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AQUEDUCT. Charles Iceland Use of Geo and Satellite Data. September 5, 2013. WATER. STRESS. Baseline Water Stress 2010. BWS = 2010 total withdrawals / mean( B a ) m ean (B a ) calculated using mean annual NASA GLDAS-2/NOAH runoff from 1950-2008 . Aqueduct water supply estimates.

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Charles Iceland Use of Geo and Satellite Data

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Charles iceland use of geo and satellite data

AQUEDUCT

Charles Iceland

Use of Geo and Satellite Data

September 5, 2013


Water

WATER

STRESS


Baseline water stress 2010

Baseline Water Stress 2010

BWS = 2010 total withdrawals / mean(Ba)

mean(Ba) calculated using mean annual NASA GLDAS-2/NOAH runoff from 1950-2008


Aqueduct water supply estimates

Aqueduct water supply estimates

  • NASA Global Land Data Assimilation System (GLDAS) plays a key role:

    • GLDAS inputs include:

      • Temperature

      • Precipitation

      • Elevation

      • Wind speed

      • Water retention of soil

      • Etc.

    • GLDAS outputs include:

      • Soil moisture

      • Evapotranspiration

      • Runoff (surface and shallow groundwater)

  • GLDAS runoff values for period 1950-2010 are used to bias-correct runoff estimates from 6 GCMs


  • Bias correcting model runoff

    Bias-correcting model runoff


    Charles iceland use of geo and satellite data

    Change in total water supply

    2040 relative to 1995 baseline

    DRAFT


    Total blue water bt 21 year window

    Total Blue Water (Bt)21-year window


    Charles iceland use of geo and satellite data

    Change in inter-annual variability of water supply

    2040 relative to 1995 baseline

    DRAFT


    Interannual variability iav 21 year window

    Interannual Variability (IAV)21-year window


    Charles iceland use of geo and satellite data

    Change in seasonal variability of water supply

    2040 relative to 1995 baseline

    DRAFT


    Seasonal variability sv

    Seasonal Variability (SV)


    Projected water stress 2020

    Projected Water Stress 2020

    DRAFT

    Water stress = 2020 projected total withdrawals / Ba

    Ba calculated using median of 6 mean annual GCM runoff from 2015-2025


    Charles iceland use of geo and satellite data

    Change in water stress for 2020

    relative to 2010 baseline

    DRAFT


    Ground

    GROUND-

    WATER


    Charles iceland use of geo and satellite data

    Groundwater Stress

    the ratio of groundwater withdrawal relative to the recharge rate to aquifer size; values above one indicate where unsustainable consumption could affect groundwater availability and dependent ecosystems

    Data Sources:

    Water Balance of Global Aquifers Revealed by Groundwater Footprint, Gleeson, T., Wada, Y., Bierkens, M.F.P., and van Beek, L.P.H., 1958-2000


    Charles iceland use of geo and satellite data

    GROUNDWATER DATA

    Gravity Recovery and Climate Experiment (GRACE)


    Surface

    SURFACE

    WATER


    Charles iceland use of geo and satellite data

    The Global Reservoir and Lake Monitor (GRLM)

    Charon Birkett, ESSIC/UMD

    Curt Reynolds, USDA/FAS

    A NASA/USDA sponsored program in collaboration with NASA/GSFC and the University of Maryland at College Park.

    Additional lake databases and

    web links.

    LAKENET

    Additional

    3-D

    imagery provided by USGS

    Application of Satellite Radar Altimetry for surface water level monitoring.

    Jason-2/OSTM

    C.Birkett ESSIC/UMD


    Floods

    FLOODS


    Charles iceland use of geo and satellite data

    FLOOD

    A COSTLY RISK

    IS GROWING

    BY 2050:

    +2.0 BILLION vulnerable to flooding

    +$70-100 BILLION/YR adaptation cost

    Source: Munich Re, 2013. Topics Geo. Natural catastrophes 2012


    Charles iceland use of geo and satellite data

    LET’S BUILD

    PREDICTIVE POWER

    1KMFLOOD MAPS

    RIVER FLOOD MODELS

    LOSS ESTIMATES

    SCENARIO ANALYSIS

    PROBABILITY

    OF LOSS


    Drought

    DROUGHT


    Charles iceland use of geo and satellite data

    Near real-time

    Global Agricultural Monitoring System (GLAM)

    Correlates significant anomalies to drought conditions and shortfalls in crop production.

    Famine Early Warning System Network (FEWS NET)

    Provides early warning on emerging and evolving food security issues.

    GLAM is a collaboration between NASA/GSFC, USDA/FAS, SSAI, and UMD Department of Geography

    FEWS NET is funded by USAID – partners include NOAA, USGS, NASA, Chemonics, and USDA/FAS


    Charles iceland use of geo and satellite data

    Long-term projections for drought

    • Projections of changes in the frequency, duration and severity of drought relative to recent experience

    • Projections will be developed for multiple types of drought:

    • Soil moisture

    • Evapotranspiration deficit

    • Hydrological drought

    Image: IPCC Fourth Assessment Report: Climate Change 2007


    Water1

    WATER

    QUALITY


    Charles iceland use of geo and satellite data

    WATER QUALITY

    CHLOROPHYL

    PHOSPHORUS

    TURBIDITY

    MODIS

    250m+ / twice per day

    1999-

    LANDSAT

    30m+ / 16 days + tasked

    1972-


    Charles iceland use of geo and satellite data

    Charles Iceland

    Senior associate [email protected]


    Appendix

    APPENDIX

    SLIDES


    Aqueduct water supply estimates1

    Aqueduct water supply estimates

    • NASA Global Land Data Assimilation System (GLDAS) plays a key role:

      • GLDAS inputs include:

        • Temperature

        • Precipitation

        • Elevation

        • Wind speed

        • Water retention of soil

        • Etc.

