International LCLUC Regional Science Meeting in Central Asia
September 23, 2013 Shahid Habib, D.Sc., PE Samarkand, Uzbekistan Chief, Office of Applied Sciences NASA Astronaut picture from ISS Earth Sciences Division NASA Goddard Space Flight Center Tashkent, Uzbekistan shahid.habib@nasa.gov 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
Earth System Interdependencies
Carbon Cycle and Ecosystem (Biosphere)
Radiation and Temperature Variability Solid Earth and Interior (Lithosphere)
Weather
Water Cycle (Hydrosphere) Atmospheric Chemistry The Shrinking Earth
1972 By 2023 there will only be 4.7 ac per person 3.9 B people 1999 6.0 B 2013
1972 7.0 B 2018 Landsat 1 Launch 1999 7.4 B 2023 9.4 ac per Landsat 7 person Launch 2013 7.9 B 6.2 ac Landsat 8 Launch 2018 5.2 ac Landsat 8 Design Life End 2023 4.9 ac Landsat 8 End of Consumables 4.7 ac J. Iron/GSFC Earth’s Response
• 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
J. Iron/GSFC NASA Earth Science Missions in Operation
Hydrology Related Missions
LCLUC Related Missions
Landsat-8 (USGS)
(Suomi) NASA Earth Science
Planned Missions (2013-2023) OCO-2 2014 SAGE-III (on ISS) 2014
Grace-FO 2017
Hydrology Related Missions OCO-3 (on ISS) 2017
LCLUC Related Missions CLARREO 2023
L-Band SAR 2021 EVI-3 2022 EVM-2 GPM PACE 2021 EVI-2 2014 2020 2020 TEMPO EVI-1, 2019 SWOT CYGNSS 2020 EVM-1, 2017 ICESat-II 2016 SMAP 2014 NASA Earth Science Data Policy is Open Source
• 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 Areas Impacting Society
Natural Disasters
Agriculture
Water Management
Ecosystems
Weather
Air Quality
Public Health Research to Application MODELS
GISS Model III GSFC GOCART
Applied Research Domain
GMAO Push Partnership Atmosphere LIS/LDAS Pull
Data & data End User/ Products Decision Support Decision Maker
Systems Benefits
Terra Aqua User Specific Operational Products Aura TRMM Validation & Calibration Science and Research Products QuikScat Landsat-7
Remote Sensing Missions Where to start??
Change radiation Precipitation Change balance and variability/Drou Albedo precipitation ght • Problems are regional to local to urban scale Mix with Dust and Atmospheric pollution transport Forest • ImpactSmoke/A may be much larger involving Depositi Fires erosols on international coordination glacier and Impact snow • NASA observations areAir global Quality Impact Economic and • NASA models are global Increase Land livelihood absorption degradation Impact • water Health Require qualityregional adaptationimpacts with help from regional partners Change stream flow Floods
Precipitation Lake Chad: an icon of African Droughts
• Damming of river for hydroelectric • Drop in precipitation • Dust transport from Bodele depression • Biomass burning impact precipitation • Water management practices
Ref: C. Ichoku/GSFC Multi Agency Partnership
THE WORLD BANK
MENA Project Partnership
MENA – Middle East North Africa 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 MENRJGCA - Royal Jordan Geographic Center Water InforLebanonmati on CNRSSys - Thete Nationalm P lCenteratfor for Remotem Sensing ProjeMoroccoct A nnuCRTSal - RThee Royalpor Centert for Remote Sensing OctoberTunisia 201 1 – SeCRTEANptem -beTher Regional 2012 Centre for Remote sensing of the States of North Africa
CNCT - Centre National de la Cartographie et de la Télédétection
UAE International Center for Biosaline Agriculture
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
Water Storage Change Precipitation Evapotranspiration Ground Water
Run Off
What is being addressed
Thematic Areas Egypt Jordan Tunisia Lebanon Morocco
Evapotranspiration x x x x x
Drought x x x x x
Floods Detection and x x Modeling Climate Change x x x x x Impact Crop Mapping & x x x x x Irrigation Hydrological x x Modeling and Analysis Locust Monitoring x
Fires (fuel loading) x Crop Yield x 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
South DeadSea irrigated agriculture North Jordan Valley irrigated agriculture
Two stage approach Natural Vegetation
–MODIS-based mapping (at 500 meter) Permanent Vegetation-olives for regional land surface and hydrological modeling Spring irrigated –Landsat-based mapping for use in local scale water and crop growth Summer irrigated assessment
Ref: M. Ozdogan/Unin of Wisc Morocco Precipitation and Flooding
Morocco Flood, 30th Nov. 2010
CREST Model Simulation
Ref: K. Thengumthara/GSFC 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
Obs.
