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Session 4 : Optimizing monitoring approaches
for modelling and management of urban and rural systems
Monitoring, managing and modellingof urban hydrological systems
Jean-Luc BERTRAND-KRAJEWSKI
3rd Water Research Horizon Conference10-11 July 2012, Berlin, Germany
CHALLENGES FOR UHS
Urban hydrological systems (wastewater + stormwater) inherited from the 19th century
changed over the last 150-160 years
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Jean-Luc Bertrand-Krajewski, INSA Lyon, 8 Oct 2009 3
SW
WW SW+WW
Overflowstructure
Separate system Combined system
ENVIRONMENTALIST PIPE SYSTEMS
WWTP WWTP
Jean-Luc Bertrand-Krajewski, INSA Lyon, 8 Oct 2009 4
NO PIPE SYSTEMS (BMP, AT, SUDS…)Hydraulic control
Hydraulic control + other use (landscape, parking, playground, etc.)
Hydraulic control + treatmentHydraulic control + treatment + other use
Hydraulic control + treatment + water resourceHydraulic control + treatment + water resource + other use
Hydraulic control + treatment + water resource + urban climatic control Hydraulic control + treatment + water resource + urban climatic control + other use
Evolution = increasing urban integration and multi-purpose approach
CHALLENGES FOR UHS
Urban hydrological systems (wastewater + stormwater) inherited from the 19th century
changed over the last 150-160 years
will change more in the near future
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Jean-Luc Bertrand-Krajewski, INSA Lyon, 8 Oct 2009 6
STORMWATER COLLECTION + USES
ourc
e : H
an, 2
009
Jean-Luc Bertrand-Krajewski, INSA Lyon, 8 Oct 2009 7
HEAT TRANFER
Heatingin winter
Coolingin summer
Sou
rce
: Lyo
nnai
se d
es E
aux
Jean-Luc Bertrand-Krajewski, INSA Lyon, 8 Oct 2009 8
FROM GREEN ROOFS TO URBAN FARMS ?S
ourc
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ttp://
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Jean-Luc Bertrand-Krajewski, INSA Lyon, 8 Oct 2009 9
Architectural Projects for Grand Paris
© Rogers Stirk Harbour + Partners – Source : http://www.linternaute.com/savoir/grand-chantier/photo/grand-paris-voici-ce-qu-ont-prevu-les-architectes/grand-paris-voici-les-projets-des-architectes.shtml
CHALLENGES FOR UHS
Urban hydrological systems (wastewater + stormwater) inherited from the 19th century
changed over the last 150-160 years
will change more in the near future multi-purpose (and potential conflicts of use) multi-disciplinary more complex decentralised and centralised : supervision + remote control adaptable
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MONITORING
Temporal dynamics and spatial variability time : on the way
space : serious progress needed
Uncertainty, variability need specific attention
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MONITORING
Need for long-term observatories OTHU (Field Observatory on Urban Hydrology)
- Lyon, France, created in 1999- urban stormwater management- multi-disciplinary (13 research groups from 9 institutions)- monitoring sites : permanent + light / temporary sites- core / common data definition : an issue- since 2011 : URBIS network of French urban water obs.,the unique urban water obs. among the SOERE
- on-line monitoring experience
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EMCs
13
10 20 50 100 200 500 1000 2000 50000
2
4
6
8
10
12
14
16
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TSS EMC (mg/L)
Num
ber o
f eve
nts
Histogram of TSS EMCs in Chassieu (log-scale)
TSS EMC (mg/L)
Num
bero
f eve
nts
TEMPORAL DYNAMICS
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05
1015I (
mm
/h)
0
0.2
0.4
0.6
Q (m
3 /s)
0
0.5
1
TSS]
(kg/
m3 )
21:36 02:24 07:12
0.5
1
[CO
D] (
kg/m3 )
Ev 1
b 24I (
mm
/h)
0
0.2
0.4
Q (m
3 /s)
0
0.1
0.2
0.3
TSS]
(kg/
m3 )16:48 21:36 02:24 07:12
0.050.1
0.150.2
[CO
D] (
kg/m3 )
Ev 4
c
MONITORING
Need for better knowledge and understanding processes
Need for spatially distributed sensors (not only outlets) affordable (low cost) sensors
more reliable sensors
new sensors (physical, chemical, biological)
new time scales for measurements (integrative sensors)
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MONITORING
Uncertainty assessment systematic application
consensus on methods
More rigorous metrological methodology
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DATA MANAGEMENT
Large (huge) amounts of data
Data checking and validation absolutely necessary
Methods and tools exist (both off-line and on-line)
Their use should become systematic
(with traceability and reversibility)
Data bases and formats : an issue. Meta data ?
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SEWER = REACTOR
dispersion
suspension
bed load
erosiondeposition
floculation
consolidation
degradation
re-aeration
oxygen
hydrolysis/degradationdissolved
BOD5
interstitialwater BOD5
sedimenyoxygendemand
sedimentBOD5
particulateBOD5
erosion/deposition
biof
ilm
BOD5 processesadapted from Garsdal et al. (1995)
MODELLING
What do we expect from models ? reproducing observation ? predicting ? explaining ?
engineering models : evaluation criterion is usefulness ?
existing models (for pollutants transfer in sewers) :insufficient
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MODEL VERIFICATION
Stricto sensu validation is impossible reproducing is not explaining
« predicting is not explaining »
Extrapolation ? Prediction ?« undue extension of the domain of application » (J.-M. Legay, 1997)
René Thom (1993)
MODELLING
New approaches accounting explicitly for uncertainties, natural variability,
equifinality, over-parameterisation,
towards a consensus among water disciplines ?
better methodologies for calibration / verification link with data sets and monitoring iterative approach
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Hypotheses on residuals
MCMC DREAM
Analysis of results
Verification of hypothesesModel improvement
Prior information
Hypotheses on residuals
MCMC DREAM
Analysis of results
Verification of hypothesesModel improvement
Sensitivity to calibration data sets
Sho
rt pe
riod
(app
rox
20 e
vent
s1-
2 m
onts
)Fu
ll pe
riod
Métadier (2011)
Hypotheses on residuals
MCMC DREAM
Analysis of results
Verification of hypothesesModel improvement
Prior information
Hypotheses on residuals
MCMC DREAM
Analysis of results
Verification of hypothesesModel improvement
Sensitivity to calibration data sets
Sho
rt pe
riod
(app
rox
20 e
vent
s1-
2 m
onts
)Fu
ll pe
riod
Métadier (2011)
MODELLING
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MODELLING
Model structure Great hope in data driven model : justified ?
Model sensitivity to data sets, data representativeness
Diversity of models use (research, operation, design, planning…)
space scales
combination of models
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WHAT IS ON THE HORIZON ?
Large dense networks of sensors for long-term monitoring affordable sensors, new substances
integrative sensors (new monitoring time scales)
Improved and certified data quality, incl. UA + data bases
Ensuring representativeness (time + space)
Modelling : better methods for model formulation and
evaluation, multi-disciplinarity, diversity vs.consensus ?
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