Lindsey Spencer

Oregon State University

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ABSTRACT:

The National Ecological Observatory Network (NEON) provides open access data products including sub-daily precipitation amounts and biweekly stable water isotope concentrations at sites across the United States. Stable water isotope (d2H, d18O) concentrations are often used in hydrometeorological studies and models, however the relatively infrequent biweekly sampling intervals complicate their applicability due to many applications requiring daily to sub-daily datasets. Here, we present statistically downscaled daily stable isotope concentration datasets based on bi-weekly data from NEON field sites. Precipitation isotope concentration is downscaled by first quantifying and removing the seasonal component from the time series. Daily time series statistics were approximated at each site and then used to generate isotope values conditioned on daily precipitation amounts. Finally, the seasonal component was added back into each time series to generate daily precipitation isotope concentrations and a residual correction was applied to ensure mass closure. This workflow was automated in a published Jupyter Notebook using Python and R scripts. The final time series contain synthetic daily values for each isotope which can then be incorporated as environmental tracers in hydrometeorological applications.

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ABSTRACT:

The National Ecological Observatory Network (NEON) provides open access data products including sub-daily precipitation amounts and biweekly stable water isotope concentrations at sites across the United States. Stable water isotope (d2H, d18O) concentrations are often used in hydrometeorological studies and models, however the relatively infrequent biweekly sampling intervals complicate their applicability due to many applications requiring daily to sub-daily datasets. Here, we present statistically downscaled daily stable isotope concentration datasets based on bi-weekly data from NEON field sites. Precipitation isotope concentration is downscaled by first quantifying and removing the seasonal component from the time series. Daily time series statistics were approximated at each site and then used to generate isotope values conditioned on daily precipitation amounts. Finally, the seasonal component was added back into each time series to generate daily precipitation isotope concentrations and a residual correction was applied to ensure mass closure. This workflow was automated in a published Jupyter Notebook using Python and R scripts. The final time series contain synthetic daily values for each isotope which can then be incorporated as environmental tracers in hydrometeorological applications.

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