An integrated vision for ultra high resolution global water cycle observation and prediction

A vision for an ultra high resolution integrated water
cycle observation and prediction system
Paul R. Houser, George Mason University
“Science exists to serve human welfare. It’s wonderful to have the opportunity given us by
society to do basic research, but in return, we have a very important moral responsibility to
apply that research to benefiting humanity.” Walter Orr Roberts

phouser@gmu.edu Paul R. Houser,25 February 2011, Page 1

Presentation Outline
• Climate Change and the Global Water Cycle

• Solution Strategies:
– Water Cycle Science Integration
• Advanced Land Modeling Vision
• Water Cycle Mission Vision
• Community of Practice

– Science making a difference
• Case Studies

Paul R. Houser, February 25, 2011, Page 2

The Water and Energy Cycle
Water in the climate system Role of the Water & Energy Cycle
functions on all time scales: in the Climate System:
From hours to centuries
•Water exists in all three phases in the climate
system; its phase transitions regulate global and
regional energy balances
•Water vapor in the atmosphere is the principal
greenhouse gas; clouds represent both positive and
negative feedbacks in climate system response
•Water is the ultimate solvent which mediates the
biogeochemical and element cycles
•Water directly impacts and constraint human
society and its well-being.

The Energy and Water Cycle is tightly intertwined
•Solar radiation drives and feedbacks with the water cycle
•Energy is transferred through water movement and phase change


“Warming is Unequivocal” IPCC AR4 Report

Rising atmospheric
temperature

Rising sea level

Reductions in NH snow
cover


Land Precipitation is changing significantly over broad areas

Smoothed annual anomalies for precipitation (%) over land from 1900 to 2005; other
regions are dominated by variability.


Water Vapor Feedback

Water vapor responds to
changes in climate, but it
doesn’t drive changes in
climate. It’s a major feedback
that amplifies global climate
change.
IPCC (2007):
Observed trends that
demonstrate the trend, in both
the upper troposphere and at
the surface.


Multi-model ensemble mean change from IPCC GCMs
Change in (P-E) for 2100 minus 2000
“Dry regions get drier, wet regions get wetter”

Held and Soden (2006)
“Thermodynamic” component

(P-E) mm/day

Paul R. Houser, February 25, 2011, Page 7 Vecchi and Soden (2007)

The importance of Water

“The Grim Arithmetic of Water”---Official Discussing
Emerging Freshwater Crisis---Source: September
2002 National Geographic

Population is dramatically increasing
Ultimately, a limited water supply will meet limited
needs

Water World: Two thirds of our planet is covered by
water. 97.5% of the water is saltwater. The majority of
freshwater is beyond our reach, locked into polar snow
and ice.
Water to Drink: At just 2% dehydration your performance
decreases by around 20%.
Water Diseases: 80% of all illness in developing
countries is caused by water related diseases. 90% of
wastewater in developing countries is discharged directly
into rivers and streams without treatment.
Clean Water: 1.1 billion people in the world still do not
have access to safe water. This is nearly 20% of the
population.
Water Future: The UN estimates that by 2025, 75% of
the world population won’t have reliable, clean water.

Water Impacts all sectors: Agriculture, industry,
transportation, urban development, insurance, health,
diaster, etc.

Paul R. Houser,25 February 2011, Page 9

Conduct research that addresses end-user needs, and nurture the transition
Challenge: of these research results into straightforward end-user solutions.

• Information about hydrologic conditions is of critical importance to real-world applications.
• A vast array of high-resolution global hydrologic data are becoming available from the next generation instruments and models.
• Water management practitioners are increasingly inundated with observations and model output in disparate formats and locations.

• We know science and technology has the potential to improve water management….
• So, why doesn’t research and technology advances always improve applications?
– Inadequate application understanding produces non-optimal science/technology investment.
– Inadequate technology (lack of useful water resource observations).
– Inadequate integration of information (lack of informative predictions, or bottlenecks in software/hardware engineering).

