This document discusses storage selection functions (SAS) as a tool for characterizing dispersion processes and solute transport at the catchment scale. SAS functions link the age distributions of water stored in a catchment to the age distributions exported from the catchment. They can be used to derive travel time distributions and model concentrations of conservative solutes. The formulation incorporates temporal variability in hydrologic fluxes and can represent spatial heterogeneity through distinct SAS functions for different catchment units. Case studies demonstrate how SAS functions capture catchment-scale age selection dynamics and can reproduce observed solute concentrations in streams.
The concepts related of the New Model of River Adige, and especially an analysys of the existing OMS components ready and their interpretation on the basis of travel time approaches
The concepts related of the New Model of River Adige, and especially an analysys of the existing OMS components ready and their interpretation on the basis of travel time approaches
A travel time model for estimating the water budget of complex catchmentsRiccardo Rigon
This is the presentation given by Marialaura Bancheri for her admission to the final exam to achieve a Ph.D. in Environmental Engineering. It contains a synthesis of her studies about spatially integrated models of the water budget, and about travel time theory. A model structure is also presented preliminarily containing five reservoirs.
This is a revision of the previous post on the same topic. There I tried to develop my own algebra of symbols to represent coarse grained (spatially integrated) hydrological system. Later on I understood that Petri networks were already there and useful to obtain the same result. The graphs obtained in such a way where, besides, studied in several places, and many contributes of literature convergent from other disciplines, can be used for hydrological scopes.
The use of Cellular Automata is extended in various disciplines for the modeling of complex system procedures. Their inherent simplicity and their natural parallelism make them a very efficient tool for the simulation of large scale physical phenomena. We explore the framework of Cellular Automata to develop a physically based model for the spatial and temporal prediction of shallow landslides. Particular weight is given to the modeling of hydrological processes in order to investigate the hydrological triggering mechanisms and the importance of continuous modeling of water balance to detect timing and location of soil slips occurrences. Specifically, the 3D flow of water and the resulting water balance in the unsaturated and saturated zone is modeled taking into account important phenomena such as hydraulic hysteresis and evapotranspiration. In this poster the hydrological component of the model will be presented and tested against well established benchmark experiments [Vauclin et al, 1975; Vauclin et al, 1979]. Furthermore, we investigate the applicability of incorporating it in a hydrological catchment model for the prediction (temporal and spatial) of rainfall-triggered shallow landslides.
Advances in Rock Physics Modelling and Improved Estimation of CO2 Saturation, Giorgos Papageorgiou - Geophysical Modelling for CO2 Storage, Leeds, 3 November 2015
The current study examines the generation and propagation of a Third order solitary water wave along
the channel. Surface displacement and wave profi le prediction challenges are interesting subjects in the
fi eld of marine engineering and many researchers have tried to investigate these parameters. To study the
wave propagation problem, here, fi rstly the meshless Incompressible Smoothed Particle Hydrodynamics
(ISPH) numerical method is described. Secondly,
DSD-INT 2017 The unsaturated zone MetaSWAP-package, recent developments - Van...Deltares
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Residence time theories of pollutants transportRiccardo Rigon
Gianluca Botter talked about the travel time distribution approach to catchment scale transport. A topic that intersects also the “old water paradox” querelle, but is, in general, pretty effective in getting the distribution of pollutants. This approach has a long story that put its roots, in Gedeon Dagan’s work, as well as in Rodriguez-Iturbe geomorphic unit hydrograph. Andrea own papers on Mass response function with Sandro Marani can also be considered at the foundations of this presentation.
GroundWater Age and Large Scale Mixing, Cargese 2015, JR de Dreuzyjrdreuzy
Cargese Summer School on Flow and Transport in Porous and Fractured Media, Development, Protection, Management and Sequestration of Subsurface Fluids, July 20th - August 1st 2015
A travel time model for estimating the water budget of complex catchmentsRiccardo Rigon
This is the presentation given by Marialaura Bancheri for her admission to the final exam to achieve a Ph.D. in Environmental Engineering. It contains a synthesis of her studies about spatially integrated models of the water budget, and about travel time theory. A model structure is also presented preliminarily containing five reservoirs.
This is a revision of the previous post on the same topic. There I tried to develop my own algebra of symbols to represent coarse grained (spatially integrated) hydrological system. Later on I understood that Petri networks were already there and useful to obtain the same result. The graphs obtained in such a way where, besides, studied in several places, and many contributes of literature convergent from other disciplines, can be used for hydrological scopes.
