tennessee technological university

Tennessee Technological University

Faisal Hossain

Tennessee TechUNIVERSITY

Sustainable Application of Water-Measuring Satellite Missions for Water Resources Management

Faisal HossainDepartment of Civil and Environmental Engineering


Past, Present and Future


Faisal Hossain


ACKNOWLEDGEMENTS

1. Former and Current Students- Amanda Harris, Preethi Raj, Nitin Katiyar, Jon Schwenk, Rahil Chowdhury, Ling Tang and Caitlin Balthrop.

2. Collaborators– University of Connecticut, University of Mississippi, Ohio State University, NASA Goddard Space Center (Laboratory of Atmospheres and Hydrologic Sciences Branch), McNeese State University, University of Oklahoma, Oregon State University, University of California-Davis, University of Dhaka, Indian Institute of Technology-Kanpur.

3. Sponsors – NASA Rapid Prototyping Capability Program; NASA Precipitation Measurement Program; NASA Earth System Science Fellowship; TTU Research Initiation Grants, TTU Water Center, Ivanhoe Foundation, Mississippi Department of Environmental Quality.

4. International Partners – Institute of Water Modeling (Bangladesh), International Precipitation Working Group (WMO), Bureau of Meteorology (Australia).


Faisal Hossain


OUTLINE

1. Primary Research Area: Scientific evolution of the concept of ‘sustainability’ for water-measuring satellites for water resources management.

2. Overview of Complementary Research and Education Agendas.


Faisal Hossain


WATER ‘MEASURING’ SATELLITES - 101

Hydrologic Remote Sensing- Microwave MW (1-20cm) and Infrared IR (< 0.1cm) Wavelengths.

Water Cycle Variables- ‘Rainfall’, ‘Soil Moisture’, ‘Discharge’.

Water has a dipole and high dielectric constant.

Orbiting and Geostationary platforms; Passive/Active.

MW sensors mostly orbiting – higher accuracy, lower sampling frequency (space-time).

IR sensors mostly geostationary platforms – lower accuracy, higher sampling frequency (space-time).


Faisal Hossain


WATER MEASURING SATELLITES - 101

HYDROS

TRMMWaTER (SWOT)

Geostationary Orbit

http://upload.wikimedia.org/wikipedia/commons/4/49/Geostat.gif


Faisal Hossain


WHAT IS ‘SUSTAINABILITY’ FOR WATER MEASURING SATELLITES?

“Sustainability is a characteristic of a process or state that can be maintained at a certain level indefinitely.” - Wikipedia

For water measuring satellites? – To make ‘optimal’ use of satellite sensor’s capability to ‘measure’ water looking down over a large area from the vantage of space.

Optimal Use? – Identify, maintain and enhance the realistic limits to which satellite hydrologic data can be used for analysis, modeling and monitoring of water resources.


Faisal Hossain


The Conceptual Appeal of Water-Measuring Satellites to the Hydrologist

In-situ networks – globally disappearing or absent; expensive maintenance; limited by point-scale.

Source: USGS

Source: Climate Prediction Center

Effective sampling area of the world’s rainfall gages is the size of a few football fields!


Faisal Hossain


Global Hydrology (Earth’s Energy/Water Budget) – Can only be supported by space-borne instruments (75% of surface is oceans).

Flood prone Tropics – sparse or non-existent network where floods are most catastrophic.

The Conceptual Appeal of Water-Measuring Satellites to the Hydrologist

Transboundary Flood Forecasting: The Story of the Niger River

Question:

How does one monitor early the evolution of river flooding across political boundaries of 5 nations, 11 administrations and a diverse landscape?

1. 4030 km long, 211,3200 km2

2. Flows through 5 countries

4. Frequent river flooding induced by heavy rainfall

3. Drainage area comprised of 11 countries

5. Diverse climate, rainfall regime, soil conditions, topography = varying response of landscape to rainfall



Faisal Hossain


Hydro-political limitations worsen at the shorter time scales

214 International River Basins in 1979 UN Register

261 in 2002 (Updated)

145 countries are associated in IRBs

Accounts for 40% of total land surface.

