (ANC) (r 2=0.94;p〈0.001) such that photobleaching was most rapid in the most alkaline lakes. The relationship between ANC and photobleaching suggests that differences in ionic conditions among lakes may influence the solubility and configuration of humic and fulvic acids and hence their susceptibility to photobleaching.
    Type of Medium: Electronic Resource
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  • 4
    Publication Date: 2024-03-22
    Description: We sampled twice 12 reservoirs between July 2016 and August 2017 in southern Spain during the summer stratification and the winter mixing. We determined reservoir area, perimeter, and capacity using the next open databases: Infraestructura de Datos Espaciales de Andalucía (IDEAndalucia; http://www.ideandalucia.es/portal/web/ideandalucia/), and the Ministerio para la Transición Ecológica (https://www.embalses.net/). The reservoir volume (m3) divided by its surface area (m2) will yield the mean depth (m). We also calculated the shoreline development ratio (DL) (Aronow, 1982) and the shallowness index (1/m) by dividing the shoreline development index (DL) by the mean depth (m). We performed the vertical geochemical profiles of the reservoirs using a Seabird 19plus CTD profiler and obtained the measurements of temperature and dissolved oxygen. We collected samples at different depths for dissolved CH4 analysis in air-tight Winkler bottles by duplicate, preserved with a solution of HgCl2 (final concentration 1mM) to inhibit biological activity and sealed with Apiezon® grease to prevent gas exchange. We measured dissolved CH4 using headspace equilibration and gas chromatography (2-3 replicates per bottle). We measured DIC, DOC, TN, and TDN by high–temperature catalytic oxidation using a Shimadzu total organic carbon analyzer (Model TOC-V CSH) coupled to nitrogen analyzer (TNM-1). We determined the NO3 concentration using the ultraviolet spectrophotometric method, using a Perkin Elmer UV-Lambda 40 spectrophotometer at wavelengths of 220 nm and correcting for DOC absorbance at 275 nm (APHA, 1992). We measured NH4 and NO2 concentrations by Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES). Dissolved inorganic nitrogen (DIN) is the addition of the NO3, NH4, and NO2 concentrations. We measured total phosphorus (TP) concentration by triplicate using the molybdenum blue method (Murphy and Riley, 1962) after digestion with a mixture of potassium persulphate and boric acid at 120 °C for 30 min (APHA, 1992). We determined chlorophyll-a concentration by extracting the pigments from filters with 95% methanol in the dark at 4 °C for 24 h (APHA, 1992). We measured chlorophyll-a (Chl-a) absorption using a Perkin Elmer UV-Lambda 40 spectrophotometer at the wavelength of 665 nm and for scattering correction at 750 nm. . To obtain the integrated mean of chlorophyll-a (μg Chl-a /l), from the discrete points along the water column, we used the trapezoidal rule (León-Palmero et al., 2019). To obtain the cumulative chlorophyll-a concentration in the whole water column (mg Chl-a/m2), we summed the concentration of chlorophyll-a from each stratum using the trapezoidal rule, as we did for the integrated chlorophyll-a before, but we omitted the division between the maximum depth. We determined the abundances of cyanobacteria and photosynthetic picoeukaryotes using flow cytometry (FACScalibur) using unfiltered water.
    Keywords: #1; #10; #11; #12; #2; #3; #4; #5; #6; #7; #8; #9; Bermejales; Beznar; Calculated; Carbon, inorganic, dissolved; Carbon, inorganic, dissolved, standard deviation; Carbon, organic, dissolved; Carbon, organic, dissolved, standard deviation; CH4; Chlorophyll a; Chlorophyll a, areal concentration; Chlorophyll a, integrated; CO2; Colomera; CTD, Seabird 19plus; Cubillas; Cyanobacteria; Cyanobacteria, integrated; Cyanobacteria, standard deviation; DATE/TIME; DEPTH, water; El_Portillo; Epifluorescence microscopy; Event label; FACSCalibur flow-cytometer (Becton Dickinson); Francisco_Abellan; Greenhouse gases; Headspace gas chromatography; ICP-OES, Inductively coupled plasma - optical emission spectrometry; Iznajar; Jandula; La_Bolera; Methane; Methane saturation; Molybdenum blue method (Murphy & Riley, 1962); MULT; Multiple investigations; N2O; Negratin; Nitrate; Nitrate, standard deviation; Nitrite; Nitrogen, inorganic, dissolved; Nitrogen, inorganic, dissolved/Carbon, organic, dissolved ratio; Nitrogen, inorganic, dissolved/Phosphorus, total ratio; Nitrogen, total; Nitrogen, total, standard deviation; Nitrogen, total dissolved; Nitrogen, total dissolved, standard deviation; Oxygen; Oxygen saturation; Period; Phosphorus, reactive soluble; Phosphorus, reactive soluble, standard deviation; Phosphorus, total; Phosphorus, total, standard deviation; Phosphorus, total dissolved; Phosphorus, total dissolved, standard deviation; Picoeukaryotes, photosynthetic; Picoeukaryotes, photosynthetic, integrated; Picoeukaryotes, photosynthetic, standard deviation; Prokaryotes; Prokaryotes, standard deviation; reservoir; Reservoir area; Reservoir capacity; Reservoir volume; Rules; San_Clemente; Shallowness index; Shimadzu TOC-V CSH total organic carbon analyzer coupled to TNM-1 nitrogen analyzer; Shoreline development ratio; Spectrophotometer Perkin-Elmer UV Lambda 40; Temperature, water; Year of establishment
    Type: Dataset
    Format: text/tab-separated-values, 5848 data points
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  • 5
    Publication Date: 2024-02-27
    Description: We estimated gross primary production (GPP), net ecosystem production (NEP), and ecosystem respiration (R) by measuring temporal changes in dissolved oxygen concentration and temperature using a miniDOT (PME) submersible water logger during the stratification period. We used the equations proposed by Staehr et al. (2010) to calculate GPP, NEP, and R.
