Shahid Habib, D.Sc., PE Chief, Office of Applied Sciences Earth Sciences Division NASA Goddard Space Flight Center shahid.habib@nasa.gov

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

Multi Agency Partnership

MENA Project Partnership

THE WORLD BANK

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

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

Nile LDAS

5 Km simulation

Drought Monitoring

Anderson et al. (2012) HESS

Modeling onset of 2011 Horn of Africa drought.

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

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