> 50% of total surface flow

Percentage Area Number of Countries

91-99% 39

81-90% 11

71-80% 14

61-70% 11

51-60% 17

41-50% 10

31-40% 10

21-30% 13

11-20% 9

1-10% 11

Transboundary Flood Forecasting: The Global Picture on International River Basins

Source: Dr. Aaron Wolf, Oregon State University


Faisal Hossain


Appeal in terms of Future Scenario

Expected launch 2013

3 hourly global rainfall products at 10X10 km scale

WaTER (SWOT)

Expected launch– 2016

Q for major rivers every 2-3 days


Faisal Hossain


Appeal in terms of Future Scenario

Hossain, F., N. Katiyar, A. Wolf, and Y. Hong. (2007). The Emerging role of Satellite Rainfall Data in Improving the Hydro-political Situation of Flood Monitoring in the Under-developed Regions of the World, Natural Hazards, Invited Paper


Faisal Hossain


Problems with Water Measuring Satellites

OLD Issues (Relatively longer known and accepted)

MW temporal sampling of rainfall was low until late 1990s. IR Rainfall data useful at > degree-monthly scales.

Scale Incongruity (satellite rainfall/moisture data too large for dynamic terrestrial hydrology).

Soil moisture accuracy limited by the need for long MW wavelength (L-band).

Passive MW (PMW) data for discharge estimation has been good only for large, ‘steady’ (glaciers) rivers on monthly timescales.

Historical solutions devised by hydrologists for handling coarse resolution data: Spatial-Temporal downscaling.

Spatial Downscaling


• Faisal Hossain


Chronology of Scale and Accuracy of

Satellite Rainfall Data

1970 – 1980 – 1990 – 2000 - 2010+IR Sensors on GEO platforms

Good space-time sampling

IR parameters weakly

related to rainfall process

PMW Sensors on LEO platforms

Poor space-time sampling

PMW parameters strongly related to rainfall process

Merging or IR with PMW began

1 Degree-Monthly

More Merged Products

Tropical Rainfall Measuring Mission- TRMM

Anticipation of GPM

3 hourly and globally coherent rainfall data

0.25 degree – 3 hourly


Faisal Hossain


Problems with Rainfall Measuring Satellites

NEW Issues on Satellite Rainfall Data (Recent Insights – post 2004 era)

Existing frameworks and metrics (bias/rmse, correlation) inadequate for assessing hydrologic potential of satellite data.

Satellite rainfall error more complex (multi-dimensional) than conventional network data.

Complexity of error increases as scales (time/space) decrease – non-negligible for dynamic hydrologic modeling.

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All Overland Satellite Rainfall Algorithms are Probabilistic at Hydrologically Relevant Scales

• Rainy/Non-rainy area delineation has a distinct spatial structure• Systematic error has a non-negligible spatio-temporal structure• Random error has a spatial structure• Regime Dependence of error structure on climate, location,

season

Four Possible Outcomes of a Rainfall Sensor at any given time:

1. Successful Rain Detection/Delineation (HIT)

2. Unsuccessful Rain Detection/Delineation (MISS)

3. Successful No-Rain Detection/Delineation (HIT)

4. Unsuccessful No-Rain Detection/Delineation (MISS)


Faisal Hossain


Our ‘Sustainable’ Solution and Framework for Old and New Problems

“As space and time scales become smaller, the passive sensor’s precipitation measurement characteristics become more complex and random.Fine-scale hydrologic assessment of satellite rainfall retrievals requires the recognition of this increasing complexity of satellite precipitation error structure.”

HYPOTHESIS: New approaches needed for hydrologists that recognize scale incongruity.

