National Engineering Handbook Research Articles

Restoring Predevelopment Hydrology with Smart Stormwater Controls in Aiken, South Carolina

For decades, large quantities of stormwater runoff from the City of Aiken flowed at destructive velocities (exceeding 5 ft/s) into the Sand River, deeply eroding the channel in the downstream Hitchcock Woods. From 1990 to 2015, the City of Aiken commissioned several consultants to produce over a dozen studies to investigate viable solutions for the destructive erosion. Upon the recommendation from a collaborative Stormwater Task Force, the City of Aiken approved a stormwater implementation plan in 2018 which recommended 20 capital improvement projects sized to capture 58 ac-ft of stormwater in order to restore the river’s predevelopment hydrology, with a goal of reducing stormwater velocities in the Sand River to reflect design considerations in the National Engineering Handbook (between 2.0 ft/s for fine sand channels and 4.0 ft/s for coarse sand channels). In November 2023, the City of Aiken completed construction on two underground detention vaults with a total storage volume of 25 ac-ft. These vaults were designed to capture stormwater runoff before it enters Hitchcock Woods and infiltrate as much stormwater runoff as possible; any additional stormwater will be released at a reduced flow rate compared to existing conditions. The 10-year, 24-hour storm event was selected to size the vaults because it was an achievable metric given numerous constraints such as available land, impacts to existing infrastructure and environmental resources, and available funding. The 10-year event also corresponded to typical stormwater requirements for new development. The vaults utilized the largest height, 15 feet, available for precast underground storage vaults in the marketplace. In addition to providing stormwater management below ground, a new public park was constructed on top of the buried vaults to be used as a gateway between the Hitchcock Woods and the City of Aiken.

Determination of Runoff Curve Numbers for the Growing Season Based on the Rainfall–Runoff Relationship from Small Watersheds in the Middle Mountainous Area of Romania

The NRCS-CN (Natural Resources Conservation Service curve number) method, developed by the USDA (U.S. Department of Agriculture) is among the most widely used for the estimation of surface runoff from watersheds. Ever since its introduction in the 1950s, although it has been used to a great extent by engineers and hydrologists, the applicability of the original procedure used to determine its main parameter, the curve number (CN), to various regions with diverse environmental conditions, is still subject to many uncertainties and debates. This study presents a comparative analysis of different methods applied to determine curve numbers from local data in four watersheds located in the central part of Romania, within the mountain region surrounding the Brașov Depression. The CN values were not only computed using rainfall–runoff records from 1991 to 2020, but also determined from the standard NRCS tables documented in the National Engineering Handbook part 630 (NEH-630), for comparison purposes. Thus, a total of 187 rainfall–runoff data records from the study watersheds and five different methods were used to assess the accuracy of various procedures for determining the CN values, namely: tabulated CN (CN values selected from NRCS tables, TAB), asymptotic fitting (AF) of both natural and ordered data, median CN (MD), geometric mean CN (GM) and the arithmetic mean CN (AM) methods. The applicability of the aforementioned methods was investigated both for the original fixed initial abstraction ratio λ = 0.2 and its adjustment to λ = 0.05. Relatively similar results were found for the curve number-based runoff estimates related to the field data analysis methods, yet slightly better when the λ was reduced to 0.05. A high overall performance in estimating surface runoff was achieved by most CN-based methods, with the exception of the asymptotic fitting of natural data and the tabulated CN method, with the latter yielding the lowest results in the study area.

Estimating the runoff response from hillslopes treated with soil and water conservation structures in the semi-arid Ethiopian highlands: Is the curve number method applicable?

