An Integrated Hydrological and Water Management Study of the Entire Nile River System – Lake

Victoria to Nile Delta

(IGARSS paper 1198: Session FR3-TR10)July 29, 2011

Vancouver, Canada

Shahid Habib, NASA Goddard Space Flight CenterBen Zaitchik, Johns Hopkins UniversityClement Alo, Johns Hopkins UniversityMutlu Ozdogon, University of WisconsinMartha Anderson, US Department of AgricultureFritz Policelli, NASA Goddard Space Flight Center

• Countries of the Nile basin face challenges related to hydrologic extremes and water resource planning.

• NASA observations and tools can provide consistent, reliable estimates of hydrological states and fluxes, even in remote areas. This information can be applied to early warning systems and decision support.

• Improved information on floods, droughts, and climate-induced changes in hydrology are critical for all countries.

Lower Nile

UpperNile

BlueNile

WhiteNile

LakeVictoria

LakeNasser

Introduction

Partners, Users and Interested Parties

PartnersNASAJohns Hopkins Global Water ProgramUSDA Hydrology and Remote Sensing LabUniversity of Wisconsin

UsersThe Regional Center for Mapping of Resources for Development(The Nile Basin Initiative and Eastern Nile Technical Regional Office)Addis Ababa UniversityFuture University, KhartoumEthiopian Mapping Authority, Ethiopian Environmental AuthorityEthiopian Meteorology

Interested EntitiesThe World BankUSAIDU.S. Department of StateUNESCO

The Nile Basin

• 3.35 million km2

• 6,650 km longLowerNile

Atbara

BlueNile

SobatWhiteNile

TheLakes

Sudd

Bahrel-Ghazal

The Nile Basin

• 3.35 million km2

• 6,650 km long• Climates range from

humid tropical to hyper-arid

The Nile Basin

• 3.35 million km2

• 6,650 km long• Climates range from

humid tropical to hyper-arid

• The vast majority or precipitation falls in the Ethiopian and Lake Victoria headwaters regions

The Nile Basin• Annual flow at Aswan: 84 BCM• 86% Ethiopia; 14% Equatorial

Lakes• Large seasonal variability in the

Blue Nile and Atbara• Interannual variability can affect

both the Blue and the White Nile

July 10, 2011Bottom of Tissisat falls

July 8, 2011Choke Mountain gorge

The Nile Basin

• 190 million people– 50% below the poverty line

• 10 nations– 8 are defined as Least Developed

Countries– 4 are nationally water scarce

today– 6 are predicted to be water scarce

by 2025– 7 have experienced war in the

past 20 years

• At present, there is no water sharing agreement or joint management plan

Lower Nile night view from satellite

NILE Basin Countries

Ref: UNEP Project GNV011,Jan-Jun 2000, Diana Karyabwite

Semi-distributedhydrological model

Dynamic water budget and allocation model

Resource analysis

Multicriteria analysis and optimization

Scenario definition

Static Parameters: Topography, Soil Types, WatershedsAtmospheric Data: Precipitation, Air Temperature, Wind speed, Incoming radiationManagement Parameters: Land cover, Crop type, Irrigation status

Routing information:River NetworkReservoirsWithdrawals

Water Allocations

Socio-economic and environmental parameters

Sectoral, spatial, temporal and

frequency distribution of benefits and

impacts

Hydrological states and

fluxes

Information Management System

Model System

Analysis System

* *

*

*

NASA’s Project Nile

Components: 1. Customized Land

Data Assimilation System

2. Land cover mapping and simulation

3. Satellite-derived evapotranspiration

4. Integration to Decision Support

Goal: improved hydrometeorological information for research, planning, and water management

LDAS

Land Cover Mapping Evapotraspiration

Decision Support System

Land Surface Model

Meteorological Data

Landscape Information

LDAS Output

SM ET Runoff

Update Observations

A Land Data Assimilation System (LDAS) is a computational system 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

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

LDAS Early Results - 2001-2009 climatology

(Using Noah Land Surface Model)Precipitation input: Rain Fall Estimate at 10 km-3 hourly, WMO Stations

