Manilla Bay DSS · 2019-11-05 · 2013 PEMSEA Total Pollutant Loading Study in the Laguna de...
Transcript of Manilla Bay DSS · 2019-11-05 · 2013 PEMSEA Total Pollutant Loading Study in the Laguna de...
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November 1, 2019
Manila Bay - Pasig River - Laguna de Bay watershed
Introduction to Decision Support System &
Model Community Initiative
Arno Nolte
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To be told
▪ Timeline for the Decision Support System (DSS)
▪ Introduction to the (available) modeling framework of Manila Bay
and Laguna de Bay
▪ Introduction to the Model Community of Practice (CoP) initiative
November 1, 2019
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Timeline for the Decision Support System
Period Project/Program
2000-2003 LLDA Sustainable Development of the Laguna de Bay
Environment (SDLBE)
2003-2005 LLDA Follow-up of the SDLBE
… … …
2013 PEMSEA Total Pollutant Loading Study in the Laguna de
Bay-Pasig River-Manila Bay Watershed (link)
2016-2018 MBCO/LLDA Updating and Application of the Nutrient Reduction
Modeling in the Laguna de Bay-Pasig River-Manila
Bay Watershed
2017 Manila Water Laguna East Bay water intake
2018-2020 NEDA Manila Bay Sustainable Development Master Plan
November 1, 2019
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Sustainable Development of the Laguna de Bay Environment (SDLBE) 2000-2003
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Decision Support System Manila Bay Area
November 1, 2019
Rainfall-
Runoff model
(WFLOW)
Water Quality
model
(Delft3D-WAQ)
Waste Load
model
(WLM)
Hydrodynamic
model
(Delft3D-FLOW)
Ecology model
(ArcGIS
Habitat)
Spatial model
(MBSM)
Currently consisting of six
interconnected components
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Modeling framework → Spatial Model (MBSM)
July 4, 2018
Time horizons:
2015
2022
2030
2040
2055
MODEL INPUT MODEL OUTPUT
0
20
40
60
80
100
120
140
160
180
200
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 2055
Ph
ilip
pin
es p
op
ula
tio
n (m
illio
n)
Population growth scenarios
1. Rich together (SSP1) 2. Middle ground (SSP2) 3. Stagnation (SSP3)
Population growth
$-
$500,000
$1,000,000
$1,500,000
$2,000,000
$2,500,000
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 2055
3 scenarios for national GDP in million US$ (constant 2010 US$)
Rich Together (SSP1) Middle ground (SSP2)
Stagnation (SSP3)
Economic growth
Spatial plans,
road network
Land use
Population and
land use on
barangay level
Rainfall-
Runoff model
(WFLOW)
Water Quality
model
(Delft3D-WAQ)
Waste Load
model
(WLM)
Hydrodynamic
model
(Delft3D-FLOW)
Ecology model
(ArcGIS
Habitat)
Spatial model
(MBSM)
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Modeling framework → Waste Load Model (WLM)
River flows
(red line)
MODEL INPUT MODEL OUTPUT
Land use6 main catchments
Bataan,
Pampanga,
Bulacan, Manila
Laguna, Cavite
Silt &
ClayBacteria
Rainfall-
Runoff model
(WFLOW)
Water Quality
model
(Delft3D-WAQ)
Waste Load
model
(WLM)
Hydrodynamic
model
(Delft3D-FLOW)
Ecology model
(ArcGIS
Habitat)
Spatial model
(MBSM)
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Sewerage
primary
treatment
Sewerage
secondary
treatment
Sewerage
tertiary
treatment
Septic
tank
desludged
Septic
tank non-
desludged
No
treatment
2015
Manila Water 14% 0 0% 41% 40% 5%
Maynilad 16% 0 0% 40% 39% 5%
Other 0% 0 0 15% 65% 20%
Reclamation 0% 0 100% 0% 0% 0%
2022
Manila Water 32% 0 0% 31% 32% 5%
Maynilad 47% 0 0% 24% 24% 5%
Other 0% 0 0 15% 65% 20%
Reclamation 0% 0 100% 0% 0% 0%
2030
Manila Water 0% 0 95% 5% 0% 0%
Maynilad 0% 0 100% 0% 0% 0%
Other 0% 0 0 15% 65% 20%
Reclamation 0% 0 100% 0% 0% 0%
2040
Manila Water 0% 0 99% 1% 0% 0%
Maynilad 0% 0 100% 0% 0% 0%
Other 0% 0 0 15% 65% 20%
Reclamation 0% 0 100% 0% 0% 0%
BOD pollution load – Reference Scenario
November 1, 2019
Preliminary result.
Not for distribution.
