SLIDE 1 Best Practices on the Application of Climate Information for Water Resources Management M.N. Ward1, U. Lall1,2, C. Brown1, H.-H. Kwon2
1International Research Institute for Climate and Society (IRI),
The Earth Institute at Columbia University, New York, USA
2Department of Earth and Environmental Engineering,
Columbia University, New York, USA
Open seminar on the applications of climate information in various socio-economic sectors. Tokyo, Tuesday 20th February 2007
SLIDE 2 Managing Water Resource Systems
- Balance Water Supply and Demand, avoid flood
- Historical rules for resource allocation
- How much, and when should these rules be
modified based on new climate technologies
- How do we assess and communicate potential
impacts of action & inaction ?
- Background risks for sustainable strategies and
infrastructure development Health Human Activity Energy Climate Water Agriculture
New City Irrigated Farms Irrigated Farms Dam 1 Dam 2 Dam 3 Electric Grid Well Field Muddy River
SLIDE 3
- 1. Monitoring and Short-term (several days)
projections
- 2. Seasonal Prediction (next 3-6 months)
- 3. Merging knowledge on natural multidecadal (e.g.
10-30 years) and global change for water resources management
Management options at different timescales of the available information
SLIDE 4
Section 1 Monitoring and Short-term projections Flood prediction and management (including Mozambique case study)
SLIDE 5
Conception of FEWS Flood Model
SLIDE 6 FEWS Flood Risk Monitoring System Flow Diagram
Preprocessing MAP MAE Basin Linkage
Routing Parameters Soil Parameters
Flood Inundation Mapping
Landsat 7 SPOT
Output / Decision Support System Data
RFE PET Soil
LU/LC
DEM
QPF
Stream Flow Model
Water Balance Lumped Routing
Updating
SLIDE 7
Case Study: surface hydrology in Sri Lanka Potential for enhanced monitoring and prediction of weather- driven component of surface hydrology
New opportunity: Reanalysis weather data
SLIDE 8 ECMWF reanalysis weather data drives stream flow simulation for Mahaweli gauge location, Sri Lanka
NASA, Mahaweli River Authority, IRI
1979 1994 Black = observed Red = simulated Flow Time
(Reanalysis rainfall is bias corrected)
SLIDE 9 Eds, Hellmuth et al., 2007. Mozambique case study by Lucio et al.
SLIDE 10 Recent climate-related natural disasters in Mozambique
***********
(Lucio et al., 2007)
SLIDE 11 Limpopo basin includes Zimbabwe, Botswana and
SLIDE 12
- 1. Seasonal forecast recognized increased risk of
flooding through the rainy season due to presence of La Nina and other climate aspects (but no methods yet to quantify increased risks)
- 2. November – National disaster committee meets
frequently and produces National Contingency Plan
Mozambique floods, Jan-Feb 2000
Aspects of good practice that were already in place
SLIDE 13
- 1. Flood risk analysis for vulnerable areas
(see section 3 of lecture)
- 2. Hydromet monitoring system enhanced
- 3. Linking monitoring/forecast information to
trigger response
- 4. Consider news media, and communication
Improvements in practice after 2000 Mozambique flood
SLIDE 14
Section 2
Bringing Seasonal Prediction Technology into Water Resources Management Especially in tropical regions, capability exists now to forecast climate patterns 3-6 months into the future
SLIDE 15 Forecasting Reservoir Inflow for Reservoir Operations
Reservoir Operation Model Reservoir Inflows
NEED AS A PDF OR ENSEMBLE
SLIDE 16 Forecasting Water Supply and Demand
General Circulation Model “Downscaling” Regional Climate Model Or Statistical Model Hydrologic Model Crop Model Reservoir Operation Model Economic Model Regional Climate Predictors Statistical Model Reservoir Inflows
NEED AS A PDF OR ENSEMBLE
SLIDE 17 Possible Procedures
Seasonal GCM Rainfall Forecast Statistical or Dynamical Downscaling Daily Weather Sequences Crop Irrigation Demand “Climate Predictors” Empirical Statistical Model Reservoir Inflow Current Reservoir Volume Probability that Demand > Supply Revise Crop Choice or Planted Area based on Expected Net Value or other criteria
Tool available
SLIDE 18
Exploring the management of Angat Dam, Philippines using seasonal inflow forecasts (Most value in such low storage to inflow ratio settings)
SLIDE 19 Rainfall-Runoff (Oct-Feb) Relation
y = 0.8331x + 27.464 R2 = 0.79 50 100 150 200 250 300 350 400 50 100 150 200 250 300 350 400 450 Rainfall (mm/month) Streamflow (Mcm/month)
SLIDE 20
Reliable Seasonal Climate Forecasts are possible in many tropical locations
Skill of Oct-Dec rainfall Predictions from a GCM
SLIDE 21
Software tool to translate GCM seasonal forecasts into a target variable
Freely available from IRI website
