Shahid Habib, D.Sc., PE Chief, Office of Applied Sciences Earth Sciences Division NASA Goddard Space Flight Center [email protected]
September 23, 2013 Samarkand, Uzbekistan NASA Astronaut picture from ISS
International LCLUC Regional Science Meeting in Central Asia
Tashkent, Uzbekistan November 11-13, 2013
Contents
• Earth Science and Remote Sensing
• Data Emphases and Applications
• Some key studies:
• MENA
• HIMALA
• Nile
• Summary and Possible Areas for collaboration
Atmospheric
Chemistry
Water Cycle
(Hydrosphere)
Solid Earth and
Interior
(Lithosphere)
Radiation and
Temperature
Variability
Carbon Cycle and
Ecosystem (Biosphere)
Weather
Earth System Interdependencies
1972
3.9 B people 1999
6.0 B 2013
7.0 B 2018
7.4 B 2023
7.9 B
1972
Landsat 1
Launch
9.4 ac per
person
1999
Landsat 7
Launch
6.2 ac 2013
Landsat 8
Launch
5.2 ac
2018
Landsat 8
Design Life End
4.9 ac
2023
Landsat 8
End of Consumables
4.7 ac
By 2023 there will only
be 4.7 ac per person
The Shrinking Earth
J. Iron/GSFC
• Land Cover and Land Use are changing at rates unprecedented in human history – Driven by population, affluence, technology, and climate
– Changes to land cover/use and ecosystems are only likely to accelerate during the next 50 years
• These changes have profound societal consequences… – Food and Fiber Production
– Water Resource Management
– Human Health and Environmental Quality
– Habitation and Urbanization
– Biodiversity
• …and also feed back to the physical climate system – Atmospheric carbon
– Energy balance
Earth’s Response
J. Iron/GSFC
NASA Earth Science Missions in Operation
Landsat-8 (USGS)
(Suomi)
Hydrology Related Missions
LCLUC Related Missions
SMAP 2014
ICESat-II 2016
SWOT 2020
PACE 2020
L-Band SAR 2021
CLARREO 2023
NASA Earth Science Planned Missions (2013-2023)
OCO-2 2014
SAGE-III (on ISS) 2014
Grace-FO 2017
OCO-3 (on ISS) 2017
GPM 2014
CYGNSS EVM-1, 2017
TEMPO EVI-1, 2019
EVI-2 2020
EVM-2 2021
EVI-3 2022
Hydrology Related Missions
LCLUC Related Missions
• Satellite data and derived scientific products are available at no cost to
all users
• NASA developed algorithms, models are open source, as applicable
• Data are made available to all users promptly
− Data product distribution can be within 3 hours of acquisition
• NASA puts great emphases on sharing data which benefits all parties including
NASA i.e., Data shared is more valuable then data NOT shared
NASA Earth Science Data Policy is Open Source
Natural Disasters
Public Health
Water Management
Weather
Ecosystems
Agriculture
Air Quality
Areas Impacting Society
Landsat-7
Terra Aqua
TRMM Aura
QuikScat
End User/ Decision Maker
MODELS
Remote Sensing Missions
Applied Research Domain
Science and Research Products
User Specific Operational Products
GMAO Atmosphere
GSFC GOCART
GISS Model III
Be
nef
its
Push Pull
Research to Application
Partnership LIS/LDAS
Decision Support Systems
Data & data Products
Validation & Calibration
Forest Fires
Smoke/Aerosols
Change Albedo
Impact Air
Quality
Mix with Dust and pollution
Atmospheric transport
Deposition
glacier and
snow
Increase absorption
Change stream flow
Change radiation balance and precipitation
Impact Economic and livelihood
Health impacts
Precipitation variability/Drought
Land degradation
Floods
Precipitation
Impact water quality
Where to start??
• Problems are regional to local to urban scale
• Impact may be much larger involving international coordination
• NASA observations are global
• NASA models are global
• Require regional adaptation with help from regional partners
Lake Chad: an icon of African Droughts
Ref: C. Ichoku/GSFC
• Damming of river for hydroelectric • Drop in precipitation • Dust transport from Bodele depression • Biomass burning impact precipitation • Water management practices
MENA Project
• Address water resources issues, understand and adapt to
climate change impacts for decision making and societal benefits Utilize NASA Earth Science satellite observations in
conjunction with ground measurements
Assist in building local expertise
Implementing Partners
Country Implementing Organization
Egypt NARSS - National Authority for Remote Sensing and Space Sciences
Jordan MW&I - Ministry of Water and Irrigation
RJGC - Royal Jordan Geographic Center
Lebanon CNRS - The National Center for Remote Sensing
Morocco CRTS - The Royal Center for Remote Sensing
Tunisia CRTEAN -The Regional Centre for Remote sensing of the States of North Africa
CNCT - Centre National de la Cartographie et de la Teledetection
UAE International Center for Biosaline Agriculture
MENA
Water Information System Platform
Project Annual Report October 2011 – September 2012
Contributors:
Dr. Shahid Habib – NASA/GSFC
Fritz Policelli – NASA/GSFC
Dr. Kunhikrishnan Thengumthara – SSAI/NASA GSFC
Maura Tokay- SSAI/GSFC
Dr. Mutlu Ozdogan - UW
Dr. Ben Zaitchik - JHU
Dr. Martha Anderson - USDA
Dr. John Mecilkalski – Jupiter’s Call/UAH
November(2,(2012(
Limited'distribution'–'for'project'use'
What are we after!
