Engineering
Desempenho de infraestrutura de WSUD: a influência da
variabilidade em projeções de precipitação de alta resolução
Felipe Fischmann (UFSC) / Dra. Cintia B. S. Dotto (Monash University)
X Encontro Nacional de Águas Urbanas
São Paulo/SP
13/11/14
80
Hydrologic Effectiveness (%)
70
Introdução
60
102
50
MUSIC Help
§  WSUD = Water Sensitive Urban Design (≈ LID) (≥SuDS)
40
I n fi l t r a t i o n m e a s u r e s
217
Infiltration Hydrologic Effectiveness
100
Diretrizes de projeto
90
30
3.6 mm/hr
80
Hydrologic Effectiveness (%)
70
20
10
Curvas de desempenho
WSUD
0.1
360 mm/hr
50
40
30
1800 mm/h
3.6 mm/hr hydraulic conductivity
3600 mm/h
36 mm/he hydraulic conductivity
20
360 mm/hr hydraulic conductivity
1800 mm/hr hydraulic conductivity
10
0
0
36 mm/he
60
3600 mm/hr hydraulic conductivity
0.2
0
0
0.3
0.1
0.2
0.3
0.4
0.4
0.5
0.6
0.5
0.7
0.8
A conceptual diagram
the Area
infiltration
system
properties
in music
below:
InfiltrationofBasin
(% impervious
catchment
(assumed
to be 1ismpresented
deep))
0.6
0.9
1
0.7
Figure 11.2 Hydrologic effectiveness of detention storages for infiltration systems in Melbourne.
Infiltration Basin Area (% impervious catchment (assumed to be 1 m deep))
11.3.1.1 Site terrain and soil salinity
ENGINEERING PROCEDURES
A combination of poor soil conditions (e.g. sodic and dispersive soils), steep terrain and shallow
Figure 11.2 Hydrologic effectiveness of detention
storages
for
infiltration
systemsDryland
in Melbourne.
saline groundwater
can render the
use of
infiltration systems inappropriate.
salinity is
STORMWATER
caused by a combination of factors, including leaching of infiltrated water and salt at ‘break-ofslope’ terrain and the tunnel erosion of dispersive soils. Soil with high sodicity is generally not
considered to be suited for infiltration as a means of managing urban stormwater.
Infiltration into steep terrain can result in the stormwater re-emerging onto the surface at
some point downstream.The likelihood of this pathway for infiltrated water depends on the soil
structure, with duplex soils and shallow soil over rock being situations where re-emergence of
infiltrated water to the surface is most likely to occur. This occurrence does not necessarily
preclude infiltrating stormwater, unless leaching of soil salt is associated with this process. The
provision for managing this pathway will need to be taken into consideration at the design stage.
11.3.1.1
Site terrain and soil salinity
A combination of poor soil conditions (e.g. sodic and dispersive soils), st
saline groundwater can render the use of infiltration systems inappropri
caused by a combination of factors, including
leaching
Melbourne
Water (2005)of infiltrated wat
WSUD infrastructure performance: the influence of variability in high-resolution rainfall projections
13/11/14 2
slope’ terrain and the tunnel erosion of dispersive soils. Soil with
high so
11.3.1.2
Hydraulic conductivity
Field hydraulic conductivity tests must be undertaken to confirm assumptions of soil hydraulic
conductivity adopted during the concept design stage. Field soil hydraulic conductivity (Kh) can
be determined using the falling head augerhole method of Jonasson (1984). The range of soil
hydraulic conductivities typically determined from a 60-minute falling head period is as follows:
Sandy soil: K60 = 5 × 10-5 m/s (180 mm/hr)
-5
-5
80
Hydrologic Effectiveness (%)
70
Introdução
60
102
50
MUSIC Help
§  WSUD = Water Sensitive Urban Design (≈ LID) (≥SuDS)
40
I n fi l t r a t i o n m e a s u r e s
217
Infiltration Hydrologic Effectiveness
100
Diretrizes de projeto
90
30
3.6 mm/hr
80
Hydrologic Effectiveness (%)
70
20
36 mm/he
60
50
Faz sentido continuar a utilizar séries temporais passadas?
10
Curvas de desempenho
40
30
3.6 mm/hr hydraulic conductivity
36 mm/he hydraulic conductivity
20
360 mm/hr hydraulic conductivity
WSUD
0
0.1
1800 mm/h
3600 mm/h
1800 mm/hr hydraulic conductivity
10
0
360 mm/hr
3600 mm/hr hydraulic conductivity
0.2
0
0
0.3
0.1
0.2
0.3
0.4
0.4
0.5
0.6
0.5
0.7
0.8
A conceptual diagram
the Area
infiltration
system
properties
in music
below:
InfiltrationofBasin
(% impervious
catchment
(assumed
to be 1ismpresented
deep))
0.6
0.9
1
0.7
Figure 11.2 Hydrologic effectiveness of detention storages for infiltration systems in Melbourne.
