MEASURING AND MODELING HYDROLOGIC MEASURING AND MODELING HYDROLOGIC RESPONSES TO TIMBER HARVEST IN A RESPONSES TO TIMBER HARVEST IN A

CONTINENTAL/MARITIME CONTINENTAL/MARITIME MOUNTAINOUS ENVIRONMENTMOUNTAINOUS ENVIRONMENT

ByByJason A. HubbartJason A. Hubbart

Alterations to WatershedHydrology with CanopyRemoval in a ComplexForested Environment

Current Research Current Research

Collaboration is Essential!

Seminar OverviewSeminar Overview

Research Context Research Context The MCEWThe MCEWStudy ApproachStudy ApproachStudy ResultsStudy Results

Harvest and water yieldHarvest and water yieldSnow deposition Snow deposition -- ablationablationModeling (WEPP, SNOBAL)Modeling (WEPP, SNOBAL)

Peripheral studies & Future workPeripheral studies & Future work

Historical StudiesHistorical StudiesQuestions:Questions:

Effect of forests on water balanceEffect of forests on water balanceWater yield augmentationWater yield augmentationImpacts on water qualityImpacts on water quality

1909: Wagon Wheel Gap, CO1909: Wagon Wheel Gap, CO1950s 1950s –– 60s: Many studies initiated60s: Many studies initiated1990: Mica Creek instrumented1990: Mica Creek instrumented

Research & Management NeedsResearch & Management NeedsData from Data from ““typicaltypical”” managed forestlandsmanaged forestlands

Second growth forestsSecond growth forestsContemporary management practicesContemporary management practicesWatershed scale studiesWatershed scale studies

Mechanistic understanding of processesMechanistic understanding of processesInterdisciplinary connectionsInterdisciplinary connections

Environmental concernsEnvironmental concernsIntegrated, spatiallyIntegrated, spatially--explicit management explicit management toolstools

Study GoalsStudy GoalsWater yieldWater yield

Assess changes in annual water yield due to contemporary timber Assess changes in annual water yield due to contemporary timber harvest harvest practices practices

Snow deposition Snow deposition –– ablationablationObtain a better understanding of the key processes that influencObtain a better understanding of the key processes that influence snow e snow accumulation and ablation in forested mountainous watershed in taccumulation and ablation in forested mountainous watershed in the he northern panhandle of Idaho northern panhandle of Idaho

Hydrological modelingHydrological modelingValidate model results by comparison to observed data, leading tValidate model results by comparison to observed data, leading to improved o improved models and hence predictive power models and hence predictive power

Inversion processesInversion processesProvide vertically stratified temperature data in forested mountProvide vertically stratified temperature data in forested mountainous terrainainous terrainAssess the magnitude to which temperature inversions affect soilAssess the magnitude to which temperature inversions affect soiltemperature by elevation temperature by elevation

Isotopes in snowIsotopes in snowDescribe effect of altitude on snow cover isotopic composition Describe effect of altitude on snow cover isotopic composition Describe the influence of elevation, and leaf area index (LAI) tDescribe the influence of elevation, and leaf area index (LAI) to the o the contribution of snowmelt to spring runoff contribution of snowmelt to spring runoff

MCEW MCEW LocationLocationUnique climate Unique climate regionregion

Continental/Maritime Continental/Maritime

Potlatch Corporation

Historical ContextHistorical Context

Extensive loggingExtensive logging19201920--19301930’’ss

Limited Limited anthropogenic anthropogenic disturbance since disturbance since that timethat time

Photo courtesy of Potlatch Corp.

