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Reed Maxwell

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Integrating models and observations to understand the hydrology and water quality impacts from beetle-impacted watersheds

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Reed Maxwell

  1. 1. Integrating models and observations to understand the hydrology and water quality impacts from beetle- impacted watersheds Colorado School of Mines, Colorado State University Lindsay Bearup, Nicole Bogenschuetz, Brent Brouillard, Stuart Cottrell, Mike Czaja, Eric Dickenson, Nick Engdahl, Mary Michael Forrester, Jennifer Jefferson, Andrew Maloney, Katherine Mattor, Reed Maxwell, John McCray, Kristin Mikkelson, Adam Mitchell, Alexis Navarre- Sitchler, Josh Sharp, Colgan Smith, John Stednick students, postdocs, faculty
  2. 2. Quantifying and predicting the impacts of land cover change presents an interesting challenge in hydrology  Loss          +          Gain   Forest  Tree         Cover   >80%         0%   Hansen et al Science (2013)
  3. 3. Temperature and insect-driven tree mortality is increasing Edburg et al FEE (2012)Williams et al NCC (2013) Forest  drought  stress  has  increased,  increasing  beetle  infesta>ons  and  tree  mortality   NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE1693 ARTICL r = 0.83 ¬2 ¬1 0 1 2 1980 1990 2000 Year 2010 Wildfirearea(km2) r = ¬0.84 r = ¬0.82 Bark-beetlearea(km2) 1 0 ¬1 Piñon Ponderosa pine Douglas-fir 1¬1 0 5 10 15 20 25 1 0 ¬1 1 10 100 1,000 10,000 2 0 ¬2 10 100 1,000 10,000 25 50 % 75 a b c d 0.35 0.40 0.45 NDVIPercentagedead FDSIFDSI2-yrFDSI6-yrFDSI Figure 2 | Measurements of forest productivity and mortality overlaid on FDSI ¬1.0 ¬0.5 0.0 0.5 1.0 1000 1200 1400 1600 Year 1800 Figure 3 | Eleven-year smoothed FDSI for AD 1000–2012. Black area: confidence range of the FDSI, representing the range of FDSI values expected if all 335 chronologies were available. Vertical grey areas hig drought events. of bark-beetle outbreaks30,35 , anomalously large wildfires31,32 widespread die-off of conifers30,31,35 . The 1899–1904 drought also associated with forest declines36 , although little documente Before the 1900s, the 1572–1587 event was the most re event exceeding the severity of the present event (Fig. 3). megadrought event37,38 ranks as the fourth most severe ad 1000 and the most severe since 1300. Although direct mor observations are not available for the 1500s event, studies of f age structure document a scarcity of trees on today’s lands that began growing before the late 1500s (refs 13,31). As lifes of SWUS conifers often greatly exceed 400 years, the scarci trees preceding the 1500s event indicates that intense dro SL Edburg et al. Bark beetle-caused tree mortality biogeochemical impacts include reductions in plant C uptake, increases in decomposition, and potential loss of nutrients. An example of “coupled” biogeophysical and biogeochemical processes is the influence of canopy struc- ture (leaf area and stem density) on the amount of precip- itation captured by the foliage (and therefore on soil mois- ture), the effects of soil moisture on soil decomposition and plant growth, and the interaction between soil nutri- ents, decomposition, and plant growth (Figure 2). Biogeophysical and biogeochemical impacts following bark beetle infestation have the potential to severely affect both natural resources and economic values. For example, snow from mountain ecosystems is the major source of water for more than 60 million people in the western US and Canada (Bales et al. 2006); changes in forest structure following bark beetle epidemics alter the amount, timing, and partitioning of this resource (Rex and Dubé 2006; Pugh and Small 2012). Post-insect-infestation tree mortal- ity also affects C and N cycling in forests. Although most of these forests are net C sinks (eg Schimel et al. 2002), insect-related disturbances may cause them to release C to the atmosphere (Kurz et al. 2008). Nutrient cycling within affected forest ecosystems will also be modified, with reduced plant uptake increasing water and nutrient export. As a result, the aggregate impact of insect outbreaks may have consequences for regional and global weather and cli- mate systems as well as for water supply and C storage. Here, we present a chronological model of ecosystem impacts to help inform future management decisions and to identify future research areas that will improve under- standing of insect-related disturbances. Our model focuses on the characteristic time scales of a mountain pine beetle (Dendroctonus ponderosae) outbreak in lodgepole pine (Pinus contorta Douglas var latifolia) forests (Figure 2), Figure 1. Areas affected by bark beetles from 1997–2010 (in
