Quantifying Dynamic River Gains and Losses Using Inverse Water Temperature Modeling

Quantifying river gains and losses is critical for effective water resource management, particularly in systems with streamflow and water quality regimes dictated by variable groundwater and surface water exchanges. Traditional methods for estimating riverine exchanges often require significant resources and can be limited in temporal or spatial scope. This study presents a practical methodology using inverse water temperature modeling and temperature data from inexpensive loggers to estimate gains and losses at different reach scales over time. A physically based, 1-D water temperature model incorporating detailed gain and loss estimates from a synoptic seepage study was calibrated at many temperature observation locations during a low-flow 2-day period. Assuming calibrated parameters held over time, gains and losses were then estimated during 18 additional 2-day periods via inverse temperature modeling using three different spatial reach segmentations (high, medium, and low resolution). Gain and loss estimates, primarily attributed to groundwater exchanges, were then interpolated over a 5-month study period to assess the effect of reach resolution on the method accuracy. Results show that a higher resolution segmentation best matched the pattern of both net and gross gains and losses observed during seepage study periods. The medium-resolution segmentation most accurately represented the total volume of observed gain and loss. This methodology effectively estimates riverine gains and losses, and temperatures over space and time at a relatively low-cost to inform hydrologic modeling and water management in systems where significant surface or subsurface exchanges alter system response.