• This leads to a paradigm lock where new science results are isolated by a lack of proven utility, and water management is
isolated by legal and professional precedence

• So, what can we do about this?
• Improved prediction of consequences is the key.
– Define research priorities based on needs
– Observe key environmental factors
– Integrate information from diverse sensors
– Assess the current environmental conditions
– Predict future environmental possibilities
– Link to decision and operation support systems

• Move from observation to predict consequences: Integrated environmental information systems adapt advanced sensor
webs, high-performance prediction systems, and decision support tools to minimize uncertainties


Linking Science to Consequences
End-to-end coordination enabling understanding and prediction of the Earth system:
Research driven by the needs of society

Use the adequate tool for the job…

To deliver social, economic and environmental benefit to stakeholders through
sustainable and appropriate use of water by directing towards improved integrated
water system management

Water Cycle Questions
What are the causes of
water cycle variations?
Are variations in the global
and regional water cycle predictable?
How are water and
nutrient cycles linked?

Objective: Quantify and predict water cycle consequences of earth system variability
and change.
– Integrate research across traditional disciplines
– An end-to-end program that transitions theoretical research to academic/public
education and real-world application,
– Cultivate partnerships with universities, government, and international agencies.
• Focused investments towards better water cycle prediction:
Observation Understanding Models Prediction Consequences
Process
Assimilation
Assessment Resolution Applications
Data Initialization
Synthesis Coverage Education
Proficiency Diagnosis
Analysis Coupling Linkages
Prognosis Solutions
Observation Validation Modeling


Water Cycle Prediction Components
• Modeling: Diagnose state-of-the-art “operational”
Earth system models; conduct sensitivity and
predictability experiments; infuse process-scale
• Observation: Quantify understanding to predict water cycle extremes,
long-term water cycle enhance prediction through observational
trends & variability; constraints; explore limits of water cycle
progress toward a predictability
coordinated water cycle
observation system;
extract knowledge and
understanding from
diverse observations.

• Solutions: Enhance
operational decision
support tools; Engage
in public and research
community
education; link to
other earth system
components

Climate Change Consequences delivered through Water & Energy Cycle
Global Water and Energy Cycle
Modeling and Computing

Key Earth Science Questions
requiring Advances in Earth
System Modeling
Variability
How are global precipitation,
evaporation and the cycling of
water changing?
Responses
What are the effects of clouds
and hydrologic processes on
global climate?
Predictability
How are variations in weather,
precipitation, and water Grand Challenge: interpret observations to instill
resources related to global
climate change? understanding and information into global prediction
Link to Biogeochemistry
systems to determine the predictable water and energy
How do ecosystems respond to, cycle variations and trends, particularly precipitation and
and affect global environmental replenishment of water resources that are associated with climate changes.
change and the carbon cycle?
Enabling Prediction
How can weather forecasts be •Explicitly simulate weather systems in climate models.
improved by new space-based
observations, data assimilation,
•Assimilate and initialize models using water cycle observations.
and modeling? •More effective representations of sub-grid scale physical processes.
Use global observations for analysis and monitoring of the Earth-system and validation,
initialization and assimilation in global prediction systems

Can climate models effectively represent water and energy cycle processes?
Use global satellite data to:
•Advance understanding and model physics
•Improve initialization and parameterization
•Diagnose and identify predictable changes
•Predict consequences & develop solutions
Global Precipitation
0.125 mm/d

Integrate
disparate 0 mm/d
observations PREDICTED

Process-scale modeling or
“Super-parameterizations”
2.8 mm/d

2.5 mm/d

OBSERVED

Weather resolving climate models?

Advance Understanding and Model Physics

Climate models’ grid-box representation
of Earth’s processes...

