Analysis of an Adaptative SCS-CN

Abstract: Alteration of the land use brings with it a change in soil moisture patterns. Moreover, land cover governs the amount of water infiltrated through the surface and water holding capacity of the soil, and thus is essential for understanding the soil hydrology dynamics and estimation of runoff.

The Soil Conservation Service Curve Number (SCS-CN), an index that represents the potential for storm water runoff within a given watershed, depends on land use and land treatment, and soil antecedent moisture conditions (AMC). Despite soil texture and topography are some of the parameters that mainly dominate soil hydrology; they do not vary much over time. On the other hand, vegetation and soil moisture do vary over time and control the overall trend of the CN, thus temporal variation of land cover and soil moisture must be taken into account when estimating the CN. To carry out that, leaf area index (LAI) and soil moisture data, from MODIS and AMSR-E respectively, will be used for CN estimation in the upper Mississippi basin, a well-known flood-prone area, where vegetation's growth cycles rapidly change affecting soil moisture which in turn is also affected by, among others, snowmelt and precipitation.

Both conventional and AMC-adjusted CNs for a given timeframe generally follow a smooth trend which does not reflect its actual behavior. This research will focus on analyzing the seasonal fluctuation of the CN using remotely sensed imagery and the subsequent estimation of the amount of runoff over time which we expect to be a more realistic and accurate approach than the traditional SCS-CN methods (conventional and adjusted). Additionally, the above-mentioned results will be compared to those obtained from modeled runoff estimation carried out by means of HEC-HMS which is compatible with gridded data.