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Acqua,suolo, foreste una prospettiva idrologica
Riccardo Rigon
Muse, 21 Marzo 2016
!2
Il buon vecchio ciclo idrologico lo conosciamo tutti
R. Rigon
Le basi
!3
+P
R. Rigon
Precipitazioni
!4
+P �Q
R. Rigon
Deflussi
!5
+P �Q� ET
R. Rigon
Evaporazioni e Traspirazioni
!6
(P �Q� ET )�t
R. Rigon
Il tempo !
!7
(P �Q� ET )�t(P �Q� ET )�t = �S
R. Rigon
Le riserve nel suolo e nelle falde
!8
Supponiamo che
la nostra superficie sia così, senza vegetazione
�S = (P �Q� ET )�t
e, naturalmente, inclinata, come si conviene in montagna
~ 1 km
R. Rigon
Un versante liscio liscio
!9
Cosa cambia se aggiungiamo un bosco ?
�S = (P �Q� ET )�t
Se il clima non cambia, P rimane uguale, ET aumenta, Q diminuisce. Ma ?
questo ce lo dicono vari esperimenti in cui interi bacini sono stati disboscati
�S
~ 1 km
R. Rigon
Add a forest
!10
P
Q
�S = 0 �t ⇠ 10 anni
R. Rigon
Cambia i flussi
!11
Se guardiamo a tempi abbastanza lunghi
la portata alla chiusura dei bacini diminuisce
A ben guardare (e c’è chi ha misurato)
questo è l’effetto di due situazioni, la diminuzione del livello della falda, che poi produrrebbe deflusso, e la diminuzione del deflusso
superficiale per se. (In verità si è osservato che …)
Ma tutto dipende anche da quanta è P
R. Rigon
Cambia i deflussi
!12
NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE2198 LETTERS
0.0
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1.0Big Thompson
Big Thompson
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tiona
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wFr
actio
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ontr
ibut
ion
to st
ream
flow
RainSnowGroundwater
Jul. Aug. Sep. Oct.
a
c d
b
2012
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tiona
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trib
utio
n to
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amflo
w20
12
0.0
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1.0North Inlet
Jul. Aug. Sep. Oct.
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Jul. Aug. Sep. Oct.19
94
Frac
tiona
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undw
ater
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ribut
ion
to st
ream
flow
0.0
0.2
0.4
0.6
0.8
1.0
Jul. Aug. Sep. Oct.
1994 Big T
2012 Big T
2012 N. Inlet
Figure 3 | Fractional contributions of endmembers to streamflow. a–c, Contributions of rain (navy), snow (cyan) and groundwater (orange) to streamflowin Big Thompson, 2012 (a); North Inlet, 2012 (b); and Big Thompson, 1994 (c). d, Groundwater contributions (orange lines) with propagated uncertainty(grey shading) for each analysis. Groundwater contributions are greatest in the most actively impacted watershed case, in a. The Big Thompson 2012groundwater fractions in a and d use the time-varying groundwater endmember and the dashed line represents the constant groundwater endmember. The1994 methodology is consistent with the dashed line of the 2012 Big Thompson study, and the solid line is consistent with the North Inlet study.
contributions to streamflow in the Big Thompson than in the NorthInlet, where tree mortality was less widespread and less recent.Sensitivity analysis of the endmembers identified two methodsof quantifying the groundwater endmember that provided thelargest range of possible groundwater contributions, includingshallow groundwater samples collected biweekly (that is, the time-varying groundwater endmember) and average pre-melt baseflowsamples (that is, the constant groundwater endmember). On thebasis of these methods, the 2012 Big Thompson mean fractionalgroundwater contribution ranged from 0.47 to 0.56 ± 0.11,compared with 0.18 ± 0.08 in 1994 and 0.30 ± 0.04 in theNorth Inlet (Fig. 3). The constant groundwater endmemberapproach is consistent with the 1994 study methodology and isused for further temporal comparisons unless otherwise noted.Both temporal and spatial analyses found greater fractionalgroundwater contributions to streams in watersheds where MPBimpact was greater. Endmember compositions naturally varyowing to di�erences in spatial characteristics such as elevationand subsurface heterogeneity or isotopic processes related tointerception and snowmelt (Supplementary Table 3). Despitethis variability, an in-depth uncertainty analysis described inSupplementary Section 4.3 reveals that significant di�erencesbetween watersheds are still observed by the end of July(Fig. 3d and Supplementary Fig. 14a), when the signal fromtranspiration may be expected to increase relative to that fromsnowmelt runo�.
Jul. Aug. Sep. Oct.
