Northern Gulf Institute - GeoSystems Research Institute

Mississippi State University

Scale-dependency and Sensitivity of Hydrological Estimations to Land Use

and Topography for a Coastal Watershed in Mississippi

Vladimir J. Alarcon and Charles G. O’Hara

ICCSA 2010 Conference, March 23-26, 2010, Fukuoka, Japan

Introduction

• The spatial variability of the physical characteristics of the terrain influences the flow regime in watersheds.

• Through Internet, physiographic datasets can be easily downloaded for geo-processing.– Topographical and land-use/land-cover datasets

are used for the purposes of watershed delineation, land use characterization, geographical positioning of hydro-chemical point sources, etc.

ICCSA 2010 Conference, March 23-26, 2010, Fukuoka, Japan

Introduction

• Several previous researches have explored the sensitivity of hydrological estimations to topographical or land use datasets.

• Few have assessed the sensitivity of hydrological estimations to combined swapping of topographic and land use datasets.– Objective: This paper explores the effects of using

several different combinations of topographical and land use datasets in hydrological estimations of stream flow.

ICCSA 2010 Conference, March 23-26, 2010, Fukuoka, Japan

Methods• Study area:

– Two river catchments• Jourdan River

– Drains 88220 ha with an average stream flow of 24.5 m3/s.

• Wolf River – Drains slightly more than

98350 ha with an average stream flow of 20.1 m3/s.

• Major contributors of flow to the St. Louis Bay, MS, USA.

Jourdan River Catchment

Wolf River Catchment

ICCSA 2010 Conference, March 23-26, 2010, Fukuoka, Japan

Methods• Topographical data:

– USGS DEM • 3 arc-second (or 1:250,000-scale) approx. 300 m spatial res.

– The National Elevation Data (NED) dataset • 1 arc second, approximately 30 m spatial res.

– NASA Shuttle Radar Topography Mission• 30 m spatial resolution near-global topography data product for

latitudes smaller than 60 degrees.

– Intermap’s Interferometric Synthetic Aperture Radar (IfSAR) Digital Elevation Models 1:24,000 scale topographic quadrangle map series, 5 m spatial res.

ICCSA 2010 Conference, March 23-26, 2010, Fukuoka, Japan

Methods• Land Use data:

– USGS GIRAS• Minimum mapping unit of 4 hectares and a maximum of 16

hectares (equivalent to 400 m spatial resolution).

– National Land Cover Data (NLCD)• 21-class land cover classification scheme, spatial resolution of the

data is 30 meters.

– The NASA MODIS MOD12Q1 Land Cover Product (MODIS/Terra Land Cover

• 1000 m spatial resolution, classified in 21 land use categories, covers most of the globe and is updated every year.

ICCSA 2010 Conference, March 23-26, 2010, Fukuoka, Japan

Methods• Geo-processing

DTED format conversion to ArcView raster (.BIL)

Mosaicing of appropriate data cubes

Delineation

Generation of the mosaiced grid

Conversion: binary floating point grid to

Arcinfo grid (floatgrid)

Mosaicing of appropriate data cubes

Pre-Delineation

Conversion to "integer grid“ with elevation in

centimeters.

Original data: floating point binary grid format

Mosaicing and clipping of the region of interest

Original data: DTED format

Optional clipping of the resulting grid

Joining of resulting polygons

Elimination of overlapping areas

SRTM IFSAR

Delineated watershed

Delineated watershed

LAND USETOPOGRAPHY

ICCSA 2010 Conference, March 23-26, 2010, Fukuoka, Japan

Methods• Data processing: all the datasets were geo-processed for input into the Hydrological

Simulation Program Fortran (HSPF).

ICCSA 2010 Conference, March 23-26, 2010, Fukuoka, Japan

Methods• Hydrological Modeling

– Hydrological Simulation Program Fortran (HSPF). • Simulation of non-point source watershed hydrology and

water quality. • Time-series of meteorological/water-quality data, land

use and topographical data are used to estimate stream flow hydrographs and polluto-graphs.

• The model simulates interception, soil moisture, surface runoff, interflow, base flow, snowpack depth and water content, snowmelt, evapo-transpiration, and ground-water recharge.

ICCSA 2010 Conference, March 23-26, 2010, Fukuoka, Japan

Methods• Processing steps for

generating the hydrological model’s data input.

• BASINS, GEOLEM, ArcGIS, and Arc Info were used.

• These GIS systems extracted land use and topographical information for input into HSPF.

• HSPF was launched from within the BASINS system.