    Keywords: #1; #10; #11; #12; #2; #3; #4; #5; #6; #7; #8; #9; Bermejales; Beznar; Calculated after Staehr etal, 2010; CH4; Chlorophyll a; CO2; Colomera; CTD, Sea-Bird SBE 911plus; Cubillas; Cyanobacteria; El_Portillo; Epifluorescence microscopy; Event label; Francisco_Abellan; Greenhouse gases; Gross primary production of oxygen; Iznajar; Jandula; La_Bolera; Latitude of event; Longitude of event; Methane; MULT; Multiple investigations; N2O; Negratin; Net primary production of oxygen; Net primary production of oxygen, integrated; Picoeukaryotes, photosynthetic; reservoir; Respiration rate, oxygen; Rules; San_Clemente
    Type: Dataset
    Format: text/tab-separated-values, 108 data points
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  • 6
    Publication Date: 2024-02-05
    Description: Simultaneous fluxes of CO2, CH4 and N2O in twelve reservoirs in Southern Spain during the stratification period and the mixing period were measured using a Picarro G2508 gas analyzer connected to a floating chamber. For each reservoir in each sampling period, we took 3-5 measurements for 40 min. We calculated the daily (from 10 am to 4 pm) average (mean) and the standard error (SE) from these measurements. Flux calculations were based on Zhao et al (2015). To obtain the reservoir radiative forcings we summed the corresponding forcing due to CO2 emissions, the warming potential (GWP) of CH4 in terms of CO2 equivalents, and the warming potential of N2O in terms of CO2 equivalents. We used 34 to convert CH4 in CO2 equivalent and 298 to convert N2O in CO2 equivalent in a 100-year time horizon, including the climate-carbon feedbacks (IPCC 2013). We measured ambient temperature, barometric pressure (HANNA HI 9828), and wind speed (MASTECH MS6252A) at the beginning of each flux measurement. We recorded dissolved oxygen concentration and water temperature using a miniDOT (PME) submersible water logger during the stratification period, and calculated the lake metabolism (i.e., gross primary production, net production and respiration rate) according to Staehr et al (2010).
    Keywords: #1; #10; #11; #12; #2; #3; #4; #5; #6; #7; #8; #9; Bermejales; Beznar; Carbon dioxide, flux, in mass carbon; Carbon dioxide, flux, standard deviation; Carbon dioxide contribution to climatic forcing; CH4; CO2; Colomera; Cubillas; DATE/TIME; Digital anemometer, MASTECH, MS6252A; El_Portillo; Event label; Francisco_Abellan; Gas analyzer (Picarro G2508 ); Greenhouse gases; Gross primary production of carbon; Iznajar; Jandula; La_Bolera; Latitude of event; Longitude of event; Methane, flux, in mass carbon; Methane, flux, standard deviation; Methane contribution to climatic forcing; Methane emission, in mass carbon dioxide equivalents; MULT; Multiparameter probe (HI9828, Hanna Instruments, Woonsocket, Rhode Island); Multiple investigations; N2O; Negratin; Net primary production of carbon; Nitrous oxide, flux, in mass nitrogen; Nitrous oxide, flux, standard deviation; Nitrous oxide contribution to climatic forcing; Nitrous oxide emission, in mass carbon dioxide equivalents; Oxygen Data Logger, MiniDOT, Precision Measurement Engineering; Period; reservoir; Respiration rate, carbon; Rules; San_Clemente; Temperature, air; Temperature, water; Total climatic forcing, in mass carbon dioxide equivalents; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 444 data points
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  • 7
    Publication Date: 2024-02-05