Hossain and Lettenmaier, 2006, Water Resources Research

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We Need Hydrologic Process-based Understanding of Scale Incongruity

Watershed = Non-linear system yavg ≠ f(xavg)

timePonding trate Rainfalli )(

p

Kii

Ktp

)(ln

0runoff surface fiWhen

rateon infiltratif )1(

p

p

pttK

F

FFF

FKf

An infiltration approach to surface runoff modeling (physically-based) as follows:

Time Space ThresholdingNon-linearity

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Our Generalized Framework for the Community (IPWG- PEHRPP) – For Rainfall

ONEHydrologically Relevant Frameworks should answer three key questions – Q1. How does the error vary in time?Q2. How does the error vary in space?Q3. How “off” is the rainfall estimate from the true value over rainy areas? TWOMetrics should have ‘Diagnostic’ and ‘Prognostic’ valueDiagnostic – Able to quantify uncertainty on a specific feature/dimension of precipitation.Prognostic – Amenable for use in a mathematical error model for synthetic generation of high resolution satellite rainfall data.

Hossain, F. and G.J. Huffman. (2008).Investigating Error Metrics for Satellite Rainfall at Hydrologically Relevant Scales, Journal of Hydrometeorology (In press)


Faisal Hossain


Two-Dimensional (x-y) Satellite Rainfall Error Model – SREM2D

Based on the concept of ‘reference’ (ground validation) rainfall.

Modular in design (collection of concepts) – for any rainfall product.

Total Error Metrics - 9 Uses Error Metrics interpretable by

both hydrologists and Data-producers.

Currently used by other research groups (MSU; UArizona; OleMiss). Preferred by NASA Laboratory of Atmospheres.

Hossain and Anagnostou (2006) IEEE Trans Geosci. Remote Sensing, 44(4).

Systematic and Random Errors in Retrieval (6) and (7)

Correlation Length of Retrieval (8)

How does the error vary in time?

Temporal Correlation of Systematic Error in Retrieval (9)

Correlation Length of Successful Detection of Rain (4)

Correlation Length of Successful Detection of No-Rain (4)

How ‘off’ is rainfall estimate from true value over rainy areas?

How does the error vary in space?

Probability of detection of Rain (As a function of

magnitude of reference or satellite rainfall (1)

Probability of detection of No-

Rain (fixed marginal value) (2)

False Alarm (Probability distribution

Parameters) (3)

Rainfall as an Intermittent Process

Rainy Areas Non-Rainy Areas

How well does satellite data delineate the rainy/non-rainy areas?

MISS ? MISS ? HIT ? HIT ?


Faisal Hossain


Transboundary Flood Monitoring: New Questions for Assessing Sustainability

General Science Question

How realistic is the use of satellite rainfall in overcoming the transboundary limitations to

flood monitoring?

Specific Questions

What specific IRBs, and downstream nations would benefit more than others from GPM?

Can we develop rules of thumb for application of satellite rainfall data in ungauged IRBs?

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Major

Minor

Improvement

Negative

‘Ball Park’ Assessment for NASA product 3B41RT

Fully Distributed Open-Book Hydrologic Model KANPUR 1.0 by Katiyar, N. and Hossain, F. 2007 Environ. Mod. Software, vol. 22(12).

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Speculations on IRBs where Satellite Rainfall Data will be ‘Sustainable’ for Flood Monitoring

Name of down stream country

International River Basin % of Total Basin Area

Cameroon Akpa/Benito/Ntem 41.8

Senegal Senegal 8.08

Ivory Coast Cavally 54.1

Benin Oueme 82.9

Botswana Okovango 50.6

Nigeria Niger 26.6

Bangladesh Ganges-Brahmaputra-Meghna 7.0

Brunei Bangau 46.0

Laos Ca/Song Koi 35.1

Cambodia Mekong 20.1

Preliminary Speculation - Setting aside ALL assumptions

Negligible Improvement

Improvement

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More Intelligent Speculation

Based on Koppen Climate Classification

Source: Encyclopedia Britannica

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Speculation on IRBs (Contd.)

Cfa & Cwa– Humid Subtropical; Bsh- Semi-aridGanges River– Bangladesh (+45%) ↑ Yalu and Tomen Rivers – North Korea (+20%)↑Limpopo River – Mozambique (+35%)↑ Senegal River – Senegal (+42%)↑La Plata River– Uruguay (+45%)↑


Faisal Hossain


Spatial Downscaling of Satellite Rainfall Data: New Questions for Assessing Sustainability

Spatial downscaling based on scale invariance.