Each grid-box can only represent the However, controlling processes of the
“average” conditions of its area. water cycle (e.g. precipitation) vary
over much smaller areas.
How can climate models effectively represent the controlling processes
of the global water cycle?
“Conventional” approach: make the model grid-boxes smaller (increase resolution)
•Slow progress: may take ~50 years to be computationally feasible
Breakthrough approach: Simulate a sample of the small-scale physics and dynamics using high
resolution process-resolving models within each climate model grid-box
•“Short-cut” the conventional approach (~10 years to implement)

State of the Water and Energy Cycle
Variable ↓ Sphere → Ocean Terrestrial Atmosphere
upper ocean currents (I/S) topography/elevation (I/S) land cover (I/S) wind (I/S )
sea surface temperature (I/S) leaf area index (I) upper air temperature (I/S)
sea level/surface topography (I/S) soil moisture/wetness (I/S) surface air temperature (I/S)
sea surface salinity (I/S) soil structure/type (I/S) sea level pressure (I)
sea ice (I/S) permafrost (I) upper air water vapor (I/S)
Internal or State wave characteristics (I/S) vegetation/biomass vigor (I/S) surface air humidity (I/S)
Variable mid- and deep-ocean currents (I) water runoff (I/S) precipitation (I/S)
subsurface thermal structure (I) surface temperature (I/S) clouds (I/S)
subsurface salinity structure (I) snow/ice cover (I/S) liquid water content (I/S)
ocean biomass/phytoplankton (I/S) subsurface temperature (I/S)
subsurface carbon(I), nutrients(I) subsurface moisture (I/S)
subsurface chemical tracers(I) soil carbon, nitrogen, phosphorus, nutrients (I)
ocean surface wind & stress (I/S) incoming SW radiation (I/S) sea surface temperature (II/S )
incoming SW radiation (I/S) incoming LW radiation (I/S) surface soil moisture (I/S) surface soil
incoming LW radiation (I/S) PAR radiation temperature (I/S)
surface air temperature (I/S) surface winds (I) surface topography (I/S)
surface air humidity (I/S) surface air temperature (I/S) land surface vegetation (I/S)
precipitation (I/S) surface humidity (I/S) CO2 & other greenhouse gases, ozone &
Forcing or Feedback evaporation (I/S) albedo (I/S) chemistry, aerosols (I/S)
Variable fresh water flux (I/S) evapotranspiration (I/S) evapotranspiration (I/S)
air-sea CO2 flux (I) precipitation (I/S) snow/ice cover (I/S)
geothermal heat flux (I) land use (I/S) SW and LW surface radiation budget
organic & inorganic effluents (I/S) deforestation (I/S) (I/S)
biomass and standing stock (I/S) land degradation (I/S) solar irradiance (S)
biodiversity (I) sediment transport (I/S)
human impacts-fishing (I) air-land CO2 flux (I)
BLUE=Water Cycle Variable; RED=Energy Cycle Variable; GREEN=Carbon/Chemistry Variable; BLACK=Boundary condition

d Q
 E  P
dt

Multi-Scale Information

Remote Sensing
Soil Moisture Variability

Data
DEM Vis/IR Vegetation Microwave Soil Microwave
Moisture Precipitation

Total
Variability
Land
Cover-Induced Precipitation-
Topography- Induced
Induced

1m 10 m 100 m 1 km 10 km 100 km
Scale

Water & Energy Cycle Prediction Strategy

Global Warming Scenarios
Operational Climate Models (GFDL, NCAR, NCEP)

Global Water& Energy Cycle
Prediction System

Next-generation
Integrated Water-Cycle Advance Understanding and Model Physics
Observation System:
Ground- and Improve Initialization & Assimilation
Space-Based
Observing Programs Diagnose and Identify Predictable Changes

Water & Energy Cycle Prediction
Developing Advanced Process-Resolving Models
Useful prediction is critical – it is the link to stakeholders.
We must move towards a new paradigm of climate models that
produce useful weather-scale, process-scale, and application-scale
prediction of local extremes (not just mean states).
We must more fully constrain climate models with observations, to
improve their realism and believability.