Infiltration Basin Area (% impervious catchment (assumed to be 1 m deep))
11.3.1.1 Site terrain and soil salinity
ENGINEERING PROCEDURES
A combination of poor soil conditions (e.g. sodic and dispersive soils), steep terrain and shallow
Figure 11.2 Hydrologic effectiveness of detention
storages
for
infiltration
systemsDryland
in Melbourne.
saline groundwater
can render the
use of
infiltration systems inappropriate.
salinity is
STORMWATER
caused by a combination of factors, including leaching of infiltrated water and salt at ‘break-ofslope’ terrain and the tunnel erosion of dispersive soils. Soil with high sodicity is generally not
considered to be suited for infiltration as a means of managing urban stormwater.
Infiltration into steep terrain can result in the stormwater re-emerging onto the surface at
some point downstream.The likelihood of this pathway for infiltrated water depends on the soil
structure, with duplex soils and shallow soil over rock being situations where re-emergence of
infiltrated water to the surface is most likely to occur. This occurrence does not necessarily
preclude infiltrating stormwater, unless leaching of soil salt is associated with this process. The
provision for managing this pathway will need to be taken into consideration at the design stage.
11.3.1.1
Site terrain and soil salinity
A combination of poor soil conditions (e.g. sodic and dispersive soils), st
saline groundwater can render the use of infiltration systems inappropri
caused by a combination of factors, including
leaching
Melbourne
Water (2005)of infiltrated wat
WSUD infrastructure performance: the influence of variability in high-resolution rainfall projections
13/11/14 3
slope’ terrain and the tunnel erosion of dispersive soils. Soil with
high so
11.3.1.2
Hydraulic conductivity
Field hydraulic conductivity tests must be undertaken to confirm assumptions of soil hydraulic
conductivity adopted during the concept design stage. Field soil hydraulic conductivity (Kh) can
be determined using the falling head augerhole method of Jonasson (1984). The range of soil
hydraulic conductivities typically determined from a 60-minute falling head period is as follows:
Sandy soil: K60 = 5 × 10-5 m/s (180 mm/hr)
-5
-5
80
Hydrologic Effectiveness (%)
70
Introdução
60
102
50
MUSIC Help
§  WSUD = Water Sensitive Urban Design (≈ LID) (≥SuDS)
40
I n fi l t r a t i o n m e a s u r e s
217
Infiltration Hydrologic Effectiveness
100
Diretrizes de projeto
90
30
3.6 mm/hr
80
Hydrologic Effectiveness (%)
70
20
36 mm/he
60
50
Faz sentido continuar a utilizar séries temporais passadas?
10
Curvas de desempenho
40
30
3.6 mm/hr hydraulic conductivity
36 mm/he hydraulic conductivity
20
360 mm/hr hydraulic conductivity
WSUD
0
0.1
1800 mm/h
3600 mm/h
1800 mm/hr hydraulic conductivity
10
0
360 mm/hr
3600 mm/hr hydraulic conductivity
0.2
0
0
0.3
0.1
0.2
0.3
0.4
0.4
0.5
0.6
0.5
0.7
0.8
A conceptual diagram
the Area
infiltration
system
properties
in music
below:
InfiltrationofBasin
(% impervious
catchment
(assumed
to be 1ismpresented
deep))
0.6
0.9
1
0.7
Figure 11.2 Hydrologic effectiveness of detention storages for infiltration systems in Melbourne.
Infiltration Basin Area (% impervious catchment (assumed to be 1 m deep))
11.3.1.1 Site terrain and soil salinity
ENGINEERING PROCEDURES
A combination of poor soil conditions (e.g. sodic and dispersive soils), steep terrain and shallow
Figure 11.2 Hydrologic effectiveness of detention
storages
for
infiltration
systemsDryland
in Melbourne.
saline groundwater
can render the
use of
infiltration systems inappropriate.
salinity is
STORMWATER
caused by a combination of factors, including leaching of infiltrated water and salt at ‘break-ofslope’ terrain and the tunnel erosion of dispersive soils. Soil with high sodicity is generally not
considered to be suited for infiltration as a means of managing urban stormwater.
