Lisbon talk for SteepStreams

Simulating the hydrology of the Sole (Sun) Valley
The SteepStream perspective
Riccardo Rigon, Stefano Tasin, Michele Bottazzi, Francesco Serafin & Marialaura
Bancheri
Anancientdoor,inValdiSole,2017
SteepStream Annual Meeting, Lisbon, October 6th, 2017

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Objective
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To study some basin taken as prototype in the project. Get they hydrology
right and, eventually, determine the solid transport, besides the water
transport, as a function of the foreseen climate change.

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Hydrometers
Meteo stations
River network
Catchment boundary
Legend
0 2.5 5 7.5 10 km
The Area
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Why we do not study the smallest, Meledrio, basin ?
Because:
•we do not have appropriate data for the basin (so far)
•it is too small for having reliable projection of
forcings.
Therefore we study the whole Noce basin closed at Malè and then,
downscale/ and or specialise to Meledrio.
We’ll also try to assess errors in estimates.
Why the whole area ?
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Now assume to have a river network
Consider the path starting in A1, for example.
It can be decomposed into steps (states)
and we can write the water budget for each
of them.
River Networks
We are using, an planning to use, spatially semi-distributed, time-
continuous models
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This paths can be described by a graph whose diagrams is
River Networks
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The squares are fluxes
The ball is a storage
This is the corresponding equation*
*Usually they are ordinary differential equations but they could be also partial differential equations
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A little of explanation
The full story @ Rigon & Bancheri, 2017

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The full network interactions can be represented as follows
River Networks
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Actually any “ball” (reservoir) can be exploded in parts (further graphs =
further ODEs)
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Embedded networks of reservoirs

!12*Image from Li, H., Hydrological consequences of climate change in Scandinavia, 2014
*
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Embedded networks of reservoirs

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Time continuous vs event based
Traditional studies on hydraulic construction refers to event-based modelling.
This is normed in the concept of return period and is based on the
assumed stationarity of climate*
*Milly, P. C. D., Betancourt, J., Falkenmark, M., Hirsch, R. M., Kundzewicz, Lettenmaier, D. P., &
Stouffer, R. J. (2008). Stationarity Is Dead: Whither Water Management? Science, 319, 1–2.
See also:
Serinaldi, F., & Kilsby, C. G. (2015). Stationarity is undead: Uncertainty dominates the distribution of
extremes. Advances in Water Resources, 77(C), 17–36. http://doi.org/10.1016/j.advwatres.2014.12.013
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In any case, our strategy I is to simulate
1. long series of climate forcing (hundreds of years) using
Breinl’s Weather Generator
2. for several times
3. the hydrological cycle of the Sole-Non valleys
From them
4. Extract relevant events and estimating their statistics
5. Cope with uncertainties
6. Feed with those results hydraulic modelling
Time continuous vs event based
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Multimodel
We do not use a single model
but at least two (three) submodels based on GEOFRAME components
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different component, in order to give the maximum flexibility of
connections. Therefore, five different components forming a MS
were used for each HRU in which the domain was discretized.
Figure 3.2: Representation of the embedded reservoir model using time-
varying Petri-Nets. Five components are storage, snow, canopy, root
zone, surface flow, and groundwater, which are represented through
circles of different colors and specifications. Snow storage is repre-
ERM model
Embedded Reservoirs Model
Bancheri, M, A flexible approach to the estimation of water budgets and its connection to the travel time
theory, Ph.D. Dissertation, 2017
Bancheri, N, Serafin, F. and R. Rigon, Travel time consequences of different modeling schemes, in preparation,
2017
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different component, in order to give the maximum flexibility of
connections. Therefore, five different components forming a MS
were used for each HRU in which the domain was discretized.
Figure 3.2: Representation of the embedded reservoir model using time-
varying Petri-Nets. Five components are storage, snow, canopy, root
zone, surface flow, and groundwater, which are represented through
circles of different colors and specifications. Snow storage is repre-
ERM model
Embedded Reservoirs Model
Bancheri, N, Serafin, F. and R. Rigon, Travel time consequences of different modeling schemes, in preparation,
2017
Snow modelling
Canopy
Root zone/
Shallow Groundwater
Hydrological Routing
Groundwater
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HBV model
Seibert, J., & Vis, M. J. P. (2012). Teaching hydrological modeling with a user-friendly catchment-runoff-
model software package. Hydrology and Earth System Sciences, 16(9), 3315–3325. http://doi.org/
10.5194/hess-16-3315-2012
Hydrologiska Byråns Vattenavdelning
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HBV model
Seibert, J., & Vis, M. J. P. (2012). Teaching hydrological modeling with a user-friendly catchment-runoff-
model software package. Hydrology and Earth System Sciences, 16(9), 3315–3325. http://doi.org/
10.5194/hess-16-3315-2012
Hydrologiska Byråns Vattenavdelning
Snow
m
odelling
Soil/Canopy
Shallow Groundwater
Hydrological
routing
Groundwater
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A few slides on OMS
OMS
Object Modelling System version 3
http://oms.colostate.edu/
David et al.
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Figure 5.7: Schema of the connection of the JGrass-NewAge components, necessaries to perform the modelling solution: the blu
arrows represents the connection out-to-in made possibile thanks to OMS3.
Schemes of work inside OMS
When you really deploy, they appear
hidden modelling parts
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A few slides on OMS

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Details upon request
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Where to ﬁnd details
The full story @ Rigon & Bancheri, 2017b and @Rigon et Al., 2017

