Skip to main contentSkip to footer content

Gap Filling

Quantifying uncertainty in gap filling of long-term hydrologic datasets for nutrient budgets: case studies from the LTER network

Craig See, Jeremy Hayward, Ruth Yanai, Doug Moore, Mark Green
In preparation for TBD


All long term datasets contain missing or unusable data (gaps). While many of these gaps are inevitable, when calculating solute inputs from precipitation or outputs from streamflow, it is not possible to simply omit missing values. The uncertainty associated with gap-filling estimates is not commonly reported or propagated into flux estimates. We hope to characterize the causes of these gaps across sites for both volume and solute chemistry in long-term precipitation and steamflow datasets. To quantify the uncertainty associated with different gap-filling methods, we are applying them to a series of "fake gaps," and comparing the estimates with measured values.


HJ Andrews Experimental Forest and LTER (Blue River, OR); Coweeta Hydrologic Laboratory and LTER (Otto, NC); Hubbard Brook Experimental Forest and LTER (West Thornton, NH); Sevilleta National Wildlife Refuge (Socorro, NM)


Datasets were collected under several cycles of NSF LTER programs.