NASA Presentation to Mini-Workshop on Hydrometeorological Products, Services and Supporting Research
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NASA Presentation to Mini-Workshop on Hydrometeorological Products, Services and Supporting Research. David Toll 1 , Stephen Ambrose 2, , Jared Entin 3 , and Christa Peters-Lidard 4 1-Hydrological Sciences NASA/GSFC Deputy Program Manager, Water Resources 2- NASA/HQ/Applied Sciences Program

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NASA Presentation to Mini-Workshop on Hydrometeorological Products, Services and Supporting Research

David Toll1, Stephen Ambrose2, , Jared Entin3,

and Christa Peters-Lidard4

1-Hydrological Sciences NASA/GSFC

Deputy Program Manager, Water Resources

2- NASA/HQ/Applied Sciences Program

Disaster Management and Water Resources

Program Manager

3-NASA/HQ/Earth Science Division

Terrestrial Hydrology and NASA Energy and Water Cycle Program Manager

4-NASA/GSFC, Head, Hydrological Sciences Branch

[email protected]

17 Sept. 2008

Goulburn-Murray Water

http://wmp.gsfc.nasa.gov


NASA Applied Sciences Program Products, Services and Supporting ResearchA Pathway Between Earth Science & Society

Results of

NASA Earth

Science Research

Uses by Partners

and Stakeholder

Communities

NASA

Applied Sciences

Program


Nasa hydrology related missions
NASA Hydrology Related Missions Products, Services and Supporting Research

SMAP

- Global Soil Moisture


Precipitation from Space Products, Services and Supporting Research

Tropical Rainfall Measurement Mission

(TRMM)

Global Precipitation Measurement (GPM)

  • TRMM is the first "space-based rain gauge" that uses microwaves to "see" how much precipitation falls from clouds around the tropics over land and ocean with unparalleled accuracy. TRMM also is the primary mission for studying El Nino.

  • Global precipitation measurement with TRMM: a great leap forward!

    • 10  85 GHz radiometers

    • 13.6 GHz precipitation radar (FIRST!)

  • Needed improvements:

  • - Longer record length

  • - High latitude precipitation including snowfall

  • - Better accuracy

  • - Spatial-temporal sampling

  • - Improved vertical resolution


Hurricane Ike in Cuba--Heavy, but not extreme rain, flooding (calculated from hydrological model) and landslide potential (from real-time landslide algorithm) indicated along south side of mountains

24-hr Rain from TRMM Multi-satellite product

http://trmm.gsfc.nasa.gov/publications_dir/potential_flood_hydro.html


GRACE Is the First Satellite to Monitor Groundwater Levels (calculated from hydrological model) and landslide potential (from real-time landslide algorithm) indicated along south side of mountains

Mississippi River basin

Rodell et al.,

Hydrogeology, 2006

New estimates match groundwater well measurements in the U.S., and can be applied globally

Illinois

Yeh et al., WRR, 2006


GRACE Data Enables More Accurate Water Budget Predictions (calculated from hydrological model) and landslide potential (from real-time landslide algorithm) indicated along south side of mountains

GRACE water storage, mm

January – December 2005 Loop

Model assimilated water storage, mm

January – December 2005 Loop

Zaitchik, Rodell, and Reichle, in preparation

Matt Rodell

NASA GSFC


  • Water, Energy & Carbon Cycles (calculated from hydrological model) and landslide potential (from real-time landslide algorithm) indicated along south side of mountains

  • Water and Food

  • Water Quality and Human Health

  • Water and the Environment

Hydros/Soil Moisture Active/Passive (SMAP) Mission Planning

  • Decadal Survey recommended that:

  • a dedicatedsoil moisture mission recently selected as one of two new Earth science mission

  • NASA fly an active / passive microwave soil moisture & F/T mission in the 2013 timeframe

  • SMAP consists of an L-Band radar & radiometer in a low Earth, sun-synchronous orbit

Societal Benefits:

  • Weather & Climate Prediction

  • Severe Storm Forecasts


Value of Soil Moisture Data to Weather and Climate (calculated from hydrological model) and landslide potential (from real-time landslide algorithm) indicated along south side of mountains

New space-based soil moisture observations and data assimilation modeling can improve forecasts of local storms and seasonal climate anomalies

NWP Rainfall Prediction

Seasonal Climate Predictability

Predictability of seasonal climate is dependent on boundary conditions such as sea surface temperature (SST) and soil moisture – soil moisture is particularly important over continental interiors.