Dai
ly d
isch
arge
(mm
)
0.0
0.5
1.0
1.5
2.0
2.51994 net streamflow 2012 net streamflow1994 groundwater 2012 groundwater
Figure 4 | Hydrograph separations presented as partitioning of the totaldaily stream discharge (in mm; full bar height) for the 1994 (blue) and2012 (orange) seasons. Groundwater discharges to streamflow weredetermined using the constant groundwater endmember. Overlappedshading in 2012 depicts the additional contribution determined from thesensitivity analysis and the time-varying groundwater endmember. Totalannual flow partitioning indicates increased groundwater discharge tostreams in 2012 despite higher total flows in 1994. Column spacing is basedon stream sampling frequency.
NATURE CLIMATE CHANGE | VOL 4 | JUNE 2014 | www.nature.com/natureclimatechange 483Bearup et al., 2014
R. Rigon
Il contributo delle acque sotterranee
!13
Di solito si è guardato cosa è successo dopo il taglio del bosco
Risultati in Australia*, dopo il taglio del 10% della foresta:
* Brown et al., 2005
• nelle foreste di conifere, il deflusso medio annuale è aumentato di 20-25 mm**
• nelle foreste di eucalipto + 6 mm • il taglio degli arbusti, + 5 mm • altre piante, decidue, +17-20 mm
Risultati similari sono stati ottenuti in Sud Africa, dove però le conifere abbassano il deflusso più che gli eucalipti.
** su circa 1000 mm annui
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Statistiche
!14
Fig. 11 depicts the response to conversion ofnative forest to pasture in the Wights catchment insouth Western Australia. As discussed in Section 4the Wights catchment is part of a series on pairedcatchment studies in south Western Australia. Inthese catchments, the interplay between the localgroundwater flow system and vegetation plays animportant role in the hydrological response.
The replacement of native forests by pastures inthese catchments has lead to a rapid increase ingroundwater discharge area (Schofield, 1996),resulting in large increases in low flows. As withFig. 10, it can be seen that all sections of the flowregime are affected by the change in vegetation.Comparing the FDC for native vegetation (1974–1976) with a period of similar climatic conditions of
Fig. 11. Flow duration curves for the Wights catchment in south Western Australia. (Based on a water year from April to March).
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
Un grafico significativo
Bro
wn
et
al.,
20
05
R. Rigon
Curve di durata
!15
Fig. 11 depicts the response to conversion ofnative forest to pasture in the Wights catchment insouth Western Australia. As discussed in Section 4the Wights catchment is part of a series on pairedcatchment studies in south Western Australia. Inthese catchments, the interplay between the localgroundwater flow system and vegetation plays animportant role in the hydrological response.
The replacement of native forests by pastures inthese catchments has lead to a rapid increase ingroundwater discharge area (Schofield, 1996),resulting in large increases in low flows. As withFig. 10, it can be seen that all sections of the flowregime are affected by the change in vegetation.Comparing the FDC for native vegetation (1974–1976) with a period of similar climatic conditions of
Fig. 11. Flow duration curves for the Wights catchment in south Western Australia. (Based on a water year from April to March).
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
Un grafico significativo
Bro
wn
et
al.,
20
05
R. Rigon
Curve di durata
!16
Altri effetti:
Il bosco: • difende dall’erosione • difende dal franamento*
*Ma quando il versante diventa instabile …
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Il dissesto (idrogeologico)
!17
• Perchè il suolo è più secco e perché le radici aumentano. • Perche le radici aumentano la coesione del terreno
R. Rigon
Frane
!18We measured characteristics of all roots with diameters
!1 mm in regions with differing vegetation communities.Field-measured root attributes included species, diameter(measured with micrometer), vertical depth relative to theground surface, whether the root was alive or decaying,whether the root was broken or intact, and cross-sectionalarea of colluvium over which roots act. The root attribute in-ventory was divided into polygons of similar soil depth witha typical length of about 2 m along the landslide scarps.Root cohesion for each polygon was calculated separatelyand the spatially weighted mean was used to represent a sin-gle value of root cohesion for a site.Live and decaying roots were identified based on their
color, texture, plasticity, adherence of bark to woody mate-rial, and compressibility. For example, live Douglas-fir rootshave a crimson-colored inner bark, darkening to a brownishred in dead Douglas-fir roots. Both are distinctive colors.Live roots exhibited plastic responses to bending and strongadherence of bark, whereas dead roots displayed brittle be-havior with bending and poor adherence of bark to the un-derlying woody material. We measured the tensile strengthof decaying root threads within clearcuts and found thattheir tensile strength was significantly lower than their ulti-mate living tensile strength. We assumed that all dead rootsin forested areas (both natural and industrial) and dead
understory roots in clearcuts had no root cohesion becausewe do not know the timing of plant mortality and hence therelative decrease in thread strength. Consequently, calcu-lated root cohesion is conservative on the low side becausedecaying roots continue to contribute a finite amount of co-hesion. We did not systematically characterize the decayfunction of all the species in the area. Instead we uniformlycharacterize the tensile strength decrease over time of all co-nifer roots (living and decaying) with the coast Douglas-firdecay function defined by Burroughs and Thomas (1977),such that
[14] TF 1.04(2.516 )r wb1.8 0.06 t" #d
where dwb is the root-thread diameter (mm) without bark, t isthe time since timber harvest (months), and TFr is expressedin kilograms. Live root tensile strength is calculated with t =0. Burroughs and Thomas determined eq. [14] by breakingroots in tension up to 14.3 mm in diameter (without bark)using a hydraulic-pressure device to anchor the root ends.