      • GLDAS outputs include:

        • Soil moisture

        • Evapotranspiration

        • Runoff (surface and shallow groundwater)

  • GLDAS runoff values for period 1950-2010 are used to bias-correct runoff estimates from 6 GCMs

  • Baseline

    • Supply = median of mean annual runoff from 6 bias-corrected GCMs for a window of time ending in 2010

  • Future

    • Supply = median of mean annual runoff from 6 bias-corrected GCMs for a window of time centered on 2020


  • Bias correcting model runoff1

    Bias-correcting model runoff

    • “quantilemapping” aka “cumulative distribution function matching” (Mason, 2007)

    • Bias correction occurs at the pixel level for each month

    • Based on generalized extreme value distribution (3 parameters)

    • Corrects for all moments, including location, spread, skew

    • Assumes stationarity of bias


    Bias correcting model runoff2

    Bias-correcting model runoff


    Example locations bias corrected raw runoff

    Example locations bias-corrected raw runoff

    GLDAS-2

    Ensemble median

    Runoff (m)

    Year

    11 yr running means


    Goals milestones

    GOALS & MILESTONES

    • Objective: Project change (from baseline) in water risk for three Aqueduct Framework indicators

      • Water stress (Water withdrawal ratio)

      • Inter-annual variability

      • Seasonal (i.e., intra-annualor monthly) variability

    • Interim results: May 2013

      • Preliminary projections for 2020

      • One draft scenario of supply and demand

      • Six climate models; one initial condition per model

    • Final release: January 2014

      • Three time periods centered on 2020, 2030, and 2040

      • Three scenarios of supply and demand

      • Six climate models; multiple initial conditions per model


    Baseline water stress

    Baseline Water Stress

    • Definition:

      • Total Annual Withdrawals / mean(Annual Available Blue Water)

      • Available Blue Water = accumulated runoff - accumulated consumptive use

    • Interpretation:

      • The degree to which freshwater availability is an ongoing concern.

      • High levels of baseline water stress are associated with:

        • Increased socioeconomic competition for freshwater supplies,

        • More reliance on engineered water supply infrastructure,

        • Heightened political attention to issues of water scarcity, and

        • Higher risk of supply disruptions.


    Change in water stress

    Change in Water Stress

    • Definition:

      • Future Water Stress / Baseline Water Stress

    • Interpretation:

      • Estimated rate of change in water stress due to:

        • Changes in use due to population growth, economic development, and technology

        • Changes in supply due to climate change

      • High rates of change associated with:

        • Faster pace of socio-economic and technological change required to keep pace


    Choosing global climate models gcms

    Choosing Global Climate Models (GCMs)

    • Select subset of 6 models from the Coupled Model Intercomparison Project Phase 5 (CMIP5; to be used for IPCC AR5)

    • Selection criteria:

      • Availability: terms of use, parameter availability (runoff and evapotranspiration)

      • Quality for this purpose: best representations of historical runoff (not global mean temperature)

        • Long-term average

        • Standard deviation

      • Data provided by Alkama et al. (2013); evaluated 15 CMIP5 models against gauge data for 18 large basins.


    Choices

    Choices


    Example locations flow accumulated runoff b t

    Example locations flow accumulated runoff (Bt)

    GLDAS-2

    Ensemble median

    Runoff (m)

    Year

    11 yr running means


    Estimating water use previous work coca cola

    Estimating water use: previous work (Coca-Cola)

    Industrial Use

    Domestic Use

    Agricultural Use

    $15,000

    $60,000

    $1,000

    • Domestic = f(population, GDP/capita) Adjusted R2=0.85

    • Industrial = f(GDP, GDP/Capita) Adjusted R2=0.70

    • Agricultural = f(population, GDP/Capita, ag land, %ag land under irrigation) Adjusted R2=0.90

    • Each sector responds differently to changing levels of economic development (GDP/Capita)

    • Cross-sectional analysis generally produces optimistic Kuznets curves


    Preliminary maps of projected change

    Preliminary maps of projected change

    • Baseline

      • Supply = mean annual 1950-2008 runoff from GLDAS-2/NOAH current release

      • Demand = 2010 use

        • FAO Aquastat withdrawals by sector, estimated for 2010 using a mean of fixed and random effects models

        • consumptive use computed by consumptive use ratio (Shiklomanov and Rodda 2003)

  • Future

    • Supply = median of mean annual 2015-2025 runoff from 6 GCMs

    • Demand = projected change in 2010 use

      • change in scenario use by sector applied to baseline use

        [2010 use] * [2020 scenario use] / [2010 scenario use]

  • Projected change maps are computed as future / baseline


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