The use of Cellular Automata is extended in various disciplines for the modeling of complex system procedures. Their inherent simplicity and their natural parallelism make them a very efficient tool for the simulation of large scale physical phenomena. We explore the framework of Cellular Automata to develop a physically based model for the spatial and temporal prediction of shallow landslides. Particular weight is given to the modeling of hydrological processes in order to investigate the hydrological triggering mechanisms and the importance of continuous modeling of water balance to detect timing and location of soil slips occurrences. Specifically, the 3D flow of water and the resulting water balance in the unsaturated and saturated zone is modeled taking into account important phenomena such as hydraulic hysteresis and evapotranspiration. In this poster the hydrological component of the model will be presented and tested against well established benchmark experiments [Vauclin et al, 1975; Vauclin et al, 1979]. Furthermore, we investigate the applicability of incorporating it in a hydrological catchment model for the prediction (temporal and spatial) of rainfall-triggered shallow landslides.
Advances in Rock Physics Modelling and Improved Estimation of CO2 Saturation, Giorgos Papageorgiou - Geophysical Modelling for CO2 Storage, Leeds, 3 November 2015
The current study examines the generation and propagation of a Third order solitary water wave along
the channel. Surface displacement and wave profi le prediction challenges are interesting subjects in the
fi eld of marine engineering and many researchers have tried to investigate these parameters. To study the
wave propagation problem, here, fi rstly the meshless Incompressible Smoothed Particle Hydrodynamics
(ISPH) numerical method is described. Secondly,
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GroundWater Age and Large Scale Mixing, Cargese 2015, JR de Dreuzyjrdreuzy
Cargese Summer School on Flow and Transport in Porous and Fractured Media, Development, Protection, Management and Sequestration of Subsurface Fluids, July 20th - August 1st 2015
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The idea is to build an algebra of object to represent (water) budgets giving a clear idea of the type of interactions that the budget is subject to.
Any symbol should correspond to a mathematical term or a group of mathematical terms. The number and the collocation of parameters of the models should be clear.
This treats from a historical point of view, first, the GIUH theories. Then It introduces the new theories of travel time, residence time distribution. Finally propose how to work out a modern statistical mechanical theory of the hydrological budgets
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
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as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
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Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
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Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
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Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
1. STORAGE
SELECTION (SAS)
FUNCTIONS:
A
TOOL
FOR CHARACTERIZING DISPERSION PROCESSES AND
CATCHMENT-SCALE SOLUTE TRANSPORT
G. Botter
Dept. Civil & Environmental Engineering, University of Padova (ITALY)
Workshop
on
coupled
hydrological
modling
Padova
|
23
–
24
April
2015
2. RIVER HYDROCHEMISTRY and CATCHMENT SCALE TRANSPORT
…why RIVER HYDROCHEMISTRY ?
Water quality has well known implications for human
well being and ecosystem services
In spite of the huge number of available models and
datasets focused on water fluxes, catchment -scale
transport models/datasets are less widespread
River hydrochemistry provides important clues for
process identification and hydrologic functioning
3. the chemical response is much more “damped” compared to the
hydrologic signal – different processes
HYDROLOGIC vs CHEMICAL SIGNALS
[Kirchner et al.., Nature 2000]
4. THE OLD WATER PARADOX
‘new’ rainfall
discharge‘old’ stored water
the hydrologic response to a rainfall event is chiefly made by water
particles already in storage before the event (old water)
5. THE OLD WATER PARADOX
TRACKS OF PAST RAINFALL EVENTS IN STREAMS…
LASTING FOR MONTHS/YEARS (LONG MEMORY)
EVENT WATER
the hydrologic response to a rainfall event is chiefly made by water
particles already in storage before the event (old water)
7. WATER RESOURCES AND WATER QUALITY
...NOT ONLY BECAUSE OF REDUCED WATER AMOUNTS,
BUT ALSO BECAUSE OF INSUFFICIENT WATER QUALITY
IN MANY REGIONS OF THE WORLD WATER RESOURCES
ARE SHRINKING...
