Journal of Hydrologic Engineering

The Soil Conservation Service (SCS) method is widely used to estimate runoff from small- to medium-sized watersheds. The most critical assumption of the SCS method is that the ratio of actual retention to potential retention is the same as the ratio of actual runoff to potential runoff; however, this assumption has not been empirically validated. The paper develops a framework to test this proportionality assumption that underpins the SCS method. Using data on rainfall intensity and storm runoff from 210 site events from Australia and Southeast Asian countries, this paper shows that a strong relationship exists between maximum retention on the basis of the SCS equation and the product of effective storm duration and spatially averaged maximum infiltration rate, and empirical support exists for the proportionality assumption for runoff estimation. Relating the maximum retention to the effective storm duration and maximum infiltration rate provides additional avenues for prediction of storm runoff amount and peak runoff rate, which are the key design parameters for storm water control and management.

The present study enhances the Soil Conservation Service curve number (SCS-CN) predictions by improving the model structure, considering the following issues of concern: implementation of antecedent moisture condition procedure, fixation of initial abstraction ratio ($\lambda$) at 0.2, usage of the potential maximum retention parameter, and incorporation of storm intensity or duration in runoff estimation. A five-parameter M3 model is proposed, with storm duration and a new parameter $S_{\mathrm{abs}}$ (potential maximum retention), to overcome most of the above limitations prevailing in the SCS-CN model. For simplicity and practical applications obviating storm-duration data, a four-parameter M4 model is also proposed. The performance of the suggested and the available models has been evaluated using the data of 82 small watersheds in the United States of America. As demonstrated, the M3 model performs the best, whereas the conventional SCS-CN model performs the poorest among all the models studied.

The Soil Conservation Service curve number (SCS-CN) method is a well-recognized technique for the estimation of direct surface runoff from a rainfall event. Most of the recently developed SCS-CN–based models including the original one ignore the effect of storm duration or rainfall intensity on surface runoff, an important aspect of the rainfall-runoff model. Some of these models have, however, included the antecedent moisture conditions. In this study, storm duration is incorporated in a recently modified version of the SCS-CN method to derive a more advanced model. This version is found to perform generally better than the other on the data of 60 small U.S. watersheds. The former model performed significantly better than the latter on the watersheds dominated by silty soils and cultivated land uses.

Four curve number (CN) determination methods are evaluated from 16 watersheds in the southwestern U.S. using 1,284 events that satisfy rainfall and runoff criteria. The use of ordered pairs versus natural pairs of rainfall and runoff data has a larger effect on the CN, whereas the difference from using a partial duration series versus an annual series was not significant. The best-available USDA soil series data were obtained for 20 Arizona watersheds and 10 groups of New Mexico natural runoff plots. The hydrologic soil groups (HSG) were determined from either direct USDA assignment or textural properties and compared with the HSGs required by the CNs and the cover condition. This study showed a standard error of about one HSG, resulting in an error in CN of approximately seven units when using the best-available data. Compared with USDA handbook table values, the CNs found from rainfall and runoff data were higher for 21 of the 30 semiarid watersheds.

Engineers and hydrologists use the curve number method to estimate runoff from rainfall for different land use and soil conditions; however, large uncertainties occur for estimates from forested watersheds. This investigation evaluates the accuracy and consistency of the method using rainfall-runoff series from 10 small forested-mountainous watersheds in the eastern United States, eight annual maximum series from New Hampshire, West Virginia, and North Carolina, and two partial duration series from Georgia. These series are the basis to compare tabulated curve numbers with values estimated using five methods. For nine of 10 watersheds, tabulated curve numbers do not accurately estimate runoff. One source of the large uncertainty is a consistent decrease in storm-event curve numbers with increasing rainfall. A calibrated constant curve number is suitable for only two of 10 watersheds; the others require a variable watershed curve number associated with different magnitude rainfalls or probabilities of occurrence. Paired watersheds provide consistent curve numbers, indicating that regional values for forested-mountainous watersheds (locally calibrated and adjusted for storm frequency) may be feasible.

Many engineers and hydrologists use the curve number method to estimate runoff from ungaged watersheds; however, the method does not explicitly account for the influence of season or forest cutting on runoff. This study of observed rainfall and runoff for small, forested watersheds that span the Appalachian Mountains of the eastern United States showed that curve numbers calibrated for the growing season tended to be smaller than for the dormant season. Forest cutting tended to increase curve numbers. However, the increase in water yield following forest cutting on these watersheds only lasted 1 year to 11 years, thereby limiting the precision of the curve numbers estimated for these brief hydrologic effect periods. This study highlights the need to account for seasonal and forest cutting when estimating runoff from some forested watersheds.

