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Title: Dissolved oxygen, temperature, chlorophyll-a, total phosphorus, total nitrogen, and dissolved organic carbon at multiple depths in 822 lakes from 1921-2022
Rapid changes in climate and land use are having substantial and interacting impacts on lake water quality around the world. Here, we synthesized time-series data for dissolved oxygen, temperature, chlorophyll-a, total phosphorus, total nitrogen, and dissolved organic carbon at multiple depths in 822 lakes to facilitate analyses of these changes. The dataset extends from 1921–2022, with a median data duration of 29 years (range 5-102) and a median of 5 unique sampling dates per year at each lake. Lakes in the dataset have a median depth of 12.5 m (range 1.5–480 m), median surface area of 85.4 ha (range: 0.5–237000 ha) and median elevation of 264 m (range: -215–2804). The lakes are located in 18 countries across 5 continents, with latitudes ranging from -42.6 to 68.3. To facilitate interoperability with other large-scale datasets, each lake is linked to a unique hydroLAKES lake ID when possible (n = 683).  more » « less
Award ID(s):
2025982
NSF-PAR ID:
10493449
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more » ; ; « less
Publisher / Repository:
Environmental Data Initiative
Date Published:
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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  4. These datasets accompany a publication in Geophysical Research Letters by Martens et al. (2024), entitled: "GNSS Geodesy Quantifies Water-Storage Gains and Drought Improvements in California Spurred by Atmospheric Rivers." Please refer to the manuscript and supporting information for additional details.

    Dataset 1: Seasonal Changes in TWS based on the Mean and Median of the Solution Set

    We estimate net gains in water storage during the fall and winter of each year (October to March) using the mean TWS solutions from all nine inversion products, subtracting the average storage for October from the average storage for March in the following year. One-sigma standard deviations are computed as the square root of the sum of the variances for October and for March. The variance in each month is computed based on the nine independent estimates of mean monthly storage (see “GNSS Analysis and Inversion” in the Supporting Information).

    The dataset includes net gains in water storage for both the Sierra Nevada and the SST watersheds (see header lines). For each watershed, results are provided in units of volume (km3) and in units of equivalent water height (mm). Furthermore, for each watershed, we also provide the total storage gains based on non-detrended and linearly detrended time series. In columns four and five, respectively, we provide estimates of snow water equivalent (SWE) from SNODAS (National Operational Hydrologic Remote Sensing Center, 2023) and water-storage changes in surface reservoirs from CDEC (California Data Exchange Center, 2023). In the final column, we provide estimates of net gains in subsurface storage (soil moisture plus groundwater), which are computed by subtracting SWE and reservoir storage from total storage.

    For each data block, the columns are: (1) time period (October of the starting year to March of the following year); (2) average gain in total water storage constrained by nine inversions of GNSS data; (3) one-sigma standard deviation in the average gain in total water storage; (4) gain in snow water equivalent, computed by subtracting the average snow storage in October from the average snow storage in March of the following year; (5) gain in reservoir storage (CDEC database; within the boundaries of each watershed), computed by subtracting the average reservoir storage in October from the average reservoir storage in March of the following year; and (6) average gain in subsurface water storage, estimated as the average gain in total water storage minus the average gain in snow storage minus the gain in reservoir storage.

    For the period from October 2022 to March 2023, we also compute mean gains in total water storage using daily estimates of TWS. Here, we subtract the average storage for the first week in October 2022 (1-7 October) from the average storage for the last week in March 2023 (26 March – 1 April). The one-sigma standard deviation is computed as the square root of the sum of the variances for the first week in October and the last week in March. The variance in each week is computed based on the nine independent estimates of daily storage over seven days (63 values per week). The storage gains for 2022-2023 computed using these methods are distinguished in the datafile by an asterisk (2022-2023*; final row in each data section).

    Dataset 1a provides estimates of storage changes based on the mean and standard deviation of the solution set. Dataset 1b provides estimates of storage changes based on the median and inter-quartile range of the solution set.

    Dataset 2: Estimated Changes in TWS in the Sierra Nevada

    Changes in TWS (units of volume: km3) in the Sierra Nevada watersheds. The first column represents the date (YYYY-MM-DD). For monthly solutions, the TWS solutions apply to the month leading up to that date. The remaining nine columns represent each of the nine solutions described in the text. “UM” represents the University of Montana, “SIO” represents the Scripps Institution of Oceanography, and “JPL” represents the Jet Propulsion Laboratory. “NGL” refers to the use of GNSS analysis products from the Nevada Geodetic Laboratory, “CWU” refers to Central Washington University, and “MEaSUREs” refers to the Making Earth System Data Records for Use in Research Environments program. The time series have not been detrended.