Planning and management of water resources is crucial to enhance agricultural productivity and ensure food security in drylands. For this, adaptable and reliable runoff prediction models are urgently needed to support water harvesting and irrigation development. In this study we explored the potential of the runoff Curve Number (CN) method to estimate daily surface runoff from representative land use types and management conditions in Tigray (north Ethiopia). For this, we use the National Engineering Handbook (NEH-Tables) to derive CN values for runoff plots treated with and without soil and water conservation (SWC) structures. Moreover, the rainfall-runoff data collected from 21 large (600–1000 m2) runoff plots during three years (2010 to 2012) were used to calculate CN-values. Results show that CN values derived from the NEH-Tables are larger by 21% compared to those values calculated from plot data. The calculated CN values vary widely (34 to 91) in response to land use type, slope gradient, and applied SWC structures. Our results show that land use and SWC structures strongly influence runoff production. Considering plots with otherwise similar characteristics, CN values are consistently larger for rangeland sites as compared to those values for cropland sites. Likewise, SWC structures greatly reduce runoff production and their presence also leads to a clearly lower performance of the CN model. The results of this study are relevant contributions towards addressing several goals of agenda 2063 of the African union either directly or indirectly and also international sustainable development goals (SDG). Based on our analysis and field observations, we propose calibrated CN values that may be used for runoff yield assessments in similar environments.

NRCS Curve Number Method: Comparison of Methods for Estimating the Curve Number from Rainfall-Runoff Data

A data set comprising rainfall-runoff data gathered at 31 Agricultural Research Service experimental watersheds was used to explore curve number calibration. This exploration focused on the calibrated value and goodness-of-fit as a function of several items: calibration approach, precipitation event threshold, data ordering approach, and initial abstraction value. Calibration methods explored were least-squares, the National Engineering Handbook (NEH) median, and an asymptotic approach. Results were quantified for events exceeding two precipitation thresholds: 0 and 25.4 mm. Natural and frequency-matched data ordering methods were analyzed. Initial abstraction ratios of 0.05 and 0.20 were examined. Findings showed that the least-squares calibration approach applied directly to rainfall-runoff data performed best. Initial abstraction ratios clearly influenced the magnitude of the calibrated curve number. However, neither ratio outperformed the other in terms of goodness-of-fit of predicted runoff to observed runoff. Precipitation threshold experiments produced mixed results, with neither threshold level producing a clearly superior model fit. Frequency-matching was not considered to be a valid analysis approach, but was contrasted with naturally ordered results, indicating a bias toward producing calibrated curve numbers that were 5–10 points larger.

Long-Term (1990–2013) Changes and Spatial Variations of Cropland Runoff across China

Quantitative information on regional cropland runoff is important for sustainable agricultural water quantity and quality management. This study combined the Soil Conservation Service Curve Number (SCS-CN) method and geostatistical approaches to quantify long-term (1990–2013) changes and regional spatial variations of cropland runoff in China. Estimated CN values from 17 cropland study sites across China showed reasonable agreement with default values from the National Engineering Handbook (R2 = 0.76, n = 17). Among four commonly used geostatistical interpolation methods, the inverse distance weighting (IDW) method achieved the highest accuracy (R2 = 0.67, n = 209) for prediction of cropland runoff. Using default CN values and the IDW method, estimated national annual cropland runoff volume and runoff depth in 1990–2013 were 253 ± 25 km3 yr−1 and 182 ± 15 mm yr−1, respectively. Estimated cropland runoff depth gradually increased from the drier northwest inland region to the wetter southeast coastal region (range: 2–1375 mm yr−1). Regionally, eastern, central and southern China accounted for 39% of the cultivated area and 53% of the irrigated land area and contributed to 68% of the national cropland runoff volume. In contrast, northwestern, northern, southwestern and northeastern China accounted for 61% of the cultivated area and 47% of the irrigated land area and contributed to 32% of the runoff volume. Rainfall was the main source (72%) of cropland runoff for the entire nation, while irrigation became the main source of cropland runoff in drier regions (northwestern and southwestern China). Over the 24-year study period, estimated cropland runoff depth showed no significant trends, whereas cropland runoff volume and irrigation-contributed percentages decreased by 7% and 35%, respectively, owing to implementation of water-saving irrigation technologies. To reduce excessive runoff and increase water utilization efficiencies, regionally specific water management strategies should be further promoted. As the first long-term national estimate of cropland runoff in China, this study provides a simple framework for estimating regional cropland runoff depth and volume, providing critical information for guiding developments of management practices to mitigate agricultural nonpoint source pollution, soil erosion and water scarcity.

