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

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. 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. 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. 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. 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. 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)

15. How might this impact water?
stry
Mikkelson, Bearup, Maxwell, Stednick, McCray, Sharp, Biogeochemistry 2013
Green
Red
Grey

16. 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)

17. 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

18. 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)

19. 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

20. 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

21. 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

22. ET decreases with MPB infestation
PINE BEETLE
Mikkelson, Maxwell, Ferguson, McCray, Stednick, Sharp, Ecohydrology 2013

23. 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

24. Decreased ET and more snow
increases in runoff and earlier
timing
Mikkelson, Maxwell, Ferguson, McCray, Stednick, Sharp, Ecohydrology 2013

25. 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?

26. 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
!
!
!
!
!
!
"#$%!&'()*+'!
"#$%&'(%#!
")*+,-.!
/*%,'0!
1+,#$*!!
2%*3!
4+55(,!
6*$&&5+,-!
/5$,7((8!!
")*+,-.!
/0).9&!
:%*'(,8%5$!
;.)$,!
!"#$%&'$(
)*+,$%-%$$.*
/0%$&#
)*123$(*
/0%$&# )*($,$405$(*
6,$%"-$*)*7%-".28*
1"##$%*2.*9024
6,$%"-$*:$"(*
;%$$&*5$%*<$8#"%$
!"#$%&'()"$*(+,-(./ 0123 420 524 4265 627
89:;(<#'=9(+>/ 5?24 520 62? 0243 526
,"="$#@"(A9#@B#;9$:(C(<#:;(+D-(8E-(>$/ ?624 327 62F 02?G 024
,"="$#@"(A9#@B#;9$:(C(89:;(+>H/ 0623 F23 625 0236 62F
IJJ9$(K#LJ#(+H/ 572G 321 62F 0257 02F
M=N9(+O/ ??2? ?20 024 02?0 42G
Water 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)

27. 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)

28. 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)

29. Our conceptual model links late summer
groundwater uptake and tree mortality
Bearup, Maxwell, Clow
and McCray Nature
Climate Change, 2014.

30. Big
T.
N.
Inlet
We use a paired-watershed approach combined
with historical observations
(Bearup et al NCC 2014)

31. 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)

32. 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)

33. 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

34. 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)

35. 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…”

36. 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)

37. 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

38. 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)

39. 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

40. 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

41. 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)

42. 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

43. 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

44. 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)

45. 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. 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.

47. https://xkcd.com/1007/xkcd FTW