Water soil woodland

Water, soil, woodland
a hydrological perspective
Riccardo Rigon
Muse, 21 Marzo 2016

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The good old hydrological cycle
we know it !
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The starting point

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+P
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Precipitation is a plus

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+P Q
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Runoff (WaterYeld) is a minus

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+P Q ET
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Evaporation and Transpiration are also a minus.The latter is the key to understand the role of vegetation

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(P Q ET) t
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The time scale matters

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(P Q ET) t(P Q ET) t = S
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Storages/Recharge

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Let’s assume
that our landscape is like this one, without vegetation*
S = (P Q ET) t
the hillslope is, obviously, sloped
~ 1 km
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A smooth linear hillslope*effectsofvegetationonriparianzonesisalsoaninterestingtopic.Nottreatedhere

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What does it change if we add a forest ?
S = (P Q ET) t
If the climate does not change,
P remains the same,
ET increases,
Q decreases.
What about ?
what happens is said by many experimental areas (e.g. Brown et al., 2005)
S
~ 1 km
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Adding a forest

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P
Q
S = 0 t ⇠ 10 anni
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Changes fluxes

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If we look at long enough time spans
discharges decrease
This (look at the experiments)
is the effect of two situations, the decrease of groundwater levels,
which therefore produces less flow, and the decrease of surface
runoff
There is a dependency on P amount indeed
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Changes runoff

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ns of endmembers to streamflow. a–c, Contributions of rain (navy), snow (cyan) and groundwater (orange) to streamflow
h Inlet, 2012 (b); and Big Thompson, 1994 (c). d, Groundwater contributions (orange lines) with propagated uncertainty
Groundwater contributions are greatest in the most actively impacted watershed case, in a. The Big Thompson 2012
use the time-varying groundwater endmember and the dashed line represents the constant groundwater endmember. The
with the dashed line of the 2012 Big Thompson study, and the solid line is consistent with the North Inlet study.
n the Big Thompson than in the North
was less widespread and less recent.
endmembers identified two methods
water endmember that provided the
roundwater contributions, including
s collected biweekly (that is, the time-
ember) and average pre-melt baseflow
nt groundwater endmember). On the
2012 Big Thompson mean fractional
ranged from 0.47 to 0.56 ± 0.11,
8 in 1994 and 0.30 ± 0.04 in the
constant groundwater endmember
the 1994 study methodology and is
comparisons unless otherwise noted.
l analyses found greater fractional
to streams in watersheds where MPB
member compositions naturally vary
atial characteristics such as elevation
ity or isotopic processes related to
(Supplementary Table 3). Despite
h uncertainty analysis described in
reveals that significant di erences
still observed by the end of July
ry Fig. 14a), when the signal from
ted to increase relative to that from
Jul. Aug. Sep. Oct.
Dailydischarge(mm)
0.0
0.5
1.0
1.5
2.0
2.5
1994 net streamflow 2012 net streamflow
1994 groundwater 2012 groundwater
Figure 4 | Hydrograph separations presented as partitioning of the total
daily stream discharge (in mm; full bar height) for the 1994 (blue) and
2012 (orange) seasons. Groundwater discharges to streamflow were
determined using the constant groundwater endmember. Overlapped
shading in 2012 depicts the additional contribution determined from the
sensitivity analysis and the time-varying groundwater endmember. Total
annual flow partitioning indicates increased groundwater discharge to
streams in 2012 despite higher total flows in 1994. Column spacing is based
on stream sampling frequency.
| JUNE 2014 | www.nature.com/natureclimatechange 483
Bearup et al., 2014
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Se how groundwaters contribute

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Often what observed was subsequent to logging
Results in Australia*, after the cut of 10% of forested areas:
* Brown et al., 2005
• in conifers forests, water held increased of 20-25 mm**
• in eucalyptus forests + 6 mm
• in bushes places, + 5 mm
• in hardwood , +17-20 mm
Similar results were obtained in South Africa, where conifers seems
to decrease runoff more that eucalyptus.
** on about1000 mm/year
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Statistics

