reed maxwell

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

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)

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

ARTICLES

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Figure 2 |Measurements of forest productivity and mortality overlaid onthe FDSI (red, right y axis). a, The annual average late-June toearly-August NDVI calculated from satellite (19811999: AVHRR,20002012: MODIS) imagery. b, Annual forest inventory and analysismeasurements of the percentage of standing dead trees in the SWUS forthe three most common conifer species. Error bars represent standarddeviation of the percentage dead when each years forest inventory andanalysis measurements are randomly resampled 1,000 times(Supplementary Information). c, Aerial-survey-derived estimates of thearea where 10 trees per acre were killed by bark-beetle attack.d, Satellite-derived moderately and severely burned forest and woodlandarea in the SWUS. See Supplementary Information and SupplementaryFig. S4 for methods to calculate burned area. The inset shows thepercentage of years within a given FDSI class that were top-10% fire-scaryears during AD 16501899 (the horizontal line is at the expectedfrequency of 10%, bins are 0.25 FDSI units wide). In all panels, the FDSIvalues for 20082012 (open red squares) were estimated by applyingclimate data to equation (1). Note the inverted y axes for the FDSI in bd.

drought has been, and remains, a primary driver of widespreadwildfires in the SWUS.

Given the exponential relationships established between theFDSI and tree mortality, severe drought events before the observedrecord probably coincided with widespread tree mortality. Asobserved climate and mortality data are unavailable for much ofthe past millennium, we use the FDSI record to identify othersevere drought events likely to have caused widespread mortalitysince ad 1000 (Fig. 3). A drought event is defined as any periodgreater than three years when the mean FDSI is negative, the FDSIis not positive for two consecutive years34, and the FDSI is lessthan two standard deviation units below the 18962007 mean forat least one year. Drought-event strength is the sum of the FDSIvalues during the event. Updating the FDSI for 20082012 withthe FDSI values estimated from equation (1), three drought eventshave occurred within the observed climate record: the presentdrought (20002012, the fifth strongest since ad 1000), 19451964(the sixth strongest) and 18991904 (the seventeenth strongest;Fig. 3). The prolonged 19451964 event was indeed associated withextensive treemortality in the SWUS as indicated by documentation

FDSI

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Figure 3 | Eleven-year smoothed FDSI for AD 10002012. Black area: 95%confidence range of the FDSI, representing the range of FDSI valuesexpected if all 335 chronologies were available. Vertical grey areas highlightdrought events.

of bark-beetle outbreaks30,35, anomalously large wildfires31,32 andwidespread die-off of conifers30,31,35. The 18991904 drought wasalso associatedwith forest declines36, although little documented.

Before the 1900s, the 15721587 event was the most recentevent exceeding the severity of the present event (Fig. 3). Thismegadrought event37,38 ranks as the fourth most severe sincead 1000 and the most severe since 1300. Although direct mortalityobservations are not available for the 1500s event, studies of forestage structure document a scarcity of trees on todays landscapethat began growing before the late 1500s (refs 13,31). As lifespansof SWUS conifers often greatly exceed 400 years, the scarcity oftrees preceding the 1500s event indicates that intense droughtconditions probably led to deaths of a large proportion of treesliving at the time. Before the late 1500s, the correspondence betweenrecords of conifer pollen buried at archaeological sites and tree-ringwidths39 suggests that widespread tree mortality (indicated bypollen) co-occurred with massive droughts in the 1200s (indicatedby tree rings). Notably, ancient Puebloan populations and land-usepractices were in great flux during this time, confounding theattribution of a dominant cause of the 1200s forest decline40.

Future forest drought-stressThe ongoing VPD-dominated drought event (Fig. 4a) is consistentwith climate-model projections (phase 3 of the Coupled ModelIntercomparison Project (CMIP3)) of increasing warm-seasonVPD (3.6%decade1) throughout the rest of the twenty-firstcentury in response to business-as-usual greenhouse-gas emissionsscenarios41 (SRES A2; Fig. 4a and Supplementary Fig. S6 foralternative emissions scenarios: SRES A1B and B1). Dynamicallydownscaled (0.5 geographic resolution)model projections indicatesimilar increases in the VPD (Fig. 4a and Supplementary Infor-mation). Furthermore, most models project a slight decrease incold-season precipitation during the second half of this century( 1.25%decade1, Fig. 4c). Applying model projections toequation (1), all models indicate negative FDSI trends throughoutthe twenty-first century (Fig. 4d). By 2050, the ensemblemean FDSIis consistently more severe than that of any megadrought since atleast ad 1000 (megadrought conditions are surpassed by 2070 in themost optimistic B1 emissions scenario, Supplementary Fig. S6d).Notably, projections of the FDSI are more severe than projectionsof gross water balance (precipitationevaporation) because water-balance projections are influenced more by decreased cold-seasonprecipitation than by increasedwarm-seasonVPD (ref. 15).

