July 10, 2007 NASA quarterly briefing

Geoprocessing using GEOLEMGeoprocessing using GEOLEMandand

HSPF in the RPC FrameworkHSPF in the RPC Framework

Vladimir Alarcon

Chuck O’Hara

July 10, 2007 NASA quarterly briefing

GEOLEM

• Library of basic geoprocessing functions, e.g., “flow direction”, “reclassify”

• Library of complex geoprocessing logic, e.g., “make map of hillslopes”, “make map of affected areas”

• Knowledge handling infrastructure• System to encode modeling knowledge

into metadata• Metadata handling infrastructure

July 10, 2007 NASA quarterly briefing

GEOLEM and HSPF

• GEOLEM was customized to provide landuse and topographical parameters to be ingested by HSPF

• How was it modified?– GEOLEM main code: changes to include new schema

files:• config.xml• concept.xml• instance.xml

– New methods codes were written:• SlopeHspfMethod.java• LanduseHspfMethod.java• ReclassLanduseHspfMethod.java

July 10, 2007 NASA quarterly briefing

GEOLEM and HSPF

July 10, 2007 NASA quarterly briefing

GEOLEM and HSPF

• How was it modified? (continued)– New parameter java codes were written:

• LandUseHspfParameters.java• SlopeHspfParameters.java• ReclassHspfLanduseParameters.java

– New providers to parameters:• LandUseHspfParametersProvider.java• SlopeHspfParametersProvider.java• ReclassHspfLanduseParametersProvider.java

July 10, 2007 NASA quarterly briefing

GEOLEM and HSPF

July 10, 2007 NASA quarterly briefing

GEOLEM and HSPF

• How was it modified? (continued)– New commands were coded into existing

JacobCommands class:• SlopePercent• TabulateArea• ReclassHSPF

July 10, 2007 NASA quarterly briefing

GEOLEM and HSPF

July 10, 2007 NASA quarterly briefing

GEOLEM and HSPF• How was it modified? (Version without changes)

HSPF

July 10, 2007 NASA quarterly briefing

GEOLEM and HSPF• How was it modified? (changed version)

HSPF

getParam: HSPFLanduseArea

getParam: HSPFSlope

getParam: HSPFLanduseArea

getParam: HSPFSlope

return: Area

return: Slope

getDimension: Sub_basin

return: Sub_basin

Subbasin: HSPFslope

Zonal Statistics: Slopes and landuse area per sub-basinParamHspfSlope

ParamHspfLanduseArea

Now the user has the option of re-using

EXISTING

Delineated subbasins instead of delineating all over again several times

OPTIO

NAL

LandUseParam.dbf

SlopeParam.dbfASCII HSPF input files

July 10, 2007 NASA quarterly briefing

HSPF in RPC

Vladimir Alarcon

Chuck O’Hara

July 10, 2007 NASA quarterly briefing

HSPF

GEOLEM

Geo-processing server

User GUI

Application schema

Python connector

Results

UCI file

Delineated Watershed

Land use classes

Characterization ---Stream

-Sub-basin

GEOLEM

Watershed boundary polygon

Delineated watershed polygon

Land use MODIS, VIIRS

Topography SRTM

GenScen

WDMutilUser Control Input file

UCI

LIS-generated data: Precip., ET, Soil Moist.

Rainfall, ET, Soil Moisture time series

Modified

Land use Topography

GEOLEM

User’s domain

Server’s domain

How does HSPF fit into RPC?

July 10, 2007 NASA quarterly briefing

HSPF in RPC

• How can HSPF be used within the RPC environment?– Study the effects of topographical datasets in

hydrograph• Some results were presented in previous briefings

– Alarcon and O’Hara, 2006– Alarcon, O’Hara et al., 2006

– Study the effects of landuse datasets in hydrograph• Some results were presented in previous briefings

– Diaz, Alarcon, O’Hara, et al.

– For this quarterly report we have prepared what could be a typical RPC application using Geolem HSPF

• Concurrent effects of landuse and topographical datasets on streamflow hydrograph simulation in a coastal watershed

July 10, 2007 NASA quarterly briefing

HSPF in RPC: research question

• Question: if NASA would like to design/launch/release a sensor/mission/product related with topographical and landuse data, what resolution would be useful for watershed hydrology modeling?

• A factorial experiment with existing LULC and topography datasets has been performed.

• GEOLEM was used to generate 12 concurrent scenarios of topographical/LULC cases for HSPF ingestion.

• HSPF was used to simulate streamflow hydrograph for each of these 12 cases.