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Fig. 11 depicts the response to conversion of
native forest to pasture in the Wights catchment in
south Western Australia. As discussed in Section 4
the Wights catchment is part of a series on paired
catchment studies in south Western Australia. In
these catchments, the interplay between the local
groundwater flow system and vegetation plays an
important role in the hydrological response.
The replacement of native forests by pastures in
these catchments has lead to a rapid increase in
groundwater discharge area (Schofield, 1996),
resulting in large increases in low flows. As with
Fig. 10, it can be seen that all sections of the flow
regime are affected by the change in vegetation.
Comparing the FDC for native vegetation (1974–
1976) with a period of similar climatic conditions of
Fig. 10. Flow duration curves for the Red Hill catchment, near Tumut, New South Wales, Australia. One year old pines and 8 year old pines
(after Vertessy, 2000).
A.E. Brown et al. / Journal of Hydrology 310 (2005) 28–61 43
A meaningful draw
Brownetal.,2005
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Duration curves change

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Fig. 11 depicts the response to conversion of
native forest to pasture in the Wights catchment in
south Western Australia. As discussed in Section 4
the Wights catchment is part of a series on paired
catchment studies in south Western Australia. In
these catchments, the interplay between the local
groundwater flow system and vegetation plays an
important role in the hydrological response.
The replacement of native forests by pastures in
these catchments has lead to a rapid increase in
groundwater discharge area (Schofield, 1996),
resulting in large increases in low flows. As with
Fig. 10, it can be seen that all sections of the flow
regime are affected by the change in vegetation.
Comparing the FDC for native vegetation (1974–
1976) with a period of similar climatic conditions of
Fig. 10. Flow duration curves for the Red Hill catchment, near Tumut, New South Wales, Australia. One year old pines and 8 year old pines
(after Vertessy, 2000).
Brownetal.,2005
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A meaningful draw
Duration curves change

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Other effects:
Woodlands diminish:
• erosion
• landslides*
*However, when the hillslope becomes unstable …
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Landsliding

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Because
• soil is more dry
• roots increase soil cohesion
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Landslides

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We measured characteristics of all roots with diameters
³1 mm in regions with differing vegetation communities.
Field-measured root attributes included species, diameter
(measured with micrometer), vertical depth relative to the
ground surface, whether the root was alive or decaying,
whether the root was broken or intact, and cross-sectional
area of colluvium over which roots act. The root attribute in-
understory roots in clearcuts had no root cohesion because
we do not know the timing of plant mortality and hence the
relative decrease in thread strength. Consequently, calcu-
lated root cohesion is conservative on the low side because
decaying roots continue to contribute a finite amount of co-
hesion. We did not systematically characterize the decay
function of all the species in the area. Instead we uniformly
Schmidt et al. 1005
Fig. 4. Photograph of broken roots (highlighted) in landslide scarp within Elliot State Forest. Roots did not simply pull out of soil ma-
trix, but broke during the landslide. Note 2 m tall person for scale in center (in center of annotated circle) and absence of roots on the
basal surface of the landslide.
•Schmidtetal.,2001
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Landslides and root strength

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https://www.ieca.org/photogallery/haymanfire.asp
Fires alter soil structure and erodibility
Fires
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You can see it when all is missed

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•Benavidesetal.,2005
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Huge sediment transport

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Snow cover is also influenced by vegetation presence
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Snow !
this, in turn, modifies plants growth and phenotype

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“However, it is important to note that the magnitude of mean
annual change, the adjustment time and seasonal response do
not tell the whole story in relation to the impact of vegetation
change on water yield “
Effects are different in different seasonsmaximum evapotranspiration and periods of minimum
evapotranspiration. Hornbeck et al. (1997) observed
that most of the increase in annual yield occurred
during the growing season as shown in Fig. 13. They
concluded that water yield increases were a result of
decreased transpiration and primarily occurred as
The difference in the results between Hornbeck
et al. (1997), who found notable seasonal differences,
and McLean (2001), who could not detect seasonal
changes, can be attributed to the deciduous nature of
the vegetation in the USA compared with the evergreen
vegetation of the pine plantations in New Zealand. The
Fig. 13. Flow duration curves for the ﬁrst year after the clear-felling treatment—Hubbard Brook experimental forest (after Hornbeck et al.,
1997).
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Not only at yearly scale