Terrestrial hydrologic cycle: many coupled processes

Weather generating processes

Biogeochemical cycles (N, C)

Water resources


Yet it is usually simulated with disconnected models

Land Surface Model

Groundwater/Vadose Model

Atmospheric Model
Surface Water Model

Land Model Developments
•Addition of carbon-nitrogen cycle model
•Dynamic Vegetation Modeling
•Urban model
•Organic soil / deeper soil column / Groundwater
•Subgrid/Fine mesh: high res land and downscaling
•Ice sheet model
•Irrigation/Landuse/Agriculture
•Integrated global crop model
•Dynamic wetlands
•Lake Model
•River Routing and Diversions
•Isotopes
•Multi-Layer Snow Modeling
•Radiance Forward Model Coupling
•Sensor Webs
•Implicit Land-Atmosphere Coupling


GLASS “LOcal-COupled” Project:“LoCo”

Overarching goal:
Accurately understand, model and predict the role of local TOA Radiation
land – atmosphere coupling in the evolution of land-
atmosphere fluxes and state variables, including clouds.
Single
Horizontal Column
Science Questions: Flux Model
Divergences
•Are the results of PILPS, GSWP, or data assimilation
experiments affected by the lack of land surface-
atmosphere coupling?
Land
•Can we explain the physical mechanisms leading to the Surface
coupling strength differences found in GLACE or other Model

coupled NWP/climate experiments? (www.arm.gov)
A Single Column Model
•Is there an observable diagnostic that quantifies the role of
local land-atmosphere coupling?


Objective: A 1/8 degree (~10km) global land modeling and assimilation system that uses all relevant observed forcing,
storages, and validation. Expand the current N. American LDAS to the globe. 1km global resolution goal
Benefits: Enable improved land-atmosphere understanding, hydrological and climate prediction, transfer research to
application, and enable consistent inter-site comparison (I.e. GEWEX).
Open Collaboration: Encourage international participation through code and data access, and cooperative evaluation.
Coupled Connections: GLDAS is the off-line land-surface development strategy for DAO, NCEP, NCAR, and NSIPP.
GLDAS Data Sources: DAO(GEOS), NCEP(GDAS), and ECMWF NWP “base” forcing.
Force with Observations: IR, TRMM, and SSM/I Precipitation, Geostationary Insolation.
Assimilation and Validation: GTS, IR derived Surface Temperature, AMSR/TRMM Soil Moisture, MODIS Snow Cover, GRACE
total water storage change.
Consistent Global Intercomparison

Land Data Observed Forcing
Assimilation

Data
Insertion of Data
into the Model
n
t io
ra
t eg
l In
de
Mo

Improved
products,
Obs 4DDA Model
CEOP
predictions,
understanding


Vision: A near-real time “patched” Global LDAS

Action: Overlay high-res regional LDAS model forcing and output over
baseline low-res GLDAS model for best local information

Advantage: Share land-hydrology data/forcing globally in a Hydrologic
“GTS” framework

Issues: Global consistency studies


Future of Hyper-Resolution Land Modeling
Future Land Modeling Development Topics:
•Dynamic water-table parameterization (i.e. TOPMODEL concepts).
•Dynamic vegetation (including human management).
•Nitrogen & carbon dynamics.
•Runoff routing, including anthropogenic abstractions.
•Land Data Assimilation - Adjoint development?

Progress toward a fully process-scale resolving model of land surface hydrology, atmospheric dynamics, and
cloud processes over the global domain.

Integrate all obviously interdependent land-atmosphere processes into a common ultra-resolution (100’s of
meters) framework for Earth system modeling, through fusion of traditional land surface hydrology modules
with boundary-layer turbulence and cloud process modules.

Envision two, eventually convergent paths toward global land-atmosphere coupling:
1) Implement traditional cloud parameterization and atmospheric turbulence schemes and implicitly
couple those to patch-based land models at highest possible resolution;
2) Develop true global process-resolving coupled land-atmosphere models in a phased approach:
(a) off-line land-cloud process resolving studies
(b) land-cloud super-parameterizations based on sampling the relevant process scales
(c) nested land-cloud resolving models in a GCM framework
(d) true global ultra-high-resolution global cloud-land process resolving model


Global Water and Energy Cycle: Observation Strategy

Future: Water Cycle Mission
Observation of water molecules through the
atmosphere and land surface using an active/passive
hyper spectral microwave instrument.