Circular utilization of urban tree waste contributes to the mitigation of climate change and eutrophication

Circular utilization of urban tree waste contributes to the mitigation of climate change and eutrophication

Evaluation of the tabulated, NEH4, least squares and asymptotic fitting methods for the CN estimation of urban watersheds

ABSTRACT The Soil Conservation Service Curve Number (SCS-CN) model is widely used to estimate the watershed runoff. However, the model may result in inaccurate estimations when assuming a tabulated CN value and an initial abstraction (λ) of 0.20. Therefore, in this paper, we evaluated the suitability of four CN estimation methods for an urban watershed: Tabulated CN, National Engineering Handbook 4, Least Squares and Asymptotic Fitting. Also, the effect of ordering the data and different λ values (0.20 or 0.05) were analyzed. Our findings suggest that the transient approach of Asymptotic Fitting method, a variance from the original Asymptotic Fitting method, provides the best runoff estimations using rank ordered data and λ equal to 0.05 (R2 = 0.79, PBIAS = 0.01% and NSE = 0.78). It is to be noted that similar results were also obtained through a simple linear model.

Estimating the curve number for conventional and soil conservation technologies using a rainfall simulator

The aim of the article was to verify the curve number (CN) values given in the National Engineering Handbook (NEH) methodology, whether they really correspond to all wide-row crops. The tested crops were maize, hops and potatoes grown using conventional and soil conservation technologies. All these crops are classified as wide-row crops, but they are very different in terms of the cultivation process. The basis for the calculation of our CN values were field measurements carried out using a rainfall simulator within the time span from 2014 to 2020 on the soil corresponding to hydrological group B in two repetitions: naturally dry soil corresponding to an ARC II curve and saturated soil corresponding to an ARC III curve. The results show that our calculated CN values for the conventional cultivation of wide-row crops are, in principle, the same as the CN values given in the NEH methodology. On the contrary, a certain difference was recorded in the soil conservation technologies with plant residues on the surface, in the case of naturally dry soil. Lower CN values are clearly seen in the technologies of no-till maize, strip-till maize and hops with catch crops, which was confirmed by the statistical tests, probably due to the interception and surface roughness.

Improved runoff curve numbers for a large number of watersheds of the USA

ABSTRACT This study presents a procedure to determine improved curve numbers (CNImp) for application of the enhanced Soil Conservation Service - Curve Number (SCS-CN) model developed by Verma et al. for improved runoff prediction. To this end, five different mathematical relationships, i.e. linear, exponential, power, logarithmic and polynomial, are developed for 57 US Department of Agriculture – Agricultural Research Service (USDA-ARS) watersheds to compute CNImp with the aid of CN derivable from the National Engineering Handbook, Section-4 (NEH-4) table (CNT) and from the enhanced SCS-CN model (CNV) with significant R 2 values. Using these relationships, CNImp values are computed for another 57 watersheds for validation. Based on common performance criteria, all five developed relationships for CNImp are found to be superior to the original model in runoff computation. When compared, CNImp are close to CNV and very different from CNT. Despite the fact all the relationships performed equally well, with similar performance statistics, the exponential relationship performed the best of all and the power relationship was the worst.

Soil conservation service curve number determination for forest cover using rainfall and runoff data in experimental forests

