SOIL CONSERVATION SERVICE RUNOFF CURVE NUMBER ESTIMATES FROM LANDSAT DATA¹

Abstract

A widely used method of calculating storm runoff developed by the Soil Conservation Service (SCS) uses storm rainfal and a curve number. A curve number is a quantitative descriptor of the land cover/soil complex and is commonly assigned based on information acquired from field surveys and interpretations of aerial photographs. Since these techniques are prohibitively expensive and time consuming for large watersheds, digital data from the Landsat‐1 satellite was used to estimate curve numbers for the Little River watershed near Tifton, Georgia, an area of about 125 square miles. A classification map of this area with four hydrologically important classes (agricultural vegetation, forest, wetland, and bare ground) was generated from the Landsat data. Since the soils data necessary for curve numbers were available for only about half the watershed, the missing soils data were interpolated from the vegetation patterns of the classification map. A modification of the SCS curve number table was developed and used to assign each pixel to a category. The average curve number for‘each of six subwatersheds and for the entire watershed was then computed and compared with SCS values for the same areas. Agreement was to within two curve number units.

Estimation of Runoff Index Using Landsat (Tm) Data and an Artificial Neural Network System

“…This is more convenient because other hydrologic models may require several input parameters, which are sometimes difficult to obtain, Two approaches in estimating CN values using remotely sensed data have been proposed (Sharma and Singh, 1992). One approach is to use remotely sensed data to classify land cover and land use, and then comPare them to those listed in the standard CN table to obtain CN values (Ragan and Jackson, 1980;Slack and Welch, 1980;Bondelid ef a/.. 1980;Still and Shih, 1985;Tiwari et at., 1991;and Sharma and Singh, 1992 any pixel classified as forest will have the CN value of 55. In reality, CN values are not precise coefficients, rather they represent the 'best fit' to the small watershed rainfall-runoff data upon which the model is based (Ragan and Jackson' 1980).…”

Rainfall-Runoff Modeling: Comparison of Two Approaches with Different Data Requirements

“…GIS allows the combination of remotely sensed data with spatial data forms such as topography, soil maps, and hydrologic variables such as rainfall distribution and soil moisture. Many researchers (Blanchard 1975;Jackson et al 1977;Ragan and Jackson 1980;Slack and Welch 1980;Bondelid et al 1982;Hill et al 1987;White 1988;Muzik 1988;Stuebe and Johnston 1990;Tiwari et al 1991Tiwari et al , 1997Das et al 1992) used land use/land cover information derived from satellite data of Landsat, SPOT, and Indian Remote Sensing Satellite (IRS) and integrated them with GIS to estimate SCS CNs and runoff. Zhan and Huang (2004) applied ArcCN-Runoff tool (an extension of ESRI's ArcGIS software) to determine CNs and to calculate runoff or infiltration from a rainfall event for a watershed in Lyon County and Osage County, KS, USA.…”

Runoff estimation using Landsat Thematic Mapper data and the SCS model

“…One involved the use of remotely sensed data to determine the land cover alone (Cermak et al, 1979;Ragan & Jackson, 1980;Slack & Welch, 1980); the other attempted to estimate the CN value itself without ancillary soils data (Blanchard, 1975;Jackson & Bondelid, 1983). However, in all those studies, Landsat Multi-Spectral Scanner (MSS) data with poor spatial resolution (80 m) were used which resulted in a large error in the estimation of runoff (Groves et ai, 1985).…”