-2
- O H +1
H
+1
+
ICEsat
Quantity Spatial Temporal Frequency
Aquarius
Jason Resolution Resolution
NPOESS Groundwater 50 km 2 weeks 100 MHz?
SMOS Soil Moisture 10 km 3 days 1.4 GHz
Landsat/SPOT Salinity 50 km 2 weeks 1.4 GH
Geostationary Freeze/thaw 1 km 1 day 1.2 GHz
DMSP Rain 5 km 3 hour 10-90 GHz
NOAA Falling Snow 5 km 3 hour 150 GHz
Hydro Altimetry Snow 1-5 km 1 day 10-90 GHz
Etc. TPW 10 km
(sea) 3 hour 6-37 GHz
(land) 3 hour 183 GHz
Need a strategy to compare and Temperature 10 km
Primary missing global integrate and make sense of (sea) 3 hour 6-37 GHz
observations: Precipitation, Soil existing observations (land) 3 hour 6-37 GHz
ET (4DDA) 5 km 3 hour 1.4-90 GHz
Moisture, Snow

Water Cycle Mission:
Microwave radiation is modified strongly by the dipole of water molecules
• Through the earth’s surface and in the atmosphere
• Dependent on the microwave radiation source and frequency and on the water phase and
concentration.
• soil moisture, rainfall, snowfall, snow cover, water vapor, total precipitable water, soil freeze-thaw,
ocean salinity, vegetation water-content, surface inundation, streamflow

There is the potential of developing a water cycle mission:
• high-resolution, active-passive, multi-frequency microwave mission
• make simultaneous observations of almost every critical water-cycle process, and bring water-cycle
science to a more compelling level.
• This mission could be built around a single, elegant, highly-integrated large aperture (10’s of meters in
size) multi-frequency active/passive microwave antenna
• Could be deployed in a geostationary orbit, or as part of a polar orbiting constellation

If we want to achieve this goal, we will need to take decisive, calculated steps:
• Focused experimental ground, air, and space based instruments (i.e.
TRMM,GPM,HYDROS,AQUARIUS).
• Development of robust microwave radiative-transfer algorithms to derive the desired quantities.
• Develop mission concept options in the near-term.

Non-microwave water cycle observations involving visible and infrared derived snow cover, surface
temperature, and cloud top temperature, as well as lidar or radar altimetry derived river and lake levels
would further increase the relevance of a potential “water cycle” mission.

Water Cycle Mission: Options?

Quantity Spatial Temporal Frequency
Resolution Resolution
Groundwater 50 km 2 weeks 100 MHz?
Soil Moisture 10 km 3 days 1.4 GHz
Salinity 50 km 2 weeks 1.4 GH
Freeze/thaw 1 km 1 day 1.2 GHz
Rain 5 km 3 hour 10-90 GHz
Falling Snow 5 km 3 hour 150 GHz
Snow 1-5 km 1 day 10-90 GHz
TPW 10 km
(land) 3 hour 183 GHz
Temperature 10 km
(land) 3 hour 6-37 GHz
ET (4DDA) 5 km 3 hour 1.4-90 GHz

Water Cycle Mission: Demonstration?
Challenge: progress from single-variable water-cycle instruments to multivariable integrated instruments.
Vision: dedicated high-resolution water-cycle microwave-based satellite mission may be possible based on large-
aperture antenna technology that can harvest the synergy that would be afforded by simultaneous multichannel active
and passive microwave measurements.
Demonstration: A partial demonstration of these ideas can be realized with existing microwave satellite observations
to support advanced multivariate retrieval methods that can exploit the totality of the microwave spectral information.
Impact: Simultaneous multichannel active and passive microwave retrieval would allow improved-accuracy retrievals
that are not possible with isolated measurements.


Observation and prediction
tools are advancing
?