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Hower seasonal changes are more difficult to observe, cause to
variability of meteo. Therefore conjectures need to be made on
what measured. and the only way to support them is using
modelling and virtual experiments.
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Modellers needed

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computationally demanding. Therefore, several eco-
hydrological models still use simpliﬁed solutions of
carbon285
) concepts that empirically link carbon
assimilation to the transpired water or intercepted
Energy exchanges
Longwave
radiation
incoming
Longwave
radiation
outgoing
Shortwave
radiation
Latent heat
Latent
heat
Sensible
heat
Soil heat flux
Geothermal heat
gain
Bedrock Bedrock Bedrock Bedrock
Momentum transfer
Rain Snow Photosynthesis
Phenology
Disturbances
Atmospheric
deposition
Fertilization
Nutrient resorption
Nutrient
uptake
Nutrients in SOM
Mineral nutrients
in solution
Mineralization and
immobilizationOccluded or not
available nutrients
Primary mineral
weathering
Biological
fixation (N)
Tectonic uplift
Denitrification (N)
Volatilization
Growth respiration
Maintenance respiration
Fruits/flowers production
Heterotrophic
respiration
Wood turnover
Litter Litter
Litterfall
nutrient flux
DecompositionMycorrhizal
symbiosis
Microbial
and soil
fauna
activity
SOM
DOC
leaching
Leaching
Fine and coarse
root turnover
Carbon allocation
and translocation
Carbon reserves (NSC)
Leaf turnover
Transpiration
Evaporation from
interception
Evaporation/
sublimation
from snow
Evaporation
Throughfall/dripping
Snow melting
Infiltration
Leakage
Root water uptake
Lateral subsurface flow
Base flow
Deep recharge
Runoff
Sensible heat
Albedo
Energy absorbed
by photosynthesis
Water cycle Carbon cycle Nutrient cycle
FIGURE 6 | Ecohydrological and terrestrial biosphere models have components and parameterizations to simulate the (1) surface energy
exchanges, (2) the water cycle, (3) the carbon cycle, and (4) soil biogeochemistry and nutrient cycles. Many models do not include all the
components presented in the ﬁgure.
WIREs Water Modeling plant–water interactions
When looking for modelling
everything seems very complex
However,afterFatichi,PappasandIvanov,2015
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Vegetation - Soil
at longer time scales
Jenny,RoleofthePlantFactorinthePedogenicFunctions,Ecology,Vol.39,No.1
concepts and classification in ecology. Trans. Roy.
Soc. S. Aust. 71: 91-136.
Hubble, G. D. 1954. Some soils of the coastal low-
values: an alternative approach
36.
Wood, J. G. 1956. Personal co
ROLE OF THE PLANT FACTOR IN THE PEDOGENIC FUNC
HANS JENNY
Universityof California,Berkeley
INTRODUCTION
The interplayofclimate,soil,and vegetationis
oftenrepresentedby a triangle:
climate
vegetation= soil
It impliesthatclimateaffectssoil and vegeta-
tionindependently,thatsoil influencesvegetation,
and thatvegetationreactsupon soil.
While at firstsightappealingthe triangleis
actuallybesetwithlogicalpitfallsand frustrations.
It has even led to the negativisticview thatthe
soil-plantcontractcannotbe interpreted.
This paper presentsa formalisticmethodfor
evaluatingthevariousinteractions.Assessingthe
rolesofclimateand vegetationin soil genesiswill
be emphasized. To do so,theconceptofthebiotic
factormustbe clearlydelineatedand given pre-
cision.
The writerfollowsa typeofpresentationwhich
is plant and animal life abo
constitutesa threedimensio
scape, an arbitraryelement
open system;matteris cont
removedfromit.
The entirelandscapecan b
composedofsuchsmalllands
pictureis comparableto the
signs on the walls of Byzan
are made up of littlecubes
called tesseras. We shall u
tessera,fora smalllandscap
The "thickness"of a tess
heightofvegetationplusthed
area ofa tesserais determin
siderations. It is a convenie
ing,a specifiedarea. A qua
one square meterin a stan
vegetationtessera. A soil m
sera. A "soil profile"colle
soiltessera,usuallyofill-def
Strictlyspeaking,a soilprof
a soiltessera.R. Rigon
Soil .. do not forget it

