Soil Conservation Service Curve Number (SCS-CN) Methodology

Hydrology is the science of water that deals with the space-time-frequency characteristics of the quantity and quality of the waters of the earth with respect to their occurrence, distribution, movement, storage, and development. Hydrology plays a fundamental role in addressing a range of issues related to environmental and ecological management and societal development. Central to addressing these issues is rainfall-runoff modeling which, in particular, is used in water resources assessment, flood and drought mitigation, and water resources planning and management. In general, rainfall-runoff modeling is basic to design of a wide variety of hydraulic structures, environmental impact assessment, evaluation of the impact of climate change, irrigation scheduling, flood forecasting, planning of tactical military operations, augmentation of runoff records, pollution abatement, watershed management, and so on.

The Soil Conservation Service Curve Number (SCS-CN) method was developed in 1954 and is documented in Section 4 of the National Engineering Handbook (NEH-4) published by the Soil Conservation Service (now called the Natural Resources Conservation Service), U.S. Department of Agriculture in 1956. The document has since been revised in 1964, 1965, 1971, 1972, 1985, and 1993. The SCSCN method is the result of exhaustive field investigations carried out during the late 1930s and early 1940s and the works of several early investigators, including Mockus (1949), Sherman (1949), Andrews (1954), and Ogrosky (1956). The passage of Watershed Protection and Flood Prevention Act (Public Law 83–566) in August 1954 led to the recognition of the method at the Federal level and the method has since witnessed myriad applications all over the world. It is one of the most popular methods for computing the volume of surface runoff for a given rainfall event from small agricultural, forest, and urban watersheds. The method is simple, easy to understand and apply, stable, and useful for ungauged watersheds. The primary reason for its wide applicability and acceptability lies in the fact that it accounts for most runoff producing watershed characteristics: soil type, land use/treatment, surface condition, and antecedent moisture condition. This chapter describes the existing SCS-CN method, the concept of curve number and factors affecting it, the procedure for its application, sensitivity of its parameters, and its advantages and limitations.

Since the inception of the SCS-CN method, the issues such as the rational derivation of the method (equation (2.5)), the rationale of initial abstraction, the analytics of the S-CN mapping relation (equation (2.7)), and the CN-AMC relations have been of major concern. These relations appear to be mysterious in a sense that no analytical or physical explanation of their development is yet available in the literature. Thus, the objective of this chapter is to revisit the existing SCS-CN method from an analytical perspective and explore the fundamental proportionality concept (equation (2.2)). The general notion that the SCS-CN method is a generalization of the Mockus (1949) method (Rallison and Miller, 1982) is proved analytically. Alternate analytical means are proposed to derive the existing SCS-CN method and these are shown to be an improvement over the existing derivations. The description of its functional behaviour leads to the development of criteria useful for field applications. The empirical S-CN relationship is investigated for its analytical derivation. The relations linking CN with AMC are also proposed and discussed. A few case studies are presented to support the analytical derivations and finally, a brief investigation is made for using the SCS-CN concept as an alternative to the power law widely used as a surrogate to the popular Manning’s equation described in Chapter 1.

In practice, the SCS-CN parameter, the potential maximum retention or postinitial abstraction retention (McCuen, 2002), S, is determined from scaled values of CN derived from the tables of National Engineering Handbook (NEH) for λ = 0.2 (Chapter 2). For computing S from rainfall-runoff data, equation (2.14) is used. Equation (2.14) is a specific form of equation (3.109). The existing SCS-CN method with λ = 0.2 is, therefore, a one-parameter model. In Chapter 3, the curve number CN is defined as the percent degree of saturation of the watershed soil by the 10-inch rainfall amount. As shown in Chapter 3, the generalization of the Mockus method leading to the SCS-CN method yields a relation between the Mockus parameter ‘b’ and S as: b =1/}S ln(10)}. Parameter ‘b’ depends on the antecedent moisture condition (AMC), vegetative cover, land use, time of the year, storm duration, and soil type; which describe CN. While attempting to theoretically justify the basis of the SCS-CN hypothesis, Yu (1998) described S as the product of the spatially averaged infiltration rate and the storm duration. In the previous chapter, the SCS-CN parameter S is distinguished from parameter s_av in the expression given by Yu (1998), for the former represents the volumetric capacity of the soil to retain water [L or L³] and the latter represents the ratio of S to the time duration of the storm [LT⁻¹]. The description of S by Mockus (1964), given in Chapter 2, appeals most for S to be equivalent to the maximum difference of (P-Q), which corresponds to the maximum possible infiltration capacity of the soil, if other losses including initial abstractions are ignored. His further explanation is, however, ambiguous in that it compares the infiltration rate with the volumetric space available in the soil profile; the former represents the rate and the latter the volume, which are not comparable. In this chapter, S is distinguished from infiltration rate to represent the volumetric retention, consistent with the definition of McCuen (2002), and it is supported by the observed infiltration data. In addition to these, the runoff factor (C) = degree of saturation (S_r) concept (Chapter 3) is used to signify S and derive a hierarchy of the SCS-CN-based methods.