Employing the advanced soil moisture accounting (ASMA) procedure and the modified subsurface drainage flow concept, a curve number (CN)–based model, named as modified long-term hydrologic simulation advance soil moisture accounting (MLTHS ASMA) model, is proposed to simulate daily flows. Its application to 17 watersheds falling in different agro-climatic zones of India and comparison with the existing long-term hydrologic simulation advance soil moisture accounting (LTHS ASMA) model reveal that the proposed model yields higher efficiency, lower standard error of estimate (SE) and percentage relative error (RE) values for high runoff producing wet watersheds, and lower efficiency for low runoff producing dry watersheds, indicating a very good model response to wet watersheds, and good to satisfactory-to-dry watersheds. On most watersheds, the proposed model performed better than the existing one. In addition, CN parameters for surface and subsurface flows were most sensitive followed by the parameters related with soil characteristics, and the significance of base flow was greater in wet watersheds than in dry watersheds.

The Natural Resources Conservation Service (NRCS) [previously Soil Conservation Service (SCS)] developed the SCS runoff curve-number (CN) method for estimating direct runoff from storm rainfall. The NRCS uses the CN method for designing structures and for evaluating their effectiveness. Structural design is usually based on a single event of a certain probability of occurrence. During the years when many floodwater-retarding watershed projects were planned and constructed (1950–1980), the CN equation was used in a continuous mode to evaluate the projects. To operate CN in a continuous mode, runoff was estimated from a daily rainfall record of approximately 30 years. For each day of recorded rainfall, the five-day antecedent rainfall was used to assign a ${\mathrm{CN}}_1$ (dry condition), ${\mathrm{CN}}_2$ (average condition), or ${\mathrm{CN}}_3$ (wet condition), and runoff was estimated with the appropriate CN. With the development of continuous hydrologic simulation models, CN was related directly to soil water content or estimated using rainfall and potential evapotranspiration (PET) to drive an index. Several methods were attempted and used with different degrees of success over a period of many years. The purpose of this study is to describe the evolution of the continuous CN method and its recent developments. Test results on the basis of the direct-link soil-moisture approach and the revised soil-moisture index method are presented for demonstration purposes. The results indicate that the revised soil-moisture index method is robust and produces realistic runoff estimates over a wide range of soil properties.

A curve-number-based, planning level, macrospreadsheet model wet pond annual efficiency simulation model (WEANES) has been developed to predict the average annual removal efficiencies (AARE) of wet detention ponds based on mass pollutant loadings. The model can be used as a design, planning, and permitting tool. WEANES uses the following three processes: (1) converts daily rainfall hyetographs to hydrographs using the curve-number (CN)—based Santa Barbara urban hydrograph method; (2) converts the inflow hydrograph to an outflow hydrograph from the wet pond outlet structure through routing; and (3) calculates annual inflow and outflow pollutant loads and pond efficiency. The model uses historical, site-specific, multiyear, rainfall data available from National Oceanic and Atmospheric Administration climatological stations. Required input parameters are: (1) watershed parameters (drainage area, pervious CN, directly connected impervious area, and time of concentration); (2) pond parameters (control and overflow elevations, pond side slopes, and surface areas at control elevation and pond bottom); (3) outlet structure parameters; (4) pollutant event mean concentrations; and (5) pond loss rate, which is defined as the net loss due to evaporation, infiltration, and water reuse. This paper provides an example of the model calibration and validation process for an existing wet pond. The effect of watershed and pond parameters on AARE is examined through sensitivity analysis. The WEANES-computed AARE values for total suspended solids ranged from 75.9 to 84%, whereas the U.S. EPA model predicted removal efficiencies of greater than 99%. The WEANES model can provide the user with greater flexibility while maintaining the simplicity of the U.S. EPA model.

Use of the Soil Conservation Service curve number (SCS-CN) approach is very popular among hydrologists. Despite this, its applications in water quality modeling have been very limited. For the first time, the present work explores the potential of the SCS-CN approach in water quality modeling of the river bank filtration process using a theoretical framework. The approach relates the curve number (CN) with the filtration/kinetic coefficient and the input applied to the system. The approach is tested for its effectiveness using the field data collected at the river bank filtration site at Haridwar, India. The CN is found to be dependent on travel time between source water and the abstraction well in addition to the influent concentration. For very low or very high values of influent concentration, the curve number exhibits an asymptotic variation approaching 100 and 0, respectively. Using the data on source water quality and the travel time, it is possible to compute the curve number and, subsequently, the filtrate quality at an abstraction well.