Site CharacteristicsSite CharacteristicsTributary of St. Joe RiverSize: 27 km2 (10.5 mi2)Elevation: 1000 – 1625m (3200 – 5240 ft)Precipitation: 1440 mm/yr (~57 in/yr)Vegetation: 65-75 yr. old mixed coniferGeology: Metamorphic - Gneiss, quartzite, granofels

MCEW Study DesignMCEW Study DesignInitiated 1990Paired & nested watershed design 6 years calibration4 years post-roads4 years post-harvest

50% clearcuts50/50 partial cuts

Annual Water Yield (mm) Annual Water Yield (mm) -- ResultsResults

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Calibration Roads Harvest

Hubbart, J.A., T.E. Link, J.A. Gravelle, and W.J. Elliot. 2007. In Press. In: Special Issue on Headwater Forest Streams, Forest Science. Timber Harvest Impacts on Hydrologic Yield in the Continental/Maritime HydroclimaticRegion of the U. S.

CalibrationQ=520-570 (22”)

P=1510 (60”)

Post-road Q = 470-550 (20")

P =1340 (53")

Post-HarvestQ = 460-760 (20")

P =1450 (57")

Clearcut

Partial cut

Figure in Press

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Annual Water Yield (mm) Annual Water Yield (mm) -- Results Results (p(p≤≤0.05)0.05)

Roads 70 (2.8”

Clearcut255 (10”)

Partial cut135 (5.3”)

Trmt vs. Ctrl130 (5.1”)

Trmt vs. Ctrl75 (3”)

Figure in Press

MonthlyMonthlyRoads Roads

NegligibleNegligibleHarvest Harvest (p (p ≤≤0.05)0.05)

Clearcut Clearcut ↑↑DecDec--June, 10June, 10--60mm/mo (0.460mm/mo (0.4--2.42.4""), 35), 35--55%55%

Partial cut Partial cut ↑↑ DecDec--June, 4June, 4--30mm/mo (0.230mm/mo (0.2--1.21.2""), 9), 9--37%37%

SeasonalDeposition season (Nov – Feb)

Clearcut, 14mm (0.5"), 38%↑Partial cut, 10mm (0.4"), 29%↑

Big Player - melt season (Mar – June)Clearcut, 45mm (1.8"), 35%↑Partial cut, 20mm (0.8"), 20%↑

ET (residual of PET (residual of P--Q)Q)CC = 62%CC = 62%↓↓to 46% annual budget to 46% annual budget PC = 63%PC = 63%↓↓to 55%to 55%

Water Yield (mm) Water Yield (mm) -- ResultsResults

MechanismsMechanisms

Snow dominated systemSnow dominated systemDeposition and ablation processesDeposition and ablation processes

Topographic influencesTopographic influencesMicroclimateMicroclimate

What are the Mechanisms What are the Mechanisms Contributing to the Changes in Contributing to the Changes in

Water Yield?Water Yield?

Snowmelt Processes Snowmelt Processes -- MethodsMethods•38 temp/data loggers

•3 m height•20 m vertical Dist.

•14 snow courses•20 m length•2 m sampling

•7 climate stations•Air temp•Windspeed•Wind direction•Relative Humidity•Soil moisture•Soil Temp•Snow Depth

0 3 6 9 12 15 18 21 24 27 30 33 361225

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Results Results –– 14 Snow courses14 Snow courses

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Apr 27May 5

May 12May 19

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SnoTel 1Con 2PC 3CC 4Con 5CC 6CC 7PC 8PC 9Con 10Con 11CC 12PC 13Rip 14Rip

Peak SWE not correlated with elevation

Positively correlated to canopy removal.

Hubbart, J.A., and T.E. Link. In: Preparation. A spatially distributed investigation of snowcover dynamics in a complex mountain environment. Targeted journal: Hydrological Processes.