140 Fit GCM
Trends
120
100
80
60
Prcp (mm/month)
40 20
Apr: Trend fit: P-value=51%; Projected trend= 1.29+-1.97 mm/month/decade 0 1960 1980 2000 2020 2040 2060 Time Calibration: Apr prcp anomaly at JENDOUBA , Tunisia using c1: R2=18%, p-value=2%.
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
Trends in groundwater storage during 2002- 08, with increases in blue and decreases in Time series of total water from GRACE, rate of groundwater red. depletion is 4 cm/yr. Inset: Seasonal cycle.
During the study period, 2002-08, 109 km3 of groundwater was lost from the states of Rajasthan, Punjab, and Haryana; triple the Ref: Rodell, Velicogna, and capacity of Lake Mead Famiglietti, Nature, 2009 Satellite-based Evapotranspiration
LAI LST
• 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.
LDAS Outputs Soil Moisture Profile Fractional Snow Coverage Snow Depth and Water Equivalent Evapotranspiration Plant CanopySoil Water Moisture Storage Soil Temperature Profile Surface Temperature Surface and Subsurface Runoff Evaporation from Soil, Snow, and Vegetation Canopy Transpiration Latent, Sensible, and Ground Heat Flux SMAP + 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 Nile LDAS
5 Km simulation Drought Monitoring
Modeling onset of 2011 Horn of Africa drought.
Anderson et al. (2012) HESS 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 ICIMOD has over a decade of experience hydrological managers mapping and monitoring glaciers in the region. HIMALA
Langtang Khola Watershed in Nepal
Accumulation zone Ablation zone Bn > 0 Bn < 0
Test dataset for HIMALA Test dataset for HIMALA Glacier ELA – Equilibrium Line Altitude for Mass balance
Racoviteanu et al, WRR Mera glacier, Khumbu
Mera glacier, Khumbu HIMALA HIMALA Architecture
Input Data (Dynamic Pars) for UEB (1980s to now) • Relative humidity (GFS or gridded gauges) • Glacier extent (ASTER) • Temperature (GFS or gridded gauges) • Precipitation (GFS or gridded gauges) • Area-Volume relationship 1 Glacier: Initial conditions for UEB • Glacier (DEM) • Wind speed (GFS or gridded gauges) • Equilibrium Line (DEM) • Water-Equivalent for • Short-wave Radiation (GFS or gridded gauges) • 2-D gridded ICIMOD glacier glaciers (2006) • Long-wave Radiation (GFS or gridded gauges) cover • Albedo Gridded (2006) • Daily albedo (MODIS, 2000-now)
Why Utah Energy Balance (UEB)? UEB model 2 UEB model SWE 4 -Enables integration of snow for Glacier Melt for Snow Melt (daily Contribution(only for by areas glacier with melt glaciers) 3 (for areasContribution with no glaciers)by snow melt and ice into hydrological system Total Melt maps)
-is simple with a small number of (in mm/day/pixel) state variables
(1-km from USGS/GLC) Land Cover (90 m) DEM Streamflow Stream discharge Key Points for HIMALA: (25 km from FAO) Soil Data Calibration / Comparisons with observed stream-flow - Integrates UEB and GeoSFM Model - Can be run at 90m to capture glaciers (GeoSFM) - 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 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 Path Forward (A) - my initial guess
Function Research/Ap Kazakhstan Kyrgyzstan Tajikistan Turkmenistan Uzbekistan Start plication 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 A x x x x x SS Security Land degradation R/A x ? ? ? x DS
Desertification R x ? ? ? x DS • Aerosol transport • Radiation balance • Albedo 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 Path Forward (B) – Integrated System
Conduct systems engineering process:
• 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
Coming together is a beginning. Keeping together is progress. Working together is success. ~ Henry Ford Visualizing Nile Basin Water Balance Utilizing NASA’s multisensor observations and models help visualize critical parameters: soil moisture, precipitation, evapotranspiration and NDVI in understanding the water balance of the entire Nile basin.
Ref: SVS/GSFC