BASINSWatershed delineation

GEOLEM/ArcGISLand use and topographical

datasets parameterization for HSPF feed

WinHSPF/GenScnGeneration and calibration of

HSPF applications

starter.uci(base file for generation of

scenarios)

*.WSD(watershed file for

generation of scenarios)

Delineated watershed(*.shp file)

Land use datasets(NLCD, GIRAS,

MODIS)

Topographical datasets

(SRTM, IFSAR, NED, DEM)

WinHSPFScenarios simulation

ICCSA 2010 Conference, March 23-26, 2010, Fukuoka, Japan

Methods

• HSPF models for Jourdan and Wolf watersheds.

• USGS stream flow gauge stations at: – Catahoula (Jourdan

River)

– Lyman, and Landon (Wolf River)

were used for hydrological calibration of the models.

ICCSA 2010 Conference, March 23-26, 2010, Fukuoka, Japan

Results• Results of statistical fit between simulated and measured

stream flow for Jourdan River watershed

Model fit efficiency (Nash-Sutcliff, NS)

MODIS (1000 m) GIRAS (400 m) NLCD (30 m)

USGS DEM (300m) 0.75 0.74 0.75

NED (30m) 0.73 0.72 0.72

SRTM (30m) 0.73 0.72 0.72

IFSAR (5m) 0.74 0.73 0.73

Coefficient of determination R2

MODIS (1000 m) GIRAS (400 m) NLCD (30 m)

USGS DEM (300m) 0.8 0.8 0.79

NED (30m) 0.77 0.78 0.76

SRTM (30m) 0.77 0.77 0.76

IFSAR (5m) 0.78 0.78 0.77

DEM(300m)NED

(30m)SRTM(30m)IFSAR

(5m)

MODIS (1000 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

0.72-0.725DEM

(300m)NED(30m)SRTM

(30m)IFSAR(5m)

MODIS (1000 m)

GIRAS (400 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

DEM(300m)NED

(30m)SRTM(30 m)IFSAR

(5m)

MODIS (1000 m)

GIRAS (400)

NLCD (30 m)

0.74

0.75

0.76

0.77

0.78

Model reliability coefficient

0.77-0.78

0.76-0.77

0.75-0.76

0.74-0.75

DEM(300m)NED

(30m)SRTM(30m)IFSAR

(5m)

MODIS (1000 m)

GIRAS (400 m)

NLCD (30 m)

0.76

0.77

0.78

0.79

0.8

0.81

0.82

0.83

Nash-Sutcliff, NS, model fit efficiency

0.82-0.83

0.81-0.82

0.8-0.81

0.79-0.8

0.78-0.79

0.77-0.78

0.76-0.77 DEM (300m)NED

(30m)SRTM(30m)IFSAR

(5m)

MODIS (1000 m)

GIRAS (400 m)

NLCD (30 m)

0.81

0.815

0.82

0.825

0.83

0.835

0.84

Coefficient of determination (R^2)

0.835-0.84

0.83-0.835

0.825-0.83

0.82-0.825

0.815-0.82

0.81-0.815

DEM(300m)NED

(30m)SRTM(30 m)IFSAR

(5m)

MODIS (1000 m)

GIRAS (400)

NLCD (30 m)

0.8

0.805

0.81

0.815

0.82

0.825

0.83

Model reliability coefficient

0.825-0.83

0.82-0.825

0.815-0.82

0.81-0.815

0.805-0.81

0.8-0.805

JourdanRiver Watershed

Wolf River Watershed

DEM(300m)NED

(30m)SRTM(30m)IFSAR

(5m)

MODIS (1000 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

0.72-0.725DEM

(300m)NED(30m)SRTM

(30m)IFSAR(5m)

MODIS (1000 m)

GIRAS (400 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

DEM(300m)NED

(30m)SRTM(30 m)IFSAR

(5m)

MODIS (1000 m)

GIRAS (400)

NLCD (30 m)

0.74

0.75

0.76

0.77

0.78

Model reliability coefficient

0.77-0.78

0.76-0.77

0.75-0.76

0.74-0.75

DEM(300m)NED

(30m)SRTM(30m)IFSAR

(5m)

MODIS (1000 m)

GIRAS (400 m)

NLCD (30 m)

0.76

0.77

0.78

0.79

0.8

0.81

0.82

0.83

Nash-Sutcliff, NS, model fit efficiency

0.82-0.83

0.81-0.82

0.8-0.81

0.79-0.8

0.78-0.79

0.77-0.78

0.76-0.77 DEM (300m)NED

(30m)SRTM(30m)IFSAR

(5m)

MODIS (1000 m)

GIRAS (400 m)

NLCD (30 m)

0.81

0.815

0.82

0.825

0.83

0.835

0.84

Coefficient of determination (R^2)