Results
Field observations illustrate that root networks of the 12dominant species (Table 1) varied from fine fibrous systems
© 2001 NRC Canada
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 thebasal surface of the landslide.
•Sc
hm
idt
et a
l., 2
00
1
R. Rigon
Frane e Radici
!19
htt
ps:
//w
ww
.iec
a.org
/ph
oto
gal
lery
/hay
man
fire
.asp
Il fuoco altera la struttura del suolo
Incendi
R. Rigon
Si vede ciò che manca, quando manca
!20
•B
enav
ides
et
al.,
20
05
R. Rigon
Trasporto di sedimento
!21
Neve
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Neve !
!22
E’ importante notare che la grandezza dei cambiamenti annuali non racconta l’intera storia dell’impatto che la vegetazione ha sulla produzione del deflusso.
Gli effetti sono diversi nelle diverse stagioni.maximum evapotranspiration and periods of minimumevapotranspiration. Hornbeck et al. (1997) observedthat most of the increase in annual yield occurredduring the growing season as shown in Fig. 13. Theyconcluded that water yield increases were a result ofdecreased transpiration and primarily occurred asaugmentation to low flows during the growing season(Fig. 13). While this seasonal break-up is obvious fordeciduous catchments, the definition of seasons is lessobvious for evergreen vegetation or catchments withuniform climate.
Using a similar approach to the analysis ofHornbeck et al. (1997) and McLean (2001) producedFDCs during winter (July–September) and summer(December–February) seasons where vegetation wasconverted from tussock to pine plantations in NewZealand. McLean (2001) concluded that:
† differences were more variable in summer flowsthan in the winter. This was due to the highvariability in the rainfall over the summer months;and
† the seasonal effects of vegetation modificationsare not easily identified using flow durationcurves.
The difference in the results between Hornbecket al. (1997), who found notable seasonal differences,and McLean (2001), who could not detect seasonalchanges, can be attributed to the deciduous nature ofthe vegetation in theUSAcomparedwith the evergreenvegetation of the pine plantations in New Zealand. Thedistinct dormant season in the USA where there are noleaves on the trees results in lower interception andtranspiration rates making the evapotranspiration ratesof forested areas very similar to those of short crops. Aswith the mean seasonal responses discussed in Section5, the response of the seasonal FDC to vegetationchange will also differ depending on the rainfallpattern. The comparison between the annual FDCs forRed Hill (Fig. 10) gives us some indication of thelikely impact of pine plantation on the seasonal FDCsin this catchment. It would be anticipated that a largerproportional reduction in low flows in the Red Hillcatchment indicates a larger change in thesummer FDC, compared to the winter FDC as themajority of low flows occur during the summermonths.
Jones and Grant (2001) noted that the nature ofthe analysis undertaken could influence the results.
Fig. 13. Flow duration curves for the first year after the clear-felling treatment—Hubbard Brook experimental forest (after Hornbeck et al.,1997).
A.E. Brown et al. / Journal of Hydrology 310 (2005) 28–61 45
R. Rigon
Non solo alla scala annuale
!23
Ma i cambiamenti stagionali, a causa della variabilità del meteo e delle precipitazioni, sono più difficilmente osservabili. Si devono fare delle congetture e supportarle con modellazione
matematica.
R. Rigon
Modelli fisico-matematici-numerici
!24computationally demanding. Therefore, several eco-hydrological models still use simplified solutions ofthe energy budget such as the Penman–Monteith orPriestley-Taylor equations (e.g., Refs260,265,269,273).
