Toll Climate Drought 1Dec09 by MO8kc34A

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									         NASA Drought Activities &
Climate Change Impacts to Water Resources


              David Toll

    Hydrological Sciences NASA/GSFC
Deputy Program Manager, Water Resources
           Dave.toll@nasa.gov



          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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