Geospatial estimation of soil moisture in rain-fed paddy fields using SCS-CN-based model

Abstract

Paddy fields are characterized by standing water and saturation condition during the entire crop growth period. However, in sub-humid and semi-arid areas, scarce rainfall and intermittent dry spells often cause soil moisture depletion resulting in unsaturated condition in the fields. These distinctive characteristics of the paddy fields have significant influence on the runoff generation and soil moisture retention characteristics of the watershed. In this study, the objective is to extend the application of the Soil Conservation Services Curve Number (SCS-CN)-based models for the geospatial and temporal simulation of soil moisture to paddy field-dominated agricultural watersheds in the water scarce areas. Different SCS-CN-based models, integrated with the soil moisture balance equation, are used to estimate the surface runoff and soil moisture content wherein, the spatial variation in the soil hydraulic characteristics is used to calculate the geospatial variation in soil moisture content. Physical significance of the terms initial abstraction ( I a ) and potential maximum retention ( S) in these models and their influence on the estimation of runoff and soil moisture are analysed in detail. A new SCS-CN-based model for soil moisture simulation (SCS-CN-SMS), to improve the soil moisture estimation, is proposed in this paper. The proposed model is built up on the soil moisture balance equation to account for the effect of ponding condition and soil moisture variation between the dry and saturation condition. The method is tested with 3 years observed surface runoff data and crop production statistics from a part of the Gandeshwari sub-watershed in West Bengal, India. The entire study area is divided into cells of 20 m × 20 m. Various components of the soil moisture balance equation are estimated for each cell as a function of the soil moisture content. Remote Sensing Technique and Geographic Information System (GIS) are used to extract and integrate the spatially distributed land use and soil characteristics. The Hortonion overland flow concept adopted in the SCS-CN method is used to estimate the soil hydraulic characteristics of each cell in which the curve number is used to infer the spatial variation of the land use and soil characteristics. Even though the original SCS-CN method and the existing modified versions are efficient for runoff estimation, these models are found to be inappropriate for the estimation of soil moisture distribution. On the other hand, the proposed SCS-CN-SMS model gives better results for both runoff and soil moisture simulation and is, therefore, more suitable for the hydrological modeling of paddy field-dominated agricultural watersheds.