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Charles Iceland Use of Geo and Satellite Data September 5, 2013 AQUEDUCT

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STRESS WATER

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Baseline Water Stress 2010 BWS = 2010 total withdrawals / mean(B a ) mean(B a ) calculated using mean annual NASA GLDAS-2/NOAH runoff from 1950-2008

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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

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Bias-correcting model runoff

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Change in total water supply 2040 relative to 1995 baseline DRAFT

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Total Blue Water (Bt) 21-year window

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Change in inter-annual variability of water supply 2040 relative to 1995 baseline DRAFT

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Interannual Variability (IAV) 21-year window

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Change in seasonal variability of water supply 2040 relative to 1995 baseline DRAFT

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Seasonal Variability (SV)

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Projected Water Stress 2020 Water stress = 2020 projected total withdrawals / B a B a calculated using median of 6 mean annual GCM runoff from 2015-2025 DRAFT

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Change in water stress for 2020 relative to 2010 baseline DRAFT

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WATER GROUND-

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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

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GROUNDWATER DATA Gravity Recovery and Climate Experiment (GRACE)

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WATER SURFACE

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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. LAKENET Additional 3-D imagery provided by USGS Additional lake databases and web links. Application of Satellite Radar Altimetry for surface water level monitoring. C.Birkett ESSIC/UMD Jason-2/OSTM

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FLOODS

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

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PROBABILITY OF LOSS

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DROUGHT

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Global Agricultural Monitoring System (GLAM) Correlates significant anomalies to drought conditions and shortfalls in crop production. GLAM is a collaboration between NASA/GSFC, USDA/FAS, SSAI, and UMD Department of Geography Famine Early Warning System Network (FEWS NET) Provides early warning on emerging and evolving food security issues. FEWS NET is funded by USAID partners include NOAA, USGS, NASA, Chemonics, and USDA/FAS Near real-time

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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 Long-term projections for drought Image: IPCC Fourth Assessment Report: Climate Change 2007

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QUALITY WATER

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SLIDES APPENDIX

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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

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Bias-correcting model runoff quantile mapping 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

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Bias-correcting model runoff

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Example locations bias-corrected raw runoff Year Runoff (m) 11 yr running means Ensemble median GLDAS-2

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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-annual or 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

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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.

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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

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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.

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Choices

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Example locations flow accumulated runoff (B t ) Year Runoff (m) 11 yr running means Ensemble median GLDAS-2

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Estimating water use: previous work (Coca-Cola) $15,000 $60,000 $1,000 Domestic Use Industrial Use Agricultural Use Each sector responds differently to changing levels of economic development (GDP/Capita) Cross-sectional analysis generally produces optimistic Kuznets curves Domestic = f(population, GDP/capita) Adjusted R 2 =0.85 Industrial = f(GDP, GDP/Capita) Adjusted R 2 =0.70 Agricultural = f(population, GDP/Capita, ag land, %ag land under irrigation) Adjusted R 2 =0.90

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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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