Snow Course ResultsSnow Course ResultsPeak SWEClearcut

1.3 times greater than peak SWE in partial cut forests1.6 times greater than the control and riparian sites

Daily MeltClearcut

1.3 times greater than average daily melt in the partial cut forests2 times greater than the control and riparian forests

Climate Stations Climate Stations -- MethodsMethods• 7 Climate stations

– Stratified– Elev & treatments

– Data collection – 2003 - present• 2006 WY to quantify

variability of snowmelt

•Air temp•W – speed•W – dir•Rel hum•Soil moist•Soil temp•Snow depth

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• Peak SWE• Clearcut: 56 cm• Partial cut: 29 cm• Control: 18 cm• Riparian: 34 cm

• Average daily melt• Clearcut: 1.08 cm/day• Partial cut: 0.66 cm/day• Control: 0.48 cm/day• Riparian: 0.5 cm/day

Preliminary Results/DiscussionPreliminary Results/DiscussionSnow cover is spatially variableCanopy reduction and topographic variability can result in considerable differences in snow accumulation and melt ratesThe largest differences in accumulation and ablation rates are observed in the clearcuts

Melt rates are approximately twice as fast at the fully forested(control) sites

Peak SWE and increased daily melt rates appear to be correlated to tree removal (e.g. clearcut), but not to elevation

Synthesis Synthesis –– Modeling Modeling -- MethodsMethodsData from 7 climate stations drive the physical process-based snowmelt model SNOBAL (Marks et al., 1999)

Output used to assess snowmelt algorithms of WEPP

Modeling outputs will be compared to observed melt, and in terms of the basic snowmelt energy balance equation:

ΔQ = Qn + Qe + Qh + Qg + QmΔQ is change in heat storage of snow coverQn is net radiation transferQe is latent heat transferQh is sensible heat transferQg is heat transfer from the soil to the snowpackQm is advected heat.

Hubbart, J.A., T.E. Link, and W.J. Elliot. In: Preparation. A detailed evaluation of snowmelt dynamics as simulated by Water Erosion Prediction Project (WEPP). Targeted journal: Trans. of ASAE.

WEPP vs. SNOBAL WEPP vs. SNOBAL Conceptual DiagramsConceptual Diagrams

Empirical/Process BasedEmpirical/Process Based Physical/Process BasedPhysical/Process Based

Preliminary Modeling Results Preliminary Modeling Results (WEPP) Simulated Snow Water Equivalent (SWE)(WEPP) Simulated Snow Water Equivalent (SWE)

Courtesy: Erin Brooks, BAE, UI

SnoTel (NRCS)Basin average approach

1991 1992 1993 1994 1995 1996 1997 1998

Peripheral ProjectsPeripheral Projects

Microclimate Microclimate -- Temperature InversionsTemperature Inversions• Temperature transect

– Elevation, topography, land-use – Cold-air drainage/temperature

inversions• Snowmelt variability• Noc cond, soil water pot (Ψs)• Soil heat, heat flux, respiration

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T e m p e r a tu r e S ta t i o n E le v a t io n ( m ) a n d L o c a t io n

Hubbart, J.A., T.E. Link, C. Campbell, and D. Cobos. 2005. An evaluation of a low-cost temperature measurement system. Hydrologic Processes, 19: 1517-1523.

Current Status & Need• Conventional thought

• “Normal” lapse rate (~6.5⁰C/km)• Why study?

• Hydrology - energy balances• Snow-melt dynamics

• Soil Temperature response• Soil heat flux• Soil respiration

• Plant physiological response(s)• Leaf/stomatal/soil conductance

Lapse Rates/Cold Air Drainage Lapse Rates/Cold Air Drainage (2004)(2004)

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Average Vapor Pressure Deficit (kPa)/Time Interval/Elevation for the Months of July and August 2004

Mean Air TemperaturesMean Air TemperaturesFull slope Full slope avgavg ELR (155m): 20.0 ELR (155m): 20.0 °°CCCold air pool (88m): 35.0 Cold air pool (88m): 35.0 °° CC

Change in VPDChange in VPDFull slope Full slope avgavg: 0.35 : 0.35 kPakPaCold air pool: 0.40 Cold air pool: 0.40 kPakPa

Hubbart, J.A., K.L. Kavanagh, R. Pangle, T.E. Link, and A. Schotzko. 2007. Cold air drainage and modeled nocturnal leaf water potential in complex forested terrain. In Special Issue Tree Physiology 27: 631-639.