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Vegetation - Soil - Topography
~ 2 km
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Feedbacks
bedrock roughness
soil surface smoothness

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Cunningham/Saigo,EnvironmentalScience,1999
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Soil itself
Vegetation - Soil - Topography
Further unknown complexity

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TagueandDugger,2010
snow
-rain
transition
m
oves
up
~ 3 km
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Introducing climate change

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TagueandDugger,2010
Increases winter runoff,
decreases the summer one
Climate change moves the
transition between snowfall and
rainfall uphill.
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snow
-rain
transition
m
oves
up

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energy limited: in this case forests is likely to
grow until is not limited by water
water limited: decresce
growth likely although
growth is possible due to
CO2 increase and more water
use efficiency
Intermediate: grows can be
positive or negative, depending on
local conditions and plants’ type
Forest grow is
modified from Tague and Dugger, 2010
West US
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Ecotones Shift

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influencing a bark beetle outbreak vary depending on the species, host tree, local
ecosystem, and geographical region, there is no single management action that is
appropriate across all affected forests.
A whitebark pine forest in Yellowstone National Park. The red trees were attacked and killed
by mountain pine beetles the year before the photo was taken in July 2007.
PHOTO BY JANE PARGITER, ECOFLIGHT, ASPEN CO
Other effects, maybe not previously
see, for instance Bearup et al., 2014
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Deseases

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the potential for cumulus convective rainfall. Therefore
vertical radiosonde soundings over adjacent locations
that have different surface conditions offer opportuni-
ties to assess alterations in thunderstorm potential. This
influence of surface conditions on cumulus cloud and
thunderstorm development has been discussed, for ex-
Pielke and Zeng, 1989]. The soundings were made prior
to significant cloud development. The radiosonde
sounding over an irrigated location had a slightly cooler
but moister lower troposphere than the sounding over
the natural, short-grass prairie location. Aircraft flights
at several levels between these two locations on July 28,
Figure 5. Same as Figure 4 except between a forest and cropland. Adapted from P. Kabat (personal
communication, 1999). Reprinted with permission.
Pielke,2001
Also the atmospheric hydrological cycle is modified
~ 10 km
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Feedbacks on the atmosphere
Also the atmosphere hydrological cycle is mo

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Precipitation recycling
~ 100 - 1000 km
Bruskeretal,1993
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Green precipitation

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MAY 1999 1379T R E N B E R T H
Trenberth, 1998
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Green precipitation quantified

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Therefore we are ready to see what happens at
larger temporal scales
Brovkin, 2002
Climate
(radiation,
precipitation,
temperature)
Vegetation
Composition
of atmospheric gases
78 % N2, 31% O2
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Everything is connected

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B.
influence the summer climate in subtropical deserts: Hadley circulation, zonal wind, monsoon-type
circulation, and convection. The vegetation model is similar to the one in the CLIMBER-2 model.
The dynamics of box model solutions in terms of precipitation from the early Holocene to the present
day are presented in Fig. 8,A. The two stable branches of the solution, the green branch with relatively
high precipitation and the desert branch with low precipitation, are separated by the unstable branch. In
the early Holocene, some 10 kyr ago, only the green equilibrium existed in the area with annual
precipitation of about 600 mm/yr. Owing to decreased summer insolation, the precipitation declined to
400 mm/yr at the end of mid-Holocene, and the stable desert equilibrium appeared about 6 kyr B.P.
Figure 8. Summary of results of a box model for
the western Sahara region for the Holocene [77].
A. Dynamics of system solution in terms of
precipitation during the last 10,000 yr. The upper
and lower curves are the green and desert
solutions, respectively. The dashed line in the
middle represents the unstable solution.
B. Multiple steady states, desert and ‘green’, are
shown in a form of Lyapunov potential for
vegetation cover. Potential minima, marked by
balls, correspond to equilibria that are stable in
absence of perturbations. Black and grey balls
indicate dominant and minor states, respectively.
8,000 yr BP. The system has only one steady
state, green Sahara.
4,000 yr BP. System underwent bifurcation;
desert state appeared and became stable. The
depths of the well for the two states are
approximately equal.
0 yr BP. Both states remain stable but desert has
a deeper well. Desert became dominant state as
precipitation fluctuations shifted the system
towards desert.
Year, kyr BP
A.
green
desert
Fig. 8,B is a simplified cartoon of the system stability under changes in the orbital forcing. The
equilibria are shown in a form of the minima of potential function (Lyapunov functional). For 8 kyr BP,
there is the single minimum that corresponds to the green equilibrium. For the present day, there are two
minima: the desert equilibrium is at an absolute minimum (dominant state) and the green equilibrium is at
a relative minimum (minor state). At some 4 kyr BP both equilibria have the same values of the potential.
In this sense, the green equilibrium became less stable than the desert equilibrium after 4 kyr BP. More
precisely, the switch from one solution to another depends on the possibility for the system to ``jump''
Brovkin, 2002
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and equilibria can be many