ABSTRACT Using the Soil Conservation Service (SCS) curve number (CN) procedure for estimating runoff volume on an ungauged forest watershed remains controversial because little guidance has been provided for defining appropriate CN values. In this study, alternative methods for assigning CN values (CNs) were assessed to determine whether these methods provide acceptable estimates of runoff on forested watersheds. The estimated CNs varied between the methods employed, showing the highest CN values when derived from a probabilistic method and lowest when derived from a graphical method. The tabulated CN values in Section 4 of the National Engineering Handbook (NEH-4) had relatively higher bias compared to those derived from measured rainfall-runoff data. The storm runoff volume was predicted using the assigned CNs and compared with the observations. The coefficients of determination and RMSE values between the measured and estimated runoff volumes varied with the methods employed. The highest watershed average RMSE value was obtained by the use of the tabulated CN values in NEH-4 (51.19 mm), while arithmetic mean approach provided the lowest average RMSE value of 24.38 mm, even though this method requires intensive data collection. Among the alternatives, probabilistic method was found to be the most reliable in determining CNs for forest cover with limited data. The estimated runoff largely agreed with the observations. Therefore, the revised CNs can be used for estimating storm runoff from ungauged, mountainous forests.

Assessment of the Soil Conservation Service–Curve Number method performance in a tropical Oxisol watershed

The Soil Conservation Service–Curve Number (SCS-CN) method is a rainfall-runoff model intended to estimate direct surface runoff (DSR) from rainfall. This method is based on an empirical approach of the relationship between rainfall (P) and ground conditions (soils, management, and antecedent moisture content). SCS-CN method can be applied using published tables containing a CN value for each combination of soil hydrology, land use, and management, and the total five-day antecedent rainfall (P₅). However, its accuracy increases as observed rainfall-runoff events are used for calibration. The standard SCS-CN method assigns 0.2 to the initial abstraction coefficient (λ), which refers to the ratio between initial rainfall abstraction (I_a) and the potential maximum retention (S). In this study, λ was evaluated for a tropical watershed with predominance of Oxisols using 15 methodologies for CN characterization, based on CN values published by <i>National Engineering Handbook</i>, Section 4 (NEH-4) and also derived from 166 monitored rainfall-runoff events. Such methodologies include the use of λ equal to 0.2—standard approach, 0.02—average value obtained from the 166 events, and 0.05—a value that has been suggested in some studies. The results demonstrated that a fixed area-weighted CN representing the entire watershed gave poor accuracy even when antecedent runoff condition (ARC) was considered. In contrast, the methodology based on calibration to the observed hydrological events provided good results. Improvements in DSR estimates were found for most of the methodologies applied when λ was set equal to 0.05 and 0.02 instead of 0.2, except for the spatially distributed CN model (Heterogeneity Model), in which the use of λ = 0.2 implied improvement of DSR estimates.

Reverification of antecedent moisture condition dependent runoff curve number formulae using experimental data of Indian watersheds

The present study evaluates the performance of five existing and three proposed antecedent moisture condition (AMC)-based runoff curve number (CN) conversion formulae utilizing the data of a large number of naturally observed rainfall (P)–runoff (Q) for an agricultural field located at Roorkee, Uttarakhand, India and available published data around the globe. For developing the proposed formulae, CNs were derived for P–Q datasets from 39 watersheds using standard initial abstraction ratio (λ) values as 0.20 and 0.030. The existing formulae outperformed the proposed formulae when tested numerically using the available National Engineering Handbook chapter–4 (NEH–4) tabular AMC-dependent CNs as target values. It might be because the existing formulae were derived from the same datasets used as targeted values (i.e. NEH–4 AMC defining tables). Therefore, when tested on large set of field data, the three proposed formulae performed better than the existing ones, the formula with λ = 0.030 the best of all.

Development of an NRCS curve number global dataset using the latest geospatial remote sensing data for worldwide hydrologic applications

ABSTRACTThe United States Natural Resources Conservation Service (NRCS) Runoff Curve Number (CN) is the most widely used rainfall-runoff modeling method. The hydrologic soil group (HSG), land use/cover, hydrologic surface condition and antecedent moisture condition jointly determine the CN value. This study aims to update the previous global CN map using the recently available geospatial remote sensing data. Based on the conventional United States Department of Agriculture (USDA) and National Engineering Handbook Section 4 (NEH-4) standard lookup tables, the newly derived CN map utilizes the latest Moderate Resolution Imaging Spectroradiometer (MODIS) land cover and the Harmonized World Soil Database (HWSD) soil data to investigate the current flood potential of the global land surface. The results show that the new CN map outperforms the previous one in detecting China’s flood event locations with an improvement in the ratio of the averaged CN value for flood events to the averaged CN value for the whole land area of China. The new CN map reveals the effects of land surface change on hydrological conditions in the 2000s. Based on the Soil Moisture Active and Passive (SMAP) data and the gauge-combined Global Satellite Mapping of Precipitation (GSMaP) product, global surface runoff (April 2015-March 2016, 0.1°×0.1°) is estimated with the new CN map. The currently derived global CN dataset can be used in multi-scale hydrological applications, such as rainfall-runoff modeling and flood hazard risk assessment across continents.