Infiltration into steep terrain can result in the stormwater re-emerging onto the surface at
some point downstream.The likelihood of this pathway for infiltrated water depends on the soil
structure, with duplex soils and shallow soil over rock being situations where re-emergence of
infiltrated water to the surface is most likely to occur. This occurrence does not necessarily
preclude infiltrating stormwater, unless leaching of soil salt is associated with this process. The
provision for managing this pathway will need to be taken into consideration at the design stage.
11.3.1.1
Site terrain and soil salinity
A combination of poor soil conditions (e.g. sodic and dispersive soils), st
saline groundwater can render the use of infiltration systems inappropri
caused by a combination of factors, including
leaching
Melbourne
Water (2005)of infiltrated wat
WSUD infrastructure performance: the influence of variability in high-resolution rainfall projections
13/11/14 4
slope’ terrain and the tunnel erosion of dispersive soils. Soil with
high so
11.3.1.2
Hydraulic conductivity
Field hydraulic conductivity tests must be undertaken to confirm assumptions of soil hydraulic
conductivity adopted during the concept design stage. Field soil hydraulic conductivity (Kh) can
be determined using the falling head augerhole method of Jonasson (1984). The range of soil
hydraulic conductivities typically determined from a 60-minute falling head period is as follows:
Sandy soil: K60 = 5 × 10-5 m/s (180 mm/hr)
-5
-5
Objetivos
§  Realizar um exercício de modelagem para avaliar a sensibilidade de
curvas de desempenho a variações entre:
Precipitação presente e futura projetada Curvas de desempenho: atenuação de vazão e remoção de poluentes –  Localizações
Dados presentes
Projeções
– 
Séries temporais
simulados e
futuras e
incerteza
– incerteza
Outros (parâmetros
construtivos, de bacia, etc.)
Faixa de incerteza
(dados futuros)
§  Propor um método para a integração de incertezas na elaboração de
Série temporal
curvas de desempenho
única (histórica)
Faixa de incerteza
(dados presentes)
WSUD infrastructure performance: the influence of variability in high-resolution rainfall projections
13/11/14
5
Métodos
WSUD infrastructure performance: the influence of variability in high-resolution rainfall projections
13/11/14
6
Métodos
Creating a Stormwater Treatment Train
103
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been corrupted. Restart your computer, and then open the file again. If the red x still appears, you may have to delete the
image and then insert it again.
Melbourne Water (2005)
FAWB (2009)
Conceptual diagram of infiltration system properties.
Melbourne Water (2005)
Location
The location name will be displayed under the infiltration system node icon on the main worksheet.
Wetlands
Biofilters
Hydrologic
effectiveness
✓
-
TSS removal
✓
✓
TN removal
✓
✓
TP removal
Infiltration systems
Inlet Properties
The Inlet Properties define the physical characteristics of the inlet section of the infiltration system.
Flow is hydrologically routed through the infiltration system, based on the characteristics defined by
the user.
✓
Low Flow Bypass
✓
✓
All of the stormwater that approaches the infiltration system below the user-defined Low Flow
Bypass amount (in units of m3/s) will bypass the system. Any flow above the Low Flow Bypass
(subject to the presence of a High Flow Bypass) will enter and be treated by the infiltration
system.
✗
High Flow Bypass
✗
When the stormwater inflow rate exceeds the user-defined High Flow Bypass amount (in units of
m3/s), only a flow rate equal to the High Flow Bypass (less that specified in any Low Flow
Bypass) will enter and be treated by the infiltration system. All of the stormwater flow in excess
of the High Flow Bypass amount will bypass the infiltration system and will not be treated.