SLIDE 22
From General Circulation Model (GCM) to Reservoir Inflow Forecast
The GCM gives a large- scale climate forecast Then apply a statistical transformation to predict reservoir inflow
SLIDE 23 50 100 150 200 250 300 350 400 1968 1978 1988 1998 Year Streamflow (MCM) ONDJF-obs ONDJF-pred
ρ ρ( (Q Qpred
pred,Q
,Qobs
):0.58
Translating large-scale forecast output from a GCM into Oct-Feb Reservoir inflow forecasts for reservoir management
SLIDE 24 Fig 4 Angat Watershed
Angat Reservoir Hydropower (200 MW) Bustos Dam Hydropower (Auxillary) – 48 MW) Bulacan Irrigation (31000 ha) Metro Manila (97%) La Mesa Dam Manila Bay
SLIDE 25 200 400 600 800 1000 1200 1400 1987 1989 1991 1993 1995 1997 1999 2001 Year Hydropower Generated (in GWH) 50 100 150 200 250 300 350 400 Observed I nflow
Actual Updated Forecast October Forecast Observed I nflow
Estimating Improved Hydropower Production using Seasonal Forecasts Output from software illustrated in previous slide
Lall and Arumugam, 2006
SLIDE 26 Two Caveats for Changing Practice Based on Seasonal Prediction 1)Technical: Care with downscaling the prediction signal 2)Societal: Participatory process and
- ften need for policy change
SLIDE 27 High mountains can make downscaling information critical and complex
(Zubair et al.)
Seasonal forecasts vary across Sri Lanka
SLIDE 28 Modeling small scale seasonal rainfall anomalies across Java in El Nino Years
(Qian et al., 2007)
Sep-Nov Dec-Feb
Brown = Below normal Green = Above normal
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Fortaleza
Jaguaribe-Metropolitano Hidrosystem
Adoption of new allocation strategies is complex process
SLIDE 30 Jan-Dec Water Macro-Allocation Plan --- Developed July-Oct
2002 Ensemble Forecast
0.0 5.0 10.0 15.0 1 3 5 7 9 11
Month Flow
Ensemble Forecast
FUNCEME/IRI
Feedback to revise offers
Water Committee
Water Users
Irrigation, Permanent
Water Users
Industry, Canning
Water Agency
COGERH
Demand & Priority Scenario Reservoirs Simulation & Optimization Assess Feasible Allocation
Communicate Propose Contracts:
- Desired Reliability
- Desired Price
Annual Allocation
User j gets Wj m3 water pj% reliability for price R$ With specified monthly pattern and priority for failure
When negotiations conclude Revise Revise
Revise
SLIDE 31
Water allocation matters to many people
SLIDE 32
- For resource management strategies
including infrastructure development
- For disaster risk management
- cf Mozambique example
Section 3 Background Hydroclimatic Risk Information
SLIDE 33 Analyses to inform strategies for infrastructure Knowledge of climate variability is a key factor Here estimates of storage volume needed by country
Brown and Lall, 2006
SLIDE 34 Multi-decadal variability is now recognized as a natural part
There is growing understanding of its sources and statistical properties Motivates finding best ways to incorporate statistics for long-term planning
SLIDE 35 Luterbacher and Xoplaki, 2003
Expression in Regional Climate Fluctuations
SLIDE 36 Developing information to support South Florida Water Management District
Kwon and Lall, 2006
Models simulate low frequency statistical properties to guide management strategies
Power Spectrum
SLIDE 37
Context of Global Change Climate/Environment and Socioeconomic
SLIDE 38
Availability of water for agriculture in the coming decades depends not only on changing climate, but also on population, economic development, and technology
Linking Regional Water Supplies and Water Demands in a changing world
(C. Rosenzweig, NASA GISS & Columbia University)
SLIDE 39 Luterbacher and Xoplaki, 2003
Expression in Regional Climate Fluctuations
SLIDE 40 50 100 150 200 250 1910 1920 1930 1940 1950 1960 1970 1980 1990 Demanda Atual Demanda 2030 Afluencia Média Quantil25%
Annual Oros Reservoir Inflow in m3/s
Projected & Existing Demand
Water Resources Setting – Study in NE Brazil How to extract the inflow statistics expected for the next 30 years?
SLIDE 41
Insurance as a natural tool to better manage climate and hydroclimatic risk
SLIDE 42 Weather / hydrology index insurance
Example for Peru, flood precipitation proxy (y-axis) x-axis is Nino index, introducing predictability to the insurance problem
Khalil et al, 2007
SLIDE 43
Insurance could be a natural partner for innovative water resources management based on probabilistic climate information
SLIDE 44
- 1. Monitoring and Short-term (several days)
projections
- 2. Seasonal Prediction (next 3-6 months)
- 3. Merging knowledge on natural multidecadal (e.g.
10-30 years) and global change for water resources management
Management options at different timescales of the available information