FDSI projections suggest that SWUS forest drought-stress isentering a new era where natural oscillations such as those apparentin Fig. 3 are superimposed on, and overwhelmed by, a trendtowards more intense drought stress. As the VPD diverges from therange of observed variability, nonlinear effects may alter droughtimpacts on forests (for example, Fig. 5 in ref. 42). During theobserved record, equation (1) was a better predictor of the FDSI

NATURE CLIMATE CHANGE | ADVANCE ONLINE PUBLICATION | www.nature.com/natureclimatechange 3

SL Edburg et al. Bark beetle-caused tree mortality

The Ecological Society of America www.frontiersinecology.org

biogeochemical impacts include reductions in plant Cuptake, increases in decomposition, and potential loss ofnutrients. An example of coupled biogeophysical andbiogeochemical 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 decompositionand plant growth, and the interaction between soil nutri-ents, decomposition, and plant growth (Figure 2).

Biogeophysical and biogeochemical impacts followingbark beetle infestation have the potential to severely affectboth natural resources and economic values. For example,snow from mountain ecosystems is the major source ofwater for more than 60 million people in the western USand Canada (Bales et al. 2006); changes in forest structurefollowing bark beetle epidemics alter the amount, timing,and partitioning of this resource (Rex and Dub 2006;Pugh and Small 2012). Post-insect-infestation tree mortal-ity also affects C and N cycling in forests. Although mostof these forests are net C sinks (eg Schimel et al. 2002),insect-related disturbances may cause them to release C tothe atmosphere (Kurz et al. 2008). Nutrient cycling withinaffected forest ecosystems will also be modified, withreduced plant uptake increasing water and nutrient export.As a result, the aggregate impact of insect outbreaks mayhave 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 ecosystemimpacts to help inform future management decisions andto identify future research areas that will improve under-standing of insect-related disturbances. Our model focuseson the characteristic time scales of a mountain pine beetle(Dendroctonus ponderosae) outbreak in lodgepole pine(Pinus contorta Douglas var latifolia) forests (Figure 2),beginning in the initial days and weeks after infestation(Stage 1; Figure 3a), proceeding through a phase in whichneedles turn red in the months to years following the out-break (Stage 2; Figure 3b), to the gray phase that occurs asneedles fall off dead trees within 35 years following attack(Stage 3; Figure 3c), and finally to tree regeneration andsnagfall in the decades following the outbreak (Stage 4;Figure 3d). Pine stands that are affected by mountain pinebeetle infestations are typically dominated by lodgepolepines (> 80% of the stem density), although spruce (Piceaspp) and fir (Abies spp) are also found therein. Understoryvegetation may be extensive in some stands (eg Brown etal. 2010). We anticipate that our conceptual model willprovide a framework for future investigations of theimpacts of bark beetles on forest ecosystems.

! Coupled biogeophysical and biogeochemicalimpacts

Stage 1: green attack (days to weeks)

Mountain pine beetles preferentially infest and kill largerdiameter host trees (pines), leaving smaller diameter trees

and understory vegetation unaffected (Shore andSafranyik 1992). Beetles introduce blue stain fungi(Grosmannia clavigera) into tree xylem, which decreaseand eventually prevent water transport (Paine et al.1997). Sap flux studies have shown that a drop in transpi-ration occurs within one month of infestation and thatthe rate of change is dependent on fungal virulence(Yamaoka et al. 1990). This finding contrasts with exper-imentally girdled trees (simulating beetles feeding onphloem) that took up to five growing seasons to die,whereas those inoculated with blue stain fungi died inone growing season (Knight et al. 1991).