Percentage of population covered by treatment type per time
horizon and per treatment area (data derived from Manila Water and
Maynilad development plans)
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Percentage of population covered by treatment type per time
horizon and per treatment area (data derived from Manila Water and
Maynilad development plans)
Sewerage
primary
treatment
Sewerage
secondary
treatment
Sewerage
tertiary
treatment
Septic
tank
desludged
Septic
tank non-
desludged
No
treatment
2015
Manila Water 14% 0 0% 41% 40% 5%
Maynilad 16% 0 0% 40% 39% 5%
Other 0% 0 0 15% 65% 20%
Reclamation 0% 0 100% 0% 0% 0%
2022
Manila Water 32% 0 0% 31% 32% 5%
Maynilad 47% 0 0% 24% 24% 5%
Other 0% 0 0 15% 65% 20%
Reclamation 0% 0 100% 0% 0% 0%
2030
Manila Water 0% 0 95% 5% 0% 0%
Maynilad 0% 0 100% 0% 0% 0%
Other 0% 0 0 15% 65% 20%
Reclamation 0% 0 100% 0% 0% 0%
2040
Manila Water 0% 0 99% 1% 0% 0%
Maynilad 0% 0 100% 0% 0% 0%
Other 0% 0 0 15% 65% 20%
Reclamation 0% 0 100% 0% 0% 0%
BOD pollution load – Reference Scenario
November 1, 2019
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
2015 2020 2025 2030 2035 2040
Reference cases: Pollution load of BOD entering Manila Bay in million tons
Rich together Middle ground Stagnation Target
Preliminary result.
Not for distribution.
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Model grid → 3D Hydrodynamic & 3D Water Quality models
▪ Laguna de Bay
and Manila Bay
connected
September 16, 2019
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Model grid – Pasig connection
April 19, 2017
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Modeling framework → 3D Water Quality model (Delft3D)
MODEL INPUT MODEL OUTPUT
1. Nutrient concentration
(N, P, Si)
2. Dissolved Oxygen
3. Suspended sediment
(turbidity)
4. Chlorophyll-a
5. BOD (Biological
Oxygen Demand)
6. Pollutants (not
included yet)
NB: And many derived variables
and statistics.
Rainfall-
Runoff model
(WFLOW)
Water Quality
model
(Delft3D-WAQ)
Waste Load
model
(WLM)
Hydrodynamic
model
(Delft3D-FLOW)
Ecology model
(ArcGIS
Habitat)
Spatial model
(MBSM)
Dissolved
oxygen
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Water Quality Model Results:
Effect of Pollution load reduction on Dissolved Oxygen
100% Waste Load
(2015)
Preliminary result.
Not for distribution.
Dissolved oxygen
concentration near the seabed
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Water Quality Model Results:
Effect of Pollution load reduction on Dissolved Oxygen
100% Waste Load
(2015)
50% Waste Load
Preliminary result.
Not for distribution.
Dissolved oxygen
concentration near the seabed
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Water Quality Model Results:
Effect of Pollution load reduction on Dissolved Oxygen
100% Waste Load
(2015)
50% Waste Load
25% Waste Load
Preliminary result.
Not for distribution.
Dissolved oxygen
concentration near the seabed
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MODEL COMMUNITY OF
PRACTICE
November 1, 2019
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Model Community of Practice
Why a Model Community of Practice?
1. Wide range of topics for which expertise is usually not available in
one organization
2. Wide range of topics requires a lot of input data from monitoring
which is usually not available in one organization
3. Turnover of trained staff results in drain of expertise
4. Multiple models for the same topic may result in confusion and
discussion if model results are not the same.
5. In general, investments are (too) large to be sustainably born by
one organization.
A Model Community of Practice is a way to share expertise, pool
knowledge transfer, combine investments, share maintenance, and
optimize developments and monitoring efforts.
November 1, 2019
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Model CoP covers more than Modeling
▪ Model application must be tailored to the Question(s) at hand.
▪ Model must be based on Understanding how the System works and reacts
to measures and/or changes. (Also, model verifies system understanding.)
▪ System understanding must be based (primarily) on data analysis.
▪ Model must be fed by and calibrated/validated against suitable Data.
November 1, 2019
Policy or Management
QUESTIONS
DATA
MODEL
SYSTEM
UNDERSTANDINGSocio-Economy
Ecology / Water Qual.
Institutional
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Organizations expressing interest
The modeling teams of the following organizations expressed interest
in a Model CoP initiative:
▪ MBCO
▪ LLDA
▪ PRRC
▪ UP Marine Science Institute
▪ UP Los Banos
▪ UP National Engineering Center
▪ Manila Water
▪ Deltares
▪ …
November 1, 2019
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Next steps?
▪ Get interested organizations together to explore possibilities
▪ Get institutional support from management
▪ Draft CoP cooperation agreement
▪ Identify and agree on common goal(s)
▪ Find balance between commitment and flexibility
▪ Voluntarily but not without obligation
▪ Set-up supporting structure
▪ People, Cooperation protocols, Computer infrastructure
▪ Define activities and actions for the next year(s)
▪ Arrange continuity
November 1, 2019
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Thank you! Salamat!
November 1, 2019