Carbon BudgetThe carbon cycle is linked to the water and energycycles because carbon assimilated through photosyn-thesis uses the same pathway between the outeratmosphere and leaf interior as transpired water (seethe ‘Stomatal Controls’ section) and because changesin vegetation properties (e.g., plant height and LAI)modify boundary conditions for energy and waterexchanges (Figure 6). For instance, a change in LAImodifies interception capacity, energy absorption andemission as well as roughness; a change in photosyn-thetic rate, An (Eq. (1)), may change stomatal con-ductance and therefore transpiration. Thecomputation of carbon assimilation can be carriedout with various degrees of complexity. Some modelsuse a biochemical model of photosynthesis in whichAn and leaf internal CO2 concentration (ci) are com-puted as prognostic variables in a non-linear equa-tion (e.g., Refs 164,165,259,275,276); others havesimpler approaches exploiting the water use effi-ciency (WUE; i.e., the ratio between net carbonassimilation and transpiration284) or light use effi-ciency (LUE; i.e., the efficiency through which radia-tion absorbed by vegetation is converted into
carbon285) concepts that empirically link carbonassimilation to the transpired water or interceptedlight (e.g., Refs 264,265,271,286,287). In some eco-hydrological models, vegetation dynamics are essen-tially reduced to the simulation of carbonassimilation only (e.g., Refs 262,276). In others, theassimilated carbon is used to grow plants and toevolve a given number of carbon pools. Carbonpools are the way models account for the size anddynamics of different plant compartments.288 Thenumber of carbon pools varies from model to model,but a typical set is composed of at least of a foliagepool, a fine-root pool, a sapwood or stem pool, and,more recently, a carbon reserve pool (e.g., Refs165,273). Carbon reserves have been ignored in earlyecohydrological models and ESMs with rare excep-tions (e.g., Ref 289), but it is currently recognizedthat plant dynamics cannot be simulated meaning-fully without accounting for carbon reserves.290–292
Models that use carbon pools can also simulate thedynamics of the biophysical structure of vegetation,e.g., LAI, vegetation height, and root biomass.
Soil BiogeochemistryWater, energy, and carbon fluxes are additionallyconnected through soil biogeochemistry and nutrientdynamics (Figure 6). Soil biogeochemistry is typicallysimulated to account for a given number of carbonand nitrogen pools.293 Other nutrients, such as phos-phorous, sulfur, or potassium, are not typically
Energy exchangesLongwaveradiationincoming
Longwaveradiationoutgoing
Shortwaveradiation
Latent heat
Latentheat
Sensibleheat
Soil heat flux
Geothermal heatgain
Bedrock Bedrock Bedrock Bedrock
Momentum transfer
Rain Snow Photosynthesis
Phe
nolo
gy
DisturbancesAtmosphericdeposition
Fertilization
Nutrient resorption
Nutrient uptake
Nutrients in SOM
Mineral nutrientsin solution
Mineralization andimmobilizationOccluded or not
available nutrients
Primary mineralweathering
Biologicalfixation (N)
Tectonic uplift
Denitrification (N)
Volatilization
Growth respiration
Maintenance respiration
Fruits/flowers production
Heterotrophicrespiration
Wood turnover
Litter Litter
Litterfallnutrient flux
DecompositionMycorrhizalsymbiosis
Microbialand soil
faunaactivity
SOM
DOCleaching Leaching
Fine and coarseroot turnover
Carbon allocationand translocation
Carbon reserves (NSC)
Leaf turnover
Transpiration
Evaporation frominterception
Evaporation/sublimationfrom snow
Evaporation
Throughfall/dripping
Snow melting
Infiltration
LeakageRoot water uptakeLateral subsurface flow
Base flowDeep recharge
Runoff
Sensible heatAlbedo
Energy absorbedby photosynthesis
Water cycle Carbon cycle Nutrient cycle
FIGURE 6 | Ecohydrological and terrestrial biosphere models have components and parameterizations to simulate the (1) surface energyexchanges, (2) the water cycle, (3) the carbon cycle, and (4) soil biogeochemistry and nutrient cycles. Many models do not include all thecomponents presented in the figure.
WIREs Water Modeling plant–water interactions
© 2015 Wiley Per iodica ls , Inc.
La complessità di una singola pianta
Non solo l’acquaaf
ter
Fati
chi, P
app
as a
nd
Iva
nov,
2
01
5
R. RigonR. Rigon
!25
Vegetazione - Suolo il tempo si allunga
Jenn
y, R
ole
of th
e Pl
ant F
acto
r in
the
Pedo
geni
c Fu
nctio
ns, E
colo
gy, V
ol. 3
9, N
o. 1
January, 1958 THE PLANT FACTOR IN THE PEDOGENIC FUNCTIONS
Beadle, N. C. W. and Costin, A. B. 1952. Ecological classification and nomenclature. Proc. Linn. Soc. N.S.W. 77: 61-82.