  4. 4. The Mountain Pine Beetle (MPB) is an endemic species (Dendroctonus ponderosae) 5mm Green Red Grey   YearSinceAttack 4 3 2 1 0 Summer Fall Winter Spring 2nd Summer Attacking Brood Adult Egg Larva Larva Pupa Adult (Figure modified from Wulder et al 2006)(Figure modified from CSFS 2013)
  5. 5. Climate drivers lead to unprecedented infestation Warmer temperatures and longer habitable summer seasons have lead to reproductive doubling -Mitton & Ferrenberg (2012) Drought conditions weaken tree defenses and correlate with infestation. -Williams et al (2013)
  6. 6. Grand Lake, Colorado -45 -40 -35 -30 -25 -20 -15 -10 1940 1960 1980 2000 2020 Min.Temperature(Nov.-Mar.,˚C) Monthly Minimum Temperature Climate drivers lead to unprecedented infestation locally in Rocky Mountain National Park (RMNP)
  7. 7. How might this impact water? stry Mikkelson, Bearup, Maxwell, Stednick, McCray, Sharp, Biogeochemistry 2013 Green   Red   Grey  
  8. 8. To address hydrologic responses to stress we need integrated tools that can evaluate managed natural systems Fig. 2. (a) Total water withdrawals, in mm/year, and (b) irrigation water with- drawals in percent of total water withdrawals, for 1998–2002. The irrigation percentage is only shown if total water withdrawals are at least 0.2 mm/year. Döll et al JoG (2012) Hansen et al Science (2013)
  9. 9. Observations are valuable but don’t tell the whole story Local measurements are difficult to scale hBp://triplemlandfarms.com/   hBp://nasa.gov   Remote sensing can’t see everything
  10. 10. We use the integrated hydrologic model ParFlow which is a tool for computational hydrology Saturated( Subsurface( Vadose( Zone( Land( Surface( No(Flow( Boundary( Overland) Flow) Lateral) Subsurface) Flow) Exfiltra8on) Infiltra8on) Z=0( P2) z2) H2) H1) z1) P1) 1) 2) dz) dx) dL) θx) Recharge) Overland)) Flow) •  Variably  saturated  groundwater  flow   •  Fully  integrated  surface  water   •  Parallel  implementa,on   •  Coupled  land  surface  processes     Maxwell (2013); Kollet and Maxwell (2008); Kollet and Maxwell (2006);Maxwell and Miller (2005); Dai et al. (2003); Jones and Woodward (2001); Ashby and Falgout (1996)
  11. 11. Saturated( Subsurface( Vadose( Zone( Land( Surface( No(Flow( Boundary( Overland) Flow) Lateral) Subsurface) Flow) Exfiltra8on) Infiltra8on) Z=0( P2) z2) H2) H1) z1) P1) 1) 2) dz) dx) dL) θx) Atmospheric) forcings) Water)) Energy) Balance) Vegeta;on( Root(zone( ParFlow’s coupling with land surface processes (CLM) allows for simulation of interactions and connections Maxwell (2013); Kollet and Maxwell (2008); Kollet and Maxwell (2006);Maxwell and Miller (2005); Dai et al. (2003); Jones and Woodward (2001); Ashby and Falgout (1996) •  Land-­‐energy  balance   •  Snow  dynamics   •  Driven  by  meteorology    
  12. 12. Models can be useful tools to provide insight •  Controlled numerical experiments elucidate process interactions under change •  A single perturbation (e.g. temperature increase) can be tracked through the entire nonlinear system •  Connections we see in simulations can provide insight and guide observations
  13. 13. We can use models to propagate tree- scale, beetle impacts to the hydrologic cycle at the hillslope scale How do changes to stomatal resistance and leaf area index impact snow, runoff, storage? ed to be 12.5 m below the ground surface including high-frequency (hourly) variability. The model he governing processes in the three simulated watersheds. Arrow lengths indicate flux magnitudes. P is precipitation, ET is evapotranspiration, O is overland flow, and I is infiltration. 67PINE BEETLE IMPACTS ON THE WATER AND ENERGY BUDGET Mikkelson, Maxwell, Ferguson, McCray, Stednick, Sharp, Ecohydrology 2013