Evaluation of the Soil Conservation Service curve number methodology using data from agricultural plots

The Soil Conservation Service curve number (SCS-CN) method, also known as the Natural Resources Conservation Service curve number (NRCS-CN) method, is popular for computing the volume of direct surface runoff for a given rainfall event. The performance of the SCS-CN method, based on large rainfall (P) and runoff (Q) datasets of United States watersheds, is evaluated using a large dataset of natural storm events from 27 agricultural plots in India. On the whole, the CN estimates from the National Engineering Handbook (chapter 4) tables do not match those derived from the observed P and Q datasets. As a result, the runoff prediction using former CNs was poor for the data of 22 (out of 24) plots. However, the match was little better for higher CN values, consistent with the general notion that the existing SCS-CN method performs better for high rainfall–runoff (high CN) events. Infiltration capacity (fc) was the main explanatory variable for runoff (or CN) production in study plots as it exhibited the expected inverse relationship between CN and fc. The plot-data optimization yielded initial abstraction coefficient (λ) values from 0 to 0.659 for the ordered dataset and 0 to 0.208 for the natural dataset (with 0 as the most frequent value). Mean and median λ values were, respectively, 0.030 and 0 for the natural rainfall–runoff dataset and 0.108 and 0 for the ordered rainfall–runoff dataset. Runoff estimation was very sensitive to λ and it improved consistently as λ changed from 0.2 to 0.03.

Approach of Land Cover Based Asymptotic Curve Number Regression Equation to Estimate Runoff

AbstractThe Natural Resources Conservation Service‐Curve Number (NRCS‐CN) method has been used widely for estimation of direct runoff. However, the determination methods of CN values for land covers from the National Engineering Handbook Chapter 4 (NEH‐4) are inadequate due to uncertainty raised in determining the of the hydrologic soil group, which could change from reported soil survey data due to soil compaction and other human activities. To overcome these drawbacks, an asymptotic CN (ACN) regression equation for the relationship between measured streamflow and rainfall for gauged river basins (ACN‐RB) was developed and used in various studies. The ACN‐RB determines various CN values depending on the amount of rainfall in an event. The ACN‐RB has limitations in application to ungauged river basin because the ACN‐based CN values do not consider various land covers. Accordingly, 13 land cover‐based asymptotic CN regression equations (LC‐ACN‐REs) were developed and evaluated in this study by comparing estimated direct runoff with measured data. The estimated direct runoff using the LC‐ACN‐REs, which were classified as “standard types”, matched observed direct runoff well, as evidenced by a Nash‐Sutcliff efficiency (NSE) value of 0.78 for Jungrang A basin and a NSE value of 0.73 for Tancheon A basin. Copyright © 2016 John Wiley &amp; Sons, Ltd.

Estimation of CN Parameter for Small Agricultural Watersheds Using Asymptotic Functions

This paper investigates a possibility of using asymptotic functions to determine the value of curve number (CN) parameter as a function of rainfall in small agricultural watersheds. It also compares the actually calculated CN with its values provided in the Soil Conservation Service (SCS) National Engineering Handbook Section 4: Hydrology (NEH-4) and Technical Release 20 (TR-20). The analysis showed that empirical CN values presented in the National Engineering Handbook tables differed from the actually observed values. Calculations revealed a strong correlation between the observed CN and precipitation (P). In three of the analyzed watersheds, a typical pattern of the observed CN stabilization during abundant precipitation was perceived. It was found that Model 2, based on a kinetics equation, most effectively described the P-CN relationship. In most cases, the observed CN in the investigated watersheds was similar to the empirical CN, corresponding to average moisture conditions set out by NEH-4. Model 2 also provided the greatest stability of CN at 90% sampled event rainfall.