Billings, W. D. 1950. Vegetation and plant growth as affected by chemically altered rocks in the Western Great Basin. Ecology 31-: 62-74.
Bryan, W. H. and Jones, 0. A. 1944. A revised glos- sary of Queensland stratigraphy. Univ. Qld. Papers Dept. Geol. N.S. 2: 77 pp.
Crocker, R. L. and Wood, J. G. 1947. Some historical influences on the development of the South Austra- lian vegetation communities and their bearing on concepts and classification in ecology. Trans. Roy. Soc. S. Aust. 71: 91-136.
Hubble, G. D. 1954. Some soils of the coastal low-
lands north of Brisbane. C.S.I.R.O. Div. Soils Rep. 1/54.
Moore, C. W. E. 1956. Nutrition of Eucalypts in re- lation to their distribution. Proc. Aust. Plant Nutri- tion Conference. Vol. I. C.S.I.R.O. Melbourne.
Prescott, J. A. 1931. The soils of Australia in relation to vegetation and climate. Coun. Sci. Ind. Res. Bull. 52.
Salisbury, F. B. 1954. Some chemical and biological investigations of materials derived from hydrother- mally altered rock in Utah. Soil Science 78: 277- 294.
Welch, B. 1951. On the comparison of several mean values: an alternative approach. Biometrika 38: 330- 36.
Wood, J. G. 1956. Personal communication.
ROLE OF THE PLANT FACTOR IN THE PEDOGENIC FUNCTIONS HANS JENNY
University of California, Berkeley
INTRODUCTION
The interplay of climate, soil, and vegetation is often represented by a triangle:
climate
vegetation = soil
It implies that climate affects soil and vegeta- tion independently, that soil influences vegetation, and that vegetation reacts upon soil.
While at first sight appealing the triangle is actually beset with logical pitfalls and frustrations. It has even led to the negativistic view that the soil-plant contract cannot be interpreted.
This paper presents a formalistic method for evaluating the various interactions. Assessing the roles of climate and vegetation in soil genesis will be emphasized. To do so, the concept of the biotic factor must be clearly delineated and given pre- cision.
The writer follows a type of presentation which he has offered for a number of years to his stu- dents in pedology. It is fair to say that both Major and Crocker attended these lectures, prior to their own pertinent publications (Major 1951, Crocker 1952). In turn the writer has profited from their considerations.
THE TESSERA AND THE LARGER SYSTEM
In a given landscape let us select a spot, say, an area of one square meter, or a square of 8 x 8 inches, or any other small area of suitable shape. There is soil with organisms below it, and there
is plant and animal life above it. The totality constitutes a three dimensional element of land- scape, an arbitrary element. The element is an open system; matter is continually added to and removed from it.
The entire landscape can be visualized as being composed of such small landscape elements. This picture is comparable to the elaborate mosaic de- signs on the walls of Byzantine churches which are made up of little cubes or dice or prisms, called tesseras. We shall use the same name, tessera, for a small landscape element.
The "thickness" of a tessera is given by the height of vegetation plus the depth of the soil. The area of a tessera is determined by operational con- siderations. It is a convenient sampling unit hav- ing, a specified area. A quadrat or rectangle of one square meter in a stand of vegetation is a vegetation tessera. A soil monolith is a soil tes- sera. A "soil profile" collected in the field is a soil tessera, usually of ill-defined areal dimensions. Strictly speaking, a soil profile is a vertical face of a soil tessera.
Table I illustrates the total nitrogen assay, by Kjeldahl analysis, of an alpine tessera collected. in 1955 on a 315-year old moraine in front of the Rhoneglacier in Switzerland. The vegetation was alpine shrub, consisting mainly of Rhododendron ferrugineum, Vaccinium myrtillus and Juniperus nana.
On the ground a square of 400 sq. cm. was marked off. Plants were clipped along the up- ward vertical projection of the square, then cut at the base, dried, weighed, ground up, and ana- lysed. Soil layers were carefully collected along
This content downloaded from 130.209.6.61 on Tue, 03 Nov 2015 18:12:33 UTCAll use subject to JSTOR Terms and Conditions
“L’interconnessione tra clima soli e vegetazione è spesso rappresentata da un triangolo. Esso implica che il clima comanda lo sviluppo dei suoli e della vegetazione; che il suolo influenza e la vegetazione e la vegetazione modifica il suolo.”
