10 - CSIRO - WRM technologies and tools-Sep-16
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Multiple tools and techniques for water resources management Modern Tools and Techniques for Water Resources Assessment and Management
Geoff Podger
16 September 2014
LAND AND WATER FLAGSHIP
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Overview
CSIRO: Who we are
IWRM
Nexus webs
Modelling framework
Model choice
Water resource management technology
Best practice modelling
Geoff Podger | Page 2
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We provide scientific responses and solutions to major national
and global challenges
Top 1% of global research
institutions in 14 of 22 research
fields Top 0.1% in 4 research fields
Darwin
Alice Springs
Geraldton 2 sites
Atherton
Townsville 2 sites
Rockhampton
Toowoomba
Gatton
Myall Vale Narrabri
Mopra
Parkes
Griffith
Belmont
Geelong
Hobart Sandy Bay
Wodonga
Newcastle
Armidale 2 sites
Perth 3 sites
Adelaide 2 sites Sydney 5 sites
Canberra 7 sites
Murchison
Cairns
Irymple
Melbourne 5 sites
CSIRO: Who we are
Werribee 2 sites
Brisbane 6 sites
Bribie Island
People
Locations
Flagships
Budget
6000
58
9
$1B+
Geoff Podger | Page 3
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CSIRO: What it brings to Water Resource Management
•Significant depth and breadth of skills/expertise
•An understanding of complex multi-disciplinary systems
•Framework for integrating science
•A focus on impact in the science that we do
Geoff Podger | Page 4
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CSIRO supporting water resource management in South Asia Brahmani – Baitarni: development of a planning model to support water resource management across three states.
Koshi Basin: development of a water balance model to support hydro-power, flood and sediment management.
Indus Basin: development of a planning model to support management of transboundary flows and within country water resource sharing.
Bangladesh: supporting development of groundwater resource plans
Geoff Podger | Page 5
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Integrated Water Resource Management
“Integrated Water Resources Management (IWRM) is a process which promotes the coordinated development and management of water, land and related resources, in order to maximise the resultant economic and social welfare in an equitable manner without compromising the sustainability of vital ecosystems.” GWP-TAC, 2000[i]
[i] GWP–TAC (2000). Integrated water resources management. Stockholm, Sweden, Global Water Partnership, Technical Advisory Committee Paper No. 4.
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• Water System o Water balance (domestic, irrigation, storage, end-of-system flows) ~Σ1 o Dynamics (hydropower flows, low flows, medium flows, flood flows) o Additional (groundwater, desalinisation)
• Assets o Infrastructure (water supply, irrigation, hydropower, dams) o Natural assets (estuary, wetlands, floodplains)
• Services o Products from infrastructure (food, energy, mitigation) o Ecosystem system services (food, nutrient cycling, recreation)
• Wellbeing o Economic (water/food/energy security, economic security) o Social (social security, social cohesion, health) o Environmental (environmental security/sustainability)
Water-Assets-Services-Wellbeing Sharing a finite and changing resource for people
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ASSETS
WELLBEING SERVICES
WATER SYSTEM
Bio-physical
Modelling
Socio-economic
Modelling
Social Decision
Modelling
NEXUS WEBS
Geoff Podger | Page 8
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Conceptual Model Design
• Climate • Rainfall/Snow/Ice • Surface/groundwater • Water quality • River systems modelling • Biophysical modelling
• Ecosystems
• Socio-economic modelling • Potable water • Hydropower • Agriculture • Ecosystem services • Social benefits
Geoff Podger | Page 9
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Integrating Conceptual Framework
Hydrological systems and assets Services
Wellbeing Bio-physical Modelling
Threats
Levers for trade-off scenarios
Risk management
Socio-
economic
Modelling
Social
Decision
Modelling
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Integrating Conceptual Framework
Cyrosphere / Snow melt
models
River Flows Groundwater
Sediment
Nutrients
Rainfall / Runoff models
Assets
Hydropower
Food production
Ecosystem Services
Hazard/Risk
Services
Energy production
Water Security
Economic Security
Food Security
Wellbeing
Energy Security
Sense of Security / Place
Bio-physical Modelling
Climate • Change • Variability
Population Growth
Land use • Forest cover • Landslides • Bushfires
Threats
Environmental Security
Storages Floodplains
Water Quantity Water Quality
Irrigation
Environment
Levers for trade-off scenarios Policy/Management, Build Infrastructure, Treatment, Targets/Standards, Education
Risk management
Socio-
economic
Modelling
Over-allocation • Surface water • Groundwater
Pollution • Point • Non point
Tra
nsp
ort
and
fa
te
Trea
tmen
t Organics
Pathogen
Heavy metals
Urban water
Industrial
Recreational
Navigation
Social
Decision
Modelling
Potable WaterEnergy
Security
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Model choice
There are lots of different models available (too many acronyms to remember) and in many cases the underlying algorithms are similar
There is no one model that can do everything
Model choice is a trade-off
Parsimony: Choose the simplest model that best answers the question
Take into consideration uncertainty. Is the model telling us something useful or is it noise?