Radar-derived next generation weather radar (NEXRAD) process 3 (P3) data offer high spatial resolution precipitation data that might improve stream-flow simulation accuracy using watershed models. The objective of this study was to assess the performance of spatially averaged subwatershed-specific P3 data on stream-flow simulations by using the soil and water assessment tool (SWAT) hydrologic model. This study was the first to evaluate the P3 product in watershed modeling. The SWAT model was chosen to simulate hydrological processes in North Fork Ninnescah Watershed ($2,400{\mathrm{km}}^2$) located in south central Kansas. A precipitation gauge (PG) station was created for each subwatershed by using an area-weighted average of P3 precipitation estimates for all $16\text{-}{\mathrm{km}}^2$ grid cells intersecting the subwatershed. Average subwatershed area ($19{\mathrm{km}}^2$) was selected to correspond roughly with NEXRAD cell area to minimize spatial aggregation of radar precipitation data. The SWAT model was calibrated with both P3 data and PG data from January 1, 2002 to December 31, 2008. The P3-calibrated model was validated by using both different precipitation source data for the same simulation period (2002–2008) and the same source data with an independent simulation period (1996–2001). The PG-calibrated model yielded slightly higher Nash-Sutcliffe efficiency ($E_{NS}$), both daily (0.40) and monthly (0.60), than the P3-calibrated model (daily: 0.35, monthly: 0.52); percent bias was very good ($<\pm 2\%$) for both P3- and PG-calibrated models at all time scales. Validation $E_{NS}$ results, however, were diminished using PG data with the P3-calibrated model (daily: 0.27, monthly: 0.52) but remained similar using P3 data with the PG-calibrated model (daily: 0.40, monthly: 0.52). The PG data appeared to demonstrate variable uncertainty among stations that was not evident for the P3 data, providing incentive for the use of P3 data. This study found evidence that the SWAT model, when run using more spatially representative precipitation data (in this case, P3), was less sensitive to minor variations in model calibration parameters. On the basis of this initial assessment of P3 data for hydrologic modeling, bias adjustment to reduce the effect of a small number of extreme P3 precipitation values may have improved results but was not required to produce watershed-outlet stream flow results similar to using PG data in this study.

A hybrid technique was used for the runoff production and its routing in an agro-forested watershed located within the Kanha National Park in Central India with the use of remote sensing and geographic information system (GIS) data. In this technique, a modified Soil Conservation Service curve number (SCS-CN) method and a two-dimensional overland flow model were combined. Modified SCS-CN method estimated daily net rainfall fractions were used as an input to the overland flow model along with other remote-sensing-derived inputs such as the digital elevation model (DEM), rainfall, and roughness factor for routing of the produced runoff. The model works on a cell basis and routs produced runoff from one cell to next following the maximum downslope directions. The flow model uses the diffusive wave approximations of the St. Venant equations for routing surface water. The model was tested by calibrating the Strickler coefficient $K$, which is inversely proportional to resistance to flow, and comparing the observed and simulated daily change in the water levels for two gauging sites. The calibrated average values of $K$ for different subcatchments were 15.7, 21.7, 23.4, and 28.4 for Kurkuti, Sijhora, between the gauging sites, and downstream catchments, respectively. The model was tested for some statistical parameters like the Nash Sutcliffe coefficientand RMSE using residuals between observed and simulated data, and found to be within the acceptable limits. The results show that the hybrid technique works well to extend the application of curve number to address the routing phase of runoff.

This study aims to simulate runoff depth using the Soil Conservation Service–curve number (SCS-CN) method with remote sensing and geographic information system (GIS) techniques. In this study, the SCS-CN technique adapted for Indian conditions has been used for the generation of CNs for an ungauged watershed. The USDA SCS-CN method is used for estimating the runoff depth in the Manimuktha subwatershed. Monthly and annual runoff values have been obtained from the daily runoff values of the watershed. A base map, soil map, and land use map of the study area have been prepared using remote sensing data and Survey of India (SOI) toposheets. The vector layers have been generated in GIS software. The SCS model is then applied to estimate the daily runoff from the watershed and validated, comparing it with the observed runoff of a few selected events of the monsoon periods of 1980–2009. The statistical analysis indicates that the SCS-CN method can be applied to predict runoff depths of an ungauged watershed. The present study reveals that the remote sensing and GIS-based SCS-CN model can be used effectively to estimate the runoff from the ungauged Indian watersheds of similar geohydrological characteristics.

This paper compares the composite and distributed curve number (CN) techniques in computation of runoff from a hypothetical watershed of $100\times 100$ cells and from a natural medium-sized agricultural Tarafeni watershed located in West Bengal, India. To this end, remote sensing (RS) satellite digital data from 1989 and 2000 were used for land use and land cover classification using a maximum likelihood classifier algorithm of supervised classification for CN-generation. Runoff was estimated for each grid by using a distributed-CN approach and averaged for the whole watershed by using a weighted-runoff approach. The estimated runoff values were compared with those from the traditional composite-CN technique. In both cases of initial abstraction ($I_a$) taken as 0.2 S and 0.3 S (where $S$ is the potential maximum retention), the estimated runoff caused by distributed CN technique was more than that caused by the composite-CN approach. The difference in runoff values was more for $I_a=0.3S$ than that for $I_a=0.2S$; runoff caused by distributed CN with $I_a=0.2S$ matched more closely with the observed. Furthermore, the difference was very high for small events, moderate for medium, and low for high rainfall events; it was very high for the watershed exhibiting greater CN variation.