Consequences Of Stand Age And Structure On Forest Water Yield
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Consequences Of Stand Age And Structure On Forest Water Yield
- 1. Consequences Of Stand Age And Structure On Forest Water Yield Chelcy Ford Miniat, Pete V. Caldwell, A. Chris Oishi, Katherine Elliott USDA Forest Service, SRS, Coweeta Hydrologic Lab, Otto, NC Steven T. Brantley Joseph W. Jones Ecological Research Center, Newton, GA Kim A. Novick University of Indiana, School of Public and Environmental Affairs, Bloomington, IN Paul V. Bolstad University of Minnesota, College of Food, Agriculture and Natural Resource Sciences, Department of Forest Resources, St. Paul, MN USDA FS SRS
- 2. USDA FS SRS Caldwell et al., 2014 • 81% Forested (24% Nat’l Forest) • 23 billion m3/yr • 588 intakes • 336 communities • 10.7 million served (4.9 M get >20%) The southern Appalachians: Headwaters for drinking water supply EPA SDWIS; WaSSI model output
- 3. Leuzinger and Korner 2010, GCB Labat et al. 2004, AWR Gedney et al. 2006, Nature Large-scale patterns in streamflow Simple Water Balance Input (Pre)– Output (Ro)= Loss (ET) Surface water supplies are increasingly vulnerable to drought. Rainfall distribution is the main driver of runoff under future warmer, higher CO2 world in a temperate deciduous forest. River discharge across the globe has been increasing at a rate of 2% for each 0.5°C increase in global temperature. USDA FS SRS Post-1960 trend Attribution of Post-1960 trend in ET
- 4. Local patterns in streamflow Locally in the southern Appalachians, we see the opposite of the global and N. American trend. Discharge in reference watersheds at Coweeta Hydrologic Lab has been decreasing at a rate of 1.3% for each 0.5°C increase in temperature. USDA FS SRS Ford et al., 2011
- 5. Hypotheses USDA FS SRS Over time water yield (Q) in unmanaged forests of the southern Appalachians has decreased Both climate (precipitation and potential ET) and species changes in evapotranspiration (ET) have contributed to the decline
- 6. Coweeta Basin N • Established 1934 • 1626 ha Basin
- 7. Coweeta Basin N • Established 1934 • 1626 ha Basin • 16 active weirs
- 8. Coweeta Basin N • Established 1934 • 1626 ha Basin • 16 active weirs • 5 climate stations
- 9. Coweeta Basin N • Established 1934 • 1626 ha Basin • 16 active weirs • 5 climate stations • 10 rain gauges
- 10. Coweeta Basin N • Established 1934 • 1626 ha Basin • 16 active weirs • 5 climate stations • 10 rain gauges • 676 vegetation plots
- 11. Elliott and Vose 2011 Changing forest species composition USDA FS SRS
- 12. Major Forest Disturbances ElmSpanworm(1961) HurricanesFrancis&Ivan(2004) MajorFlood(1964) FallCankerworm(1969-1977) MajorDrought(1984-1988)SPBandOakMortality HemlockWoollyAdelgid(2003-present) HurricaneOpal(1995) 1900 1950 2000 Drought(1999-2001)SPBmortality 1850 Chestnutblight(1928-1938) Logging(late1800s-1920s) HomesteadsandGrazing NativeAmericanuseofFire Hurricane(1835) Drought(2006-2008) USDA FS SRS
- 13. Control Watersheds at Coweeta USDA FS SRS
- 14. Quantifying changes in annual Q AutoRegressive Integrated Moving Average Time Series Modeling • Can account for: • autocorrelation (memory) • transfer functions can combine independent series (e.g., Q~P, PET) • external perturbations affecting the time series (interventions, e.g., species data) • Akaike’s Information Criterion (AIC) for model selection USDA FS SRS
- 15. Q has changed in low elevation watersheds Low elevation High elevation • For WS14 and WS18, Q decrease began in 1974, declined by 22% or 64mm/decade
- 16. Hypotheses Over time water yield (Q) in unmanaged forests of the southern Appalachians has decreased Both climate (precipitation and potential ET) and species changes in evapotranspiration (ET) have contributed to the decline USDA FS SRS
- 17. Precipitation has not changed Change in P 1938-1946 +373 mm/decade (p=0.0835) Checking standard rain gauge at CS01 (1952) USDA FS SRS
- 18. PET has increased in recent years USDA FS SRS 1938-1949 -50 mm/decade p=0.0463 1997-2013 +46 mm/decade p=0.0023 CS01 Evaporation Pan: Measured daily since 1936
- 19. ET initially declined, but has increased markedly since the 1980s Low elevation High elevation • Increase began in 1980-1999, increasing by 37-93 mm/decade
- 20. Elliott and Vose 2011 Changing forest species composition USDA FS SRS
- 21. Water use varies by species Ford et al., 2011; Vose and Ford, 2011 Diffuse porous and tracheid xylem Ring porous xylem Red maple Tulip poplar White pine Hemlock Red oak Chestnut oak Semi-ring porous xylem Hickory
- 22. Disturbance has shifted tree abundance and composition • Residuals from a model predicting Q as a function of climate only (P and PET)
- 23. Climate can explain some of the changes in Q, but not all Low elevation High elevation • Residuals from a model predicting Q as a function of climate only (P and PET) • Residuals from a model predicting Q as a function of climate (P and PET) and species change
- 24. Low elevation High elevation These changes in forest structure and species composition may have decreased water yield by as much as 18% in a given year since the mid-1970s after accounting for climate.
