Charles Iceland Use of Geo and Satellite Data

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

2. WATER STRESS

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

4. 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

5. Bias-correcting model runoff

6. Change in total water supply 2040 relative to 1995 baseline DRAFT

7. Total Blue Water (Bt)21-year window

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

9. Interannual Variability (IAV)21-year window

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

11. Seasonal Variability (SV)

12. 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

13. Change in water stress for 2020 relative to 2010 baseline DRAFT

14. GROUND- WATER

15. 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

16. GROUNDWATER DATA Gravity Recovery and Climate Experiment (GRACE)

17. SURFACE WATER

18. 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

19. FLOODS

20. 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

21. LET’S BUILD PREDICTIVE POWER 1KMFLOOD MAPS RIVER FLOOD MODELS LOSS ESTIMATES SCENARIO ANALYSIS PROBABILITY OF LOSS

22. DROUGHT

23. 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

24. 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

25. WATER QUALITY

26. WATER QUALITY CHLOROPHYL PHOSPHORUS TURBIDITY MODIS 250m+ / twice per day 1999- LANDSAT 30m+ / 16 days + tasked 1972-

27. Charles Iceland Senior associate CICELAND@WRI.ORG

28. APPENDIX SLIDES

29. 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

30. 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

31. Bias-correcting model runoff

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

33. 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

34. 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.

35. 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

36. 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.

37. Choices

38. Example locations flow accumulated runoff (Bt) GLDAS-2 Ensemble median Runoff (m) Year 11 yr running means

39. 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

40. 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