Grid Computing
Decision Support
Systems
Can we link these advanced tools
Prediction Models reduce uncertainties in end-uses? Critical Application
Satellite Sensor Web A vision for the future:
Integrated environmental information systems
• Integration of
• Advanced/smart sensor webs (in-situ, airborne, and space-based)
• High-performance prediction systems (Global-scale, locally relevant)
• Decision support tools (planning, management, operations)
• Enables system adaptation to minimize uncertainties
• Sensing, monitoring, prediction, and application at global, regional and local.
• Collaborative, coherent, consistent and consolidated environmental data collection, fusion, prediction and
distribution.
• What does it do?
• interoperable: different kinds of sensors, models, and DSS can be linked;
• intelligent: components communicate with each other;
• dynamic: components are "position aware" and mobile;
• flexible: components handle various modes of data transmission;
• expandable: new components will need to be easily added.

Today: Tomorrow:
Large space-based Observatories Integrated environmental information system

Smart Sensor Web

SWARM
Adaptive Resource ManagerRM
C2

Single sensor retrievals Coordination for distributed monitoring,
Spatial/temporal inconsistency processing, and decision making
Parameter-driven requirements Easy deployment of technology and scalability
Multiple sensor retrievals
Spatial/temporal consistency
Integrated cross-sensor calibration
System-driven requirements
Reconfigurable ground and space information systems

Earth Science Vision
Distributed Information-System-in-the-Sky

Commercial Optical Crosslink
Communication
Network
Ka Crosslink Optical Interoperating Measurement
Crosslink Systems (Air / Spacecraft /
In-situ) with modeling
systems
Flexible Measurement Network
Architecture
Direct Distribution of Derived
Active Products
Optical Ka
Network Computing-in-the-Sky
Ka
Ka
Passive
Optical

In-situ User
PC Based GS
Comm Gateway Digital
Library Metadata
Warehouse


4DDA Value-
Added Products

Prediction Models

Airborne Sensors
Decision Support
Systems

Integrated environmental
information system
The “internet” of Earth Information

Satellite Sensor Web

Critical Application
Surface
Remote-Sensing
Data Mining

In-Situ
Sensors
Grid Computing

Linking Science to Consequences
End-to-end coordination enabling understanding and prediction of the Earth system:
Research driven by the needs of society

To deliver social, economic and environmental benefit to stakeholders through
sustainable and appropriate use of water by directing towards improved integrated
water system management

WaterNet: A Water Cycle Community of Practice Demonstration
• Advances are currently available to develop solutions to the global water
crisis, but they remain largely unused. Effective communication is the critical
task to enable using these advances to develop solutions and set future
research priorities.

•WCCoP (Water Cycle Community of Practice)
• has knowledge of both the decision support needs and the cutting-edge
research results
• can formulate a broad array of water cycle partnerships and solutions.

Questions remain:
•Is a WCCoP really needed?
•How do we enable the formulation of a WCCoP?
•What partners & elements already exist?
•What tools & services are available?
•If we build it, will they come?


Objective: A 1/4 degree (and other) global land modeling and assimilation system that uses all relevant observed forcing,
storages, and validation. Expand the current N. American LDAS to the globe. 1km global resolution goal

Consistent Global Intercomparison

Soil
SW down Moisture
ET
(May 2001)

Observed
Forcing
U.MD AVHRR- Tsurface
Veg Cover

Land Data
Assimilation
Data CEOP
Insertion of Data
into the Model
on
at i
gr
te
l In
de
Mo
Merged Ppt Snow WE
Forcing
Improved
products,
Obs 4DDA Model predictions,
understanding


Coupled Model Forecast: 1988 Midwestern U.S. Drought
(JJA precipitation anomalies, in mm/day)
Observations Predicted: AMIP