    We highlight that we have added changes in reservoir storage (see Dataset 8) back into the JPL solutions, since reservoir storage had been modeled and removed from the GNSS time series prior to inversion in the JPL workflow (see “Detailed Description of Methods” in the Supporting Information). Thus, the storage values presented here for JPL differ slightly from storage values pulled directly from Dataset 6 and integrated over the area of the Sierra Nevada watersheds.

    Dataset 3: Estimated Changes in TWS in the Sacramento-San Joaquin-Tulare Basin

    Same as Dataset 2, except that data apply to the Sacramento-San Joaquin-Tulare (SST) Basin.

    Dataset 4: Inversion Products (SIO)

    Inversion solutions (NetCDF format) for TWS changes across the western US from January 2006 through March 2023. The products were produced at the Scripps Institution of Oceanography (SIO) using the methods described in the Supporting Information.

    Dataset 5: Inversion Products (UM)

    Inversion solutions (NetCDF format) for TWS changes across the western US from January 2006 through March 2023. The products were produced at the University of Montana (UM) using the methods described in the Supporting Information.

    Dataset 6: Inversion Products (JPL)

    Inversion solutions (NetCDF format) for TWS changes across the western US from January 2006 through March 2023. The products were produced at the Jet Propulsion Laboratory (JPL) using the methods described in the Supporting Information.

    Dataset 7: Lists of Excluded Stations

    Stations are excluded from an inversion for TWS change based on a variety of criteria (detailed in the Supporting Information), including poroelastic behavior, high noise levels, and susceptibility to volcanic deformation. This dataset provides lists of excluded stations from each institution generating inversion products (SIO, UM, JPL).

    Dataset 8: Lists of Reservoirs and Lakes

    Lists of reservoirs and lakes from the California Data Exchange Center (CDEC) (California Data Exchange Center, 2023), which are shown in Figures 1 and 2 of the main manuscript. In the interest of figure clarity, Figure 1 depicts only those reservoirs that exhibited volume changes of at least 0.15 km3 during the first half of WY23.

    Dataset 8a includes all reservoirs and lakes in California that exhibited volume changes of at least 0.15 km3 between October 2022 and March 2023. The threshold of 0.15 km3 represents a natural break in the distribution of volume changes at all reservoirs and lakes in California over that period (169 reservoirs and lakes in total). Most of the 169 reservoirs and lakes exhibited volume changes near zero km3. Datasets 8b and 8c include subsets of reservoirs and lakes (from Dataset 8a) that fall within the boundaries of the Sierra Nevada and SST watersheds.

    Furthermore, in the JPL data-processing and inversion workflow (see “Detailed Description of Methods” in the Supporting Information), surface displacements induced by volume changes in select lakes and reservoirs are modeled and removed from GNSS time series prior to inversion. The water-storage changes in the lakes and reservoirs are then added back into the solutions for water storage, derived from the inversion of GNSS data. Dataset 8d includes the list of reservoirs used in the JPL workflow.

    Dataset 9: Interseismic Strain Accumulation along the Cascadia Subduction Zone

    JPL and UM remove interseismic strain accumulation associated with locking of the Cascadia subduction zone using an updated version of the Li et al. model (Li et al., 2018); see Supporting Information Section 2d. The dataset lists the east, north, and up velocity corrections (in the 4th, 5th, and 6th columns of the dataset, respectively) at each station; units are mm/year. The station ID, latitude, and longitude are listed in columns one, two, and three, respectively, of the dataset.

    Dataset 10: Days Impacted by Atmospheric Rivers

    A list of days impacted by atmospheric rivers within (a) the HUC-2 boundary for California from 1 January 2008 until 1 April 2023 [Dataset 10a] and (b) the Sierra Nevada and SST watersheds from 1 October 2022 until 1 April 2023 [Dataset 10b]. File formats: [decimal year; integrated water-vapor transport (IVT) in kg m-1 s-1; AR category; and calendar date as a two-digit year followed by a three-character month followed by a two-digit day]. The AR category reflects the peak intensity anywhere within the watershed. We use the detection and classification methods of (Ralph et al., 2019; Rutz et al., 2014, 2019). See also Supporting Information Section 2i.

