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Dataset Abstract Measurements of soil moisture began in 1989 for all treatments on the LTER main site and in 1993 on the successional and forest sites. Soil moisture is analyzed on the baseline soil samplings which are collected twice monthly or monthly during the growing season. The percent gravimetric moisture content is calculated on a dry weight basis. Other datasets from the baseline soil samplings include Inorganic nitrogen and Total N and Total C. original data source http://lter.kbs.msu.edu/datasets/18
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Soil Inorganic Nitrogen on the Main Cropping System Experiment at the Kellogg Biological Station, Hickory Corners, MI (1989 to 2018)
Dataset AbstractMeasurement of soil inorganic nitrogen began in 1989 for all treatments on the LTER Main Site and 1993 on the Successional and Forest sites. Ammonium and nitrate are analyzed twice monthly or monthly during the growing season on baseline soil samplings. Additional datasets from the Baseline Soil Samplings include soil moisture, total N and total C.original data source http://lter.kbs.msu.edu/datasets/24
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- Award ID(s):
- 1832042
- PAR ID:
- 10357099
- Publisher / Repository:
- Environmental Data Initiative
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Dataset Abstract Trace gases (nitrous oxide, methane, and carbon dioxide) have been measured on the LTER Main Site since 1991 and on Successional and Forest sites since 1993. Trace gas fluxes are measured twice monthly or monthly until the ground freezes using permanently-installed, in-situ static chambers. CH4 and N2O are analyzed with gas-chromatography and CO2 with an infrared gas analyzer. Soil moisture and temperature are measured during sampling. original data source http://lter.kbs.msu.edu/datasets/16more » « less
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Abstract Anthropogenic climate change has already affected drought severity and risk across many regions, and climate models project additional increases in drought risk with future warming. Historically, droughts are typically caused by periods of below‐normal precipitation and terminated by average or above‐normal precipitation. In many regions, however, soil moisture is projected to decrease primarily through warming‐driven increases in evaporative demand, potentially affecting the ability of negative precipitation anomalies to cause drought and positive precipitation anomalies to terminate drought. Here, we use climate model simulations from Phase Six of the Coupled Model Intercomparison Project (CMIP6) to investigate how different levels of warming (1, 2, and 3°C) affect the influence of precipitation on soil moisture drought in the Mediterranean and Western North America regions. We demonstrate that the same monthly precipitation deficits (25th percentile relative to a preindustrial baseline) at a global warming level of 2°C increase the probability of both surface and rootzone soil moisture drought by 29% in the Mediterranean and 32% and 6% in Western North America compared to the preindustrial baseline. Furthermore, the probability of a dry (25th percentile relative to a preindustrial baseline) surface soil moisture month given a high (75th percentile relative to a preindustrial baseline) precipitation month is 6 (Mediterranean) and 3 (Western North America) times more likely in a 2°C world compared to the preindustrial baseline. For these regions, warming will likely increase the risk of soil moisture drought during low precipitation periods while simultaneously reducing the efficacy of high precipitation periods to terminate droughts.more » « less
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Wildfires have continued to increase in frequency and severity in Southern California due in part to climate change. To gain a further understanding of microbial soil communities’ response to fire and functions that may enhance post-wildfire resilience, soil fungal and bacterial microbiomes were studied from different wildfire areas in the Gold Creek Preserve within the Angeles National Forest using 16S, FITS, 18S, 12S, PITS, and COI amplicon sequencing. Sequencing datasets from December 2020 and June 2021 samplings were analyzed using QIIME2, ranacapa, stats, vcd, EZBioCloud, and mixomics. Significant differences were found among bacterial and fungal taxa associated with different fire areas in the Gold Creek Preserve. There was evidence of seasonal shifts in the alpha diversity of the bacterial communities. In the sparse partial least squares analysis, there were strong associations (r > 0.8) between longitude, elevation, and a defined cluster of Amplicon Sequence Variants (ASVs). The Chi-square test revealed differences in fungi–bacteria (F:B) proportions between different trails (p = 2 × 10−16). sPLS results focused on a cluster of Green Trail samples with high elevation and longitude. Analysis revealed the cluster included the post-fire pioneer fungi Pyronema and Tremella. Chlorellales algae and possibly pathogenic Fusarium sequences were elevated. Bacterivorous Corallococcus, which secretes antimicrobials, and bacterivorous flagellate Spumella were associated with the cluster. There was functional redundancy in clusters that were differently composed but shared similar ecological functions. These results implied a set of traits for post-fire resiliency. These included photo-autotrophy, mineralization of pyrolyzed organic matter and aromatic/oily compounds, potential pathogenicity and parasitism, antimicrobials, and N-metabolism.more » « less
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Weather data are the key forces that drive hydrological processes so that their accuracy in watershed modeling is fundamentally important. For large-scale watershed modeling, weather data are either generated by using interpolation methods or derived from assimilated datasets. In the present study, we compared model performances of the Soil and Water Assessment Tool (SWAT), as driven by interpolation weather data, and NASA North American Land Data Assimilation System Phase Two (NLDAS2) weather dataset in the Upper Mississippi River Basin (UMRB). The SWAT model fed with different weather datasets were used to simulate monthly stream flow at 11 United States Geological Survey (USGS) monitoring stations in the UMRB. Model performances were evaluated based on three metrics: coefficient of determination (R2), Nash–Sutcliffe coefficient (NS), and percent bias (Pbias). The results show that, after calibration, the SWAT model compared well at all monitoring stations for monthly stream flow using different weather datasets indicating that the SWAT model can adequately produce long-term water yield in UMRB. The results also show that using NLDAS2 weather dataset can improve SWAT prediction of monthly stream flow with less prediction uncertainty in the UMRB. We concluded that NLDAS2 dataset could be used by the SWAT model for large-scale watersheds like UMRB as a surrogate of the interpolation weather data. Further analyses results show that NLDAS2 daily solar radiation data was about 2.5 MJ m−2 higher than the interpolation data. As such, the SWAT model driven by NLDAS2 dataset tended to underestimate stream flow in the UMRB due to the overestimation in evapotranspiration in uncalibrated conditions. Thus, the implication of overestimated solar radiation by NLDAS2 dataset should be considered before using NLDAS2 dataset to drive the hydrological model.more » « less
