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 (Water Yeld) 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*e

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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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NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE2198 LETTERS

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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�.

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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

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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

!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

A meaningful draw

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Duration curves change

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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

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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

!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.

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Landslides and root strength

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Fires

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You can see it when all is missed

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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 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

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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

!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

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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.

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Vegetation - Soil at longer time scales

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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

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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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Soil itself

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Further unknown complexity

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Introducing climate change

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Increases winter runoff,

decreases the summer one

Climate change moves the transition between snowfall and rainfall uphill.

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Introducing climate change

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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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Introducing climate change

!31

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Ecotones Shift

Introducing climate change

!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

Other effects, maybe not previously

see, for instance Bearup et al., 2014

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Deseases

!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

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Also the atmospheric hydrological cycle is modified

~ 10 km

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Feedbacks on the atmosphere

Also the atmosphere hydrological cycle is mo

!34

Precipitation recycling

~ 100 - 1000 km

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Green precipitation

!35

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

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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.

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

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 11.35Long

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

R. Rigon

]

The challenge of modelling all of this

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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

Posing water budget snapshot

R. Rigon

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

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

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 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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can be win, but …

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Find this presentation at

http://abouthydrology.blogspot.com

Ulr

ici, 2

00

0

Other material at

Questions ?

R. Rigon

http://www.slideshare.net/GEOFRAMEcafe/acqua-suolo-foreste-59814511

orhttp://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.

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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