Harmonizing Multiple Information Sources to Predict River Corridor Respiration at the Watershed Scale


Francisco Guerrero* ([email protected]), Matthew Kaufman ([email protected]), Xingyuan Chen, Brieanne Forbes, Stephanie Fulton, Z. Jason Hou, Xinming Lin, Lupita Renteria, Kyongho Son, James Stegen, Tim Scheibe


Pacific Northwest National Laboratory, Richland, WA



Respiration in river corridors is an essential process by which organic matter is converted to CO2 and released into the atmosphere. River corridors are also highly sensitive to changing climate conditions. Understanding the factors that control the location and timing of organic matter processing in river corridors is critical to predicting their role in climate feedback. Using a ModEx approach, the research team addressed this challenge in two ways: (1) evaluating a basin-scale model-generated hypothesis with field estimates of riverbed sediment respiration and (2) using model-generated sediment respiration hypotheses to quantify variations in spatial scaling.

Researchers conducted this work in the Yakima River Basin (YRB), which is representative of the environmental gradients in the larger Columbia River Basin. Process-based modeling predicted a consistent spatial pattern for respiration across the YRB: respiration rates increased with stream order, reaching an average maximum at fifth-order streams, then decreased toward the main Yakima River channel. Field measurements also revealed a similar spatial organization, although researchers observed the potential for maximum respiration in fourth-order streams. The team also found that sediment respiration makes up >98% of total respiration across all 48 field sites. Sediment respiration is, therefore, dominant in the river system. Still, spatial respiration patterns differ markedly between model predictions and other measurements of microbial processes (e.g., cotton strip experiments or lab incubations). These results indicate that existing models could be missing key processes and that prediction-observation mismatches can be used to guide further model development. From a spatial scaling perspective, the process-based model was used as a digital twin to explore scaling relationships between sediment respiration and watershed area.

This study used variations in spatial scaling relationships as additional model-generated hypotheses that researchers could evaluate with their field respiration estimates. Specifically, predicted scaling relationships tend to be super-linear in more homogeneous landscapes with longer residence times when compared with more heterogenous landscapes. On the other hand, predicted scaling behavior becomes more uncertain as human-influenced land cover increases. These hypotheses indicate a strong coupling between in-stream and hillslope processes, modulated by climate, vegetation, and land cover. Thus, scaling relationships represent simple quantitative hypotheses ripe for evaluation via ongoing ModEx, including independent testing through field measurements.