The initial impact of mountain pine beetle-inducedmortality on lodgepole pine trees is hypothesized to occurin three stages. First, water transport in the stem shutsdown, which results in the same response of stomatal clo-sure as tree response to drought. Stomatal conductanceand plant hydraulics are closely coordinated (Ewers et al.2007). Second, a drop in stomatal conductance leads to a

Figure 1. Areas affected by bark beetles from 19972010 (inthe western US) and 20012010 (in British Columbia,Canada). Color of each grid cell represents the last year thatbark beetle damage was observed by aerial surveys.

The Mountain Pine Beetle (MPB) is an endemic species (Dendroctonus ponderosae)

5mm

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(Figure modified from Wulder et al 2006) (Figure modified from CSFS 2013)

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)

Grand Lake, Colorado

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Climate drivers lead to unprecedented infestation locally in Rocky Mountain National Park (RMNP)

How might this impact water?

compared to bark beetle infestation are tree harvestand fire. Adams et al. (2011) conducted an extensive

review on the ecohydrological similarities and differ-

ences between tree mortality associated with canopydie-off, forest harvesting practices and fire and

highlighted below are some of the different responses

that are important to distinguish. One of the maindifferences is that in high severity fires and many tree

harvesting practices, there is entire loss of theoverstory canopy, while in bark beetle infestations

mortality is not necessarily continuous across the

entire watershed. In general, fire is not as similar tobark beetle infestations as tree harvesting, as fire

completely alters ground surface vegetation and soil

surface properties. Forest harvesting is similar in manyways to bark beetle infestations, inasmuch as both

disturbances result in diminished canopy and reduced

vegetative nutrient and water uptake; however, forestharvesting also often includes soil disturbance, soil

compaction and road construction and maintenance,

all of which contribute to differences in hydrologicaland biogeochemical processes. Beetle-induced tree

mortality also occurs in phases, with the tree initially

losing its ability to take up water and nutrients,followed by needle discoloration and several years

later actual needle drop. This slower transitioning

results in less stark changes to forest biogeochemistry,as soil buffering and surviving vegetation can often

compensate lowering nutrient export and observedhydrological changes (Griffin et al. 2013). In compar-

ison, forest harvesting occurs on a much shorter

timescale with complete canopy removal associatedwith immediate tree death. With the above highlighted

differences between bark beetle infestations and other

land disturbances, it becomes apparent that even withsimilarities we can draw upon from the extensive pool

of literature on non-bark beetle canopy-changing

disturbances, it is necessary to review and synthesizethe impacts bark beetle infestations can have on

hydrological and biogeochemical processes.

Fig. 1 A conceptual image depicting the continuously chang-ing hydrologic and biogeochemical cycles during the variousphases of bark beetle infestation. The top portion of the figurecontains the visual representation of the three primary phases ofinfestation, with the accompanying elements of the hydrologiccycle. Fluxes are denoted with arrows and storage reservoirs asrectangles with the associated increase or decrease in theprocess depicted by the fill departure above or below the N.S.C.(no significant change) line. While differences in changes ofeach variable have been observed due to catchment character-istics, climate and infestation characteristics, the filled-inportion displays the general trend even though magnitude may

vary. T transpiration, E ground evaporation, I interception, SWEsnow water equivalent, h soil moisture, A ablation and Q wateryield. The bottom of the figure displays the temporal trendsassociated with the different phases of infestation and theexpected alterations in biogeochemical cycling. An up arrowindicates concentrations above baseline, a down arrow indicatesconcentrations below baseline and a horizontal dash indicatesno significant change (with the size of the arrow indicatingmagnitude). Asterisks(*) indicate this trend depicts the majorityof results published, although occasional studies have notobserved this trend. See Table 1 and 2 for additionalclarification

Biogeochemistry

123

Mikkelson, Bearup, Maxwell, Stednick, McCray, Sharp, Biogeochemistry 2013

Green Red Grey

To address hydrologic responses to stress we need integrated tools that can evaluate managed natural systems

P. Dll et al. / Journal of Geodynamics 59 60 (2012) 143 156 147

to a lack of data on drainage in irrigated areas in many countries, wehad to combine data on drained irrigated area with data on drainedarea (without distinction of rainfed and irrigated agriculture). Val-ues of frgi between 0.2 and 0.4 occur in regions where irrigatedareas are strongly drained: the Nile in Egypt, the southern part ofthe Indus, Japan, Philippines, Indonesia and parts of Australia. Thenorthern part of the Indus as well as Northeastern China show val-ues between 0.4 and 0.6. In most of the USA, frgi is between 0.6and 0.7, in India, the value is about 0.75. In the rest of the world,irrigated areas are not drained much and frgi is close to 0.8.

