All long term datsets contain missing or unusable data (gaps). While many of these gaps are inevitable, it is not possible to simply omit missing values when calculating solute fluxes from precipitation or streamflow. The uncertainty associated with gap-filling estimates is not commonly reported or propagated into these flux estimates. We hope to characterize the causes of these gaps across sites for both volume and solute chemistry in long-term precipitation and streamflow datasets. To quantify the uncertainty associate with different gap-filling methods, we will apply them to a series of "fake gaps," and compare the estimates with measured values.
Weather events can cause gaps in long-term datasets. (D. Buso)
To do this we need datasets! We're interested in hearing form potential co-authors with streamflow and precipitation datasets to discuss the frequency and causes of gaps, and how they fill them. We are especially in need of solute concentration datasets. We're also interested in hearing from statistically minded individuals with new ideas on how to quantify the uncertainty associated with these gaps.