R. Rigon
Il suolo
!26
Vegetazione - Suolo - Topografia sulla scala più lunga
~ 2 km
R. Rigon
Feedbacks
!27
Cu
nn
ingh
am/S
aigo, E
nvi
ron
men
tal
Scie
nce
, 19
99
Vegetazione - Suolo - Topografia una complessità ancora da
conoscere bene
R. Rigon
Il suolo da solo
!28
Tag
ue
and
Du
gger
, 20
10
tran
sizi
one
neve
-pio
ggia
~ 3 km
R. Rigon
Cambiamenti Climatici
!29
Tag
ue
and
Du
gger
, 20
10
tran
sizi
one
neve
-pio
ggia
Aumenta il deflusso invernale,
diminuisce il deflusso estivo
Il cambiamento climatico sposta la transizione tra precipitazioni
nevose e piovose verso le alte quote
R. Rigon
Cambiamenti Climatici
!30
limitata dall’energia: la foresta è probabile cresca: fintantoché anche l’acqua non diventa limitante
limitata dall’acqua: è probabile che la foresta diminuisca, anche se l’aumento di CO2 potrebbe favorirne la crescita
Intermedio: la crescita può essere positiva o negativa dipende dal tipo di pianta e dalle risorse locali
Bosco a crescita:
modificato da Tague and Dugger, 2010
Nell’ovest degli USA
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Cambiamenti Climatici
!31
Cambiamenti Climatici
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Shift degli ecotoni
!32
BARK BEETLE OUTBREAKSIN WESTERN NORTH
AMERICA: CAUSES ANDCONSEQUENCES
Given the complexity of bark beetle community dynamics and the specific ecosys-
tems they inhabit, the roles these factors play differ from forest to forest.
Although research has uncovered a great deal of information about the life cycles
and host interactions of some species of bark beetles, many gaps in our knowl-
edge remain. In addition, because changing climate and forest disturbances have
altered outbreak dynamics in recent years, some of what has been learned from
past outbreaks may no longer hold true. There may be no equivalent in the 100
or so years of recorded history for the current outbreaks.
These recent infestations may result in dramatic changes to the long-term eco-
logical pathways of some ecosystems, radically shifting vegetation patterns in
some hard-hit forests. The visual landscape cherished by many nearby landown-
ers and visitors is altered as well, as once-green trees turn brown and then lose
their needles. In addition, bark beetle-killed trees pose some hazards when dead
trees fall in areas of forest that humans frequent.
Although there are no known management options to prevent the spread of a
large-scale bark beetle outbreak, land-use activities that enhance forest hetero-
geneity at the regional scale—such as creating patches of forest that contain
diverse species and ages of trees—can reduce susceptibility to bark beetle out-
breaks. However, because resource objectives often differ, and because the factors
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.
2
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
Effetti che magari non si erano considerati
si veda, per esempio Bearup et al., 2014
R. Rigon
Insetti
!33
2. VERTICAL PERSPECTIVE
As overviewed in section 1, any aspects of land-surface characteristics which influence the heating andmoistening of the atmospheric boundary layer will affectthe potential for cumulus convective rainfall. Thereforevertical radiosonde soundings over adjacent locationsthat have different surface conditions offer opportuni-ties to assess alterations in thunderstorm potential. Thisinfluence of surface conditions on cumulus cloud andthunderstorm development has been discussed, for ex-
ample, by Clark and Arritt [1995], Crook [1996], Cutrim etal. [1995], Garrett [1982], and Hong et al. [1995].
Figure 6 illustrates two soundings made over twolocations in northeastern Colorado at 1213 local stan-dard time (LST) on July 28, 1987 [Segal et al., 1989;Pielke and Zeng, 1989]. The soundings were made priorto significant cloud development. The radiosondesounding over an irrigated location had a slightly coolerbut moister lower troposphere than the sounding overthe natural, short-grass prairie location. Aircraft flightsat several levels between these two locations on July 28,
Figure 4. Schematic of the differences in surface heat energy budget and planetary boundary layer over atemperate forest and a boreal forest. The symbols used refer to equation (1). Horizontal fluxes of heat andheat storage by vegetation are left out of the figure. Adapted from P. Kabat (personal communication, 1999).Reprinted with permission.
Figure 5. Same as Figure 4 except between a forest and cropland. Adapted from P. Kabat (personalcommunication, 1999). Reprinted with permission.