    Dataset 10c provides a list of days and times when ARs made landfall along the California coast between October 1980 and September 2023, based on the MERRA-2 reanalysis using the methods of (Rutz et al., 2014, 2019). Only coastal grid cells are included. File format: [year, month, day, hour, latitude, longitude, and IVT in kg m-1 s-1]. Values are sorted by time (year, month, day, hour) and then by latitude. See also Supporting Information Section 2g.

     
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  5. Excessive phosphorus (P) applications to croplands can contribute to eutrophication of surface waters through surface runoff and subsurface (leaching) losses. We analyzed leaching losses of total dissolved P (TDP) from no-till corn, hybrid poplar (Populus nigra X P. maximowiczii), switchgrass (Panicum virgatum), miscanthus (Miscanthus giganteus), native grasses, and restored prairie, all planted in 2008 on former cropland in Michigan, USA. All crops except corn (13 kg P ha−1 year−1) were grown without P fertilization. Biomass was harvested at the end of each growing season except for poplar. Soil water at 1.2 m depth was sampled weekly to biweekly for TDP determination during March–November 2009–2016 using tension lysimeters. Soil test P (0–25 cm depth) was measured every autumn. Soil water TDP concentrations were usually below levels where eutrophication of surface waters is frequently observed (> 0.02 mg L−1) but often higher than in deep groundwater or nearby streams and lakes. Rates of P leaching, estimated from measured concentrations and modeled drainage, did not differ statistically among cropping systems across years; 7-year cropping system means ranged from 0.035 to 0.072 kg P ha−1 year−1 with large interannual variation. Leached P was positively related to STP, which decreased over the 7 years in all systems. These results indicate that both P-fertilized and unfertilized cropping systems may leach legacy P from past cropland management. Experimental details The Biofuel Cropping System Experiment (BCSE) is located at the W.K. Kellogg Biological Station (KBS) (42.3956° N, 85.3749° W; elevation 288 m asl) in southwestern Michigan, USA. This site is a part of the Great Lakes Bioenergy Research Center (www.glbrc.org) and is a Long-term Ecological Research site (www.lter.kbs.msu.edu). Soils are mesic Typic Hapludalfs developed on glacial outwash54 with high sand content (76% in the upper 150 cm) intermixed with silt-rich loess in the upper 50 cm55. The water table lies approximately 12–14 m below the surface. The climate is humid temperate with a mean annual air temperature of 9.1 °C and annual precipitation of 1005 mm, 511 mm of which falls between May and September (1981–2010)56,57. The BCSE was established as a randomized complete block design in 2008 on preexisting farmland. Prior to BCSE establishment, the field was used for grain crop and alfalfa (Medicago sativa L.) production for several decades. Between 2003 and 2007, the field received a total of ~ 300 kg P ha−1 as manure, and the southern half, which contains one of four replicate plots, received an additional 206 kg P ha−1 as inorganic fertilizer. The experimental design consists of five randomized blocks each containing one replicate plot (28 by 40 m) of 10 cropping systems (treatments) (Supplementary Fig. S1; also see Sanford et al.58). Block 5 is not included in the present study. Details on experimental design and site history are provided in Robertson and Hamilton57 and Gelfand et al.59. Leaching of P is analyzed in six of the cropping systems: (i) continuous no-till corn, (ii) switchgrass, (iii) miscanthus, (iv) a mixture of five species of native grasses, (v) a restored native prairie containing 18 plant species (Supplementary Table S1), and (vi) hybrid poplar. Agronomic management Phenological cameras and field observations indicated that the perennial herbaceous crops emerged each year between mid-April and mid-May. Corn was planted each year in early May. Herbaceous crops were harvested at the end of each growing season with the timing depending on weather: between October and November for corn and between November and December for herbaceous perennial crops. Corn stover was harvested shortly after corn grain, leaving approximately 10 cm height of stubble above the ground. The poplar was harvested only once, as the culmination of a 6-year rotation, in the winter of 2013–2014. Leaf emergence and senescence based on daily phenological images indicated the beginning and end of the poplar growing season, respectively, in each year. Application of inorganic fertilizers to the different crops followed a management approach typical for the region (Table 1). Corn was fertilized with 13 kg P ha−1 year−1 as starter fertilizer (N-P-K of 19-17-0) at the time of planting and an additional 33 kg