8. THE AGE OF WATER & WATER QUALITY ISSUES
LAND MANAGEMENT AND CATCHMENT RESILIENCE
9. A CHALLENGING PROBLEM...
SPATIAL and TEMPORAL
PATTERNS of SOLUTE INPUT
LANDSCAPE HETEROGENITY
TEMPORAL VARIABILITY
OF CLIMATE FORCING
HYDROLOGIC PROCESSES
12. X0
Xt(t;X0,t0)
X1
X3
X2
INJECTION
AREA
CONTROL
VOLUME
Lagrangian transport model:
water parcels traveling through a
control volume
[e.g. Dagan, 1989; Cvetkovic and Dagan, 1994; Rinaldo et al., 1989]
TRAVEL TIME FORMULATION of TRANSPORT
),(
),;( 00
t
dt
ttd
t
t
XV
XX
=
particle’s trajectory:
INPUT
OUTPUT
CONTROL
PLANE CP
KINEMATIC DEFINITION of TRAVEL TIME : CPtTt ∈);( 0XX
13. KERNEL of SPATIALLY INTEGRATED INPUT-OUTPUT CONVOLUTIONS
AGE DISTRIBUTION of the outflows
T
T
OUT(t)
Storage
IN (t)
TRAVEL TIME PDF
conditional to the exit time t
pout (T , t )
( ) ( ) ( )∫∞−
−=
t
iioutiINout dttttptCtC ,
output flux concentration
(OUTPUT MEMORY of the INPUT)
PDF
UNSTEADY FLOW CONDITIONS, TYPICAL OF MOST HYDROLOGIC SYSTEMS
14. THE FOKKER PLANK EQUATION
( )=0|, ttg x
[Benettin, Rinaldo and Botter, WRR 2013]
displacement pdf (injection in t0)
ADVECTION DISPERSION
EULERIAN
CONCENTRATION
15. AGE MASS DENSITY
T
T
T
AGE MASS DENSITY [Ginn, WRR 1999]
...REPRESENTS THE AGE (T) DISTRIBUTION
AT A GIVEN POINT x AND AT A GIVEN TIME t
mass input in
t-T (age T)
displacement
pdf
TIME SPENT INSIDE THE SYSTEM SINCE ENTRY
(ages increase during the parcels’ journey
within the control volume)
AGE OF WATER/SOLUTE PARCEL T
16. SPATIAL INTEGRATION OF THE FOKKER PLANCK EQUATION
AGE PDF IN THE OUTFLOW (TRAVEL TIME PDF):
)(
)(
),(
),(),(
tM
t
tTp
T
tTp
t
tTp out
out
SS φ
−=
∂
∂
+
∂
∂
xx dtT
tM
tTp
V
S ∫= ),,(
)(
1
),( ρ
σρρ dtTttTt
tΦ
tTp
outV
out
out nxxDxxu •∇−= ∫∂
)],,(),(),,(),([
)(
1
),(
SPATIALLY AVERAGED MASS AGE CONSERVATION
AGE PDF IN THE STORAGE:
...as a function of spatially integrated fluxes and storage
[Botter et al., GRL 2011]
17. The particles leaving the system are sampled among those in storage,
and so their age:
ω(T, t)pout (T,t) = pS(T, t)
PREFERENCE
StorAGE
SELECTION
FUNCTION
LOW AVAILABILITY or LOW PREFERENCE IMPLIES LOW SAMPLING
– AGES POORLY REPRESENTED IN THE OUTPUT
[Botter et al., GRL 2011]
OUT(t)
pout(T,t)
pS(T, t)
AGES SAMPLED AGES AVAILABLE
StorAGE selection: LINKING AGE DISTRIBUTIONS
18. The particles leaving the system are sampled among those in storage,
and so their age:
1
SAMPLING
through
SAS
func?ons
1
1
uniform
preference
over
all
ages
ω decreases
for
older
ages
𝝎(𝑻, 𝒕)
𝝎(𝑻, 𝒕)
𝝎(𝑻, 𝒕)=const
ω
increases
for
older
ages
random sampling
preference for old water
preference for new water
T
T
𝑻
𝝎
𝝎
𝝎
ω(T, t)pout (T,t) = pS(T, t)
AGES AVAILABLE PREFERENCE
StorAGE
SELECTION
FUNCTION
StorAGE selection: LINKING AGE DISTRIBUTIONS
AGES SAMPLED
19. SAS as SPATIALLY INTEGRATED DESCRIPTORS of TRANSPORT
SAS seen from a full 3D KINEMATIC FORMULATION ...