39, 2 / REVIEWS OF GEOPHYSICS Pielke: PREDICTION OF CUMULUS CONVECTIVE RAINFALL ● 155
Pie
lke,
20
01
Feedbacks - Retroazioni sull’atmosfera
~ 10 km
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Cambia il cielo
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Precipitation recycling
~ 100 - 1000 km
Bru
sker
et
al, 1
99
3
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Cambiano le precipitazioni
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MAY 1999 1379T R E N B E R T H
FIG. 10. For annual mean conditions, r, the recycling (%) for L 5 1000 km over land.
typically and the mean recycling is 16.8% globally,15.4% over land and 17.3% over the oceans.Over the Amazon, the results depend greatly upon
whether or not the maximum over the southern part ofthe basin, where r . 20%, is included. The South Amer-ican region defined by Brubaker et al. (1993) is from
2.58N to 158S, 508 to 758W (which is a reasonable ap-proximation of the Amazon River Basin), for which theaverage annual recycling on 500-km scales is 8.7%,ranging from 5.7% in JJA to 11.9% in DJF. But if theregion is cut off at 108S, the values drop to 6.4% forthe annual mean and with a range of 4.8% in JJA to
Trenberth, 1998
R. Rigon
Cambiano le precipitazioni
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Così siamo pronti a scale temporali ancora più grandi
Brovkin, 2002
Clima (radiazione,
precipitazioni, temperatura)
Vegetazione
Composizione dei gas dell’atmosfera 78 % N2, 31% O2
R. Rigon
E’ un tutt’uno
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B.
Using the conceptual model, the results of ECHAM-BIOME model have been interpreted [77]. Itwas shown that present-day climate corresponds to three equilibria (case II in Fig. 7), and the mid-Holocene corresponds to the unique green equilibrium (case III). The Lyapunov functional reveals oneminimum for the mid-Holocene but two for present-day climate (see analogue in Fig. 8,B). Moreover, theminimum corresponding to the green equilibrium for the present day is rather flat. Therefore anysufficiently large perturbation, for instance a prolonged drought such as is observed to occur at thedecadal timescale (see, e.g., [85]), could take the system to the desert equilibrium.
The spatial resolution of the CLIMBER-2 model is too coarse to investigate multiple equilibria in thewestern Saharan region [86]. A box model for climate-vegetation interaction has been developed andapplied for Holocene climate [77]. The dynamic climate model accounts for the main processes whichinfluence the summer climate in subtropical deserts: Hadley circulation, zonal wind, monsoon-typecirculation, 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 presentday are presented in Fig. 8,A. The two stable branches of the solution, the green branch with relativelyhigh precipitation and the desert branch with low precipitation, are separated by the unstable branch. Inthe early Holocene, some 10 kyr ago, only the green equilibrium existed in the area with annualprecipitation of about 600 mm/yr. Owing to decreased summer insolation, the precipitation declined to400 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 forthe western Sahara region for the Holocene [77].
A. Dynamics of system solution in terms ofprecipitation during the last 10,000 yr. The upperand lower curves are the green and desertsolutions, respectively. The dashed line in themiddle represents the unstable solution.
B. Multiple steady states, desert and ‘green’, areshown in a form of Lyapunov potential forvegetation cover. Potential minima, marked byballs, correspond to equilibria that are stable inabsence of perturbations. Black and grey ballsindicate dominant and minor states, respectively.8,000 yr BP. The system has only one steadystate, green Sahara.4,000 yr BP. System underwent bifurcation;desert state appeared and became stable. Thedepths of the well for the two states areapproximately equal.0 yr BP. Both states remain stable but desert hasa deeper well. Desert became dominant state asprecipitation fluctuations shifted the systemtowards desert.
Year, kyr BP
A.
green
desert
Fig. 8,B is a simplified cartoon of the system stability under changes in the orbital forcing. Theequilibria 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 twominima: the desert equilibrium is at an absolute minimum (dominant state) and the green equilibrium is ata 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. Moreprecisely, the switch from one solution to another depends on the possibility for the system to ``jump''
Brovkin, 2002
R. Rigon
Fondato su equilibri che possono variare
!38
A qualsiasi scala spaziale e temporale
Come si partizionano evaporazione e deflussi a diverse scale spaziali e temporali ?
45.75
45.80
45.85
11.20 11.25 11.30 11.35Long
Lat
Figure 1: Posina basin and the HRU partition used for simulation. The triangle points are the discharge measurement stations. The location andelevation of the basin is described in Abera et alXXX.
elling system JGrass-NewAge (from now on, simply NewAge),which o↵ers a set of model components built accordingly to theObject Modelling System version 3 (OMS) framework (Davidet al., 2013). OMS, modelling framework based on component-based software engineering, uses classes as fundamental modelbuilding blocks (components) and uses a standardized inter-faces supporting component communication. In OMS3, the in-teraction of each component is based on the use of annotations.This enable model connectivity, coupling and maintaining easyand fast (David et al., 2013).
NewAge covers most of the processes involved in the wa-ter budget and its components were discussed with detail in:Formetta et al. (2011, 2013a); Formetta (2013); Formetta et al.(2013b, 2014b), and they are not fully re-discussed here. Com-ponents that can be used in various combinations inside ofNewAge are shown in Table 1.