P ha−1 year−1 was added as superphosphate in spring 2015. Corn also received N fertilizer around the time of planting and in mid-June at typical rates for the region (Table 1). No P fertilizer was applied to the perennial grassland or poplar systems (Table 1). All perennial grasses (except restored prairie) were provided 56 kg N ha−1 year−1 of N fertilizer in early summer between 2010 and 2016; an additional 77 kg N ha−1 was applied to miscanthus in 2009. Poplar was fertilized once with 157 kg N ha−1 in 2010 after the canopy had closed. Sampling of subsurface soil water and soil for P determination Subsurface soil water samples were collected beneath the root zone (1.2 m depth) using samplers installed at approximately 20 cm into the unconsolidated sand of 2Bt2 and 2E/Bt horizons (soils at the site are described in Crum and Collins54). Soil water was collected from two kinds of samplers: Prenart samplers constructed of Teflon and silica (http://www.prenart.dk/soil-water-samplers/) in replicate blocks 1 and 2 and Eijkelkamp ceramic samplers (http://www.eijkelkamp.com) in blocks 3 and 4 (Supplementary Fig. S1). The samplers were installed in 2008 at an angle using a hydraulic corer, with the sampling tubes buried underground within the plots and the sampler located about 9 m from the plot edge. There were no consistent differences in TDP concentrations between the two sampler types. Beginning in the 2009 growing season, subsurface soil water was sampled at weekly to biweekly intervals during non-frozen periods (April–November) by applying 50 kPa of vacuum to each sampler for 24 h, during which the extracted water was collected in glass bottles. Samples were filtered using different filter types (all 0.45 µm pore size) depending on the volume of leachate collected: 33-mm dia. cellulose acetate membrane filters when volumes were less than 50 mL; and 47-mm dia. Supor 450 polyethersulfone membrane filters for larger volumes. Total dissolved phosphorus (TDP) in water samples was analyzed by persulfate digestion of filtered samples to convert all phosphorus forms to soluble reactive phosphorus, followed by colorimetric analysis by long-pathlength spectrophotometry (UV-1800 Shimadzu, Japan) using the molybdate blue method60, for which the method detection limit was ~ 0.005 mg P L−1. Between 2009 and 2016, soil samples (0–25 cm depth) were collected each autumn from all plots for determination of soil test P (STP) by the Bray-1 method61, using as an extractant a dilute hydrochloric acid and ammonium fluoride solution, as is recommended for neutral to slightly acidic soils. The measured STP concentration in mg P kg−1 was converted to kg P ha−1 based on soil sampling depth and soil bulk density (mean, 1.5 g cm−3). Sampling of water samples from lakes, streams and wells for P determination In addition to chemistry of soil and subsurface soil water in the BCSE, waters from lakes, streams, and residential water supply wells were also sampled during 2009–2016 for TDP analysis using Supor 450 membrane filters and the same analytical method as for soil water. These water bodies are within 15 km of the study site, within a landscape mosaic of row crops, grasslands, deciduous forest, and wetlands, with some residential development (Supplementary Fig. S2, Supplementary Table S2). Details of land use and cover change in the vicinity of KBS are given in Hamilton et al.48, and patterns in nutrient concentrations in local surface waters are further discussed in Hamilton62. Leaching estimates, modeled drainage, and data analysis Leaching was estimated at daily time steps and summarized as total leaching on a crop-year basis, defined from the date of planting or leaf emergence in a given year to the day prior to planting or emergence in the following year. TDP concentrations (mg L−1) of subsurface soil water were linearly interpolated between sampling dates during non-freezing periods (April–November) and over non-sampling periods (December–March) based on the preceding November and subsequent April samples. Daily rates of TDP leaching (kg ha−1) were calculated by multiplying concentration (mg L−1) by drainage rates (m3 ha−1 day−1) modeled by the Systems Approach for Land Use Sustainability (SALUS) model, a crop growth model that is well calibrated for KBS soil and environmental conditions. SALUS simulates yield and environmental outcomes in response to weather, soil, management (planting dates, plant population, irrigation, N fertilizer application, and tillage), and genetics63. The SALUS water balance sub-model simulates surface runoff, saturated and unsaturated water flow, drainage, root water uptake, and evapotranspiration during growing and non-growing seasons63. The SALUS model has been used in studies of evapotranspiration48,51,64 and nutrient leaching20,65,66,67 from KBS soils, and its