Leaf Water Potential Leaf Water Potential (nocturnal)(nocturnal)

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Hourly Nocturnal Leaf Potential (mPa) for Western Red Cedar Based on Mean Vapor Pressure Deficit (kPa)

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TimeFull Slope

(155m)Inversion

Layer (88m)1:00 -0.15 -0.152:00 -0.18 -0.183:00 -0.14 -0.144:00 -0.14 -0.145:00 -0.14 -0.1420:00 -0.14 -0.1421:00 -0.18 -0.1822:00 -0.18 -0.1823:00 -0.18 -0.180:00 -0.15 -0.15

Change in Yleaf (mPa) From Bottom to Top of Slope

Nocturnal ELR, VPD, and modeled Ψleafbased on mean temperatures for the months of July through August 2004

DiscussionDiscussion (2004)(2004)

Inversions/Cold Air DrainageInversions/Cold Air DrainagePersistent (the norm)Persistent (the norm)

Mean tempsMean tempsELR = 28.0ELR = 28.0⁰⁰C full slopeC full slopeELR = 48.3ELR = 48.3⁰⁰C w/in inversion layer (42%C w/in inversion layer (42%↑↑full slope)full slope)

VPD tracksVPD tracks

Based on mean tempsBased on mean temps⍦⍦leaf leaf increasingly negative (+25%) towards top of inversion (88m)increasingly negative (+25%) towards top of inversion (88m)

Max Temps = 37%Max Temps = 37%Min Temps = 13%Min Temps = 13%

Clear night (Worse case scenario cold air drainage)Clear night (Worse case scenario cold air drainage)ELR = 75ELR = 75⁰⁰C (88m inversion)C (88m inversion)⍦⍦leaf leaf was was --0.39mPa more negative from bottom to top of COD0.39mPa more negative from bottom to top of COD

Cloudy nightCloudy nightELR = 1.85ELR = 1.85⁰⁰C (88m inversion layer)C (88m inversion layer)⍦⍦leaf leaf ––0.04mPa more negative from bottom to top of COD0.04mPa more negative from bottom to top of COD

Effects of Cold Air Drainage on Soil Effects of Cold Air Drainage on Soil Temperature Analysis Temperature Analysis (Summer 2006)(Summer 2006)

Hubbart, J.A., T.E. Link, J. Marshall, E. Du, K. Kavanagh, P. Koeniger, and J.A. Gravelle. In: Preparation. Soil Temperature and Modeled Soil Respiration Response to Persistent Temperature Inversions and Timber Harvest in Northern Idaho. Targeted journal: Journal of Agricultural and Forest Meteorology.

Summer 2006 Soil TemperaturesSummer 2006 Soil TemperaturesClearcutClearcut

Ts(5)Ts(5)AvgAvg SLR (263m): SLR (263m): 6.5 6.5 °°CC

Ts(15)Ts(15)AvgAvg SLR 3.0 SLR 3.0 °° CC

ControlControlTs(5)Ts(5)

AvgAvg SLR (155m): SLR (155m): 53.5 53.5 °°CC

Ts(15)Ts(15)AvgAvg SLR 11.8 SLR 11.8 °° CC

Control (Fully Forested)

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Take Home MessagesTake Home MessagesNot safe to assume a normal temp regimeNot safe to assume a normal temp regime

I.e. Cooling with elevationI.e. Cooling with elevationClear night Clear night not same as cloudy nightnot same as cloudy night

Classic assumption of normal lapse rates may not be Classic assumption of normal lapse rates may not be true true

↓↑↓↑ hydrologic processeshydrologic processesClassic assumptions of plant physiological responses Classic assumptions of plant physiological responses based on assumed lapse rates may not be truebased on assumed lapse rates may not be true

↓↑↓↑nocturnal transpirationnocturnal transpirationAssumptions of carbon allocation, forest productivity Assumptions of carbon allocation, forest productivity may not be truemay not be true

↓↑↓↑forest productivityforest productivitySoil RespirationSoil Respiration

Inversions Inversions -- Future DirectionsFuture Directions5 slope analysis5 slope analysis