Preserves the mean of rainfall.

Stochastic in nature – yields equi-probable realizations.

Mimics the expected variance of rainfall at downscaled resolution.

Downscaling schemes preserve the mean and mimic the expected variance. Is that good enough for flood prediction needs for GPM?

Satellite rainfall data has scale-dependent complex error–

i) Does the downscaling scheme add artifacts to downscaled satellite rainfall data?

ii) What are the hydrologic implications of using a spatial downscaling scheme for satellite rainfall on flood prediction uncertainty?

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VIRGINIA

LeeBell

Scott

Clay

Wise

Harlan

Leslie

Hawkins

Letcher

Knox

Claiborne

Perry

Hancock Sullivan

Dickenson

Knott Pike

WashingtonGreeneUnion Grainger

Norton

Owsley

Whitley

Laurel

Legend

Rivers

State Boundaries

County Boundaries

Subbasins

Satellite Pixels

0 4 8 12 16Miles

KENTUCKY

TENNESSEE

End-to-End system conceptualized, developed and tested over Upper Cumberland

River basin in Kentucky.

St. Louis Basin

An end-to-end system for NASA real-time satellite rainfall data analysis

0.25˚SREM2D

Downscaling0.125˚

0.0625˚

0.03125˚

0.12.5˚

0.0625˚

0.03125˚Downscaling

0.25˚SREM2D

HEC-HMS

Aggregated NEXRAD

Data (0.04˚ to 0.25˚)

3B42RT

NASA TMPA Data @ 0.25˚

3B41RT

0.125 deg 0.25 deg

0.0625 deg 0.03125 deg

Upper Yazoo Basin

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Downscaling of 3B41RTIncreases streamflow simulation uncertainty(?!)

0.125 degree 0.0625 degree

0.03125 degreeStream flow simulation

uncertainty using downscaled 3B41RT

data

0

10000

20000

30000

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Time

Ou

tlet

flo

w(c

fs)

ObservedMean+1*StdvMean-1*StdvmeanRadar obs simulation 0.125 deg

0

10000

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Time

Out

let f

low

(cfs

)

ObservedMean+1*StdvMean-1*StdvmeanRadar obs simulation_0.0625 deg

0

10000

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Time

Ou

tlet

flo

w(c

fs)

ObservedMean+1*StdvMean-1*Stdv

meanRadar obs simulation_0.03125 deg


Faisal Hossain


Physically-based Investigation of Spatial Downscaling on Overland Runoff Generation

Downscaling

Upscaling

Rainy grid boxes can be non-rainy

Non-rainy grid boxes can be rainy

Redistribution and bias of downscaled rainfall can be significant


Faisal Hossain



1i1

2i2

1i1

2i2

1 1

2 2

p

Kt

c ci K

1 1 1

K2 ( ) ( )pt ci ci K i i K

1 1 2 2

2p p

p

t i t iF

1 2

2p p

i iF t

1 21 2 or

2

i ii i

1 2/c i iWatershed = Non-linear system yavg ≠ f(xavg)

What role does ‘C’ – subgrid rainfall variability play in runoff simulation?

= f (avg. rainfall)

= yavg

= f (avg. rainfall)

= yavg

i


Faisal Hossain



Ksat - fieldRainfall- field

Clayey Loam

Silty Loam

Sandy Loam

High spatial Correlation-200km

Medium spatial Correlation-100km

Low spatial Correlation-50km


Faisal Hossain



Spatial downscaling technique improves the estimation of accumulated runoff parameters when compared to estimates derived from lower resolution rainfall data. Not suitable for improving the estimation of time sensitive runoff parameters such as the time to a flood peak.

