Modeling thermal effects of climate change from landform predictors of groundwater influence in Chesapeake Bay headwater streams Zachary C. Johnson Nathaniel P. Hitt Craig D. Snyder USGS Leetown Science Center Aquatic Ecology Branch

Many factors regulate stream temperature

Advection from groundwater and hyporheic flow important in headwater streams

Thermal Sensitivity Time Temperature (C) Low sensitivityHigh sensitivity High GW influenceLow GW influence Air Water

Thermal Sensitivity Time Temperature (C) Water Temp (C) Air Temp (C) Low sensitivityHigh sensitivity High GW influenceLow GW influence Air Water

Modeling GW influence on stream temperature Air and water temperature – Mean daily Accumulated Degree Day (ADD) – Sum of daily air degrees above summer mean

Shenandoah National Park Sampling Design N = 80+ in 9 watersheds Watersheds stratified by geology, solar radiance Paired air-water loggers Began summer of 2012 Whiteoak Jeremy’s Big Run Staunton Meadow Paine Piney Hughes Hazel

Previous work As much variation within watersheds as between them 1 What physical factors influence this variation? 1 C.D. Snyder et al., Eco. Apps. 2015

Landform Factors Surficial and bedrock geology – Colluvium, alluvium, debris fan, etc. – Siliciclastic, Basaltic, Granitic Geomorphology – Elevation, slope, contributing area, etc. – Topographic wetness index, stream power index Network topology – Tributary confluences, drainage density, etc.

Preliminary Results Statistical Test Parameter TypeParameterSignPearsonCARTRandom Forest Step AIC Geology%Granite-XXXX %Basalt+XXX Soil Wtr Stor+XXX GeomorphologySymmetry+XX Network%Tributaries-XXX Drain Dnsty-XX

Preliminary Results Statistical Test Parameter TypeParameterSignPearsonCARTRandom Forest Step AIC Geology%Granite-XXXX %Basalt+XXX Soil Wtr Stor+XXX GeomorphologySymmetry+XX Network%Tributaries-XXX Drain Dnsty-XX Geology – Less GW influence in granitic basins – More GW influence in basaltic basins – More GW influence with greater soil water storage

Preliminary Results Statistical Test Parameter TypeParameterSignPearsonCARTRandom Forest Step AIC Geology%Granite-XXXX %Basalt+XXX Soil Wtr Stor+XXX GeomorphologySymmetry+XX Network%Tributaries-XXX Drain Dnsty-XX Geomorphology – More GW influence with more symmetric cross- sections

Preliminary Results Statistical Test Parameter TypeParameterSignPearsonCARTRandom Forest Step AIC Geology%Granite-XXXX %Basalt+XXX Soil Wtr Stor+XXX GeomorphologySymmetry+XX Network%Tributaries-XXX Drain Dnsty-XX Network topology – Less GW influence with increasing contribution from tributaries

Taken after S.J. Dugdale et al., 2015 Confined Semi- confined Unconfined Steep gradient Gentle gradient Less GW Influence More GW Influence Less GW Influence

Confined/ Semi-confined Confined/ Unconfined Semi-confined/ Unconfined Steep gradient Gentle gradient Steep gradient Less + More GW Influence? Less + Less GW Influence? More + Less GW Influence?

10m DEM vs LiDAR (< 1m) Tributary Main Stem NHD Stream Layer LiDAR-derived Stream Layer A B A B A B A B

Lessons Learned Complex interactions between geology, geomorphology, and network topology – No single dominant parameter or category New hypotheses – Confinement asymmetry – Spatial organization of confinement Less confinement could be good or bad – Good: more surface-subsurface interactions – Bad: could result in significant flow loss/drying

Challenges & Ongoing Work Limited discharge information – Detecting drying from temperature data – Precipitation data Spatial scale of available data – 10m vs. LiDAR Multiple years ( ) – Annual temperature data Not only summer

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References Snyder, C.D., N.P. Hitt, J.A. Young (2015) “Accounting for groundwater in stream fish thermal habitat responses to climate change”. Ecological Applications, 25, Dugdale, S.J., N.E. Bergeron, A. St-Hilaire (2015) “Spatial distribution of thermal refuges analysed in relation to riverscape hydromorphology using airborne thermal infrared imagery”. Remote Sensing Environment, 160,

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