Model Choice
Problem Space
Complexity Data
Geoff Podger | Page 12
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Model choice Questions
Problem Space (What are the issues to be considered?) Planning (scale, sectors, sharing rules, WQ) Operations (dams, structures, hydro, irrigation, environment, culture, WQ) Forecasts (scale, lead time, extent, floods, allocations)
Data (What is needed and available?)
Global/Local, Observed/Inferred, Historic/Realtime
What is the uncertainty in the data?
Complexity (What is justified given data and problem space?)
Spatial and temporal scale
Process description
Run times
Number or parameters
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Water Resource Management Technology
Remote sensing (LIDAR, DEM, ET and Land use)
Climate surfaces, GCMs and downscaling
Water Resource Information Systems
Flood forecasting systems
Flexible hydrological modelling frameworks
Workflow tools (integrating hydrological, environmental and economic models)
Modelling uncertainty and risk
Technology in the cloud and web
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The spectrum of DEM products
9 second SRTM 3 second DEM-H 1 second Lidar 5 m
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Catchment delineation
Error in
delineated
watershed
area
5%
0.5%
3 arcsec: 90 m 1” : 30 m
DEM Cell Size
1990’s
2000’s
From: Maidment (2011)
Geoff Podger | Page 16
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Climate data for South Asia
Data sources
• Local climate stations and local data products
• Aphrodite Asian precipitation data base – 0.25o gridded daily rainfall data for Monsoon Asia – 1951–2007 – http://www.chikyu.ac.jp/precip/products/index.html
• IMD Indian precipitation data base – 0.25o gridded daily rainfall data for India – 1901–2010 – http://www.imd.gov.in/doc/nccraindata.pdf
• Princeton global reanalyses climate data base – 0.5o gridded daily climate data – 1948–2008 – http://hydrology.princeton.edu/data.pgf.php
Climate variables
• Precipitation (rainfall, snow)
• Temperature
• Potential evaporation
• Humidity
• Solar radiation
• Wind speed/run
Geoff Podger | Page 17
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Comparison of annual precipitation series
500
600
700
800
900
1000
1100
1200
1300
1400
1500
190
1
190
3
190
5
190
7
190
9
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1
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3
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5
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7
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9
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3
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5
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7
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5
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7
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9
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1
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3
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5
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7
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9
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1
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3
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5
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7
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1
200
3
200
5
200
7
200
9
201
1
201
3
Rai
nfa
ll (m
m)
Year
Aphrodite (SA)
Princeton (SA)
IMD
Aphrodite (India)
Princeton (India)
Geoff Podger | Page 18
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Precipitation, APET and runoff across South Asia
Mean annual PRECIPITATION
Mean annual RUNOFF
Mean annual AREAL POTENTIAL
EVAPORATRANSPIRATION
From Aphrodite database Estimated from Princeton climate data using
Morton’s Ew formulation
Estimated using Budyko relationship
Geoff Podger | Page 20
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2046–2065 Dec–Feb temperature relative to 1986–2005 [RCP4.5]
2046–2065 Jun–Aug temperature relative to 1986–2005 [RCP4.5]
IPCC AR5 future temperature projections for South Asia
MEDIAN
MEDIAN
From IPCC AR5 “Atlas of global and regional climate projections”, http://www.climatechange2013.org/report/full-report/