0.835-0.84

0.83-0.835

0.825-0.83

0.82-0.825

0.815-0.82

0.81-0.815

DEM(300m)NED

(30m)SRTM(30 m)IFSAR

(5m)

MODIS (1000 m)

GIRAS (400)

NLCD (30 m)

0.8

0.805

0.81

0.815

0.82

0.825

0.83

Model reliability coefficient

0.825-0.83

0.82-0.825

0.815-0.82

0.81-0.815

0.805-0.81

0.8-0.805

JourdanRiver Watershed

Wolf River Watershed

DEM(300m)NED

(30m)SRTM(30m)IFSAR

(5m)

MODIS (1000 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

0.72-0.725DEM

(300m)NED(30m)SRTM

(30m)IFSAR(5m)

MODIS (1000 m)

GIRAS (400 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

DEM(300m)NED

(30m)SRTM(30 m)IFSAR

(5m)

MODIS (1000 m)

GIRAS (400)

NLCD (30 m)

0.74

0.75

0.76

0.77

0.78

Model reliability coefficient

0.77-0.78

0.76-0.77

0.75-0.76

0.74-0.75

DEM(300m)NED

(30m)SRTM(30m)IFSAR

(5m)

MODIS (1000 m)

GIRAS (400 m)

NLCD (30 m)

0.76

0.77

0.78

0.79

0.8

0.81

0.82

0.83

Nash-Sutcliff, NS, model fit efficiency

0.82-0.83

0.81-0.82

0.8-0.81

0.79-0.8

0.78-0.79

0.77-0.78

0.76-0.77 DEM (300m)NED

(30m)SRTM(30m)IFSAR

(5m)

MODIS (1000 m)

GIRAS (400 m)

NLCD (30 m)

0.81

0.815

0.82

0.825

0.83

0.835

0.84

Coefficient of determination (R^2)

0.835-0.84

0.83-0.835

0.825-0.83

0.82-0.825

0.815-0.82

0.81-0.815

DEM(300m)NED

(30m)SRTM(30 m)IFSAR

(5m)

MODIS (1000 m)

GIRAS (400)

NLCD (30 m)

0.8

0.805

0.81

0.815

0.82

0.825

0.83

Model reliability coefficient

0.825-0.83

0.82-0.825

0.815-0.82

0.81-0.815

0.805-0.81

0.8-0.805

JourdanRiver Watershed

Wolf River Watershed

.)(

)(

1

1

2

1

2

n

jjj

n

jjj

OO

SO

NS

GIRAS (400 m)

ICCSA 2010 Conference, March 23-26, 2010, Fukuoka, Japan

Results• Results of statistical fit between simulated and measured

stream flow for Wolf River watershed

JourdanRiver Watershed

Wolf River Watershed

JourdanRiver Watershed

Wolf River Watershed

JourdanRiver Watershed

Wolf River Watershed

Model fit efficiency (Nash-Sutcliff, NS)

MODIS (1000 m) GIRAS (400 m) NLCD (30 m)

USGS DEM (300m) 0.82 0.76 0.81

NED (30m) 0.81 0.81 0.8

SRTM (30m) 0.81 0.81 0.8

IFSAR (5m) 0.82 0.82 0.81

Coefficient of determination R2

MODIS (1000 m) GIRAS (400 m) NLCD (30 m)

USGS DEM (300m) 0.83 0.83 0.82

NED (30m) 0.82 0.82 0.82

SRTM (30m) 0.82 0.82 0.82

IFSAR (5m) 0.83 0.83 0.82

.)(

)(

1

1

2

1

2

n

jjj

n

jjj

OO

SO

NS

ICCSA 2010 Conference, March 23-26, 2010, Fukuoka, Japan

Conclusions• Scale and spatial resolution of digital land use and topography

datasets do not affect the statistical fit of measured and simulated stream flow time-series, when using the Hydrological Program Fortran (HSPF) as the hydrological model recipient of land use and topographical data.

• When the input to HSPF comes from the coarsest spatial resolution datasets (MODIS land use and USGS DEM topography), HSPF simulated stream flow show equivalent statistical fit to measured stream flow as when finer spatial resolution datasets are combined.

• This suggests that stream flow simulation is not sensitive to scale or spatial resolution of land use and/or topographical datasets.

ICCSA 2010 Conference, March 23-26, 2010, Fukuoka, Japan

Conclusions• Since HSPF is a lumped-parameter hydrological model, the

summarization of physiographic information (before it is input to the model) is a required step.

• This pre-processing of raw land use and topographical data (re-classification, averaging of data per sub-basin, etc.) may be a factor that explains the insensitivity of simulated stream flow to land use and topographical datasets swapping, when using HSPF.

• Future studies should explore the effects that this swapping may have when using distributed hydrological models.