Simplified sediment yield index model incorporating parameter curve number

Runoff sediment process modeling is highly variable and nonlinear in nature. In the present study, an attempt has been made to develop a relationship between Soil Conservation Service Curve Number (SCS-CN) and Sedimen Yield Index (SYI) for the Narmada watersheds (Madhya Pradesh). Only area (A), delivery ratio (DR), and curve number (CN) were used as input for model development. High value of Nash–Sutcliffe efficiency of (η) 98.60 and 88.19 % for Shakkar watershed and for combined watersheds, respectively, shows that the proposed simplified SYI model is found to be suitable for the study area. The resulting higher (0.98 for Shakkar and 0.88 for combined watersheds) coefficient of determination (R 2) values strongly support the versatility of the derived relationship and invokes assessment of SYI from the available National Engineering Handbook (NEH-4) CN values. Thus, the prediction of SYI is important in order to adopt the suitable soil conservation measure in the study watershed for minimizing the soil erosion.

Curve Number: Empirical Evaluation and Comparison with Curve Number Handbook Tables in Sicily

AbstractThe curve number (CN) method is widely used for estimating direct runoff depth from rainstorms. The procedure is on the basis of the parameter CN, a lumped expression of basin absorption and runoff potential, and a second parameter, initial abstraction (IA), which represents the interception, infiltration, and surface depression during the early part of a storm. The evaluation of CN in Sicily at a basin scale from rainfall-runoff multiday events is done using rainfall-runoff observations during the period 1940–1997 (mean record length of 20 years) in 61 Sicilian watersheds using three different methods: (1) the national engineering handbook, section 4 hydrology (NEH4) method (NEH4M) (the median CN for the annual flood events); (2) asymptotic fitting of ordered and natural data; and (3) least-squares method using rainfall-runoff using both ordered and natural data. Asymptotic fitting showed a major occurrence of the standard CN response (43 basins), with a lesser complacent response (10 basins), an...

Relationship between SCS-CN and Sediment Yield

Accurate estimation of runoff and sediment yield amount is not only an important task in physiographic but also important for proper watershed management. This paper proposes a seasonal relationship between Soil Conservation Services, runoff curve number (CN) and sediment yield (SY). Short-term sediment yield value of duration range from 1 to 30 day was correlated with the runoff CN derived for the respective duration from observed rainfall–runoff data. It is derived empirically from short-term (10 years) daily rainfall–runoff data of the Shakkar watershed of Narmada Basin falling in Madhya Pradesh (India). The resulting coefficient of determination (R2) values range (0.76–0.79) strongly support the versatility of the derived relationship and invokes determination of SY from the available National Engineering Handbook (NEH-4) CN values.

Relationship between Runoff Curve Number and PET

This paper presents a relationship between the runoff curve number (CN) and mean potential evapotranspiration (PET) using long- term daily rainfall-runoff data. To this end, CNs were derived for different durations from rainfall-runoff data of eight different agro-climatic river basins in India, and these were correlated with PET derived for respective watersheds using the standard Penman-Monteith method. The resulting high coefficient of determination (R 2 ) values, viz, 0.99 for Hemawati and Mohegoan; 0.98 for Haridanagar, Kalu, and Seonath; 0.96 for Manot, Ghodahadho, and Ramganga strongly support the versatility of the derived relationship. This relationship also invokes deter- mination of PET from the available published CN values, and therefore may be quite useful in field applications. DOI: 10.1061/(ASCE)HE .1943-5584.0000780. © 2014 American Society of Civil Engineers. Author keywords: Curve number; National Engineering Handbook hydrology (NEH-4); Penman-Monteith; Potential evapotranspiration (PET); Soil conservation service; Water balance.