CMIP5 data portal, http://cmip-pcmdi.llnl.gov/cmip5/data_portal.html
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2046–2065 Oct–Mar precipitation relative to 1986–2005 [RCP4.5]
2046–2065 Apr–Sep precipitation relative to 1986–2005 [RCP4.5]
IPCC AR5 future precipitation projections for South Asia
MEDIAN
MEDIAN
From IPCC AR5 “Atlas of global and regional climate projections”, http://www.climatechange2013.org/report/full-report/
CMIP5 data portal, http://cmip-pcmdi.llnl.gov/cmip5/data_portal.html
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Change in mean annual runoff for 2oC warming
2080 flood frequency for 20th century 100-year flood
• Changed water availability
• Enhanced variability and reduced water security: - longer droughts - more precipitation falling as rain rather than snow - retreat of glaciers - increased seasonality of flow
• Enhanced flood risk
Climate change impact on water
Geoff Podger | Page 23
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Water Resource Information Systems: Geofabric
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• 3 month probabilistic outlook of
unregulated total streamflow volumes
• Ensemble forecasts at 74 sites in 32
river basins
• Uses CSIRO developed statistical
model (BJP)
• Further testing on sites in all states
and territories
• Extend to 200 sites by the mid 2015
• www.bom.gov.au/water/ssf
Seasonal streamflow forecasting
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Flood & short-term forecasting
Ensemble forecast of a flood event in the Stanley River
Geoff Podger | Page 26
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Short term streamflow forecasting
• Flow forecasts up to 10 days ahead
• Unregulated inflows to regulated systems
• Includes rainfall forecasts
• R&D conducted through CSIRO
• Ovens River pilot for registered users
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CLIMATE
LAND USE
ECOLOGICAL ASSETS
DAMS & WEIRS
IRRIGATION CITIES
Flexible hydrological modelling
frameworks
Geoff Podger | Page 28
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Tamor daily rainfall runoff model
• Area: 4058 km2
• Elevation: 422 – 8505 m
• Glacier area: 13%
Rainfall runoff model
• GR4J+snow+glacier
• 7 parameters
• 44 HRUs
• Bias 2% NSE 0.87
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Brahmani-Baitarani river model
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Dam break modelling
• CSIRO 2 and 3d hydrodynamic models
• Geheyan Dam in China
• Different dam break scenarios
Scenario 1 Scenario 2 Scenario 3
Scenario 4 Scenario 5 Scenario 6
Geoff Podger | Page 31
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Urban water management
Geoff Podger | Page 32
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Irrigation modelling
Farm scale bio-physical models (e.g. APSIM) consider:
Physical properties of different crops over growing period
Soil type
Water and heat stress
Irrigation efficiency
Fertiliser application
Yield and production
Regional scale crop models (e.g. Source) consider:
Supply storages both regional and local
Water access rules
Multiple water sources (surface and groundwater)
Distribution and return systems (irrigation districts)
FAO 56 crop factors to drive current use and future demands
Losses (channel, escapes, deep percolation)
Crude yield and production estimates
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Floodplain modelling hydrologic vs 2d hydrodynamic
a) Simulated inundation by LiDAR based approach b) Simulated inundation by 2D HD model
22 month event
10 minute run time
55 day event
10 day run time
Geoff Podger | Page 34
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Revised annualised Ungauged Losses and Adjusted Losses (ML): Willara to Wanaaring. (Positive values are losses, negative values are gains)
-200000
-100000
0
100000
200000
300000
1975 1980 1985 1990 1995 2000 2005 2010
Ungauged Loss Function
Adjusted Loss Function
0
20000
40000
60000
80000
1/01/2000 1/04/2000 2/07/2000 1/10/2000 1/01/2001
-10000
-5000
0
5000
10000
Raw Flow (Fit + UGL)
Groundwater Loss
Ungauged Loss Function
Adjusted ULF (ULF - GWL)
Revised daily flow components for the year 2000 (ML/d), Willara to Wanaaring. River flow and GW loss are on left-axis, loss/gain functions on the right-axis.