• Those simulated streamflow hydrographs were compared to measured streamflow and the simulated-output reliability was assessed

July 10, 2007 NASA quarterly briefing

HSPF in RPC: factorial experiment

Topography\LanduseMODIS (1000 m)

GIRAS (900 m)

NLCD (30 m)

DEM (300 m)

NED (30 m)

SRTM (30 m)

IFSAR (5 m)

Statistical indicators of fit between HSPF simulated streamflow and measured streamflow

•Nash-Sutcliff (NS) number

•Coefficient of determination R2

•Model reliability coefficient:

2

222 NSR

Good fit when these coefficients are close to 1

July 10, 2007 NASA quarterly briefing

• Jourdan River:– Located in the Saint Louis

Bay watershed (Mississippi Gulf coast)

• Largest contributor of flow to the Saint Louis Bay

• Drains 882 sq. km• Average flow: 24.5 cms

Jourdan River Catchment

HSPF in RPC: Study area

July 10, 2007 NASA quarterly briefing

HSPF in RPC: NED & NLCD(GOOD FIT)

July 10, 2007 NASA quarterly briefing

HSPF in RPC: DEM & MODIS (BETTER FIT)

July 10, 2007 NASA quarterly briefing

HSPF in RPC: fit between simulated and measured streamflow

DEM(300m)NED

(30m)SRTM(30m)IFSAR

(5m)

MODIS (1000 m)

GIRAS (900 m)

NLCD (30 m)

0.72

0.725

0.73

0.735

0.74

0.745

0.75

0.745-0.75

0.74-0.745

0.735-0.74

0.73-0.735

0.725-0.73

0.72-0.725

Model fit efficiency (Nash-Sutcliff NS)MODIS (1000 m) GIRAS (900 m) NLCD (30 m)

DEM (300m) 0.75 0.74 0.75NED (30m) 0.73 0.72 0.72SRTM (30m) 0.73 0.72 0.72IFSAR (5m) 0.74 0.73 0.73

MODIS (1000 m) GIRAS (900 m) NLCD (30 m)DEM (300m) 0.75 0.74 0.75NED (30m) 0.73 0.72 0.72SRTM (30m) 0.73 0.72 0.72IFSAR (5m) 0.74 0.73 0.73

July 10, 2007 NASA quarterly briefing

DEM (300m)NED

(30m)SRTM(30m)IFSAR

(5m)

MODIS (1000 m)

GIRAS (900 m)

NLCD (30 m)

0.76

0.765

0.77

0.775

0.78

0.785

0.79

0.795

0.8

0.795-0.8

0.79-0.795

0.785-0.79

0.78-0.785

0.775-0.78

0.77-0.775

0.765-0.77

0.76-0.765

MODIS (1000 m) GIRAS (900 m) NLCD (30 m)DEM (300m) 0.8 0.8 0.79NED (30m) 0.77 0.78 0.76SRTM (30m) 0.77 0.77 0.76IFSAR (5m) 0.78 0.78 0.77

Coefficient of determination R2

MODIS (1000 m) GIRAS (900 m) NLCD (30 m)DEM (300m) 0.8 0.8 0.79NED (30m) 0.77 0.78 0.76SRTM (30m) 0.77 0.77 0.76IFSAR (5m) 0.78 0.78 0.77

HSPF in RPC: fit between simulated and measured streamflow

July 10, 2007 NASA quarterly briefing

HSPF in RPC: fit between simulated and measured streamflow

Model reliabilityMODIS (1000 m) GIRAS (900) NLCD (30 m)

DEM (300m) 0.78 0.77 0.77NED (30m) 0.75 0.75 0.74SRTM (30 m) 0.75 0.75 0.74IFSAR (5m) 0.76 0.76 0.75

DEM(300m)NED

(30m)SRTM(30 m)IFSAR

(5m)

MODIS (1000 m)

GIRAS (900)

NLCD (30 m)

0.74

0.745

0.75

0.755

0.76

0.765

0.77

0.775

0.78

0.775-0.78

0.77-0.775

0.765-0.77

0.76-0.765

0.755-0.76

0.75-0.755

0.745-0.75

0.74-0.745

Model reliability coefficientMODIS (1000 m) GIRAS (900) NLCD (30 m)

DEM (300m) 0.78 0.77 0.77NED (30m) 0.75 0.75 0.74SRTM (30 m) 0.75 0.75 0.74IFSAR (5m) 0.76 0.76 0.75

July 10, 2007 NASA quarterly briefing

Conclusions from the experiment

• The combination of low resolution topographical datasets (such as DEM, 300m) and low resolution landuse datasets (such as MODIS, 1000m) produce good statistical fit between simulated and measured streamflow hydrographs.

• Also: the finer the topographical grid (such as IFSAR, 5m) combined with coarse resolution landuse datasets (such as MODIS or GIRAS) seem to produce good statistical fit.

• Medium-resolution topographical datasets(such as SRTM or NED, 30m) combined with medium-resolution landuse datasets (NLCD, 30 m) give the lowest goodness of fit.

July 10, 2007 NASA quarterly briefing

Future steps

• Include simulated VIIRS in similar topography/LULC and streamflow hydrograph assessments

• Include distributed meteorological forcings in the exploration:– NASA LIS precipitation– NASA LIS soil moisture– NASA LIS evapotranspiration