Curve Number Derivation for Watersheds Draining Two Headwater Streams in Lower Coastal Plain South Carolina, USA

Epps, Thomas H.; Hitchcock, Daniel R.; Jayakaran, Anand D.; Loflin, Drake R.; Williams, Thomas M.; Amatya, Devendra M.

doi:10.1111/jawr.12084

Cited by 26 publications

(44 citation statements)

References 27 publications

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“…This discussion aims to analyze the UDC watershed behavior, furnishing an interpretation of the basin hydrologic response that differs from the one proposed by Epps et al . (), also using WTE to represent the source area which produces runoff, according to Model (5).…”

Section: Discussionmentioning

confidence: 99%

Discussion

D’Asaro

Grillone

2014

J American Water Resour Assoc

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Epps et al. (2013) derived Curve Number (CN) values for two forested headwater watersheds in the Lower Coastal Plain of South Carolina during the 2008-2011 period from rainfall-runoff data, resulting in 23 events for the Upper Debidue Creek (UDC) watershed and in 20 events for Watershed 80 (WS80). D'Asaro and Grillone analyzed the P, CN data of the UDC watershed finding an evident "complacent" behavior, characterized by a declining CN with increasing P but without approaching a stable value at large storms. In this case, the traditional runoff CN equation does not fit well with the rainfall-runoff data that indicate a partial source area watershed behavior and are more aptly modeled by the equation introduced by D'Asaro and Grillone (2012), who introduced a C parameter in the well-known runoff CN formula. The C value, that represents the source area (fraction of drainage area) of the basin that produces runoff with a fixed CN < 100, is here considered to vary with the water table elevation (WTE), to which it is well correlated.

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Section: Discussionmentioning

confidence: 99%

Discussion

D’Asaro

Grillone

2014

J American Water Resour Assoc

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“…Those models were selected because the ARC can be evaluated based on the initial soil moisture content. Although in the literature there are models based on different variables to represent the ARC, such as the Antecedent Precipitation Index (Beck et al, 2009;Huang et al, 2007), base flow (Longobardi et al, 2003;Shaw and Walter, 2009;Tramblay et al, 2010) or water table elevation (Epps et al, 2013), our work is focused on ISMC as it is the best indicator of the antecedent conditions (Tramblay et al, 2010). Thus, the models analysed in this section are: the SMA procedure over the NRCS-CN model developed by Michel et al (2005), called SS a model, the adaptation of the CN parameter depending on ISMC exposed in Brocca et al (2009a) and Brocca et al (2009b), called S_f(θ) model, and a new model based on a reinterpretation of the parameters involved in Michel (2005) to evaluate the initial conditions, called RSS a model.…”

Section: Methodsmentioning

confidence: 99%

Improvement of the integration of Soil Moisture Accounting into the NRCS-CN model

Durán‐Barroso

González

Valdés

2016

Journal of Hydrology

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Rainfall-runoff quantification is one of the most important tasks in both engineering and watershed management as it allows the identification, forecast and explanation of the watershed response. This non-linear process depends on the watershed antecedent conditions, which are commonly related to the initial soil moisture content. Although several studies have highlighted the relevance of soil moisture measures to improve flood modelling, the discussion is still open in the literature about the approach to use in lumped model. The integration of these previous conditions in the widely used rainfall-runoff models NRCS-CN (e.g. National Resources Conservation Service -Curve Number model) could be handled in two ways: using the Antecedent Precipitation Index (API) concept to modify the model parameter; or alternatively, using a Soil Moisture Accounting (SMA) procedure into the NRCS-CN, being the soil moisture a state variable. For this second option, the state variable does not have a direct physical representation. This make difficult the estimation of the initial soil moisture store level. This paper presents a new formulation that overcomes such issue, the rainfall-runoff model called RSS a . Its suitability is evaluated by comparing the RSS a model with the original NRCS-CN model and alternatives SMA procedures in 12 watersheds located in six different countries, with different climatic conditions, from Mediterranean to Semi-arid regions. The analysis shows that the new model, RSS a , performs better when compared with previously proposed CN-based models.Finally, an assessment is made of the influence of the soil moisture parameter for each watershed and the relative weight of scale effects over model parameterization.

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“…Therefore, in accordance with the three ARC levels, the precipitation threshold for the definition of the three ARC levels in studied sites or the linear or non-linear relationship between CN and antecedent precipitation was developed; this development increased the precision of runoff prediction by SCS-CN to a certain extent (Haith and Andre 2000;Miliani et al 2011). Aside from antecedent precipitation, base flow discharge (Shaw and Walter 2009), groundwater table (Epps et al 2013), in situ soil moisture (Brocca et al 2009b;Tessema et al 2014;Tramblay et al 2010), or data retrieved from satellite products (Brocca et al 2009a;Nagarajan and Poongothai 2012) has been utilized as a predictor to obtain better estimates for ARC. Runoff prediction in the SCS-CN framework can be improved by correlating these predictors with potential maximum retention; however, more parameters and input data need to be added.…”

Section: Introductionmentioning

confidence: 98%

“…Runoff prediction in the SCS-CN framework can be improved by correlating these predictors with potential maximum retention; however, more parameters and input data need to be added. As indicated by the abovementioned revisions of the SCS-CN method, ARC can be effectively estimated as long as a predictor and its relationship with potential maximum retention effectively describe the storage and depletion processes of watershed water amount (Epps et al 2013). Thus, a better revision of the SCS-CN method would be to select available data as an ARC predictor and develop a mathematical expression of the predictor to represent watershed water storage and depletion.…”

Section: Introductionmentioning

confidence: 99%

Improved SCS-CN Method Based on Storage and Depletion of Antecedent Daily Precipitation

Jiao

Wang

et al. 2015

Water Resour Manage

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The accurate simulation or prediction of storm runoff is one of the most important bases of water resource management and environmental quality assessment of water and soil. The soil conservation service-curve number (SCS-CN) method cannot effectively determine the effect of antecedent precipitation storage and depletion on runoff, which limits the accuracy of the method's runoff prediction. Thus, potential initial abstraction and decay constant were developed in this study to improve the SCS-CN method. Potential initial abstraction determined the maximum rainfall storage before runoff and the threshold of daily effective rainfall, and the decay constant was used to describe the dynamic depletion of antecedent daily effective rainfall induced by evapotranspiration and seepage. The improved SCS-CN method was evaluated withthe runoff data observed in four cropping systems and three drainage areas, and it proved that the improved SCS-CN method predicted runoff more accurately than the original SCS-CN method. The inproved SCS-CN method increased both NSE and R 2 values by more than 20 % for the four cropping systems, and increased R 2 values by 11.9, 13.9 and 9.6 % for the plot, field, and catchment, respectively, compared with the original SCS-CN method. The decay constant did not vary with cropping systems and drainage areas.

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Curve Number Derivation for Watersheds Draining Two Headwater Streams in Lower Coastal Plain South Carolina, USA

Cited by 26 publications

References 27 publications

Discussion

Discussion

Improvement of the integration of Soil Moisture Accounting into the NRCS-CN model

Improved SCS-CN Method Based on Storage and Depletion of Antecedent Daily Precipitation

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