The goals of this paper are to determine the water budget ofeach Hydrologic Response unit (HRU) of a small catchment athigh temporal resolution i.e hourly, that can be aggregated todi↵erent temporal scale, and define a methodology that can beused to analysed larger basins. A HRU represents a part of thebasin that can be treated as a single unit, and a single controlvolume k for which eq. (1) is solved.
The working scheme followed are:
• For each model’s component determine the parameters bycalibration by using an automatic calibration algorithm,
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 organizationThe paper is organized as follows: section 2 provides
methodologies of modeling the ”output” terms of the waterbalance equation, particularly rainfall-runo↵ modeling and dis-charge estimation (subsection 2.3) and evapotranspiration andwater balance residual estimations (section 2.4). Brief descrip-tion of the basin is at section 3. At section 4, the results ofthe hourly time series simulations for three components (snowwater equivalent (sec 4.1), discharge (sec 4.2), and evapotran-spiration and storage (sec 4.3), and results of basin scale waterbudget closure (sec 4.4)) is presented. Finally, the conclusionsabout the water flux of the basin follows.
2
~ 10 km ~ 100 km ~ 500 km
Posina Adige Nilo Blu
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]
La sfida della modellazione
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Figure 13 shows the long term monthly means, of course itprovides seasonal inference too as each month is sampled fromeach season, spatial distribution of water budget componentsover the basin. The result confirms the monthly analysis givenfor the year 2012 (figure 12).
The trend in Q follows the trend in precipitation, but actuallynot in a linear way. This could have been deduced from the dataalone, However, seeing it with the other budget componentsenlighten the complexity of the interactions actually in place.
Figure 12: The same as figure 11, but monthly variability for the year2012.
J
80 120 160 200
Q
40 80 160
ET
20 40 60
S
Jan
Apr
Jul
Oct
−150 −100 −50 0 50Figure 13: The spatial variability of the long term mean monthly waterbudget components (J, ET, Q, S). For reason of visibility, the colorscale is for each component separately.
5. Conclusions
The water budget of the river Posina basin has been ana-lyzed with the NewAge system at hourly time-steps by using18 years of meteorological data (rainfall and temperature) anddischarge. The analyses include estimations of the four compo-nents of water budget (precipitation, discharge, relative storage,and evapotranspiration) under the hypothesis of stationarity (i.e.null storage) in one of the years where measurements are avail-able. NewAge model components are used to capture the basinbehaviour and forecast the water cycle, in presence of knownprecipitation inputs, and to fill the gaps where distributed dataare necessary and only local measures of rainfall and dischargeare present. The procedure implemented is general, and can betransposed to all the basins.
In NewAge Adige rainfall-runo↵ component, the inputs forruno↵ production such as precipitation and temperature are pro-duced for each hillslope. To asses the impact of precipitationinterpolation and its coarse graining at hillslope scale, as re-quested by the rainfall-runo↵model, we analyzed the dischargeforecasting by using four kriging methods. The DK and LDKperformances were found to give better results that the OK andLOK. The GOF index of the simulated against observed dis-charge shows that the model performances are acceptable (es-pecially considering that we are simulating hourly discharges).Using discharge measures inside the basin, it was possible toquantify the reliability of internal discharges estimations by as-suming the validity of model parameters calibrated at the fur-thermost downstream outlet. The model performances actu-ally maintain similar performances at the interior sites, which
12
After Abera et al., in preparation, 2016a
Il bilancio del Posina
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La sfida della modellazione
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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
MO
DIS
ET
New
Age
ET
35 36 37 38 39 40 35 36 37 38 39 40 35 36 37 38 39 40 35 36 37 38 39 40long
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.
Figure 7: Time series ET estimation with NewAge and MOD16 at 8-days of time steps for four subbasin 2 (a), subbasin 248 (b), subbasin 107 (c)and subbasin 386 (d). The location of the subbasin are indicated in the map inside the plots.
9
After Abera et al., in preparation, 2016b
Nilo Blu
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0.25
0.50
0.75
1.00
Gen 1994 Apr 1994 Lug 1994 Ott 1994 Gen 1995Time [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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La sfida della modellazione
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Find this presentation at
http://abouthydrology.blogspot.com
Ulr
ici, 2
00
0
Other material at
Domande ?
R. Rigon
http://www.slideshare.net/GEOFRAMEcafe/acqua-suolo-foreste-59814511
orhttp://abouthydrology.blogspot.it/2016/03/water-soil-forests-man-who-planted.html
!43
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