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At any spatial and temporal scale
How are fluxes portioned ?
45.75
45.80
45.85
11.20 11.25 11.30 11.35
Long
Lat
Posina basin and the HRU partition used for simulation. The triangle points are the discharge measurement stations. The location and
of the basin is described in Abera et alXXX.
stem JGrass-NewAge (from now on, simply NewAge),
↵ers a set of model components built accordingly to the
Modelling System version 3 (OMS) framework (David
13). OMS, modelling framework based on component-
ftware engineering, uses classes as fundamental model
blocks (components) and uses a standardized inter-
pporting component communication. In OMS3, the in-
of each component is based on the use of annotations.
ble model connectivity, coupling and maintaining easy
(David et al., 2013).
Age covers most of the processes involved in the wa-
et and its components were discussed with detail in:
a et al. (2011, 2013a); Formetta (2013); Formetta et al.
2014b), and they are not fully re-discussed here. Com-
and comparing appropriate measured and simulated data;
• Validate the models using various goodness of ﬁt methods
(GoFs) to assess the model performances.
• Estimate the outputs of the budgets’ terms i.e. discharge,
actual evapotranspiration, and storage and thier errors
1.3. Paper organization
The paper is organized as follows: section 2 provides
methodologies of modeling the ”output” terms of the water
balance equation, particularly rainfall-runo↵ modeling and dis-
charge estimation (subsection 2.3) and evapotranspiration and
water balance residual estimations (section 2.4). Brief descrip-
~ 10 km ~ 100 km ~ 500 km
Posina Adige Nilo Blu
R. Rigon
]
The challenge of modelling all of this

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term monthly means, of course it
oo as each month is sampled from
ion of water budget components
nﬁrms the monthly analysis given
trend in precipitation, but actually
d have been deduced from the data
ith the other budget components
he interactions actually in place.
J
80 120 160 200
Q
40 80 160
ET
20 40 60
S
JanAprJulOct
−150 −100 −50 0 50
Figure 13: The spatial variability of the long term mean monthly water
budget components (J, ET, Q, S). For reason of visibility, the color
scale is for each component separately.
5. Conclusions
After Abera et al., in preparation, 2016a
Posing water budget snapshot
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can be win, but …

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2005−01−01 2005−04−06 2005−07−03 2005−11−08
8
9
10
11
12
8
9
10
11
12
MODISETNewAgeET
35 36 37 38 39 40 35 36 37 38 39 40 35 36 37 38 39 40 35 36 37 38 39 40
long
lat
25
50
75
ET (mm/8-days)
Figure 6: The Spatial and temporal variability of ET in 8-day intervals for in the study area.
After Abera et al., in preparation, 2016b
Upper Blue Nile
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can be win, but …

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0.25
0.50
0.75
1.00
Gen 1994 Apr 1994 Lug 1994 Ott 1994 Gen 1995
Time [h]
Θ
January
February
March
April
May
June
July
August
September
October
November
Partitioning coefficient Θ
After Rigon et al., in preparation, 2016
⇥ :=
Q(t)
ET (t) + Q(t)
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can be win, but …

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Find this presentation at
http://abouthydrology.blogspot.com
Ulrici,2000
Other material at
Questions ?
R. Rigon
http://www.slideshare.net/GEOFRAMEcafe/acqua-suolo-foreste-59814511
or
http://abouthydrology.blogspot.it/2016/03/water-soil-forests-man-who-planted.html