ClearcutClearcutPartial cutPartial cutControlControl

Winter Winter –– snowmelt implicationssnowmelt implicationsSummerSummer

Hydrological implicationsHydrological implicationsPlant physiological implicationsPlant physiological implications

Improved forest productivity analysesImproved forest productivity analysesSoil moisture, and othersSoil moisture, and othersNPPNPP

Improved forest managementImproved forest managementNew and improved predictive modelsNew and improved predictive models

Temperature Inversions: Dynamic fluid modelTemperature Inversions: Dynamic fluid modelCollaboration with Dr. Paul Collaboration with Dr. Paul GesslerGessler (GIS)(GIS)

Inversions and fire effectsInversions and fire effectsMovement and transport, modelingMovement and transport, modeling

Isotopes in SnowIsotopes in SnowWhy Isotopes?

Helpful tool for investigating snowmelt contributions to runoff.Initial isotope signals are stored in snow layers (Stichler et al. 2000).

Need:Consideration of spatial variations of isotopes in snowpacks as tracer inputs to catchment studies (Unnikrishna et al. 2002).

Objectives:Quantify the isotope distribution in snow layers vs. variations in meteorological parameters and management practices (e.g. clear-cut, partial-cut and fully forested sites). Quantify spatial variability of elevation, canopy structure on snow cover isotopic composition (18O), and the contribution of snowmelt to spring runoff.

Isotopes in Isotopes in snowmeltsnowmelt

- No altitude effect in snow - Spatial variability vs. LAI- Differences in spring snowmelt

event

Koeniger, P., J.A. Hubbart, T. Link, and J.D. Marshall. In Submission. Stable isotope variability in snowcover and snowmelt in response to forest management at the Mica Creek Experimental Watershed, Northern Idaho. Hydrological Processes.

Research SynopsisResearch SynopsisWater YieldWater Yield

~30% increase post clearcut~30% increase post clearcut~20% increase post partial cut~20% increase post partial cut

Snow deposition/ablation processesSnow deposition/ablation processesPeak SWE ~1.5x higher in clearcut relative to controlPeak SWE ~1.5x higher in clearcut relative to controlMelt ~2x higher in clearcut relative to controlMelt ~2x higher in clearcut relative to control

Modeling Modeling –– coming sooncoming soon…………..InversionsInversions

PersistentPersistentEffects Effects –– tree physiological response, soil heat flux, soil respirationtree physiological response, soil heat flux, soil respiration

IsotopesIsotopesProvide mechanism to better understand relative contributions toProvide mechanism to better understand relative contributions to runoffrunoff

SedimentSedimentIncrease 3x post clearcut for 12 monthsIncrease 3x post clearcut for 12 months

TstreamTstreamIncreased from clearcutIncreased from clearcutHighly variable (hyporheic exchange?)Highly variable (hyporheic exchange?)

BryophytesBryophytesBioindicatorsBioindicators of forest management impactsof forest management impacts

Thank YouThank You

Research FundingResearch FundingUSFS Research Joint Venture Agreement #03USFS Research Joint Venture Agreement #03--JVJV--1122206511222065--068068USFS Grant 04DG11010000037USFS Grant 04DG11010000037USDAUSDA--CSREES 2003CSREES 2003--0126401264U.I. Graduate Student Fellowship (2006U.I. Graduate Student Fellowship (2006--2007)2007)A whole lot of peopleA whole lot of people……John Gravelle, John Gravelle, EnhaoEnhao Du, Diana Du, Diana KarwanKarwan, , John Marshall, Paul John Marshall, Paul KoenigerKoeniger, Justin , Justin BroglioBroglio, Reviewers of , Reviewers of Manuscripts, CoManuscripts, Co--authorsauthors…………, ,

Graduate Committee MembersGraduate Committee MembersTimothy LinkTimothy LinkKaty KavanaghKaty KavanaghJan BollJan BollBill ElliotBill Elliot