New Technologies for Better Water Management

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End-to-end coordination enabling understanding
and prediction of the Earth system Research
driven by the needs of society
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GEWEX Americas Prediction Project
A community based, international and interagency
effort bringing hydrologists, land surface
specialists, atmospheric scientists, and
end-users together to advance climate prediction
and improved resource management.
GAPP Objectives
Develop a capability to predict water cycle
variables on monthly to seasonal time scales
based on improved understanding and
representation of land-atmosphere
interactions. Interpret climate predictions for
better water management.
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GAPP Background GAPP has a successful history
rooted in international and interagency
cooperation, scientific advisement, and
operational relevance. International
WCRP-UNESCO-IGOS-IAHS-IGBP Interagency
NOAA-NASA-BoR-USGS-USDA-DOE-USACE Research
Community 50 university, federal, state, and
private researchers
YEAR
91 92 93 94 95
96 97 98 99 00
01 02 03 Beyond
ISIP
NRC COHS
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GAPP is a leader in a new thrust for water
management involving new management efficiencies
through integrated support systems and science
IGOS-P
WCRP
PUBs
TOWARDS A NEW ERA IN WATER MANAGEMENT
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GAPP Approach Operational prediction
improvements through research community
engagement with operational NOAA-based GAPP
core project.
The ESP Process (OHD)
Corrects bias, meteorological uncertainty
Corrects bias, hydrologic uncertainty
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GAPP Ideally positioned to make significant
contributions to CCSP-Global Water Cycle, ISIP,
and end-user goals.
GAPP Leads or Co-leads CCSP Global Water Cycle
deliverables 0-2 Y 2 (OUT OF 5) 2-4 Y 5 (OUT
OF 19) gt 4 Y 2 (OUT OF 14)
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Examples of GAPP Products Monitor and
Observe Radar/gage/satellite precipitation
(Stage 3-4, Higgins. Etc.) Surface shortwave
radiation (NESDIS, UMD) Soil moisture (Oklahoma
Mesonet, San Pedro) Snow (CLPX) Understand and
Describe Monsoon phenomenon (NAME) Predictabilit
y studies Assess and Predict Land Data
Assimilation Systems (CONUS and Global) Regional
reanalysis Ensemble hydrologic
predictions Model intercomparisons Improved
operational prediction models Engage, Inform and
Advise BoR water management improvement NCEP
and OHD operations (core project) USGS model
development and assessment
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GAPP Examples of legacy data sets.

• 5-year NEXRAD rain data set for the
• Mississippi Basin is complete.(1996-2000)

• Reanalysis of solar radiation products
• nearing completion.(1996-2000)

• Regional Reanalysis is producing 25 - years
• of 32-km resolution products for North America.

• Soil Moisture Data sets from Oklahoma

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Analysis in support of model development
Analysis of subsurface flows for the Little
Washita Indicates that two distinct flow regimes
exit with different time scales. Hydrologic
models should be able to reproduce these response
features (Duffy).
Analysis of below canopy wind and snow shows the
importance of topography and vegetation cover
(Marks).
Heterogeneity of surface fluxes above different
land cover types controls the intensity of summer
convection (Pielke Sr).
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North American and Global Land Data Assimilation
System
LDAS concept Optimal integration of land
surface observations and models to operationally
obtain high quality land surface conditions and
fluxes. Continuous in timespace multiple
scales retrospective, realtime, and forecast
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Water Cycling Research coupling LDAS results

• Objective To better understand the water cycle
  by quantifying geographic sources (local and
  remote) of precipitating waterSoil water
  anomalies likely affect the local continental
  source of water for precipitation in the monsoon
  (e.g. Atlas et al. 1993)
• Controlled sensitivity experiments can be
  performed, using GLDAS initial conditions for the
  FVGCM
• Using realistic perturbations, what is the impact
  of wet and dry anomalies on the monsoon
  precipitation, and the relative sources of water