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Bearup, L. A., Maxwell, R. M., Clow, D. W., & McCray, J. E. (2014). Hydrological effects of forest
transpiration loss in bark beetle-impacted watersheds. Nature Climate Change, 4(6), 481–486. http://
doi.org/10.1038/nclimate2198
Benavides-Solorio, J. de D., & MacDonald, L. H. (2005). Measurement and prediction of post-fire erosion at
the hillslope scale, Colorado Front Range. International Journal of Wildland Fire, 14(4), 457–18. http://
doi.org/10.1071/WF05042
Bentz, B., Logan, J., MacMahon, J., Allen, C. D., Ayres, M., Berg, E., et al. (2013). Bark beetle outbreaks in
western North America: Causes and consequences, 1–46.
Brovkin, V. (2002). Microsoft Word - brov_er5.doc. Journ. Phys. IV France, (12), 57–72.
Blöschl, G., Ardoin-Bardin, S., Bonell, M., Dorninger, M., Goodrich, D., Gutknecht, D., et al. (2007). At what
scales do climate variability and land cover change impact on flooding and low flows? Hydrological
Processes, 21(9), 1241–1247. http://doi.org/10.1002/hyp.6669
Brown, A. E., Zhang, L., McMahon, T. A., Western, A. W., & Vertessy, R. A. (2005). A review of paired
catchment studies for determining changes in water yield resulting from alterations in vegetation.
Journal of Hydrology, 310(1-4), 28–61. http://doi.org/10.1016/j.jhydrol.2004.12.010
Brown, A. E. (2008, March 10). Predicting the effect of forest cover changes on flow duration curves. (L.
Zhang, A. Western, & T. A. McMahon, Eds.).
Brubaker, K., Entekhabi, D., & Eagleson, P. S. (1993). Estimation of Continental Precipitation Recycling.
Water Resources Res., 6(6), 1077–1089.
Eltahir, A. B., & Bras, R. L. (1994). Precipitation Recycling in the Amazon Basin. Quarterly Journal of the
Royal Meteorological Society, 120, 861–880.
Alcune buone letture
R. Rigon

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Entekhabi, D., Rodriguez-Iturbe, & Bras, R. L. (1992). Variability in Large-Scale Water Balance with Land Surface-
Atmosphere Interaction. Journal of Climate, 5, 798–813.
Fatichi, S., Pappas, C., & Ivanov, V. Y. (2015). Modeling plant-water interactions: an ecohydrological overview from
the cell to the global scale. Wiley Interdisciplinary Reviews: Water, n/a–n/a. http://doi.org/10.1002/wat2.1125
Jenny, H. (1958). Role of the plant factor in the pedogenic functions. Ecology, 39(1), 5–16.
Johansen, M. P., Hakonson, T. E., & Breshears, D. D. (2001). Post-fire runoff and erosion from rainfall simulation:
contrasting forests with shrublands and grasslands. Hydrological Processes, 15(15), 2953–2965. http://doi.org/
10.1002/hyp.384
Johnson, D. L., Keller, E. A., & Rockwell, T. K. (1990). Dynamic pedogenesis: New views on some key soil concepts,
and a model for interpreting quaternary soils. Quaternary Research, 33(3), 306–319. http://doi.org/
10.1016/0033-5894(90)90058-S
Miles, J. (1985). The pedogenic effects of different species and vegetation types and the implications. Journal of
Soil Science, 36, 371–384.
Pielke, R. A., Sr. (2001). Influence of the spatial distribution of vegetation and soils on the prediction of cumulus
convective rainfall, 1–28.
Tague, C., & Dugger, A. L. (2010). Ecohydrology and Climate Change in the Mountains of the Western USA - A
Review of Research and Opportunities. Geography Compass, 4(11), 1648–1663. http://doi.org/10.1111/j.
1749-8198.2010.00400.
Trenberth, K. E. (1999). Atmospheric Moisture Recycling: Role of Advection and Local Evaporation. Journal of
Climate, 12, 1368–1381.
Alcune buone letture
R. Rigon

Water soil woodland

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