NASA Drought Activities & Climate Change Impacts to Water Resources David Toll Hydrological Sciences NASA/GSFC Deputy Program Manager, Water Resources Dave.firstname.lastname@example.org 1 December 2009 Water Cycle & Capacity Building Workshop Lima, Peru Goulburn-Murray Water http://wmp.gsfc.nasa.gov Climate Impacts on Water Resources 18 March 2009 - How to Use Remote Sensing to Improve Water Management Climate Change: variability over 450,000 years Last 150yrs: greenhouse gases driving temperature change greenhouse gases change in response to climate change Warm periods (warm, wet, calm) Ice Ages (cold, dry, windy) 3 18 March 2009 - How to Use Remote Sensing to Improve Water Management 18 March 2009 - How to Use Remote Sensing to Improve Water Management 18 March 2009 - How to Use Remote Sensing to Improve Water Management Global Climate Forcing Climate Change Forcing in the Industrial Era (1850-2000) ► CO2 Is Largest Forcing ► Air Pollutants (O3, CH4, BC) Cause Large Forcing ► Aerosol Effects (direct + on clouds) Most Uncertain Conclusion: CO2 Largest Forcing, But Others Significant References: ►Trends of measured climate forcing agents, Proc.Natl.Acad.Sci., 98, 14778, 2001. ►Efficacy of climate forcings, J. Geophys. Res., in press, 2005. Changes in Precipitation More Rain Less Rain 18 March 2009 - How to Use Remote Sensing to Improve Water Management IPCC Report on Climate Change and Water (http://www.ipcc.ch/#) Fifteen-model mean changes in (a) precipitation (%), (b) soil moisture content (%), (c) runoff (%), and (d) evaporation (%). Climate Downscaling & Impacts Assessment Observed Modeled Climate Downscaling ● Regional Climate Modeling (Left) - Examples from NASA Goddard Precipitation Institute for Space Studies (Left) - Especially useful for assessing extreme events of flooding & droughts ● Statistical Down Scaling Modeling - Regional Ensemble Multi-Model - Percent likelihoods for Precipitation & Temperature Temperature Hydrologic Downscaling ● Land Data Assimilation Systems Hydrologic Modeling (Streamflow, ET, Snowpack, etc.) ● Climate Impacts for Hydro-Power, Dams and Levees, Snow Pack, Agriculture Planning, Groundwater Depletion, etc. CCSR, GISS, UCONN 18 March 2009 - How to Use Remote Sensing to Improve Water Management Seasonal Hydrologic Dynamics due to Precipitation Change CLM simulation results on Runoff using IPCC projections Relative % Change = Seasonal Change/Annual Change California WGM Orange Ganges Krishna Huai Congo 100 100 Relative Runoff Response Relative Runoff Response 50 50 due to dP (%) due to dP (%) 0 0 DJF MAM JJA SON DJF MAM JJA SON -50 -50 -100 -100 Runoff response in semi-arid basins due to Runoff response in humid basins due to decreasing precipitation in JJA show a different precipitation change is dominated by the response for each basin Asian monsoon season 11 U. Illinois 18 March 2009 - How to Use Remote Sensing to Improve Water Management Dinajpur Irrigated Dry-Season Rice % change in potential rice Drought Region yield for each impact CO2 T&P Floods SLR Combined component Ishwardi Monsoon Rice Ganges- Key Brahmaputra CO2 effects Confluence Temp. and Precip Basin Floods CO2 T&P Floods SLR Combined Sea Level Rise Khulna Monsoon Rice Combined Effects (median displayed) Coastal Region R. Horton – NASA/GISS, Columbia U. CO2 T&P Floods SLR Combined Water Availability Linked to Drought Vulnerability Ethiopia Volume of potentially available annual surface water per family in 1,000 m3 units (assumes 7 persons per family). FEWS NET estimated food security conditions, October-December, 2008. Image obtained from www.fews.net. 18 March 2009 - How to Use Remote