Revisiting the Antecedent Moisture Content-Based Curve Number Formulae

Rainfall-Runoff modeling is a hydrological modeling which is extremely important for water resources planning, development, and management. In this paper, Natural Resource Conservation Service-Curve Number (NRCS-CN) method along with Geographical Information System (GIS) approach was used to evaluate the runoff resulting from the rainfall of four stations, namely, Bilodra, Kathlal, Navavas and Rellawada of Sabarmati River basin. The rainfall data were taken for 10 years (2005-2014). The curve number which is the function of land use, soil and antecedent moisture condition (AMC) was generated in GIS platform. The CN value generated for AMC- I, II and III were 57.29, 75.39 and 87.77 respectively. Using NRCS-CN method, runoff depth was calculated for all the four stations. The runoff depth calculated with respect to the rainfall for Bilodra, Kathlal, Navavas and Rellawada shows a good correlation of 0.96. The computed runoff was compared with the observed runoff which depicted a good correlation of 0.73, 0.70, 0.76 and 0.65 for the four stations. This method results in speedy and precise estimation of runoff from a watershed.

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Improved SMA-based SCS-CN method incorporating storm duration for runoff prediction on the Loess Plateau, China

Hydrology Research ◽

10.2166/nh.2020.140 ◽

2020 ◽

Vol 51 (3) ◽

pp. 443-455

Author(s):

Wenhai Shi ◽

Ni Wang

Keyword(s):

Loess Plateau ◽

Curve Number ◽

The Loess Plateau ◽

Moisture Condition ◽

Soil Moisture Accounting ◽

Antecedent Moisture ◽

Storm Duration ◽

Service Curve ◽

Scs Cn ◽

Runoff Events

Abstract In the Soil Conservation Service Curve Number (SCS-CN) method for estimating runoff, three antecedent moisture condition (AMC) levels produce a discrete relation between the curve number (CN) and soil water content, which results in corresponding sudden jumps in estimated runoff. An improved soil moisture accounting (SMA)-based SCS-CN method that incorporates a continuous function for the AMC was developed to obviate sudden jumps in estimated runoff. However, this method ignores the effect of storm duration on surface runoff, yet this is an important component of rainfall-runoff processes. In this study, the SMA-based method for runoff estimation was modified by incorporating storm duration and a revised SMA procedure. Then, the performance of the proposed method was compared to both the original SCS-CN and SMA-based methods by applying them in three experimental watersheds located on the Loess Plateau, China. The results indicate that the SCS-CN method underestimates large runoff events and overestimates small runoff events, yielding an efficiency of 0.626 in calibration and 0.051 in validation; the SMA-based method has improved runoff estimation in both calibration (efficiency = 0.702) and validation (efficiency = 0.481). However, the proposed method performed significantly better than both, yielding model efficiencies of 0.810 and 0.779 in calibration and validation, respectively.

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Sara4r: an R graphical user interface (GUI) to estimate watershed surface runoff applying the NRCS – curve number method

Journal of Hydroinformatics ◽

10.2166/hydro.2020.087 ◽

2020 ◽

Author(s):

Rafael Hernández-Guzmán ◽

Arturo Ruiz-Luna ◽

Eduardo Mendoza

Keyword(s):

User Interface ◽

Graphical User Interface ◽

Modular Design ◽

Curve Number ◽

Land Use Land Cover ◽

Rainfall Runoff ◽

R Software ◽

Antecedent Moisture ◽

Curve Number Method ◽

Moisture Conditions

Abstract This paper introduces a graphical user interface (GUI) for the R software that allows calculating the rainfall-runoff relationship, using the Curve Number method. This GUI is a raster-tool whose outputs are runoff estimates calculated using land use/land cover and hydrologic soil group maps. The package allows the user to select among three different antecedent moisture conditions and includes modifications about the initial abstraction parameter. We tested this GUI with data derived from two watersheds in Mexico and the outputs were compared with those produced using a well-established GIS tool in a vector environment. The results produced by these two approaches were practically the same. The main advantages of our package are: (1) ‘Sara4r’ is faster than previous vector based tools; (2) it is easy to use, even for people with no previous experience using R; (3) the modular design allows the integration of new routines and, (4) it is free and open source.

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