Summer Rainfall differences: 1993 (flood) minus 1988 (drought)

Buffalo Creek

Basin

Observed Rainfall

0000Z to 0400Z 13/7/96

(Chen et al., 2001)

Without Realistic Soil Moisture

Observations

24-Hours Ahead High-Resolution Atmospheric Model Forecasts

Prediction driven by SST and soil moisture

Prediction driven just by SST

With Realistic Soil Moisture

High resolution soil moisture data will improve numerical weather prediction (NWP) over continents by accurately initializing land surface states

(Schubert et al., 2002)

-5 0 +5

Rainfall Difference [mm/day]


Surface Water Mission Concept (SWOT) (calculated from hydrological model) and landslide potential (from real-time landslide algorithm) indicated along south side of mountains

Stream Discharge and Surface Water Height

  • Motivation:

  • critical water cycle component

  • essential for water resource planning

  • stream discharge and water height data are difficult to obtain outside US

  • find the missing continental discharge component

Mission Concepts:

Laser Altimetry Concept

e.g. ICESat (GSFC)

Radar Altimetry Concept

e.g. Topex/Poseidon over Amazon R.

Interferometer Concept

(JPL)

Targeted path

Coincident w/ river reach


Land Information System Heritage: (calculated from hydrological model) and landslide potential (from real-time landslide algorithm) indicated along south side of mountains

NLDAS and GLDAS

North American LDAS

1/8 Degree Resolution

Mitchell et al., JGR, 2004

Global LDAS

1/4 Degree Resolution

Rodell et al., BAMS, 2004

Land Information System (http://lis.gsfc.nasa.gov)

Multi-Resolution Ensemble LDAS Software Framework

Kumar et al., EMS, 2006


LSM Initial Conditions (calculated from hydrological model) and landslide potential (from real-time landslide algorithm) indicated along south side of mountains

LIS Running Modes

Uncoupled or

Analysis Mode

Coupled or

Forecast Mode

Station Data

WRF/

GFS/

GCE

Global, Regional

Forecasts and

(Re-)Analyses

ESMF

Land Sfc Models

(Noah, Catchment,

CLM, VIC, HYSSiB)

Satellite Products

Kumar, S. V., C. D. Peters-Lidard, J. L. Eastman and W.-K. Tao, 2008. An integrated high-resolution hydrometeorological modeling testbed using LIS and WRF. Environmental Modelling & Software, Vol. 23, 169-181.


LIS Uncoupled/Analysis Mode (calculated from hydrological model) and landslide potential (from real-time landslide algorithm) indicated along south side of mountains

Inputs

Physics

Outputs

Applications

Topography,

Soils

Land Surface Models

Soil

Moisture &

Temperature

Weather/

Climate

Water

Resources

Agriculture

Drought

Military

Ops

Natural

Hazards

Land Cover,

Vegetation

Properties

Evaporation

Sensible Heat

Flux

Meteorological

Forecasts,

Analyses, and/or

Observations

Runoff

Data Assimilation Modules

Snow

Soil Moisture

Temperature

Snowpack

Properties


Drought monitoring comparison

D4 (calculated from hydrological model) and landslide potential (from real-time landslide algorithm) indicated along south side of mountains

D3

D2

D1

D0

Drought Monitoring Comparison


Drought monitoring comparison1

D4 (calculated from hydrological model) and landslide potential (from real-time landslide algorithm) indicated along south side of mountains

D3

D2

D1

D0

Drought Monitoring Comparison


Lis wrf coupled example 2 0 10 cm initial soil moisture 1200 utc 6 may 2004
LIS-WRF Coupled Example 2: (calculated from hydrological model) and landslide potential (from real-time landslide algorithm) indicated along south side of mountains0-10 cm initial soil moisture (%)(1200 UTC 6 May 2004)

Eta soil moisture

LIS soil moisture

  • Much more detail in LIS (as expected)

  • LIS drier, especially over N. FL & S. GA

  • LIS slightly more moist over Everglades

Difference (LIS – Eta)

LIS Substantially

Drier

17


LIS-WRF Coupled Example 2: (calculated from hydrological model) and landslide potential (from real-time landslide algorithm) indicated along south side of mountains

Sea Breeze Evolution Difference(1800 UTC 6 May to 0300 UTC 7 May)

Case, Jonathan L., William L. Crosson, Sujay V. Kumar, William M. Lapenta, Christa D. Peters-Lidard, 2008. Impacts of High-Resolution Land Surface Initialization on Regional Sensible Weather Forecasts from the WRF Model. In press, Journal of Hydrometeorology.