  14. 14. ET decreases with MPB infestation PINE BEETLE Mikkelson, Maxwell, Ferguson, McCray, Stednick, Sharp, Ecohydrology 2013
  15. 15. Snow Water Equivalent (SWE) increases with MPB Infestation As infestation progresses we see a greater snowpack and a shorter snow season re 2. The complete water balance and average mo tal monthly ET, row B is total monthly overland fl monthly aMikkelson, Maxwell, Ferguson, McCray, Stednick, Sharp, Ecohydrology 2013
  16. 16. Decreased ET and more snow increases in runoff and earlier timing Mikkelson, Maxwell, Ferguson, McCray, Stednick, Sharp, Ecohydrology 2013
  17. 17. Aspen  Water  Treatment   Plant   LETTERS PUBLISHED ONLINE: 28 OCTOBER 2012 | DOI: 10.1038/NCLIMATE1724 Water-quality impacts from climate-induced forest die-off Kristin M. Mikkelson1,2 *, Eric R. V. Dickenson1,3 , Reed M. Maxwell2,4 , John E. McCray1,2 and Jonathan O. Sharp1,2 Increased ecosystem susceptibility to pests and other stres- sors has been attributed to climate change1 , resulting in un- precedented tree mortality from insect infestations2 . In turn, large-scale tree die-off alters physical and biogeochemical processes, such as organic matter decay and hydrologic flow paths, that could enhance leaching of natural organic matter to soil and surface waters and increase potential formation of harmful drinking water disinfection by-products3,4 (DBPs). Whereas previous studies have investigated water-quantity alterations due to climate-induced, forest die-off5,6 , impacts on water quality are unclear. Here, water-quality data sets from water-treatment facilities in Colorado were analysed to determine whether the municipal water supply has been perturbed by tree mortality. Results demonstrate higher to- tal organic carbon concentrations along with significantly Changes in TOC characteristics and increased loading can lead to human health concerns as humic and fulvic fractions of natural organic matter (NOM) have been correlated with the formation of DBPs, such as trihalomethanes (THMs, known carcinogens), during chlorination3,13,14 . Hence, the potential for exceedance of regulatory limits, human health impacts and increased treatment costs are potential concerns for water-treatment facilities associated with bark-beetle-infested watersheds. The objective of this study was to collect and analyse archived, publicly available water-quality data from water-treatment facilities located in the Rocky Mountain region of Colorado. Water-quality data were compared between MPB-infested watersheds and regionally analogous facilities located in watersheds that did not experience the same degree of MPB infestation (control watersheds). Archived water-quality data were collected from nine different What can observations tell us about carbon cycle and water quality?
  18. 18. Legend 14050001 14010004 14010003 14010001 MutipleClip <all other values> SurfText <Null> channery loam clay loam coarse sandy loam cobbly loam fine sandy loam gravelly loam gravelly sandy loam loam sandy loam very cobbly loam very cobbly sandy loam Multiple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ater treatment facilities in the Rocky Mountains are already experiencing MPB impacts *Beetle  kill  was  the  only  sta>s>cally  significant  variable  between  MPB  and  control   watersheds.   (Mikkelson et al NCC 2013)
  19. 19. 0" 2" 4" 6" 8" 10" 12" MPB" Non.MPB" TOC$(mg/l)$ min" median" max" 0" 20" 40" 60" 80" 100" MPB" Non"MPB" HAA5$(ug/l)$ b" a" c" 0" 40" 80" 120" 160" MPB" Non"MPB" TTHM$(ug/l)$ Control" TOC  (mg/l)  HAA5  (ug/l)  TTHM  (ug/l)   Higher TOC and DBP concentrations are observed in MPB-impacted facilities than at control facilities. But what is the mechanism? (Mikkelson et al NCC 2013)
  20. 20. 0" 2" 4" 6" 8" 10" 12" MPB" Non.MPB" TOC$(mg/l)$ min" median" max" 0" 20" 40" 60" 80" 100" MPB" Non"MPB" HAA5$(ug/l)$ b" a" c" 0" 40" 80" 120" 160" MPB" Non"MPB" TTHM$(ug/l)$ Control" TOC  (mg/l)  HAA5  (ug/l)  TTHM  (ug/l)   Higher TOC and DBP concentrations are observed in MPB-impacted facilities than at control facilities. MPB   Control   But what is the mechanism? (Mikkelson et al NCC 2013)
  21. 21. Our conceptual model links late summer groundwater uptake and tree mortality Bearup, Maxwell, Clow and McCray Nature Climate Change, 2014.