• Establishing relationships in losing and
gaining streams
• Validating the relationships
• Integrating with the river system model
Preliminary results from Paroo
SW-GW connectivity map (MDBSY)
SW-GW interaction
Geoff Podger | Page 35
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Physically based groundwater models
Geoff Podger | Page 36
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Ecological Systems: Environmental Flow Modelling
Understanding Ecosystem Complexity
• Species, habitats and refugia
Predicting Ecological Outcomes
• Ecological Response Models
• Driver-Pressure-Stressor-Impact-Response
Integration: Models & Assessment
• Scenario-based tools
• Optimisation-based tools
Time Since Last Wet (Low_Floodplain)
current
up to 05y
btn 05 to 1
btn 1 to 2
btn 2 to 5
btn 5 to 10
greater 10
0
0
0
100
0
0
0
Suitabiliy_TSLW1
Extreme DryDryOptimal
0 0
100
Redgum_Forest
Extreme DryDryOptimalWet
0 0
100 0
Suitabiliy_Duration1
Extreme DryDryOptimalWet
0 0
100 0
Duration1
irrelevantup to 1moup to 2moup to 4moup to 6moup to 12mo
100 0 0 0 0 0
Suitabiliy_FF1
Extreme DryDryOptimalWet
0 0
100 0
FF_last_ten_yrs (Low Floodplain)
one in oneone in twoone in fiveone in tenless one in ten
0 100
0 0 0
Geoff Podger | Page 37
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Impacts of….? Vulnerability of ecosystems to….? • River Regulation? • Climate Change? • Hydropower? • Land cover change?
What to consider in Basin planning? • Ecosystem Services
• Water Quality • Ecosystem function • Fish for food • Tourism / Recreation • Habitat • Cultural values
• Conservation • System assets • Biodiversity • Threatened species, Ramsar sites
Environmental Flows: Why? What?
Geoff Podger | Page 38
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Predicting Ecological Outcomes: Models that are ‘Fit for Purpose’
No Data, High uncertainty
Some Data, Some Knowledge
Lots of Data, Good Knowledge
Data complete, System well defined
Conceptual, Hydrologic Alteration
Expert system, Habitat models, Uncertainty analysis
Population dynamics, Function dynamics
Dynamic ecosystem models
Pa
rtic
ipa
tio
n
Geoff Podger | Page 39
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Ecological Context | Danial Stratford
Prioritisation of Effort: A Data Poor Basin
• Defining Ecosystem Classes: Which classes are predicted to change hydrologically (dam development, climate change?)
What are the essential services, functions and assets by class?
Classes for extrapolation
Assessment: Uniqueness, Vulnerability to change
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Economic modelling
Local scale (farm, hydro station)
Regional scale (irrigation districts, countercyclical trade)
National scale (computable general equilibrium)
Trans-boundary scale (trade between countries)
Price,
$
Quantity, GL
Supply
Demand
Qe
Pe
Pareto surface
Geoff Podger | Page 41
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Many different workflow tools are available
Allow different models, data sources and outputs to be connected together in a windows based interface.
Can connect different scale models
Can link hydrologic, environmental and economic models.
Can use HPC to run things many times on lots of computers.
Provides provenance so that you can reproduce results
Workflows to integrate and run models
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419053 Manilla R @ Black Springs
Quantifying rating uncertainty Quantifying river model uncertainty
Quantifying groundwater model uncertainty
BATEA Uncertainty analysis
Uncertainty and risk assessment
Quantifying rainfall uncertainty
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Decision Support
Geoff Podger | Page 44
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Emerging Technologies
The cloud – Running models as a service
– Running workflows as a service
Semantic web, linked open data – Common data formats (e.g. WaterML2.0)
– Linking models to data and parameters via the web
– Making results publicly available in a standard form
Geoff Podger | Page 45
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Best practice modelling framework
Geoff Podger | Page 46
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Thank you
CSIRO Land and Water Geoff Podger Project Director South Asia SDIP t +61 2 6246 5851 m +61 418 454 283 E [email protected] w www.csiro.au
LAND AND WATER FLAGSHIP