18


LIS-WRF Coupled Example 2: (calculated from hydrological model) and landslide potential (from real-time landslide algorithm) indicated along south side of mountains

Sea Breeze Evolution Difference(Meteogram plots at 40J and CTY)

19


Mapping Evaporation and Moisture Stress from Space Using Thermal Remote Sensing (Anderson and others)

SURFACE TEMPERATURE

EVAPOTRANSPIRATION

ALEXI

(GOES Sounder)

Continental

ALEXI

(GOES Imager)

Regional

DisALEXI

(Landsat)

Watershed

DisALEXI

(USU aircraft)

Temperature (C)

CORN

Field scale

Latent Heat (Wm-2)

SOY


Water Resource and Climate Variability Thermal Remote Sensing (Anderson and others)

As the Earth has warmed, what has happened to Earth’s water resources?

Recent Trends

● Precipitation Intensity Increases

● Increased floods and droughts & intensity

● Snowfall decrease

● Earlier runoff

● Melting of ice sheets and glaciers

● Sea level rise

● Increase in hurricanes and intensity

38

105

67

393

355

38

1000 km3/yr


Climate Downscaling & Impacts Assessment Thermal Remote Sensing (Anderson and others)

Observed

Modeled

Climate Downscaling

● Regional Climate Modeling (Left)

- Examples from NASA Goddard Institute for Space Studies (Left)

● Statistical Down Scaling Modeling

- Regional Ensemble Multi-Model

- Percent likelihoods for Precipitation & 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.

Precipitation

Temperature

CCSR, GISS, UCONN


Water Resources Program Functional Themes –Projects Summary

http://wmp.gsfc.nasa.gov

FLOODS & STREAMFLOW FORECASTING

Four projects

- NOAA led examining improved streamflow for River Forecast Centers.

- Using NASA satellite and modeling products for Seasonal Forecast in W. US

- NASA-NOAA-USGS Flash Flood Project.

- NASA Snow Products and LIS for NOAA Snow and Water Forecast

IRRIGATION & WATER DELIVERY

Three projects are currently addressing various aspects of improving ET estimates for use in the BoR AWARDS-ET Toolbox and similar DSTs. Focus on using MODIS data and LDAS/LIS estimates of ET & soil moisture.

DROUGHT

Four Projects. Two using satellite (AMSR-E, Quickscat/Sea Winds and MODIS) and modeling/data assimilation products to improve the US Drought Monitor. Two other improving seasonal predictions and the downscaling of climate predictions for US Drought Outlook. In support of the National Integrated Drought Information System (NIDIS).

WATER QUALITY

Nonpoint source pollution project to assess the impact of MODIS land data products and impact of LIS precipitation and ET products to improve the continuous hydrologic model, HSPF, used in the EPA BASINS DST.


Priorities of the future

NASA Approved Missions – Global Precipitation Measurement (GPM) and Soil Moisture Active Passive (SMAP) Phase II and III Satellite Sensors for Hydrology (SWOT, GRACE-II, and SCLP)

Integrate Land Components for Earth System Models

- Atmospheric (WRF/GCE/GFS/GEOS); Land Surface Models (LIS); Coastal/Ocean Models

Downscaling climate predictions to local/regional scales

- Hydrologic (streamflow, ET, snow)

- Including impacts, vulnerability and adaptations

Infusing short-term, seasonal and decadal forecasting information into decision tools

New Focus on Regional and Topical Area (Gulf Coast, Water Quality, Climate Impacts)

Water, Water, Water

Actual water

- Consumption, industry, ecosystems, etc.

Water as the underlying element

- Drought, wildfires, energy production, agriculture

Assessing these Internationally

Priorities of the Future


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