  22. 22. Big  T.   N.  Inlet   We use a paired-watershed approach combined with historical observations (Bearup et al NCC 2014)
  23. 23. We use end-member mixing to determine contributions to the hydrograph End Member Mixing Analysis (EMMA) Three end-member hydrograph separation −15 −10 −5 0 5 10 −5051015 U1 U2 1 2 3 4 5 Baseflow * . ..................... . . 56 7 89 1011 12 13141516 17181920212223 Rain Snow Stream  Flow   Snow   Rain   Ground-­‐ water   (Bearup et al NCC 2014)
  24. 24. 0.00.20.40.60.81.0 Big Thompson FractionalContributiontoStreamflow rain snow groundwater Jul Aug Sept Oct a) 2012 0.00.20.40.60.81.0 North Inlet Jul Aug Sept Oct b) 0.00.20.40.60.81.0 FractionalContributiontoStreamflow Jul Aug Sept Oct c) 1994 0.00.20.40.60.81.0 Jul Aug Sept Oct d) 1994 Big T 2012 Big T 2012 N. Inlet Temporal   Spa>al   We found an increase in GW contributions for impacted watersheds (Bearup et al NCC 2014)
  25. 25. Tree Scale Sap flux: 16 L/day (Hubbard et al 2013, CO) Stand Scale Potometers: 3.4 mm/day (Knight et al 1981, WY) Hillslope to Watershed Scale ParFlow ET: - 20-35% (Mikkelson et al 2013, CO) ‘Watershed’ Scale MODIS ET: (Maness et al 2013, BC) Eddy Covariance: 0.7 mm/day (Brown et al 2014, BC; Biederman et al 2014, CO/WY; Reed et al 2014; WY) 02040 ET(m July Aug Sep Oct 020406080 ET(mm) July Aug Sep Oct 8% Slope 020406080 ET(mm) July Aug Sep Oct 15% Slope Estimating evapotranspiration is challenging across scales
  26. 26. 0 20 40 60 80 100 0.00.51.01.5 Percent of net trees killed in impacted area FluxChange(mm/day) M ODIS Com parison (M aness et al 2013) Sap Flux Com parison (Hubbard etal2013) Temporal Control Spatial Control Temporal Control (Constant EM) a) 0.00.51.01.5 T T T T T T T T T T C C C C C C C C C C S S S S S S S S S S Jul Aug Sept Oct b) T C S Temporal Control Constant EM Spatial Control Model Grey Phase Model Red Phase Which allowed a scale-up of ET fluxes to the watershed (Bearup et al NCC 2014)
  27. 27. Using models to predict streamwater age and composition is an important topic in hydrology “What are the physical processes and material properties that control transit time distribution? How and why do these processes vary with time, ambient conditions, and place?” “How can we deal with the effects of … ET partitioning in ‘predicting’ transit time distributions…”
  28. 28. Integrated hydrologic models may be used to attribute source and to study the effects of disturbances such as ET Outflow         Sublimation Snowfall Interception Transpiration Stream  Flow   Snow   Rain   Ground-­‐ water   (Bearup  et  al,  in  review)  
  29. 29. Outflow         Evaporation Rainfall Interception Transpiration Stream  Flow   Snow   Rain   Ground-­‐ water   (Bearup  et  al,  in  review)   Integrated hydrologic models may be used to attribute source and to study the effects of disturbances such as ET
  30. 30. 0.00.20.40.60.81.0 Big Thompson FractionalContributiontoStreamflow rain snow groundwater Jul Aug Sept Oct a) 2012 0.00.20.40.60.81.0 North Inlet Jul Aug Sept Oct b) 0.00.20.40.60.81.0 FractionalContributiontoStreamflow Jul Aug Sept Oct c) 1994 0.00.20.40.60.81.0 Jul Aug Sept Oct d) 1994 Big T 2012 Big T 2012 N. Inlet Model Results Field Observations (Bearup  et  al,  in  review)   Transient model simulations allow a virtual hydrograph separation and show an increase in groundwater contribution and demonstrate similar behavior to observations (Bearup  et  al  NCC  2014)  