- 25. Summary • There have been changes in water yield (Q) in unmanaged forests of the southern Appalachians • Q increased 30-55% in low elevation watersheds from 1938-1970s • Q decreased 22% from early 1970s to 2013 in some low elevation watersheds • There have been changes in P and PET that explain some the changes in Q and ET • Changes in forest species composition and structure also played a role on Q and ET • Significant interventions indicate changes in Q by up to 10% • Canopy interception increases with age • Shift to dominance of diffuse porous species
- 26. USGS gauge 03500000, Little Tennessee River at Prentiss Reference gage How widespread are these changes? Departures from 1960-1980 line of fit USDA FS SRS Caldwell et al., in prep
- 27. Dr. Steven Brantley, AFRI project post- doc, now permanent Eco-hydrologist at Joseph W. Jones Ecological Research Center Training, Mentoring and Products Changes in interception with forest age USDA FS SRS Brantley et al., in prep This AFRI project has successfully trained and mentored 3 post-doctoral research scientists, 2 graduate students, and 1 undergraduate student
- 28. Brantley, S. T., M. Schulte, P. V. Bolstad, and C. F. Miniat. 2016. Equations for estimating biomass, foliage area and sapwood of small trees in the southern Appalachians. Forest Science 62: 414–421 Equations for estimating structural characteristics of mid-canopy trees and shrubs in the Southern Appalachians. USDA FS SRS Ms. Morgan Schulte, AFRI project undergraduate student
- 29. Dr. Kim Novick, FS/AFRI project post-doc, now faculty at University of Indiana Bloomington USDA FS SRS Novick, K. A., et al. (2013). Eddy covariance measurements with a new fast- response, enclosed-path analyzer: Spectral characteristics and cross-system comparisons. Ag For Met, 181, 17-32. Novick, K., Brantley, S., Miniat, C. F., Walker, J., & Vose, J. M. (2014). Inferring the contribution of advection to total ecosystem scalar fluxes over a tall forest in complex terrain. Ag For Met, 185, 1-13.
- 30. Dr. Chris Oishi, AFRI project post-doc, now permanent Ecologist with USDA Forest Service Coweeta Hydrologic Lab ET flux from canopy and subcanopy across forest ages forest age USDA FS SRS Oishi et al., in prep
- 31. Benson, M., Oishi, A.C., Miniat, C.F., Domec, J.-C., and Novick, K.A. “Climate and age influence on drought-induced cavitation vulnerability in eastern deciduous forests” Oral presentation, Ecological Society of America Annual Meeting Ft. Lauderdale, FL. August 7-12, 2016. Poster presentation, AmeriFlux Annual Meeting, Golden, Colorado, September 21-23, 2016 Safety Margin (P50 - ΨL) across chronosequence: • LITU – juvenile stands in more mesic site are at highest risk for drought induced mortality. • QUAL – mature species in more mesic site are at the highest risk for drought induced mortality Mr. Michael Benson, AFRI project undergraduate, now a MS student with Novick at IU
- 32. Oishi, A.C., et al. "Baseliner: An open-source, interactive tool for processing sap flux data from thermal dissipation probes." SoftwareX (2016). Dehnam, S.O., Oishi, A.C., Miniat, C.F., Brantley, S.T., Novick, K.A. “Tree water use dynamics across different sites and age classes in the Southern Appalachians”. Poster presentation, Ecological Society of America, Annual Meeting Ft. Lauderdale, FL. August 7-12, 2016. Ms. Sander Denham, AFRI project post-MS student, now a PhD student with Novick at IU
- 33. USDA FS SRS Special thanks to Chris Sobek, Charles Marshall, Neal Muldoon, and all scientists, technicians and students who have contributed to the long-term dataset. This project was supported by: Agriculture and Food Research Initiative Competitive Grant number 2012-67019-19484 from the USDA National Institute of Food and Agriculture USDA FS SRS USFS/UMN cooperative agreement (agreement #12-CS-11330140-128) Acknowledgements
Editor's Notes
- Magnitude and direction of streamflow trends vary across space and time Impact of ET on water yield thought to be small compared to impact of precipitation at large scale, but may be important locally Increasing debate over how ET will change and impact streamflow in the future
- There is indirect evidence suggesting that forest ET is changing in response to changing climate; however, evidence largely comes from observational studies over time that use proxies for ET, such as estimates of ET from the balance of precipitation and river discharge records. River discharge across the globe has been increasing at a rate of 4% for each 1°C increase in global temperature (Labat et al. 2004). The increase in discharge has been attributed to the physiological effect of CO2 decreasing ET and not to the effect of changing land use (Gedney et al. 2006). Striped bars refer to trends over the whole analysis period and solid bars refer to post-1960 trends. Bars to right show the attribution of the post-1960s trend
