Gordon E. Grant USDA Forest Service PNW Research Station M. Safeeq & S.Lewis Oregon State University C.Tague, University of California Santa Barbara Where’s Water? Forecasting future streamflow regimes in the Pacific Northwest

J F M A M J J A S O N D precipitation water use The paradox of water in the West…

Develop a theoretical model of streamflow sensitivity to warming Apply this model to long- term data from basins across western US; examine empirical trends in streamflow Explore sensitivity to warming across basins across Oregon Compare with downscaled models Today’s menu

A quick primer on climate change in the Pacific Northwest –Warmer historic temperatures –Changes in precipitation likely but uncertain (storminess?) –Snowpack is smaller and melting earlier –Glaciers are retreating

(Nolin and Daly, 2006) Snow at risk in a warming climate 22%Oregon Cascades 12%Washington Cascades 61%Olympic Range <3%Pacific Northwest study area Red = rain instead of snow in the winter

But…. It’s not just about snow…. Location (geology) matters too… So…where, when, and how much water will be available in the future – and what will its quality be? –Start with summer streamflow

Filter 1:Timing and Magnitude of Recharge Filter 2: Drainage Efficiency Tague & Grant, 2009

Simple model (from Tague and Grant, 2009) Q t – streamflow at time t (in days) Q o – streamflow at beginning of recession k – recession constant

Treating recharge as a single event, we develop a model for summer baseflow: Q r – summer streamflow k - drainage efficiency t r - days between snowmelt (t pk ) and time of interest (t summer ) pk 15-day - snowmelt input (peak reduction in a watershed areal mean of a 15 day running average pk 15-day trtr k (Tague & Grant, 2009)

Summer flow sensitivity to changes in snowmelt dynamics (first derivatives) Magnitude (pk 15-day ) Timing (t r ) Both contain k, drainage efficiency (Tague & Grant, 2009)

unit change in daily streamflow (mm/day) sensitive Not sensitive deep/slowshallow/fast short long

Now, how do we go about: forecasting the sensitivity of watersheds across the region without modeling everything in sight?

…can we? It would be nice if we could break the world into four distinct classes: Rain Slow Snow Slow Rain Fast Snow Fast Snowmelt dominated Filter 1: Climate / Precipitation Filter 2: Drainage Efficiency Rain dominated Fast draining Slow draining

Interpreting streamflow trends across western US 81 unregulated basins Drainage area: 20 to 36,000 km 2 (median 550 km 2 ) Gage elevation: 6.5 to 2,245 m (median 431 m ) Time period for analysis:

Extracting metrics from hydrologic records 1. Centroid Timing (CT) 2a. Recession (k) 2b. Base Flow Index (BFI) Recession Constant (k) Base Flow Index (BFI) annual value; mean for period of record daily value; mean for period of record event value; median for period of record

Interpreting streamflow trends across western US Early CT = rain-dominated Intermediate CT = Rain-on- snow / mixed Late CT = snowmelt- dominated 1. Timing

Interpreting streamflow trends across western US Low BFI = fast draining Medium BFI = somewhere in between High BFI = slow draining 2. Efficiency

Late CT Snowmelt dominated Filter 1: Climate / Precipitation Early CT Rain dominated Filter 2: Drainage Efficiency Low BFI Fast draining High BFI Slow draining Summer Runoff Ratio (summer flow/ annual precip) water year

Late CT Snowmelt dominated Filter 1: Climate / Precipitation Early CT Rain dominated Filter 2: Drainage Efficiency Low BFI Fast draining High BFI Slow draining Summer Runoff Ratio (summer flow/ annual precip) water year All trends are negative

Late CT Snowmelt dominated Filter 1: Climate / Precipitation Early CT Rain dominated Filter 2: Drainage Efficiency Low BFI Fast draining High BFI Slow draining Summer Runoff Ratio (summer flow/ annual precip) water year Slopes steepen with increasing BFI

Late CT Snowmelt dominated Filter 1: Climate / Precipitation Early CT Rain dominated Filter 2: Drainage Efficiency Low BFI Fast draining High BFI Slow draining Summer Runoff Ratio (summer flow/ annual precip) water year Precipitation trends can trump geology

Key initial findings: Snowpack dynamics and drainage efficiency (mediated through hydrogeology) are both first- order controls on streamflow response to climate warming Simple theory predicts greatest low flow sensitivity to changes in timing of snowmelt are in basins with intermediate snowmelt timing and low drainage efficiencies Basins can be categorized in terms of their snowpack dynamics and drainage efficiencies using simple metrics Historical trends in streamflow are consistent with model predictions Changes in precipitation can trump changes due to warming alone

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