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 method Data and Parameters Digital Elevation model Watershed delineation Land use / land cover Soil hydrologic group Precipitation data Streamflow record Stream baseflow estimation Antecedent moisture condition = C urve N umber } * NRCS – Natural Resources Conservation Service

Essential observations of a surface water system Precipitation (rainfall) Infiltration Runoff Streamflow Infiltration Soil moisture RS direct quantification Passive microwave methods very coarse spatial resolution poor temporal resolution expensive data moderate spatial resolution excellent temporal resolution free data RS proxy characterization Landscape state and energy flux

Data and Methodology Remote Sensing Data MODIS 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 ( km 2 pixel dim) NEXRAD radar (Stage III, MPE) precipitation estimates USGS gauged streamflow records Model parameterization based on: USGS_Pic2488r 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 February 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 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:

Coupling vegetation to soil moisture visiblenear infraredmiddle infrared Leaf structure Leaf water content Leaf chemistry

nir red

Band 2 Band 7Band 6

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

RS Model Development (2004) 6 MODIS parameters (LST day, LST night, 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, LSTday r (1), EVI r (0) Final equation: where Q = streamflow, P = precipitation, T = LST, and I = EVI All β 1,2,3 estimates significant at P < β 0 estimate significant at P < 0.04

2002 – 07* time series of daytime LST and precipitation

Sandies Creek calibration and validation results Model periodE log series Bias Calibration All (n = 174) (-0.471)* 2002 (n = 43) (-0.399)* 2003 (n = 42) (n = 45) (n = 44) Validation All (n = 57) (n = 46) (n = 11) Calibration Validation * Exclusion of July 2002 flood event

Sandies Creek validation results (linear space)