Evapotranspiration at 5 km resolution

Precipitation – Evapotranspiration = Surface and Subsurface runoff -5 km

LDAS Early ResultsUsing last 30 years (1980-2010) ENSO data

C. Alo, JHU

El Nino years 4 months precipitation- Jun, Jul, Aug, & Sep

La Nina years 4 months precipitation- Jun, Jul, Aug, & Sep

Warmer years produce less precipitation over E. Africa

Cooler years produce more precipitation over E. Africa

Land Cover Mapping

A Nested Approach:• Continental scale maps (from MODIS) with

general land cover categories (used for deriving the model)– Used for landscape scale sampling

• Regional maps (from Landsat - agriculture) with detailed land use categories– 1:25,000 scale– Detailed description of the land cover

• Local scale mapping (commercial)– For detailed analyses and true area estimation

M. Ozdogan, Univ. of Wisconsin

MODIS-based regional map(VIS to 2.4 micron – reflected domain)

Friedl et.al., 1999 IEEE TGARS

• Collect one year of 8-day composited MODIS surface reflectance data

• Identify representative temporal profiles for general land cover classes

• Apply an automated Decision Tree algorithm (using band comparison) to classify each pixel

• Use this map as a guide for sampling the landscape for detailed analyses

Root

Leaf nodes

Internal nodes

M. Ozdogan, Univ. of Wisconsin

Data

Decision criteria

Final classified label

forestshrublandgrasslandagriculturebarren

M. Ozdogan, Univ. of Wisconsin

Yearly product based on MODIS 8-day composite at continental scale - 2005

forestshrublandgrasslandagriculturebarren

Landsat footprint for regional mapping

Landsat – 30M Scale - 2005Winter - December

ShrubsAgriculture

Forest

Clouds

Spring, Summer, Fall and Winter averaged

Topographic view

Choke Mountain Caldera

Landsat

MODIS

Commercial with 0.5 m resolution

Northern Ethiopia heterogeneous landscape requires high resolution imagery

TSoilTSoil

Tsoil & Tveg Tsoil & Tveg

Given known radiative energy inputs, how much water loss is required to keep the soil and vegetation at the observed temperatures?

transpiration & evaporation

soil evaporation

SURFACE TEMPERATURESURFACE TEMPERATURE

TvegTveg

Satellite-derived Evapotranspiration

M. Anderson, USDA

Regional scaleΔTRAD - Geostationaryfc - MODIS (vegetation cover function)

Landscape scaleTRAD - TM, ASTER, MODISfc - TM, ASTER, MODIS

Rsoil

TcTac

Hs

Ts

RaH = Hc + Hs

Rx

Hc

Ta

ABL

Ta

ALEXI DisALEXI

5 km30 m

Tw

o-S

ou

rce M

odel

TRAD (φ), fc

TRAD,i(φi), fc,i

i

Ra,i

Blending height

Rsoil

TcTac

Hs

Ts

RaH = Hc + Hs

Rx

Hc

TaTa

ABL

Ta Ta

ALEXI DisALEXI

5 km30 m

Tw

o-S

ou

rce M

odel

TRAD (φ), fc

TRAD,i(φi), fc,i

i

Ra,i

Blending height

Surface temp:Cover fraction:

ET: The Atmosphere-Land Exchange Inverse (ALEXI) Model

(Atmospheric Boundary Layer)

Sensible Heat:Canopy Heat:

(from Landsat thermal band 100m)

Early Results: clear-sky ET composites (2008)

June JulyWm-2

M. Anderson, USDA

(~ 6 km resolution)

2009 FEBRUARY

Average ALEXI ET Average LDAS ET

(MJ m-2 d-1)

Note ET from Sudd and Nile Delta in ALEXI, not captured in LDAS.

2009 JANUARY-DECEMBER

Average ALEXI ET (MJ m-2 d-1) Average ALEXI ET/PET

Nile-LDAS

Applications: Decision Support

Management and DSS

Lan

d C

over

Map

sS

ate

llit

e E

TP

recip

itati

on

• Drought monitoring• Water resource analysis• Early warning systems• Planning for change

Summary

• Many researchers have studied this region over the last three decades

• NASA is taking another integrated look at the entire region using satellite observations and multitude of land surface/hydrological models

• The most significant aspect of this work is to validate using in situ measurements and depends on the regional partners willingness to share in situ data

• As a starting point, we are working with Ethiopian hydrology and Meteorology offices to get such data for the Blue Nile head waters

• We also plan to simulate future climate impact on hydrology using IPCC scenarios

• Our work will be published on a scientific basis Ancient map drawn by Ptolemy