In the previous chapter, the SCN-CN hypothesis was described using the C (runoff factor) = S_r (degree of saturation) concept derived from the volumetric concept described in Chapter 3. The SCS-CN parameter S was shown to represent the maximum possible retention of water in the soil profile or the maximum cumulative amount of dynamic infiltration. This description is closely associated with the discussion of Mockus (1964) (Chapter 2) on the physical significance of S. The Mockus description, however, does not distinguish the dynamic portion of infiltration from the static one and takes into account only the volumetric properties of the soil, excluding its transmission characteristics. His description also limits S to the value of the maximum infiltration rate occurring at the beginning of the infiltration phenomenon. Except for an unreasonable comparison between infiltration rate and volumetric space, Mockus attempted to account for both the volumetric and transmission properties of soils, which are fundamental to the description of a dynamic system. Defining S by the amount of water storage available in the soil profile, one excludes the transmission property of the soil.

As discussed in Chapter 2, the SCS-CN method was originally developed for computing the direct surface runoff (or rainfall-excess) amount from a given amount of storm rainfall occurring on agricultural sites, where it performs best (Ponce and Hawkins, 1996). It performs fairly on range sites and poorly in applications to forest sites (Hawkins, 1984; 1993). The method is also recommended for use in urban environments (SCS, 1975). One of the several problems associated with the SCS-CN method (Mockus, 1964; Ponce and Hawkins, 1996; and others) is that the method does not contain any expression for time and ignores the impact of rainfall intensity and its temporal distribution. Since the SCS-CN method is also construed as an infiltration model (Aron et al., 1977; Chen, 1982; Ponce and Hawkins, 1996; Mishra, 1998; Mishra and Garg, 2000), there exists a possibility for incorporating time in the SCS-CN method for predicting infiltration rates (Chapter 4) and consequently, the runoff hydrograph.

Long-term hydrologic simulation is required for augmentation of hydrologic data. It is useful for water resources planning and watershed management. Long-term hydrologic data are specifically required for analyses of water availability; computation of daily, fortnightly, and monthly flows for reservoir operation; and drought analyses. Since the rainfall data are generally available for a much longer period than are the stream flow data, long-term hydrologic simulation helps extend the gauged data required for the above applications. Since this book deals with the SCS-CN method, the available methods utilizing the SCS-CN method for hydrologic simulation are reviewed and their application is demonstrated using simple examples. Finally, the modified version of the SCS-CN method (Chapter 4) along with its variants is applied to the data set of Hemavati watershed (area = 600 sq. km) in India.

The environmental pollution has been a matter of concern since long. It has affected almost all phases of the modern life the world over, especially more in the past two decades. Consequently, it has attracted globally the attention of scientists, engineers, hydrologists, planners, economists, sociologists, and environmentalists. Water pollution can occur above, on, or below the land surface in several forms. This chapter, however, deals specifically with the pollution of the surface water and its transport in urban areas during the processes of rainfall-runoff, river flow, and snowfall.

Soil erosion is the result of soil exposure to erosive energy of rainfall and flowing waters. Although extensive efforts have been made in the past to model the processes of erosion and sediment transport, the understanding on the subject is still less than complete. Consequently, there exists lack of a universally accepted formula for determination of the sediment yield from a watershed (Shen and Julien, 1992). Recent advancements have employed approximate solutions of the full dynamic equations for determination of sediment yield. Erosion, degradation, and sediment yield from watersheds are related to a complex interaction between topography, geology, climate, soil, vegetation, land use, and man-induced influences. Water, wind, and ice are the primary agents of soil erosion, with water being the most prominent of them.