North America Water evaporates from the
Caribbean Sea moving westward (white isosurface)
as the circulation changes this water is
transported northward into the US. (The red
isosurface shows water that has evaporated from
the central US)
Bosilovich and Schubert, 2002 Bosilovich 2002
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1988 Midwestern U.S. Drought (JJA precipitation
anomalies, in mm/day)
Without soil moisture initialization
With soil moisture initialization
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3.
1.
0.5
0.2
0
-0.2
-0.5
-1.
-3.
-10
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GAPP and BoR DSS Environment for Interactive Web
and River System Management
BoR AWARDS - ET Toolbox System
GAPP Products
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CCSP-GWC Thrust Improve Predictions of Water
Cycle Variables at Seasonal to Interannual (SI)
and Longer Time Scales
Accurate SI prediciton of extremes could result
in billions of savings

• CCSP-GWC Priorities
• Seasonal prediction of precipitation.
• Prediction of hydrologic extremes.
• Improved representation of water cycle processes
  in climate models.

Forecast improvements can be obtained by better
parameterizations, model initialization, data
assimilation, and ensembles
Global Water Cycle process representation in
climate model how can we break the cycle of
mediocrity?
Poor rain processes
Limited land surface physics
Evapotranspiration is incorrect
Incorrect soil moisture
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CCSP-GWC Thrust Water cycle information for
improved decisions
BoR Internet river system models and tools.

• Priorities
• Develop better mechanisms for making knowledge
  available to users.
• Develop more relevant information for users.
• Assess implications of water management practices
  for climate feedbacks, long-term water supplies,
  and strategies for adapting to climate
  variability and change.

Decision support calendars
Seasonal products from the advanced hydrologic
prediction system (AHPS)
(A. Ray)
(OHD)
Specialized forecast systems
(GLERL)
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GAPP contributions to ISIP

• NOAA Intra-Seasonal-to-Interannual Prediction
  Program (ISIP) Goals
• A Requirement Based, Integrated, and Products
  Driven RD Program
• A Seamless suite of NWS forecast guidance
• Multi-model ensemble forecast system(s)
• Applications and products
• International assessments, predictions and
  applications
• Improve operational intra-seasonal-to-Interannual
  climate prediction?

• International -- Interagency GEWEX Americas
  Prediction Project (GAPP) Goals
• Develop a capability to predict water cycle
  variables on monthly to seasonal time scales
  based on improved understanding and
  representation of land-atmosphere interactions.
• Interpret climate predictions for better water
  management.

• GAPP contributions to ISIP
• GAPP has well established products (data,
  analysis understanding, prediction).
• GAPP strongly contributes to CCSP-GWC.
• GAPP has significant international and
  interagency partnerships
• GAPP has strong end-user and operational
  connections.
• With its strong research community, international
  and interagency partnerships, legacy data sets,
  established prediction skill, operational core
  project, and established end-user connections,
  GAPP is already meeting many of the ISIP vision
  and goals.

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GAPP-PACS Synergy

• There is a large degree of commonality between
  GAPP and PACS, especially with regard to warm
  season precipitation and NAME.
• Critical GAPP-PACS partnership questions
• PACS is explicitly PanAmerican in geographic
  scope whereas the current GAPP study areas do not
  extend south of Mexico. Will GAPP endorse and
  support "PanAmerican" study areas?
• To what extent can GAPP and PACS emphasize the
  same time scales? There is some sense that GAPP
  and its land surface emphasis is focused more on
  shorter time scales than PACS with its
  CLIVAR-based oceanic component. To what extent
  will either program consider decadal variability?
  
• To what extent will PACS support applications
  research, which is currently one of the focus
  areas of GAPP?
• To what extent will GAPP support field studies
  and climate observing system enhancements, which
  are now a primary focus of PACS?
• It was noted that neither (c) nor (d) represents
  a complete change in direction for either
  program, merely an integration of program
  emphases.