Remote sensing for surface water hydrology

• RS applications for assessment of hydrometeorological states and fluxes– Soil moisture, snow cover, snow water equivalent,

evapotranspiration, vegetation cover and water content, land surface energy balance, water quality

• The above parameterize numerous physical, conceptual, and empirical models of surface water dynamics, such as runoff, infiltration, and streamflow

• Can runoff/streamflow be directly observed and quantified with RS?

Not with any current technology

NRCS* Curve number methodData and Parameters

• Digital Elevation model

• Watershed delineation

• Land use / land cover

• Soil hydrologic group

• Precipitation data

• Streamflow record

• Stream baseflow estimation

• Antecedent moisture condition

= CurveNumber}

* NRCS – Natural Resources Conservation Service

Essential observations of a surface water system

Precipitation (rainfall)

Infiltration

Runoff

Streamflow

Infiltration

Soil moistureRS directquantificationPassive microwave

methods

very coarse spatial resolutionpoor temporal resolution

expensive data

moderate spatial resolutionexcellent temporal resolution

free data

RS proxycharacterization

Landscape stateand energy flux

Data and Methodology

• Remote Sensing DataMODIS NASA’s Moderate Resolution Imaging Spectroradiometer - Surface temperature (LST)- Albedo- Vegetation state

- NDVI (Normalized Difference Vegetation Index)- EVI (Enhanced Vegetation Index)- User derived MSI (Moisture Stress Index) and others

AMSR-E Advanced Microwave Scanning Radiometer - Soil Moisture (resolution issues?)- Vegetation water content and roughness

General methodology

• MODIS time-series landscape biophysicals – High temporal resolution (daily but composited as 8 and 16 day

products)– Moderate spatial resolution (0.25 - 1km2 pixel dim)

• NEXRAD radar (Stage III, MPE) precipitation estimates• USGS gauged streamflow records

Model parameterization based on:

http://malibusurfsidenews.com/blog/uploaded_images/USGS_Pic2488r-764415.jpg

NEXRAD MPE radar estimate of hourly precipitation rate for 4 July 2006 (21:00 GMT) for Sandies Creek watershed and surrounding region. Rates ranged from 0.0 mm/hr (black pixel) to 14.6 mm/hr (white pixel) for cells within the watershed

Daytime LST (8 day composite) for the Sandies Creek watershed for the period 18 - 25 February 2002. Mean temperatures for this period ranged from 24.9 C (dark pixels) to 29.3 C (light pixels).

NDVI (16 day composite) image of the Sandies Creek watershed for the period 18 February – 6 March 2002. Dark-toned and light-toned pixels represent low and high NDVI values (stressed vegetation vs healthy), respectively.

How is LST coupled to soil moisture (or vice versa)

• Heat flux from the earth’s surface– Radiative flux (long wave thermal 9-13 μm)– Sensible heat flux (convection and conduction)– Latent heat flux (phase change)

• Is soil surface emissivity affected by soil moisture? would this affect radiative, sensible, or latent heat loss?

• Would a loss or gain of near-surface soil moisture likely impact sensible or latent heat flux?

From: http://upload.wikimedia.org/wikipedia/en/6/69/LWRadiationBudget.gif

Coupling vegetation to soil moisture

A Typical Vegetation Reflectance Spectra

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visible near infrared middle infrared

Leaf structure Leaf water content

Leaf chemistry

Spectral response of leaf drydown as % water loss

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Spectral response of leaf drydown as % water loss

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Development of a benchmark model (CN) for Sandies Creek for 2004

RS Model Development (2004)

• 6 MODIS parameters (LSTday, LSTnight, NDVI, EVI, NDWI, MSI) x 2 states (raw, deseasoned) x 3 antecedent offsets (0, 8, 16 days) = 36 regressors evaluated (plus precipitation)

• Streamflow log transformed (normality assumptions)

• Final model: Prec, LSTdayr(1), EVIr(0)

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logQ Predicted P<.0001 RSq=0.84

RMSE=0.6876

Final equation:

ITPQ 331.7192.0439.0957.0log where Q = streamflow, P = precipitation, T = LST, and I = EVI

All β1,2,3 estimates significant at P < 0.0001β0 estimate significant at P < 0.04

2002 – 07* time series of daytime LST and precipitation

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Sandies Creek calibration and validation results

Model period E log series Bias

Calibration All (n = 174) 0.677 0.207 (-0.471)*

2002 (n = 43) 0.616 1.037 (-0.399)*

2003 (n = 42) 0.477 -0.467

2004 (n = 45) 0.705 -0.516

2005 (n = 44) 0.785 -0.627

Validation All (n = 57) 0.453 -0.322

2006 (n = 46) -0.028 -0.593

2007 (n = 11) 0.871 -0.293

Calibration Validation

* Exclusion of July 2002 flood event

Sandies Creek validation results (linear space)