✗
Tip Box
The Low and High Flow Bypasses are assumed to occur simultaneously. So for a Low Flow
Bypass of 2m3/s, a High Flow Bypass of 8m3/s, and inflow of 10m3/s:
WSUD infrastructure performance: the influence of variability in high-resolution rainfall projections
13/11/14
7
Métodos
§  2008 – 2009
10.320
estimativas de
desempenho
§  Dados de radar + 3 projeções = 4 séries temporais por local
§  Resolução temporal: 1 km x 1km
§  Discretização temporal: 6 min
§  3 locais
WSUD infrastructure performance: the influence of variability in high-resolution rainfall projections
13/11/14
8
Resultados preliminares
§ 
Locais distintos
§ 
Mesma projeção
§ 
Mesmos parâmetros
TN removal efficiency in wetlands (%) 100 90 MRO Radar data 0.36 0.5 80 MRO Sim 0 0.36 0.5 70 MRO Sim 1 0.36 0.5 MRO Sim 2 0.36 0.5 60 Weribee Radar data 0.36 0.5 50 Weribee Sim 0 0.36 0.5 40 Weribee Sim 1 0.36 0.5 30 Weribee Sim 2 0.36 0.5 Dand. Ranges Radar data 0.36 0.5 20 Dand. Ranges Sim 0 0.36 0.5 10 Dand. Ranges Sim 1 0.36 0.5 0 0 1 2 3 4 5 6 7 8 9 10 Dand. Ranges Sim 2 0.36 0.5 Surface Area (% Catchment’s imperviousness) WSUD infrastructure performance: the influence of variability in high-resolution rainfall projections
13/11/14
9
Resultados preliminares
§ 
Locais distintos
§ 
Mesma projeção
§ 
Mesmos parâmetros
TN removal efficiency in wetlands (%) 100 90 80 70 60 MRO Sim 1 0.36 0.5 50 Weribee Sim 1 0.36 0.5 40 Dand. Ranges Sim 1 0.36 0.5 30 20 10 0 0 1 2 3 4 5 6 7 8 9 10 Surface Area (% Catchment’s imperviousness) WSUD infrastructure performance: the influence of variability in high-resolution rainfall projections
13/11/14
10
Resultados preliminares
§ 
Locais distintos
§ 
Mesma projeção
§ 
Mesmos parâmetros
TN removal efficiency in wetlands (%) 100 90 80 70 60 45%
MRO Sim 1 0.36 0.5 50 Weribee Sim 1 0.36 0.5 40 Dand. Ranges Sim 1 0.36 0.5 30 20 10 0 0 1 2 3 4 5 6 7 8 9 10 Surface Area (% Catchment’s imperviousness) WSUD infrastructure performance: the influence of variability in high-resolution rainfall projections
13/11/14
11
Resultados preliminares
§ 
Mesmo local
§ 
Mesmos parâmetros
§ 
Séries temporais distintas
TN removal efficiency in wetlands (%) 100 90 MRO Radar data 0.36 0.5 80 MRO Sim 0 0.36 0.5 70 MRO Sim 1 0.36 0.5 MRO Sim 2 0.36 0.5 60 Weribee Radar data 0.36 0.5 50 Weribee Sim 0 0.36 0.5 40 Weribee Sim 1 0.36 0.5 30 Weribee Sim 2 0.36 0.5 Dand. Ranges Radar data 0.36 0.5 20 Dand. Ranges Sim 0 0.36 0.5 10 Dand. Ranges Sim 1 0.36 0.5 0 0 1 2 3 4 5 6 7 8 9 10 Dand. Ranges Sim 2 0.36 0.5 Surface Area (% Catchment’s imperviousness) WSUD infrastructure performance: the influence of variability in high-resolution rainfall projections
13/11/14
12
Resultados preliminares
§ 
Mesmo local
§ 
Mesmos parâmetros
§ 
Séries temporais distintas
TN removal efficiency in wetlands (%) 100 90 80 70 60 MRO Sim 0 0.36 0.5 50 MRO Sim 1 0.36 0.5 40 MRO Sim 2 0.36 0.5 30 20 10 0 0 1 2 3 4 5 6 7 8 9 10 Surface Area (% Catchment’s imperviousness) WSUD infrastructure performance: the influence of variability in high-resolution rainfall projections
13/11/14
13
Resultados preliminares
§ 
Mesmo local
§ 
Mesmos parâmetros
§ 
Séries temporais distintas
TN removal efficiency in wetlands (%) 100 90 80 70 45%
60 MRO Sim 0 0.36 0.5 50 MRO Sim 1 0.36 0.5 40 MRO Sim 2 0.36 0.5 30 20 10 0 0 1 2 3 4 5 6 7 8 9 10 Surface Area (% Catchment’s imperviousness) WSUD infrastructure performance: the influence of variability in high-resolution rainfall projections
13/11/14
14
Resultados preliminares
§ 
Mesmo local
§ 
Mesmos parâmetros
§ 
Séries temporais distintas
TN removal efficiency in wetlands (%) 100 90 80 70 MRO Radar data 0.36 0.5 60 MRO Sim 0 0.36 0.5 50 MRO Sim 1 0.36 0.5 40 MRO Sim 2 0.36 0.5 30 20 10 0 0 1 2 3 4 5 6 7 8 9 10 Surface Area (% Catchment’s imperviousness) WSUD infrastructure performance: the influence of variability in high-resolution rainfall projections
13/11/14
15
Resultados preliminares
§ 
Mesmo local
§ 
Mesmos parâmetros
§ 
Séries temporais distintas
TN removal efficiency in wetlands (%) 100 90 80 70 MRO Radar data 0.36 0.5 60 45%
MRO Sim 0 0.36 0.5 50 MRO Sim 1 0.36 0.5 40 MRO Sim 2 0.36 0.5 30 20 10 0 0 1 2 3 4 5 6 7 8 9 10 Surface Area (% Catchment’s imperviousness) WSUD infrastructure performance: the influence of variability in high-resolution rainfall projections
13/11/14
16
Conclusões e Observações
§  Método para a geração de curvas de desempenho para diversas configurações
(= rápida estimativa preliminar da demanda de área)
§  Análise e comparação entre:
–  Localizações
–  Projeções
–  Características construtivas, etc.