Manage and Plan water resources in the MENA countries i.e., Know the water balance in near real time
Precipitation
Evapotranspiration
Water Storage Change
Ground Water
Run Off
Thematic Areas Egypt Jordan Tunisia Lebanon Morocco
Evapotranspiration x x x x x
Drought x x x x x
Floods Detection and Modeling
x x
Climate Change Impact
x x x x x
Crop Mapping & Irrigation
x x x x x
Hydrological Modeling and Analysis
x x
Locust Monitoring x
Fires (fuel loading) x
Crop Yield x
What is being addressed
Formula for Success
• Engage Users: Must involve the users/decision makers from onset e.g.,
hydrological, meteorological and agricultural organizations
• Build Capacity: Establish subject matter “champions” who interfaces with
NASA expert(s) in order to establish core capability per thematic area
• Empower Talent: Must involve young scientists and engineers in this process
• Involve Academia: Establish scholarships for involving students to work on
real life problems
• Share Data: Apply in situ data to validate and calibrate NASA provided
models
NASA Contribution
• Satellite data products from multibillion dollar investments in space
• Algorithms to generate data products
• Open source models: drought, evapotranspiration, flood detection and
mapping, flood modeling, and hydrological modeling
• Climate data down scaling for conducting impact assessment
• Initial training on accessing and using data products and models
Crop Mapping and Irrigation
North Jordan Valley irrigated agriculture South DeadSea irrigated agriculture
Two stage approach
–MODIS-based mapping (at 500 meter) for regional land surface and hydrological modeling
–Landsat-based mapping for use in local scale water and crop growth assessment
Natural Vegetation
Permanent Vegetation-olives
Spring irrigated
Summer irrigated
Ref: M. Ozdogan/Unin of Wisc
Morocco Flood, 30th Nov. 2010
CREST Model Simulation
Ref: K. Thengumthara/GSFC
Morocco Precipitation and Flooding
Climate Data Downscaling
• Future climate patterns are projected based on past variability patterns
– Climate change will alter the frequency and intensity of historically observed patterns
• Analyzing both Statistical downscaling and dynamical downscaling
• Statistical downscaling is more flexible and easily transferred
1960 1980 2000 2020 2040 2060
02
04
06
08
01
00
12
01
40
Calibration: Apr prcp anomaly at JENDOUBA , Tunisia using c1: R2=18%, p-value=2%.
Time
Prc
p (
mm
/mo
nth
)
Obs.FitGCMTrends
Apr: Trend fit: P-value=51%; Projected trend= 1.29+-1.97 mm/month/decade
Ref: B. Zaitchik/JHU
GRACE Reveals Massive Depletion of
Groundwater in NW India
The water table is declining at an average rate of 33 cm/yr
During the study period, 2002-08, 109 km3 of groundwater was lost from the states of Rajasthan, Punjab, and Haryana; triple the capacity of Lake Mead
Trends in groundwater storage during 2002-08, with increases in blue and decreases in
red.
Time series of total water from GRACE, rate of groundwater depletion is 4 cm/yr. Inset: Seasonal cycle.
Ref: Rodell, Velicogna, and Famiglietti, Nature, 2009
LAI LST
Satellite-based Evapotranspiration
• Meteosat analysis at 3km resolution, daily
• MODIS thermal bands to downscale to 1km
• Further downscaling possible with Landsat and ASTER
ALEXI: Atmosphere-Land Exchange Inverse (ALEXI) model
Ref: M. Anderson/USDA
Hydrological Modeling
A Land Data Assimilation System (LDAS) is a computational tool that
merges observations with numerical models to produce optimal
estimates of land surface states and fluxes.