SURFACE INTEGRAL: flux of
ages across the boundaries
VOLUME INTEGRAL: ages stored
T
[Benettin, Rinaldo and Botter, WRR 2013]
20. 1D ADVECTION DISPERSION WITH CONSTANT u AND D
VELOCITY FIELD and BC:
>> 1D FINITE DOMAIN
>> CONSTANT D, u
>> ABSORBING/REFLECTING
BARRIERS
SOLUTE INPUT:
>> IMPULSIVE/CONTINUOUS
>> POINT/DISTRIBUTED
𝜕 𝐶/𝜕𝑡 + 𝑢 𝜕 𝐶/𝜕𝑥 = 𝐷 𝜕↑2 𝐶/𝜕 𝑥↑2
21. 1D CONVECTION DISPERSION WITH CONSTANT u AND D
normalized age
SASSASPDFPDF
STORAGE SELECTION FUNCTION
STORAGE SELECTION FUNCTION
STORAGE
OUTFLOW
AGE DISTRIBUTIONS and
SAS FUNCTIONS for
POISSON INPUTS
(...for selected times, but SAS
are almost stationary)
normalized age
22. STORAGE SELECTION FUNCTIONS AND PECLET NUMBER
normalized age [%]
ω(T)
STORAGE SELECTION FUNCTIONS FOR DIFFERENT DEGREE OF DISPERSION
HIGH DISPERSION COEFFICIENTS (low Pe) INCREASES
UNIFORMITY OF SAS (- RANDOM SAMPLING)
23. SPATIAL PATTERNS of CONCENTRATION and SAS FUNCTIONS
SPATIAL PATTERNS of
CONCENTRATION
..for low Pe:
C_out = mean C in (0,L)
BUT
NOT A WELL MIXED
SYSTEM
storAGE selection
[Benettin, Rinaldo and Botter, WRR 2013]
RANDOM SAMPLING
normalized age
CONCENTRATION PROFILE
24. SPATIALLY DISTRIBUTED INJECTIONS AND SAS FUNCTIONS
SPATIALLY DISTRIBUTED INJECTIONS... INCREASE SAS UNIFORMITY
storAGE selection function (SAS)
RANDOM SAMPLING
normalized age
25. WHY SHOULD WE CARE ABOUT SAS FUNCTIONS?
)(
)(
),(
),(),(
tM
t
tTp
T
tTp
t
tTp out
out
SS φ
−=
∂
∂
+
∂
∂
),(),(),( tTptTtTp Sout ω=
>> derive ps(T,t) and pout(T,t) for water based on SAS
and integral fluxes/storage
( ) ( ) ( )∫∞−
−=
t
iioutiINout dttttptCtC ,
>> water age distributions can be used to compute
concentrations of conservative (or reactive) solutes:
SPATIALLY AVERAGED MASS AGE CONSERVATION
{
[Botter et al., GRL 2011; Botter WRR 2012; Rinaldo et al., WRR 2011]
26. WHY SHOULD WE CARE ABOUT SAS FUNCTIONS?
)(
)(
),(
),(),(
tM
t
tTp
T
tTp
t
tTp out
out
SS φ
−=
∂
∂
+
∂
∂
),(),(),( tTptTtTp Sout ω=
>> derive ps(T,t) and pout(T,t) for water based on SAS
and integral fluxes/storage
( ) ( ) ( )∫∞−
−=
t
iioutiINout dttttptCtC ,
>> water age distributions can be used to compute
concentrations of conservative (or reactive) solutes:
SPATIALLY AVERAGED MASS AGE CONSERVATION
{
[Botter et al., GRL 2011; Botter WRR 2012; Rinaldo et al., WRR 2011]
RANDOM SAMPLING: ANALYTICAL
SOLUTIONS
27. ADVANTAGES of THE FORMULATION
DRY WET
INCORPORATES THE TIME VARIABILITY
of HYDROLOGIC FLUXES (dynamic TTDs)
10/2007 11/2007
DISCHARGE[mm/h]CONCENTRATION[mg/l]
SILICA
CHLORIDE
(data from UHF @ Plynlimon, UK)
Late
OCT 2007
INPUT
Mid
NOV 2007
28. DIRECT INTEGRATION OF HYDROLOGIC AND CHEMICAL DATA/MODELS
INCORPORATES THE TIME VARIABILITY
of HYDROLOGIC FLUXES (dynamic TTDs)
ADVANTAGES of THE FORMULATION
29. «CREATION» OF UNAVAILABLE AGES IS NOT ALLOWED reducing
the risk of getting the right answer for the wrong reason
DIRECT INTEGRATION OF HYDROLOGIC AND CHEMICAL DATA/MODELS