2.4. Model runs with WaterGAP 2.1h

For the period 19012002, WGHM and GIM were driven bymonthly climate data from the Climate Research Unit (CRU) with aspatial resolution of 0.5 (covering the global land surface). The CRUTS 2.1 data set includes gridded data for the climate variables tem-perature, cloudiness and number of rain days from 1901 to 2002.This data set is based on station observations and uses anomalyanalysis for spatial interpolation (Mitchell and Jones, 2005). Torun the model for the GRACE period 20022009, monthly data ontemperature, cloudiness and number of rain days for 20032009from the European Centre for Medium-Range Weather Forecasts(ECMWF) operational forecast system were used. For precipitation,the Global Precipitation Climatology Centre (GPCC) full data prod-uct v3 provided gridded monthly values for 19512004 (Rudolf andSchneider, 2005), also with a spatial resolution of 0.5, except thatfor GIM the GPCC full data product v4 was used until 2002. Forthe years 20052009, the so-called GPCC monitoring product witha spatial resolution of 1 was used, which is based on a smallernumber of station observations. In WGHM, monthly precipitation isdistributed equally over the number of rain days within one month.The monthly precipitation data are not corrected for measurementerrors, but precipitation, in particular snow is generally underesti-mated by measurements mainly due to wind induced undercatch.As this has a strong influence on simulated snow water storage,precipitation was corrected in WGHM using mean monthly catchratios and taking actual monthly temperatures into account (Dlland Fiedler, 2008). Water use in the three sectors households, man-ufacturing, and cooling of thermal power plants was computed for19012005, and assumed to be equal to the values for 2005 from2006 to 2009. For livestock, the year with the last statistics availablewas 2002, and livestock use in 20032009 was assumed to be thesame as in 2002. For the runs with WaterGAP 2.1h, the calibrationparameter values of version 2.1g were used.

2.5. GWS variations developed from measured groundwaterlevels

For the High Plains aquifer, which covers about 450,000 km2,Strassberg et al. (2009) developed a time series of GWS vari-ations for the aquifer as a whole from measured groundwaterlevels in 1989 wells. Four seasonal values were developed per year(JanuaryMarch, AprilJune, JulySeptember, OctoberDecember),covering the years 20032006, based on an average of 983 wellsper season. To convert water level variations to GWS variations, theformer were multiplied by a constant specific yield of 0.15, whichrepresent the area-weighted specific yield (McGuire, 2009). Forthe entire Mississippi basin (approx. 3,248,000 km2), Rodell et al.(2007) developed a monthly time series of GWS from water levelobservations in 58 wells in unconfined aquifers. These wells weredistributed almost evenly over the basin. To convert water levelvariations to GWS variations, the former were multiplied by spe-cific yield values determined individually for each well, rangingfrom 0.02 to 0.32, with a mean of 0.14. For our comparison, we

used an updated data set covering the period January 2002June2006.

2.6. Total water storage variations from GRACE satelliteobservations

We compared monthly TWS variations simulated with Water-GAP to values derived from GRACE satellite observations. Tobetter understand the uncertainty in GRACE data, solutions com-puted by three different GRACE processing groups were used. Tocompute ITG-GRACE2010 monthly solutions (http://www.igg.uni-bonn.de/apmg/index.php?id=itg-grace2010), a set of sphericalharmonic coefficients for degrees n = 1. . .120 was estimated foreach month from August 2002 to August 2009 without applying anyregularization. More detailed information on this GRACE solutioncan be found in Mayer-Grr et al. (2010). To determine continentalwater storage variation from the total GRACE signal, the followingbackground models were taken into account: ocean, Earth and poletides, atmospheric and oceanic mass variations as well as glacialisostatic adjustment (as described in Eicker et al., in press). Degreen = 1 (geo-center variations) was set to zero as GRACE does notdeliver realistic estimates of these coefficients. This is justifiableas a comparison using GPS geo-center estimates has revealed thatthere is no significant contribution of degree 1 in the two investiga-tion areas. In addition, GFZ release 4 monthly solutions (Flechtneret al., 2010) and CSR monthly solutions (Bettadpur, 2007) wereconsidered.