Sensing to Improve Water Management C. Funk/USGS Biophysical Impacts on Agriculture are Complex Possible benefits CO2 Carbon dioxide Longer fertilization growing Increased season precipitation Possible drawbacks Soil resources permitting More frequent Pest droughts Faster growing Increased Heat periods flooding and stress salinization Bongaarts, J., Scientific American, 1992 How to handle external effects? R. Horton – NASA/GISS, Columbia U. 18 March 2009 - How to Use Remote Sensing to Improve Water Management DROUGHT 18 March 2009 - How to Use Remote Sensing to Improve Water Management 18 March 2009 - How to Use Remote Sensing to Improve Water Management Water Management: National Drought Monitoring System Georegistration Compositing Surface Reflectance AVHRR Existing Stacking Smoothing Anomaly Detection Vegetation Dynamics Metrics Calculation System (SOS, SG, PASG) Data Translation EMODIS System Partners get MODIS Vegetation “Regular data over 2009> Dynamics the Nation and VegDRI served quickly” Models Satellite Data Services User/Decision Data Support System 18 March 2009 - How to Use Remote Sensing to Improve Water Management Drought Monitoring with QSCAT, AMSR-E & ‘NLDAS’ 1.7 3.3 5.0 6.7 8.4 10.0 % 1.7 3.3 5.0 6.7 8.4 10.0 % QuikSCAT Scatterometer Surface Wetness 9/4/2004 9/5/2005 AMSR-E Soil Moisture Land Data Assimilation System Multi- Modeling AMSR-E ‘LDAS’ Modeling Drought Monitor Comparison Mosaic LSM Total Column Soil Moisture Percentile Mini-Ensemble (Noah and Mosaic) Total Column Soil Moisture Percentile July 1st, 2007, Based on 28 Year Climatology July 1st, 2007, Based on 28 Year Climatology D4 D3 D2 D1 D0 D4 D3 D2 D1 D0 Noah LSM Total Column Soil Moisture Percentile July 1st, 2007, Based on 28 Year Climatology D4 D3 D2 D1 D0 Soil moisture percentiles from each LSM combined to form ensemble mean percentile map Project will eventually use Mosaic, Noah, VIC, Sacramento, CLM3, HySSiB, and Catchment models with a variety of lineages (climate modeling, weather forecasting, hydrological) Ensembles often offer more accurate depictions of drought GRACE water storage, mm Model assimilated water storage, mm Rodell January – December 2005 Loop January – December 2005 Loop Seasonal Predictions – Drought Outlook Initial conditions (Dec. 1, 2008) 1-month lead (Jan. 1, 2009) Root zone soil moisture anomaly (expressed as standard normal deviate) 2-month lead (Feb. 1, 2009) 3-month lead (Mar. 1, 2009) Drought conditions given a probability to persist into early March. Project: Development of a Robust Drought Index for Agricultural Applications. PI: R. Koster, NASA/GSFC http://gmao.gsfc.nasa.gov/forecasts/# http://www.cpc.ncep.noaa.gov/products/fews/ 18 March 2009 - How to Use Remote Sensing to Improve Water Management NASA funded project to Enhance the Malaria and Famine Early Warning System (FEWS) with NASA data Using NASA data to assist FEWS NET to Projecting MODIS anticipate and warn of humanitarian crises. NDVI data 4 months • Projecting Rainfall and NDVI data 1-4 months in ahead will give future for improved decision support. advance warning to • Integrated climate data for WHO HealthMapper for early identification of malaria epidemics. food and fodder production shortfalls. NASA Data Incorporated: AURA MLS Relative Humidity TRMM Precipitation MODIS NDVI GIMMS AVHRR NDVI Southern Africa Epidemic Malaria Regions where rainfall data guides health Funk and Brown, RSE 2006 v 101 p 249-256 interventions. Benefits: Improved response and recovery Funded from 2007-2009 from food crises and epidemics, reducing costs to US Government and saving lives.
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