- *Controls with mixed hardwoods stands remaining undisturbed since 1927. Unmanaged ~85 year old stands that were once logged. Watersheds on the left are higher elevation with relatively higher P while watersheds on the right are low elevation with relatively lower P
- Observed annual water yield for low‐elevation (a) and high‐elevation (b) watersheds (WS). Black lines and circles are the observed water yield, solid red lines are the modeled mean with ramp interventions in 1949 for WS02, and 1974 for WS14 and WS18. Dashed red lines are the upper and lower 95% confidence intervals about the modeled mean. Ramp interventions shown were significant at α = 0.10.
- Observed annual precipitation (P) (a) and potential evapotranspiration (PET) (b) at low-elevation standard rain gage 19 (SRG19) and climate station 01 (CS01), respectively. Black lines and circles are the observations; solid red lines are the modeled mean with ramp interventions in 1946 for precipitation and 1949 and 1997 for evaporation. Dashed red lines are the upper and lower 95% confidence intervals about the modeled mean. Ramp interventions shown were significant at α = 0.10.
- Observed annual precipitation (P) (a) and potential evapotranspiration (PET) (b) at low-elevation standard rain gage 19 (SRG19) and climate station 01 (CS01), respectively. Black lines and circles are the observations; solid red lines are the modeled mean with ramp interventions in 1946 for precipitation and 1949 and 1997 for evaporation. Dashed red lines are the upper and lower 95% confidence intervals about the modeled mean. Ramp interventions shown were significant at α = 0.10.
- Observed annual evapotranspiration (ET) for low‐elevation (a) and high‐elevation (b) watersheds (WS). Black lines and circles are the observed ET, solid red lines are the modeled mean with ramp interventions in 1967 and 1999 (WS02), 1974 and 1980 (WS14), 1978 and 1985 (WS18), 1959 and 1999 (WS34), 1965 (WS27), and 1978 and 1998 (WS36). Dashed red lines are the upper and lower 95% confidence intervals about the modeled mean. Ramp interventions shown were significant at α = 0.10.
- Diffuse porous- more functinoal sapwood Ring porous- less functional sapwood
- Cumulative Q residuals (predicted–observed) for low‐elevation (a) and high‐elevation (b) watersheds (WS). Red circles are the cumulative residuals using models that predict Q as a function of climate only (model 9 Table b). Blue triangles are cumulative residuals using models that also include interventions (Climate and forest structure and species composition models in Table ). Interventions added to the models minimized the relationship between residuals and water year as indicated by the white noise statistical test for residuals in Table S6, and represent changes in Q due to changes in forest structure and species composition.
- Cumulative Q residuals (predicted–observed) for low‐elevation (a) and high‐elevation (b) watersheds (WS). Red circles are the cumulative residuals using models that predict Q as a function of climate only (model 9 Table b). Blue triangles are cumulative residuals using models that also include interventions (Climate and forest structure and species composition models in Table ). Interventions added to the models minimized the relationship between residuals and water year as indicated by the white noise statistical test for residuals in Table S6, and represent changes in Q due to changes in forest structure and species composition.
- Relative (blue) and absolute (red) change in annual water yield (Q) not explained by climate for low‐elevation (a) and high‐elevation (b) watersheds (WS). Changes in Q were quantified by computing the difference between Q predicted using the selected intervention models for each watershed in Table (i.e., Q affected by both climate and forest change) and Q predicted using these models but with intervention coefficients ω4 and ω5 set to zero (Q affected by climate only).
- Litter interception methods: Collected forest floor mass since November 2012 Derived equations for litter wetting and drying from Helvey (1964) Used equations to create a model to estimate the quantity of throughfall intercepted by litter with monthly litter mass and daily rainfall data as input variables Currently validating the modeled interception data by taking litter wet and dry weights and using litter moisture sensors