All three GRACE solutions were smoothed using the non-isotropic filter DDK3 (Kusche et al., 2009). To allow a consistentcomparison to WGHM results, the filtered results were interpo-lated to the WGHM 0.5 grid such that basin averages of TWS couldbe computed as averages over the respective WGHM grid cells. Inorder to compare TWS modeled with WGHM to GRACE-derived

Fig. 2. (a) Total water withdrawals, in mm/year, and (b) irrigation water with-drawals in percent of total water withdrawals, for 19982002. The irrigationpercentage is only shown if total water withdrawals are at least 0.2 mm/year.

Dll et al JoG (2012) Hansen et al Science (2013)

Observations are valuable but dont tell the whole story

Local measurements are difficult to scale

hBp://triplemlandfarms.com/ hBp://nasa.gov

Remote sensing cant see everything

We use the integrated hydrologic model ParFlow which is a tool for computational hydrology

Saturated(Subsurface(

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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)

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ParFlows 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

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

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?

Bedrock is assumed to be 12.5m below the ground surfaceand is a no flow boundary. Once water reaches the confinesof the domain as either subsurface lateral flow or overlandflow, it crosses the boundary and is no longer considered tobe part of the domain, leading to subsurface storage lossesor gains depending on the scenario.This hillslope-scale study was simulated with a hypothe-

tical domain, 500m (x-direction) by 1000m (y-direction) by12.5m (z-direction). The domain was discretised usingx= 100m, y= 200m and z=0.5m. Three differentsimulations were run with topographic slopes of 0.01, 0.08and 0.15m/m (Figure 1) that were selected to represent thetopography found both in high-elevation, mountainous,MPB-infested watersheds in the Rocky Mountain West,along with the flatter MPB-infested watersheds found insouthernWyoming. The saturated hydraulic conductivity wasset to 0.1m/h. Porosity and vanGenuchten parameters wereassigned for the watershed. Porosity was held constantthroughout at 0.390 (!) and the van Genuchten parameterswere set as follows: a =3.5 (1/m), n=2 and Sres = 0.01 torepresent an idealised, relatively fast draining, sandy clayloam soil. Soils in mountain hillslopes are of course veryheterogeneous with great uncertainty about both point valuesand spatial distribution. The assumption of homogeneityinvoked here is used to isolate the impacts of land-coverchanges on the hydrology of the system, whereas minimisingother confounding signals. Approaches that directly includeheterogeneity at the hillslope scale (Rihani et al., 2010;Atchley and Maxwell, 2011) are beyond the scope of thiscurrent study and are excellent topics for future work.Hourly meteorological forcings were taken from the

North American Land Data Assimilation System(NLDAS), forcing dataset (Cosgrove et al., 2003) for the2008 water year (1 September 2007 to 31 August 2008) atPennsylvania Gulch, Blue River, Colorado. The climateduring 2008 was typical of an average year for this regionby comparing precipitation with seasonal averages, andtherefore a good representation of what a climatologicalweather pattern will be in the Rocky Mountains, yet still

including high-frequency (hourly) variability. The modelwas run for each phase for 3 years, re-running the 2008meteorological data for each year, to minimise theinfluence of initial conditions (but not running the modelto equilibrium) on simulated results with results focusingon the third year.

In the model simulations, we defined the process ofmortality in an affected tree to have four distinct phases: (1)green phase: the tree is alive and transpiring; (2) red phase:the tree has been attacked and has ceased transpiring andinterception has slightly decreased; (3) grey phase: the treeis dead, has no remaining needles, transpiration has ceased,and interception is significantly decreased; and (4) diebackphase: the tree has fallen to the ground and begundecomposing as new vegetation begins to take its place.During an actual MPB infestation, the vegetation distribu-tion throughout the four phases is likely to be hetero-geneous; however, for the sake of simplicity and tounderstand the magnitudes of difference in the hydrologicand energy regimes between each phase, we are assuming ahomogeneous distribution of vegetation during each phase.