+ SMAP
Soil Moisture Evapotranspiration
LDAS Outputs
Soil Moisture Profile
Fractional Snow Coverage
Snow Depth and Water Equivalent
Plant Canopy Water Storage
Soil Temperature Profile
Surface Temperature
Surface and Subsurface Runoff
Evaporation from Soil, Snow, and Vegetation
Canopy Transpiration
Latent, Sensible, and Ground Heat Flux
Snow Phase Change Heat Flux
Snowmelt
Snowfall and Rainfall (as % of Total Precipitation)
Net Surface Shortwave Radiation
Net Surface Longwave Radiation
Aerodynamic Conductance
Canopy Conductance
Surface Albedo
HIMALA
• ICIMOD (International Center for Integrated Mountain Development), – a regional knowledge
development and learning center
– eight regional member countries of the Hindu Kush-Himalayas Afghanistan, Bangladesh, Bhutan, China, India, Myanmar, Nepal, and Pakistan
• HIMALA focuses on providing new decision support capability that integrates information about snow and glacier ice melt water in stream flow models for hydrological managers
ICIMOD has over a decade of experience mapping and monitoring glaciers in the region.
HIMALA
Test dataset for HIMALA
Langtang Khola Watershed in Nepal
Mera glacier, Khumbu
Test dataset for HIMALA
Mera glacier, Khumbu
Accumulation zone Bn > 0
Ablation zone Bn < 0
Glacier ELA – Equilibrium Line Altitude for Mass balance
Racoviteanu et al, WRR
HIMALA
Notes: 1 Volume / Area relationship is need to estimate initial conditions for Glacier Water-Equivalent (This could be constant (e.g. 1.36) or based on models, empirical relationships)
2 Utah Energy Balance (UEB) model - It will be run at 90m resolution / 6-hour time-step – Will be run sub-basin by sub-basin (start with sub-basin with largest glacier contribution) 3 (i) snow over ice, (ii) ice, or (iii) debris over ice (IF albedo=snow-albedo THEN this is snow-over-ice , so the regular (snow-melt) UEB will be run. IF albedo=ice-albedo THEN the new( glacier-melt) UEB model component will be run) 4 Initial conditions for SWE will be estimated based on precipitation
Streamflow Model
(GeoSFM)
(1-km from USGS/GLC) Land Cover (90 m) DEM
(25 km from FAO) Soil Data Stream discharge
Total Melt
(in mm/day/pixel)
Contribution by glacier melt Contribution by snow melt
UEB model for Snow Melt (for areas with no glaciers)
Calibration / Comparisons with observed stream-flow
• Relative humidity (GFS or gridded gauges) • Temperature (GFS or gridded gauges) • Precipitation (GFS or gridded gauges) • Wind speed (GFS or gridded gauges) • Short-wave Radiation (GFS or gridded gauges) • Long-wave Radiation (GFS or gridded gauges) • Daily albedo (MODIS, 2000-now)
• Water-Equivalent for
glaciers (2006) • Albedo Gridded (2006)
Glacier: Initial conditions for UEB
• Glacier extent (ASTER)
• Area-Volume relationship 1 • Glacier (DEM) • Equilibrium Line (DEM) • 2-D gridded ICIMOD glacier cover
Input Data (Dynamic Pars) for UEB (1980s to now)
HIMALA Architecture
SWE 4 (daily maps)
UEB model 2 for Glacier Melt (only for areas with glaciers) 3
Key Points for HIMALA: - Integrates UEB and GeoSFM - Can be run at 90m to capture glaciers - Will provide access to downscaled MERRA - New GUI tool: MapWindow BASINS Why GeoSFM? A decade of use in the region
Asia Flood Network with training of partners
Why Utah Energy Balance (UEB)? -Enables integration of snow and ice into hydrological system -is simple with a small number of state variables
Path Forward (A) - my initial guess
Function Research/Application
Kazakhstan Kyrgyzstan Tajikistan Turkmenistan Uzbekistan Start
Precipitation change A x x x x x QS
Evapotranspiration A x x x x x DS
Crop Mapping A x x x x x SS
Hydrological system R/A x x x x x DS
Ground Water R/A ? ? ? ? ? DS
Glacier-snow melt R/A ? x x ? ? DS
Drought/Food Security
A x x x x x SS
Land degradation R/A x ? ? ? x DS
Desertification • Aerosol transport • Radiation balance • Albedo
R x ? ? ? x DS
Climate Impact R x x x x x DS
Invasive species R/A x ? ? ? x SS
Floods A x x x x x SS
Fires A x ? ? x x QS
QS- Quick Start SS- Slow Start DS- Delayed Start
• Build a baseline/per country or region - Analyze and evaluate what has been done - Identify the gaps - Develop a pathway to complete the gaps
• Identify data sets and tools - Identify in situ data sets - Identify local technical capacity - Get users involved
• Complete analysis - Conduct scenarios backward/ forward
• Start small – Identify pilot projects - Identify Champions to lead - Continue to look for donors
• Gradually move on to bigger things
Path Forward (B) – Integrated System
Conduct systems engineering process:
Coming together is a beginning. Keeping together is
progress. Working together is success. ~ Henry Ford