predictions of growing-season evapotranspiration are consistent with independent measurements based on growing-season soil water drawdown53 and evapotranspiration measured by eddy covariance68. Phosphorus leaching was assumed insignificant on days when SALUS predicted no drainage. Volume-weighted mean TDP concentrations in leachate for each crop-year and for the entire 7-year study period were calculated as the total dissolved P leaching flux (kg ha−1) divided by the total drainage (m3 ha−1). One-way ANOVA with time (crop-year) as the fixed factor was conducted to compare total annual drainage rates, P leaching rates, volume-weighted mean TDP concentrations, and maximum aboveground biomass among the cropping systems over all seven crop-years as well as with TDP concentrations from local lakes, streams, and groundwater wells. When a significant (α = 0.05) difference was detected among the groups, we used the Tukey honest significant difference (HSD) post-hoc test to make pairwise comparisons among the groups. In the case of maximum aboveground biomass, we used the Tukey–Kramer method to make pairwise comparisons among the groups because the absence of poplar data after the 2013 harvest resulted in unequal sample sizes. We also used the Tukey–Kramer method to compare the frequency distributions of TDP concentrations in all of the soil leachate samples with concentrations in lakes, streams, and groundwater wells, since each sample category had very different numbers of measurements. Individual spreadsheets in “data table_leaching_dissolved organic carbon and nitrogen.xls” 1.    annual precip_drainage 2.    biomass_corn, perennial grasses 3.    biomass_poplar 4.    annual N leaching _vol-wtd conc 5.    Summary_N leached 6.    annual DOC leachin_vol-wtd conc 7.    growing season length 8.    correlation_nh4 VS no3 9.    correlations_don VS no3_doc VS don Each spreadsheet is described below along with an explanation of variates. Note that ‘nan’ indicate data are missing or not available. First row indicates header; second row indicates units 1. Spreadsheet: annual precip_drainage Description: Precipitation measured from nearby Kellogg Biological Station (KBS) Long Term Ecological Research (LTER) Weather station, over 2009-2016 study period. Data shown in Figure 1; original data source for precipitation (https://lter.kbs.msu.edu/datatables/7). Drainage estimated from SALUS crop model. Note that drainage is percolation out of the root zone (0-125 cm). Annual precipitation and drainage values shown here are calculated for growing and non-growing crop periods. Variate    Description year    year of the observation crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” precip_G    precipitation during growing period (milliMeter) precip_NG    precipitation during non-growing period (milliMeter) drainage_G    drainage during growing period (milliMeter) drainage_NG    drainage during non-growing period (milliMeter)      2. Spreadsheet: biomass_corn, perennial grasses Description: Maximum aboveground biomass measurements from corn, switchgrass, miscanthus, native grass and restored prairie plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Data shown in Figure 2.   Variate    Description year    year of the observation date    day of the observation (mm/dd/yyyy) crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” replicate    each crop has four replicated plots, R1, R2, R3 and R4 station    stations (S1, S2 and S3) of samplings within the plot. For more details, refer to link (https://data.sustainability.glbrc.org/protocols/156) species    plant species that are rooted within the quadrat during the time of maximum biomass harvest. See protocol for more information, refer to link (http://lter.kbs.msu.edu/datatables/36) For maize biomass, grain and whole biomass reported in the paper (weed biomass or surface litter are excluded). Surface litter biomass not included in any crops; weed biomass not included in switchgrass and miscanthus, but included in grass mixture and prairie. fraction    Fraction of biomass biomass_plot    biomass per plot on dry-weight basis (Grams_Per_SquareMeter) biomass_ha    biomass (megaGrams_Per_Hectare) by multiplying column biomass per plot with 0.01 3. Spreadsheet: biomass_poplar Description: Maximum aboveground biomass measurements from poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Data shown in Figure 2. Note that poplar biomass was estimated from crop growth curves until the poplar was harvested in the winter of 2013-14. Variate    Description year    year of the observation method    methods of poplar biomass sampling date    day of the observation (mm/dd/yyyy) replicate    each crop has four replicated plots, R1, R2, R3 and R4 diameter_at_ground    poplar diameter (milliMeter) at the ground diameter_at_15cm    poplar diameter (milliMeter) at 