  31. 31. Groundwater-generated outflow is greater in infested watersheds at early times, but shows less memory Living Hillslope Dead Hillslope 1 year 10 years3 months 100 years (Bearup et al, in review) STEADY STATE RESULTS  
  32. 32. Big  Thompson  Model:   100  m  Resolu>on   1  km2  Forested  Domain:     ET  at  Variable  Resolu>on   for  8%  slope   2  m  resolu>on   Colorado  Model:  1  km  Resolu>on    100  m  resolu>on    500  m  resolu>on   Denver   East  Inlet  Model:  10  m   Resolu>on   We are using a multi-scale modeling approach
  33. 33. We are using an integrated hydrologic model to study scaling implications of beetle infestation Big  T.   0 1 2 3 4 Green Phase August Depth (m) 0 1 2 3 4 Differ (Penn  et  al,  in  review)  
  34. 34. Green Phase June Depth (m) 0 1 2 3 4 Difference (Grey − Green) −1.0 −0.5 0.0 0.5 1.0 Green Phase August Depth (m) 0 1 2 3 4 Difference (Grey − Green) −1.0 −0.5 0.0 0.5 1.0 (Penn  et  al,  in  review)   0 1 −1.0 −0.5 Green Phase August Depth (m) 0 1 2 3 4 Difference (Grey − Green) −1.0 −0.5 0.0 0.5 1.0 Depth  to  water  table  difference  (grey  –  green)   -­‐1.0             -­‐0.5             0.0             0.5             1.0   Models indicate higher groundwater tables in infested areas
  35. 35. 0 5 10 15 20 25 30 35 40 45 50 55 A) Transpiration Green Phase Grey Phase 0 5 10 15 20 25 30 35 40 45 50 55 B) Intercepted Evaporation Nov Jan Mar May Jul Sep 0 5 10 15 20 25 30 35 40 45 50 55 C) Soil Evaporation TranspirationorEvaporation(mm) Nov Jan Mar May Jul Sep 0 10 20 30 40 50 60 70 80 90 100 D) Total Evapotranspiration Models exhibit compensation in evapotranspiration (Penn  et  al,  in  review)        Transpira>on                    Intercepted  Evapora>on   Soil  Evapora>on                                      Total  Evapotranspira>on  
  36. 36. Modeled streamflow response is muted 0 2 4 6 8 10 12 14 16 18 20 22 Outflow(m3 /s) Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct 0 1 2 3 4 5 6 7 8 9 10 CumulativeRunoff(x107 m3 ) Grey Phase Green Phase A) B) 11% (Penn  et  al,  in  review)  
  37. 37. Bridging scales allows us to help quantify the cascade of impacts from the mountain pine beetle epidemic •  The mountain pine beetle infestation of North America is the first observable climate change impact on water quality and helps us quantify transpiration •  We see increased groundwater contributions from beetle-killed watersheds which allow us to estimate transpiration •  We can use hydrologic models to predict source contribution and water age •  Models allow us to scale impacts from the hillslope to the watershed
  38. 38. 46   Thank You! This  material  was  based  upon  work  supported  by  the  Na>onal  Science  Founda>on  (WSC-­‐1204787)  and  U.S.  Geological   Survey  (G-­‐2914-­‐1).  Any  opinions,  findings,  and  conclusions  or  recommenda>ons  expressed  in  this  material  are  those  of  the   authors  and  do  not  necessarily  reflect  the  views  of  these  organiza>ons.    
  39. 39. https://xkcd.com/1007/xkcd FTW

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