§  Resultados (até agora) demonstraram sensibilidade aos parâmetros escolhidos
§  Procedimento / método para a análise estatística dos resultados
§  Aplicação a outros sistemas e configurações
§  Adaptação ao Brasil (?)
§  Necessidade de dados apropriados:
–  Alta resolução temporal
–  Séries temporais mais longas
WSUD infrastructure performance: the influence of variability in high-resolution rainfall projections
13/11/14
17
Referências
§  FAWB. Adoption Guidelines for Stormwater Biofiltration Systems. Facility for
Advancing Water Biofiltration, Monash University. 2009
§  INTERGOVERNMENTAL PANEL ON CLIMATE CHANGE. IPCC fifth assessment
report climate change 2013. Geneva : Intergovernmental Panel on Climate Change,
2013.
§  MANGANGKA, I. R. Role of hydraulic factors in constructed wetland and
bioretention basin treatment performance. 2013. (Doctor of Philosophy). Science and
Engineering Faculty, Queensland University of Technology.
§  MELBOURNE WATER. WSUD engineering procedures: stormwater. Collingwood,
Vic.: CSIRO Publishing, p.2005.
§  ______. Design, Construction & Maintenance of WSUD. 2010
§  Wong, T., et al., MUSIC Version 5.0, Software, 213 pp, MUSIC Development Team, CRC
for Catchment Hydrology, Melbourne. 2005
WSUD infrastructure performance: the influence of variability in high-resolution rainfall projections
13/11/14
18
Agradecimentos
§  Profa. Dra. Nádia B. Bonumá
§  Prof. Dr. César Augusto Pompêo
§  Profa. Dra. Ana Deletic
WSUD infrastructure performance: the influence of variability in high-resolution rainfall projections
13/11/14
19
Obrigado
Felipe Fischmann
[email protected]
WSUD infrastructure performance: the influence of variability in high-resolution rainfall projections
13/11/14
20
Slides adicionais
WSUD infrastructure performance: the influence of variability in high-resolution rainfall projections
13/11/14
21
Comparação preliminar de séries temporais
WSUD infrastructure performance: the influence of variability in high-resolution rainfall projections
13/11/14
22
TP removal efficiency in wetlands (%)
100
90
80
70
60
45%
MRO Radar data 0.036 0.25
50
MRO Radar data 0.036 0.5
40
MRO Radar data 0.036 0.75
30
20
10
0
0
2
4
6
8
10
12
14
16
18
20
% Catchment’s imperviousness
WSUD infrastructure performance: the influence of variability in high-resolution rainfall projections
13/11/14
23
TSS removal efficiency in wetlands (%)
100
90
80
70
60
MRO Sim 2 0.36 0.25
50
Weribee Sim 2 0.36 0.25
40
Dand. Ranges Sim 2 0.36 0.25
30
20
10
0
0
1
2
3
4
5
6
7
8
% Catchment’s imperviousness
WSUD infrastructure performance: the influence of variability in high-resolution rainfall projections
13/11/14
24
TSS removal efficiency for biofilters (%)
100
90
80
70
60
Dand. Ranges Radar data 0.36 0.3
0.25
50
Dand. Ranges Sim 0 0.36 0.3 0.25
40
Dand. Ranges Sim 1 0.36 0.3 0.25
30
Dand. Ranges Sim 2 0.36 0.3 0.25
20
10
0
0
1
2
3
4
5
% Catchment’s imperviousness
WSUD infrastructure performance: the influence of variability in high-resolution rainfall projections
13/11/14
25
Ilustração do modelo MUSIC
WSUD infrastructure performance: the influence of variability in high-resolution rainfall projections
13/11/14
26
Modelo de chuva-vazão simplificado adotado
no MUSIC
WSUD infrastructure performance: the influence of variability in high-resolution rainfall projections
13/11/14
27
Escolha de parâmetros de nó de tratamento
no MUSIC
WSUD infrastructure performance: the influence of variability in high-resolution rainfall projections
13/11/14
28