    Description: We collected data of reservoir area, capacity, age, and location from open databases: Infraestructura de Datos Espaciales de Andalucía (IDEAndalucia; http://www.ideandalucia.es/portal/web/ideandalucia/) and the Ministerio para la Transición Ecológica (https://www.embalses.net/). The reservoir capacity (m3) divided by its surface area (m2) will yield the mean depth (m). We obtained the lithology and land-use maps using ArcGIS® 10.2 software (ESRI 2012) under the Universidad de Granada license. First, we delimited the watershed of each reservoir using the rivers and hydrographical demarcations, and, second, we calculated the area for each different type of lithology and land-use within watersheds. We used the databases: Infraestructura de Datos Espaciales (IDE) from the Ministerio de Agricultura, Pesca y Alimentación (MAPA; https://www.mapa.gob.es/es/cartografia-y-sig/ide/default.aspx); the Infraestructura de Datos Espaciales de Andalucía(IDEAndalucia; http://www.ideandalucia.es/portal/web/ideandalucia/); the Instituto Geológico y Minero de España (IGME; http://www.igme.es/default.asp); the Confederación Hidrográfica del Segura (CHSEGURA; https://www.chsegura.es/chs/); and The Junta de Comunidades de Castilla-La Mancha (IDE-JCCM; https://castillalamancha.maps.arcgis.com/home/index.html). We defined the next categories: water-covered area; carbonate-rich rocks; limestones, marls, and dolomites; gravels, conglomerates, sands and silts; and non-calcareous rocks. The soils with high capacity to solubilize dissolved inorganic carbon are carbonate-rich rocks and limestones, marls, and dolomites. In contrast, non-calcareous rocks include igneous rocks like basalt and metamorphic rocks like marble, schist, quartzite, phyllite, gneiss, and slate have less capacity to leach dissolved inorganic carbon. The land-use categories were: crops, forest, urban, treeless area, and water covered area. The forestry area includes trees, plantation trees, sparse trees, and dispersed trees.
    Keywords: #1; #10; #11; #12; #2; #3; #4; #5; #6; #7; #8; #9; Anthropogenic land-use ratio; Areal extent, carbonate-rich rocks; Areal extent, crops; Areal extent, forest; Areal extent, limestones, marls and dolomites; Areal extent, non-calcareous rocks; Areal extent, urban area; Bermejales; Beznar; Calculated, see abstract; CH4; CO2; Colomera; Cubillas; DEPTH, water; El_Portillo; Event label; Francisco_Abellan; Greenhouse gases; Iznajar; Jandula; La_Bolera; Latitude of event; Longitude of event; MULT; Multiple investigations; N2O; Negratin; reservoir; Reservoir area; Reservoir capacity; Rules; San_Clemente; UTM Easting, Universal Transverse Mercator; UTM Northing, Universal Transverse Mercator; UTM Zone, Universal Transverse Mercator; Watershed area; Year of establishment
    Type: Dataset
    Format: text/tab-separated-values, 168 data points
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  • 8
    Publication Date: 2024-06-12
    Description: This dataset presents weekly raw data on atmospheric deposition collected in Sierra Nevada (Granada, Spain) during the period June-September of 2022. Separate samples of dry and wet atmospheric deposition were collected using a MTX ARS 1010 automatic deposition sampler located in the University Hostel, Hoya de la Mora (37°05'N, 3°23'W, 2500 m a.s.l.), in Sierra Nevada National Park, southeastern of the Iberian Peninsula. From the dry and wet deposition samples, we took aliquots for the next variables: Particulate matter (PM, mg m⁻² d⁻¹), Particulate inorganic matter (PIM, mg m⁻² d⁻¹), Particulate organic matter (POM, mg m⁻² d⁻¹), Total Nitrogen (TN, µmol m⁻² d⁻¹), Total Phosphorus (TP, µmol m⁻² d⁻¹), and total bacteria (cells m⁻² d⁻¹).
    Keywords: atmospheric deposition; Automatic deposition sampler, MTX, MTX ARS 1010; Bacteria; Bacteria, cell, flux; Comment; DATE/TIME; Filtration; Flow cytometry; Inorganic matter, particulate; Matter, particulate, flux; Nitrogen, total, flux; Nutrients; Organic matter, particulate, flux; particulate matter; Phosphorus, total, flux; Sample type; SN_deposition_2022; Spectrophotometry; Time in weeks; University Hostel, Hoya de la Mora, Sierra Nevada, Spain
    Type: Dataset
    Format: text/tab-separated-values, 304 data points
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  • 9
    Publication Date: 2007-11-12
    Print ISSN: 1015-1621
    Electronic ISSN: 1420-9055
    Topics: Biology
    Published by Springer
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  • 10
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