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Source code OMS projects
Community blog Documentation
Manca Mailing list
General Information
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Methods
We estimate the whole hydrological cycle budget
Marialaura Bancheri – A TRAVEL TIME MODEL FOR WATER BUDGET OF COMPLEX
CATCHMENTS
Figure 5.12: Waterfall charts of the relative contributes of the water
balance for the canopy, root zone and groundwater reservoirs. Green
bars represent the inputs of the storage, blu bars represents the outputs
and red bars represent the change in storage. Two selected month,
March 1994 and September 1994, are shown in order to compare the
annual variability of each contribute.
Plot like like Figure 5.12and 5.13 can be produced for any of the
HRU, for any hour or more aggregated temporal scale.
Figure 5.13 shows the daily total actual evapotranspiration
(evaporation from the wet canopy plus evapotranspiration from
the root zone) in selected days of the years. As it is clear from the
maps, the AET does not vary much over the year. This situation
is common to many other places in humid areas, (Lewis et al. ,
2000; Oishi et al. , 2010). Some HRUs presents values of AET
always smaller than the rest of the basins. This is mainly given to
for any reservoir, actually. Closing the budget thought
necessary for an accurate assessment of fluxes and runoff.
Especially in view of climate projections.
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Methods
Standard procedure:
• calibration first
• projection (driven by the weather generation)
eventually
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Very ﬁrst trials: the whole basin

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Very ﬁrst trials: the whole basin

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Very ﬁrst trials: Meledrio

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Suppose for end of March 2018 or before we
have solved all the calibration issues, and and
made the simulations required what we could do next
(besides projections):
• We give our hydrography (for channels where debris flow
or sediment transport happens) to our friends to do
hydraulic of sediment transport
• We can move to evaluate sediment
production outside channels
For this we need a different type of modeling
(os, as a minimum, idealise some sub-grid processes)
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Beyond that

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GEOtop 2.0 ?
Rigonetal,2006;Bertoldietal.,2006
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Parflow,AsbyanFalgout,1996
Hydrogeosphere,TherrienandSudicki,1996
Catflow,Zeheetal.,2001
InHM,VanderKwaak,andLoague,2001
Cathy,PaniconiandPutti,1994
tRIBS,Ivanovetal,2004
DHSVM,Wigmostaetal.,1994

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Rigonetal,2006
BATS,Dickinsonetal.,1986,
NoahLSM,Chenetal.,1996,
LSM,Bonan,1996
SEWAB,Megelkampetal.,1999
CLM,Daietal.,2003
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GEOtop 2.0 ?

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In What GEOtop is different ?
Snow height, density, temperature)
Freezing Soil - Permafrost
Zanotti et al, 2004; Dall’Amico et al., 2011
CROCUS,Brunetal.,1992
Alpine3D,Lenhingetal.,2006
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GEOtop 2.0 ?

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Some relevant references:
Giuseppe, F., Simoni, S., Godt, J. W., Lu, N., & Rigon, R. (2016). Geomorphological control on variably saturated
hillslope hydrology and slope instability. Water Resources Research, 52(6), 4590–4607. http://doi.org/
10.1002/2015WR017626
Formetta, G., Capparelli, G., David, O., Green, T., & Rigon, R. (2016). Integration of a Three-Dimensional Process-
Based Hydrological Model into the Object Modeling System. Water, 8(1), 12–15. http://doi.org/10.3390/
w8010012
Simoni, S., Zanotti, F., Bertoldi, G., & Rigon, R. (2008). Modelling the probability of occurrence of shallow
landslides and channelized debris flows using GEOtop-FS. Hydrological Processes, 22(4), 532–545. http://
doi.org/10.1002/hyp.6886
Lanni, C., Borga, M., Rigon, R., & Tarolli, P. (2012). Modelling shallow landslide susceptibility by means of a
subsurface flow path connectivity index and estimates of soil depth spatial distribution. Hydrology and Earth
System Sciences, 16(11), 3959–3971. http://doi.org/10.5194/hess-16-3959-2012
Endrizzi, S., Gruber, S., Dall'Amico, M., & Rigon, R. (2014). GEOtop 2.0: simulating the combined energy and water
balance at and below the land surface accounting for soil freezing, snow cover and terrain effects. Geoscientific
Model Development, 7(6), 2831–2857. http://doi.org/10.5194/gmd-7-2831-2014
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GEOtop about landslides

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Simoni, S., Zanotti, F., Bertoldi, G., & Rigon, R. (2008). Modelling the probability of occurrence of shallow landslides and channelized debris flows
using GEOtop-FS. Hydrological Processes, 22(4), 532–545. http://doi.org/10.1002/hyp.6886
Rigon et Al.
Further data for GEOtop

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Simoni, S., Zanotti, F., Bertoldi, G., & Rigon, R. (2008). Modelling the probability of occurrence of shallow landslides and channelized debris flows
using GEOtop-FS. Hydrological Processes, 22(4), 532–545. http://doi.org/10.1002/hyp.6886
Rigon et Al.
To obtain something like this

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As paper testifies, we have the tools for doing it since
quite a long time. However,
• Numerics we use now is much more refined with respect to ten
years ago
• Geotechnics is better and based on new theories by Ning Lu
• Possibly we could use a new model, say GEOtop-w, which uses
even more refined algorithms, derived from recent numerical
work by colleagues.
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This we can already do (in principle)

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Some missing steps:
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This we have to solve

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Moving sediment to the channel network
Adding it to the flood
See which effects it has to the flood waves
Some missing steps:
This we have to solve

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Find this presentation at
http://abouthydrology.blogspot.com
Ulrici,2000?
Other material at
Questions ?
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