INCORPORATES THE TIME VARIABILITY
of HYDROLOGIC FLUXES (dynamic TTDs)
ADVANTAGES of THE FORMULATION
30. DIRECT INTEGRATION OF HYDROLOGIC AND CHEMICAL DATA/MODELS
SPATIAL HETEROGENEITY
CAN BE REPRESENTED
«CREATION» OF UNAVAILABLE AGES IS NOT ALLOWED reducing
the risk of getting the right answer for the wrong reason
INCORPORATES THE TIME VARIABILITY
of HYDROLOGIC FLUXES (dynamic TTDs)
ADVANTAGES of THE FORMULATION
31. INCLUDING SPATIAL HETEROGENEITY
Identify distinct INTERNAL UNITS (VERTICAL and/or HORIZONTAL
HETEROGENEITY) and then define UNIT-SCALE SAS FUNCTIONS
𝝎1(T) (unit 1)
1(T) (unit 1)
[see e.g. Birkel et al., WRR 2014; HP 2015]
𝝎2(T) (unit 2)
2(T) (unit 2)
𝝎3(T) (unit 3)
3(T) (unit 3)
Bruntland Burn(UK): ongoing work in collaboration with C. Soulsby and D. Tetzlaff
32. SAS-BASED LUMPED HYDROCHEMICAL MODEL @ PLYNLIMON (UK)
SERIES OF TWO
STORAGES WITH
UNIFORM SAS
+
LUMPED
HYDROLOGIC
MODEL
OBSERVED
ROOT ZONE
GROUNDWATER
OBSERVED
MODEL
CHLORIDECONCENTRATIONDISCHARGE
[Benettin et al., WRR 2015]
33. DYNAMICAL AGE SELECTION @ PLYNIMON (UK)
CATCHMENT-
SCALE
AGE SELECTION
is controlled by
the catchment
«STATE»
StorAGE SELECTION FUNCTIONS
YOUNG
OLD
normalized age
ω
[Benettin, Kirchner, Rinaldo and Botter, WRR 2015]
34. DYNAMICAL AGE SELECTION @ PLYNIMON (UK)
CATCHMENT-
SCALE
AGE SELECTION
is controlled by
the catchment
«STATE»
FAST flows
(young)
StorAGE SELECTION FUNCTIONS
YOUNG
OLD
normalized age
ω
[Benettin, Kirchner, Rinaldo and Botter, WRR 2015]
INPUT
Mid
NOV 2007
35. DYNAMICAL AGE SELECTION @ PLYNIMON (UK)
StorAGE SELECTION FUNCTIONS
YOUNG
OLD
normalized age
ω
[Benettin, Kirchner, Rinaldo and Botter, WRR 2015]
Late
OCT 2007
CATCHMENT-
SCALE
AGE SELECTION
is controlled by
the catchment
«STATE»
FAST flows
(young)
vs
GW flows
(older)
36. OBSERVED AND MODELED Cl CONCENTRATIONS @ HUPSEL BROOK
SHORT TERM FLUCTUATIONS RELATED TO
THE ROOT ZONE (short travel times)
in WINTER the Cl concentration is sustained by GW (long travel times)
[Benettin et al., WRR 2013]
38. LONG-TERM SILICA & SODIUM DYNAMICS @ HUBBURD BROOK (US)
RIVER HYDROCHEMISTRY is driven by the chemical
differentiation between fast flows (short memory)
and slow flows (long-memory)
SILICON (Si) SODIUM (Na)
39. CONCLUDING REMARKS
High dispersion coefficients in 1D
advection – disersion models lead
to uniform SAS (random sampling)
Use of spatially distributed models to analyze
SAS dynamics .. implications for lumped
catchment-scale hydrochemical models
Storage selection functions (SAS) are effective spatially
integrated descriptors of mixing/dispersion
processes in heterogeneous media
The method provides consistent results
in diverse settings (climate, solutes)
40. ACKNOWLEDGMENTS
K. McGuire, J. Kirchner
D. Tetzlaff, C. Soulsby
Andrea Rinaldo, Paolo Benettin, Enrico Bertuzzo
...more details will be provided by Paolo Benettin tomorrow ...