The hydrologic and land-energy impacts of each of thefour phases of MPB infestation were simulated byperturbing two vegetative parameters: stomatal resistanceand leaf area index. Table 1 shows how we defined eachphase of MPB infestation, along with its correspondingland surface classification, maximum and minimum LAI(depending on season, recall that LAI is dynamicallycalculated in CLM) and the minimum stomatal conduc-tance. When the MPB infects a stand of trees, it introducesa blue-stain fungi (Ceratostomella montia and Europhiumclavigerum) that essentially clogs the xylem and phloemtubes (Amman, 1978). This renders the tree unable to takeup water and nutrients from its roots. Stomatal resistancewas manipulated to represent this phenomenon duringthe red and grey stages of infestation by increasing theminimum stomatal conductance until it was maximised andtranspiration approached zero. During the red and greyphases, it is assumed that there is no new undergrowth in

Figure 1. Displays the governing processes in the three simulated watersheds. Arrow lengths indicate flux magnitudes. P is precipitation, ET isevapotranspiration, O is overland flow, and I is infiltration.

67PINE BEETLE IMPACTS ON THE WATER AND ENERGY BUDGET

Copyright 2011 John Wiley & Sons, Ltd. Ecohydrol. 6, 6472 (2013)

Mikkelson, Maxwell, Ferguson, McCray, Stednick, Sharp, Ecohydrology 2013

ET decreases with MPB infestation

because increased radiation penetration through the canopycauses earlier and faster snowmelt. This phenomenon isseen during both periods of transition, fall and spring, inassociation with the snow season.Surface saturation increases through each phase of

infestation, but the differences are most apparent duringtimes of moisture stress (Figure 3). In steeper watersheds,the difference in surface saturation is not as drastic becausesoils are drier owing to increased baseflow runoff andsubsequent drainage of shallow soils. Saturation also drivesmany of the other hydrologic processes. For example,during November, ET decreases in the red and greenphases partly because of the dry top surface layer (Figures 2(A) and 3) with a minimum in mid-winter months. In the1% slope case, the dieback phases ET is able to rebound inthe summer months, surpassing the grey phase ET. This isnot seen for the steeper slopes and is due to the fact that the

shallow, surface soil moisture is higher for the 1% slope(Figure 3(A)) than for the other two slopes, allowing moreET to occur in the dieback phase. This relationship betweendieback rebound and slope exists because the deeper rootsof the phreatophytic lodgepole pine are able to accessgroundwater whereas the shrub vegetation cannot. Theshrub vegetation used in the dieback phase has roots thatextend less than 3.1m into the subsurface. For the steeperslopes, the water table is deeper than 3.1m, therebylimiting the amount of water the plants can transpire.

For example, when looking at the monthly differences inET during the late fall, ET does not follow the typicalyearly pattern (Figure 2(A)): the grey phase exhibits anincrease in ET whereas all the other phases experience adecrease in ET. To further explore this phenomenon, wecompared the daily average ground temperature, SWE andET for the green and grey phases during a 2-month period

Figure 2. The complete water balance and average monthly snow water equivalent for the four phases of infestation at 1%, 8% and 15% slopes. Row Ais total monthly ET, row B is total monthly overland flow, row C is the change in storage from the beginning to the end of that month, and row D is the

monthly average snow water equivalent. Values are all in mm.

Figure 3. Top layer saturation, averaged hourly over the entire domain for (A) 1%, (B) 8%, and (C) 15% slopes.













Snow Water Equivalent (SWE) increases with MPB Infestation

As infestation progresses we see a greater snowpack and a shorter snow season




















Decreased ET and more snow increases in runoff and earlier timing











Aspen Water Treatment Plant

LETTERSPUBLISHED ONLINE: 28 OCTOBER 2012 | DOI: 10.1038/NCLIMATE1724

Water-quality impacts from climate-inducedforest die-offKristin M. Mikkelson1,2*, Eric R. V. Dickenson1,3, Reed M. Maxwell2,4, John E. McCray1,2and 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 biogeochemicalprocesses, such as organic matter decay and hydrologic flowpaths, that could enhance leaching of natural organic matterto soil and surface waters and increase potential formationof harmful drinking water disinfection by-products3,4 (DBPs).Whereas previous studies have investigated water-quantityalterations due to climate-induced, forest die-off5,6, impactson water quality are unclear. Here, water-quality data setsfrom water-treatment facilities in Colorado were analysedto determine whether the municipal water supply has beenperturbed by tree mortality. Results demonstrate higher to-tal organic carbon concentrations along with significantlymore DBPs at water-treatment facilities using mountain-pine-beetle-infested source waters when contrasted with thoseusing water from control watersheds. In addition to this dif-ferentiation between watersheds, DBP concentrations demon-strated an increase within mountain pine beetle watershedsrelated to the degree of infestation. Disproportionate DBP in-creases and seasonal decoupling of peak DBP and total organiccarbon concentrations further suggest that the total organiccarbon composition is being altered in these systems.