Estimation Bias (%)

Rainfall 50 KM (LOW) 100 KM (MEDIUM) 200 KM (HIGH)

Soil Clay Silt Sand Clay Silt Sand Clay Silt Sand

Ponding Time

Scale Effect -98.2 -97.8 -79.5 -89.6 -94.8 -98.1 -97.9 -88.8 -89.9

Downscaling Effect

-90.1 -90.0 10.0 -91.1 -69.9 -77.8 -98.0 -51.2 -17.4

Runoff Volume

Scale Effect -75.3 -75.5 -80.5 -75.0 -75.1 -75.8 -75.1 -75.3 -77.0

Downscaling Effect

0.1 1.13 -3.46 -7.6 -7.6 -8.8 -0.1 -1.1 4.8


Faisal Hossain


Discharge Estimation of Braided Rivers:Sustainability of the SWOT Mission

What is the uncertainty of satellite interferometry (SRTM) -based discharge estimation of large braided rivers?

SRTM Overpass – Feb 20, 2000


Faisal Hossain


Discharge Estimation of Braided Rivers:Value of SWOT Mission

Hamski et al (2008) – ASLO Conference March 2-7, Orlando, Florida.

Estimated dry season discharge comparable to the natural low-flow variability.


Faisal Hossain


The Future on Sustainability of Application of Water Measuring Satellites

NASA’s vision for the post-GPM era (2013+)

- To produce routine high-level uncertainty information of their global and real-time rainfall products for users to identify sustainable application on their own (George Huffman of Laboratory of Atmospheres-NASA).

The Unresolved Paradox:Satellite rainfall will be most useful over ungauged (non-GV) regions – so how can we generate routine uncertainty estimates for satellite data over those regions ?


Faisal Hossain


The Future on Sustainability of Application of Water Measuring Satellites

Our Strategy for Solving the Paradox

Global similarity of US climate zones

Rainfall Climatology over US

Study Regions over US; 8 years of data; Radar as ground validation


Faisal Hossain


Overview of Other Research Agenda:New Paradigms for Improving Spatial Mapping

Development of NLDMAP 1.0

(Non-linear Dynamic Mapping) for rural settings.

Test cases: 1)Arsenic contamination of groundwater in Bangladesh; 2) USGS monitored aquifers in Connecticut.

Improving geostatistical (kriging) methods using Chaos Theory and Neural Networks.

Chaos and ANN analysis are complete (merging of schemes on-going – collaboration with Dept. of ECE/TTU).

Hossain,, F., and B. Sivakumar. (2006). Spatial Pattern of Arsenic Contamination in Shallow Tubewells of Bangladesh: Regional Geology and Non-linear Dynamics Stochastic Environmental Research and Risk Assessment, vol 20(1-2), pp. 66-76

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1. Uncertainty is omni-present in natural or man-made water resources systems.

2. Need good understanding of Stochastic Theory, e.g. Random Functions, Geostatistics, Time series, – to model/predict the variability.

Overview of Education Agenda on Water Resources Engineering Education

• More and more research conducted at graduate level involving stochastic theory applications.

• Bloom’s learning level of entering graduate students should be ‘Analysis’ or ‘Application’.

• Are we doing a good job with instruction of stochastic theory in CE/Water resources?


Faisal Hossain


Overview of Education Agenda:Stochastic Theory Education through Visualization Environment

Total Number of Universities Surveyed 67

Number of Universities with www listing of relevant courses 57

Total number of courses identified (having the generic terms ‘stochastic’, ‘statistics’, ‘numerical’ etc in CE curricula)

241

% Graduate(Dual listed) and Undergraduate 84(4.5)11.5

Number of schools with integrated courses on Stochastic Theory 40

Number of courses on Stochastic Theory 84 (35%)

Number of courses on Stochastic Theory in Water Resources and Environmental Engineering

27 (11.2%

)

Number of courses on Stochastic Theory in Water Resources only 23 (9.5%)Schwenk, Hossain and Huddleston (2008) Computer Applications in Engineering Education (In press)

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A Long-term Vision

http://iweb.tntech.edu/saswe

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THANK YOU!

QUESTIONS?

tennessee technological university

Documents

wikipediafor water

ttu water center

monitoring of water

university of mississippi

university of oklahoma

university of california

university of dhaka

mcneese state university