Without
soil moisture
initialization

With soil
moisture
initialization

10
Predicted: LDAS Predicted: Scaled LDAS
3.
1.
0.5
0.2
0
-0.2
-0.5
-1.
-3.
-10

Koster et al., 2004 Paul R. Houser, February 25, 2011, Page 40

Land Information System http://lis.gsfc.nasa.gov
Co-PIs: P. Houser, C. Peters-Lidard
2005 NASA SOY co-winner!!
Summary: LIS is a high performance set of land
surface modeling (LSM) assimilation tools.
Applications: Weather and climate model initialization
and coupled modeling, Flood and water
resources, precision agriculture, Mobility
assessment …
External

LIS

Internal

Memory Wallclock time CPU time
(MB) (minutes) (minutes)
LDAS 3169 116.7 115.8
LIS 313 22 21.8
reduction factor 10.12 5.3 5.3

200 Node “LIS” Cluster
Optimized I/O, GDS Servers Paul R. Houser, February 25, 2011, Page 41

LIS Future: Enabling Process-Resolving Earth System Models
LIS uses interoperability standards:
•The Earth System Modeling Framework (ESMF)
•Assistance for Land Modeling Activities (ALMA)
•GrADS Data Server (GDS) Atmos.
•Open-source Project for a Network Data Access
Protocol (OPeNDAP) Models

Enables LIS integration
Ocean
with other components: LIS
• Weather Research and Forecasting (WRF) model Models
• Goddard Cumulous Ensemble (GCE) model
• etc.

LIS Impact Example: Coupling LIS to a Weather Model
Observed
Rainfall With Without
LIS LIS
12-Hours Ahead
Atmospheric Model
Forecasts


Global Scale Initiative as an Solution Network Demonstration
Links Geophysics of H20, Governance, Vulnerability, Supply Limitation Imposed by Pollution,
Ecosystem Flow Requirements
(Links to NASA NEWS, GWSP, GEWEX/HAP, IGWCO, ICSU Socioeconomics Group)

Meta-data & Link Archive Integrative
GWSP Models
Value-added
External GWSP And
Outputs
Indicator Generators
Data ATLAS
(e.g. NASA,
ESA, JAXA) Evaluate & Improve
Internal Geospatial Archive
Paul R. Houser,25 February 2011, Page
45

The Coral Reef Early Warning System (CREWS) Network:
marine environmental monitoring to support
research and marine sanctuary management

A CREWS Station is a "smart" meteorological and oceanographic monitoring
platform installed near coral reef areas, software-configured
to ensure the gathering of high quality data and the eliciting of automated
alerts when specified environmental conditions occur (e.g., those thought
to be conducive to coral bleaching)

...and information synthesis products
Surface-truth for satellite products, coral bleaching alerts, data quality
alerts; and matching patterns as proscribed by biologists, oceanographers
and the public (fish & invertebrate spawning, migration, bloom conditions,
good fishing and/or diving conditions, etc.)


CNRFC Demonstration Project

Goals:
• Demonstrate value added to National Weather Service Decision Support Tools (DSTs) by
various remote sensing and land surface modeling research applications to improve flood
forecasting, estimation of snow pack and rainfall runoff, stream flow and water supply forecasts.
• Evaluate impacts of improved forecasts on water management Decision Support Tools
• Focus future NASA resources on gaps identified

Bureau of Reclamation Study
Soil Moisture Analysis

Snow Water Equivalent
Integration of Land Products: Land Cover, Snow,
Evapotranspiration, Streamflow, Soil Moisture, Other
Goal to produce successful demonstration of these applications-
based studies using satellite data for applications such as Hydro-
energy management.


A vision for making a difference
Apply state-of-the-art capabilities in water cycle observation, analysis, and modeling to
reach new frontiers in earth system prediction, and enabling real benefit to society.

– Integrate research across traditional disciplines
– An end-to-end program that transitions theoretical research to academic/public education and
real-world application,
– Cultivate universities, government, and international partnerships...
• Focused investments towards better water cycle solutions:
Observation Understanding Models Prediction Consequences
Process
Assimilation
Assessment Resolution Applications
Data Initialization
Synthesis Coverage Education
Proficiency Diagnosis
Analysis Coupling Linkages
Prognosis
Validation

An integrated vision for ultra high resolution global water cycle observation and prediction

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