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Research Project: Strategic Investigations to Improve Water Quality and Ecosystem Sustainability in Agricultural Landscapes

Location: Water Quality and Ecology Research

Title: To model or measure: estimating gas exchange to measure metabolism in shallow, low gradient stream habitats

Author
item Nifong, Rachel
item Taylor, Jason
item Yasarer, Lindsey

Submitted to: Freshwater Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/26/2019
Publication Date: 12/17/2019
Citation: Nifong, R.L., Taylor, J.M., Yasarer, L.M. 2019. To model or measure: estimating gas exchange to measure metabolism in shallow, low gradient stream habitats. Freshwater Science. 39(1):70-85. https://doi.org/10.1086/707460.
DOI: https://doi.org/10.1086/707460

Interpretive Summary: Stream metabolism is an important measure of stream ecosystem health and can help evaluate responses to reduction in agricultural runoff to the Nation’s streams. Measuring stream metabolism is based on measuring daily patterns in dissolved oxygen, a key limiting factor to aquatic life in freshwater lakes and rivers. A critical variable necessary to measure stream metabolism is the rate of gas transfer from water to the atmosphere based on temperature and barometric pressure. Unfortunately this variable is very difficult to measure. ARS scientists conducted a study comparing novel approaches to measure gas transfer with the purpose of refining methods for estimated stream metabolism responses to implementation of agricultural best management practices. Results indicate that different approaches to measuring gas transfer produce similar results in shallow vegetated streams. However, in unvegetated streams, modeling approaches to estimating gas transfer produced much higher estimates compared to direct measurements, and these inconsistencies impacted measures of stream metabolism. Therefore it is recommended that when using stream metabolism as a measure of stream health, water resource scientists should measure gas transfer in shallow low gradient streams and ditches, a dominant feature in agricultural landscapes. These results should be of interest to both scientists and agricultural land managers as they work to ensure sustainable agricultural production and resource use.

Technical Abstract: Stream metabolism is an important metric of ecosystem function. Accurate estimates of gross primary production (GPP) and respiration (ER) require estimates of rates of gas transfer at the surface water interface as inputs to metabolism models. Recent advances in methods to quantify gas exchange include the use of argon tracer tests for direct measurements and inverse modeling methods to simultaneously solve for GPP, ER, and gas exchange rate, K (d-1). However, a comparison of these methods and the resulting effects on ecosystem metabolism is lacking. We compared these methods in both vegetated and unvegetated open canopy, shallow, low-gradient streams experiencing a range in hydrologic conditions. Methods largely agreed for estimates of gas exchange in vegetated streams. In unvegetated streams, we observed higher estimates for K based on inverse modeling methods compared to measured K and inconsistencies in K propagated to GPP and ER estimates. Despite this, linear relationships between ER and GPP were not significantly different among methods of estimating gas exchange for vegetated or unvegetated streams. This study demonstrates that given high GPP and low gas transfer in shallow, low-gradient, open canopy streams, practitioners can consider inverse modeling approaches to estimate gas exchange rates within these habitats. This is especially apparent in vegetated streams where considerable agreement among methods for incorporating K into metabolism estimates was observed. In unvegetated streams, model fits struggled to replicate dissolved O2 data and metabolism estimates based on modeled K trended higher, indicating that reliable estimates in these dynamic habitats can be hindered by uncertainty in measured K in low gas transfer environments and processing errors in existing models.