15 cm height biomass_tree    biomass per plot (Grams_Per_Tree) biomass_ha    biomass (megaGrams_Per_Hectare) by multiplying biomass per tree with 0.01 4. Spreadsheet: annual N leaching_vol-wtd conc Description: Annual leaching rate (kiloGrams_N_Per_Hectare) and volume-weighted mean N concentrations (milliGrams_N_Per_Liter) of nitrate (no3) and dissolved organic nitrogen (don) in the leachate samples collected from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for nitrogen leached and volume-wtd mean N concentration shown in Figure 3a and Figure 3b, respectively. Note that ammonium (nh4) concentration were much lower and often undetectable (<0.07 milliGrams_N_Per_Liter). Also note that in 2009 and 2010 crop-years, data from some replicates are missing.    Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” crop-year    year of the observation replicate    each crop has four replicated plots, R1, R2, R3 and R4 no3 leached    annual leaching rates of nitrate (kiloGrams_N_Per_Hectare) don leached    annual leaching rates of don (kiloGrams_N_Per_Hectare) vol-wtd no3 conc.    Volume-weighted mean no3 concentration (milliGrams_N_Per_Liter) vol-wtd don conc.    Volume-weighted mean don concentration (milliGrams_N_Per_Liter) 5. Spreadsheet: summary_N leached Description: Summary of total amount and forms of N leached (kiloGrams_N_Per_Hectare) and the percent of applied N lost to leaching over the seven years for corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for nitrogen amount leached shown in Figure 4a and percent of applied N lost shown in Figure 4b. Note the fraction of unleached N includes in harvest, accumulation in root biomass, soil organic matter or gaseous N emissions were not measured in the study. Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” no3 leached    annual leaching rates of nitrate (kiloGrams_N_Per_Hectare) don leached    annual leaching rates of don (kiloGrams_N_Per_Hectare) N unleached    N unleached (kiloGrams_N_Per_Hectare) in other sources are not studied % of N applied N lost to leaching    % of N applied N lost to leaching 6. Spreadsheet: annual DOC leachin_vol-wtd conc Description: Annual leaching rate (kiloGrams_Per_Hectare) and volume-weighted mean N concentrations (milliGrams_Per_Liter) of dissolved organic carbon (DOC) in the leachate samples collected from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for DOC leached and volume-wtd mean DOC concentration shown in Figure 5a and Figure 5b, respectively. Note that in 2009 and 2010 crop-years, water samples were not available for DOC measurements.     Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” crop-year    year of the observation replicate    each crop has four replicated plots, R1, R2, R3 and R4 doc leached    annual leaching rates of nitrate (kiloGrams_Per_Hectare) vol-wtd doc conc.    volume-weighted mean doc concentration (milliGrams_Per_Liter) 7. Spreadsheet: growing season length Description: Growing season length (days) of corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in the Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Date shown in Figure S2. Note that growing season is from the date of planting or emergence to the date of harvest (or leaf senescence in case of poplar).   Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” year    year of the observation growing season length    growing season length (days) 8. Spreadsheet: correlation_nh4 VS no3 Description: Correlation of ammonium (nh4+) and nitrate (no3-) concentrations (milliGrams_N_Per_Liter) in the leachate samples from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2013-2015. Data shown in Figure S3. Note that nh4+ concentration in the leachates was very low compared to no3- and don concentration and often undetectable in three crop-years (2013-2015) when measurements are available. Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” date    date of the observation (mm/dd/yyyy) replicate    each crop has four replicated plots, R1, R2, R3 and R4 nh4 conc    nh4 concentration (milliGrams_N_Per_Liter) no3 conc    no3 concentration (milliGrams_N_Per_Liter)   9. Spreadsheet: correlations_don VS no3_doc VS don Description: Correlations of don and nitrate concentrations (milliGrams_N_Per_Liter); and doc (milliGrams_Per_Liter) and don concentrations (milliGrams_N_Per_Liter) in the leachate samples of corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2013-2015. Data of correlation of don and nitrate concentrations shown in Figure S4 a and doc and don concentrations shown in Figure S4 b. Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” year    year of the observation don    don concentration (milliGrams_N_Per_Liter) no3     no3 concentration (milliGrams_N_Per_Liter) doc    doc concentration (milliGrams_Per_Liter) 
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