The mountain pine beetle (MPB, Dendroctonus ponderosae)infestation has reached epidemic proportions and is generatinggrowing concern for regional water resources with little knownabout potential impacts. In the Rocky Mountains, warmer winterminimum temperatures and persistent drought conditions havecontributed to an ongoing MPB epidemic7 that has affectedmore than 4 million acres of lodgepole pine forests in Coloradoand Wyoming (Fig. 1). Changes in hydrology following bark-beetle infestation such as decreased interception, increased erosionand particulate transport8, increased soil moisture and increasedradiation to the forest floor5 can lead to altered degradation andtransport of soil organic matter in both particulate and dissolvedforms9. Total organic carbon (TOC, which comprises particulateand dissolved organic carbon) increases have been observed acrosslarge areas of the Northern Hemisphere, and although there isno scientific consensus on the driving mechanisms of increasedTOC a number of different factors have been proposed includingchanges in acid deposition10, variability in climate11 and land-usechanges12. We propose that the recent bark-beetle epidemic isanother mechanism that may alter TOC loading and compositionin surface and groundwaters.

1Department of Civil and Environmental Engineering, Colorado School of Mines, Golden, Colorado 80401, USA, 2Hydrological Science and EngineeringProgram, Colorado School of Mines, Golden, Colorado 80401, USA, 3Water Quality Research and Development Division, Southern NevadaWaterAuthority, Henderson, Nevada 89015, USA, 4Department of Geology and Geological Engineering, Colorado School of Mines, Golden, Colorado 80401,USA. *e-mail: [email protected].

Changes in TOC characteristics and increased loading can leadto human health concerns as humic and fulvic fractions of naturalorganic matter (NOM) have been correlated with the formationof DBPs, such as trihalomethanes (THMs, known carcinogens),during chlorination3,13,14. Hence, the potential for exceedance ofregulatory limits, human health impacts and increased treatmentcosts are potential concerns for water-treatment facilities associatedwith bark-beetle-infested watersheds. The objective of this studywas to collect and analyse archived, publicly available water-qualitydata from water-treatment facilities located in the Rocky Mountainregion of Colorado. Water-quality data were compared betweenMPB-infested watersheds and regionally analogous facilities locatedin watersheds that did not experience the same degree of MPBinfestation (control watersheds).

Archived water-quality data were collected from nine differenttreatment plants in Colorado, which included four control (Aspen,Carbondale, Glenwood Springs and Gypsum) and five MPB-impacted facilities (Kremmling, Steamboat Springs, Winter Park,Dillon and Granby). The average level of infestation at the controlsites was about a quarter (0.8 0.2 trees killed per hectare) thatof the MPB-impacted sites (3.0 0.8 trees killed per hectare)and sites were as geographically similar as the available datawould allow (Supplementary Fig. S4). Most treatment facilitiescollect their water from surface-water sources; however, WinterPark and Dillon primarily use a groundwater supply whereasCarbondale, Aspen and Steamboat Springs use both groundwaterand surface-water supplies (Supplementary Table S2). Water-quality samples were collected and analysed by the treatmentfacilities in quarterly intervals in compliance with EnvironmentalProtection Agency (EPA) standardized procedures15 and spanthe years of 20042011, during which the impacted lodgepolepine forests experienced heavy MPB infestation (Fig. 1). Althoughfacilities were analysed individually (Supplementary Table S3),trends were more significant owing to larger sample sizes whenanalysed as a group (that is, MPB or control) and thus aggregatedresults will be presented.

Quarterly TOC samples taken before water treatment weregrouped for these distinct facilities (Fig. 2a). Our analysis demon-strates significantly more TOC in the MPB watersheds versusthe control watersheds with respect to both mean and maximumconcentrations (p< 0.0001 fromMannWhitney test). As is typicalin most watersheds, surface-water sources in impacted watershedshad significantly higher TOC concentrations than groundwatersources; however, this interpretation is limited as the only availableTOCdata from an impacted groundwater source was from the town

NATURE CLIMATE CHANGE | ADVANCE ONLINE PUBLICATION | www.nature.com/natureclimatechange 1

What can observations tell us about carbon cycle and water quality?

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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

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.

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