Resources Conservation Service Curve Number Research Articles

The significance of basin slope for curve number estimation and the impact on flood prediction in arid basins

The Natural Resources Conservation Service (NRCS) method was developed in agricultural basins with slopes less than or equal to 5%. Extension of the method to arid regions and at higher slopes is needed. This research aims to investigate the relationship between the basin slope and runoff depth in arid basins by using the Natural Resources Conservation Service Curve Number (NRCS-CN). The results show that there is a clear effect of the proposed curve number equation with slope (the slope adjusted CN, CNIIα ) on the runoff depth in the studied basins, and this is consistent with the literature. However, large basin areas with a high percentage of alluvium area, vegetation, and high infiltration rates countereffects the slope of the basin, and consequently, the basin slope will not have a significant effect on runoff. The correlation coefficient of various developed equations varies between (0.89-0.93) and the root mean square error of runoff depth varies between 11 mm for λ=0.2 and 5.2 mm for λ=0.01.

Estimation of water inflows to reservoirs of irrigation dams in the Guinea Savannah ecological zone of Ghana

ABSTRACT The study was carried out in the northern Ghana to estimate water inflows to nine reservoirs of irrigation dams for a period of 20 years (1999–2018) using the USDA Natural Resources Conservation Service Curve Number model. The key input parameters of the model were hydrologic soil group, rainfall amount, landuse/landcover, weighted curve number, antecedent moisture condition, and potential maximum soil moisture retention. The annual rainfall within the reservoir catchments was found to vary between 617.20 and 1,382.30 mm. The estimated annual runoff depths ranged from 54.10 to 125.55 mm. A high degree of positive correlation was found between rainfall and runoff depths with a coefficient of determination (R2) ranging from 79.80 to 90.70%. Hydrologic soil groups accounted for about 67.2 and 62.5% of the variation in runoff depth and percentage runoff, respectively. The estimated annual water inflows to the reservoirs ranged from 145,206 ± 125,814 m3/y at Gambibgo to 47,074,634 ± 4,395,860 m3/y at Tono. The variation in the annual water inflows and annual storage capacity recharge among the various reservoirs was noted to be influenced by reservoir size, catchment size, rainfall, and soil characteristics in the reservoir catchments.

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.

A modified NRCS-CN method for eliminating abrupt runoff changes induced by the categorical antecedent moisture conditions

The popular Natural Resources Conservation Service Curve Number (NRCS-CN) (earlier known as Soil Conservation Service Curve Number (SCS-CN) method of rainfall-runoff modeling has often faced the criticism of exhibiting quantum jumps in runoff computations because of the sudden jumps appearing in CN-values derived from NEH-4 tables for three antecedent moisture conditions (AMC), viz., AMC-I, AMC-II, and AMC-III valid for dry, normal, and wet conditions, respectively. The variability of antecedent soil moisture within an AMC category is responsible for the abrupt jump and other deficiencies in the CN method for runoff estimation. This paper suggests a novel procedure to account for the antecedent moisture (M), preventing quantum jumps and eliminating deficiencies in determination of CN and, in turn, estimation of direct runoff. Its validity was verified utilizing the observed rainfall (P)-runoff (Q) events from 36 US watersheds, four sub-catchments of the Godavari basin, and small agricultural plots at Roorkee, India. The performance of the proposed model (M5) for runoff prediction was compared with the existing NRCS-CN (M1), Mishra and Singh (2002) (M2), Singh et al. (2015) (M3), and Verma et al. (2021) (M4) model using various performance indices. Using the CNs derived from observed events, model M5 was seen to have performed better than M1-M4 in terms of Nash Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), and Percent Bias (PBIAS) for the data of US watersheds, and CN-P correlation improved as the coefficient of determination (R2) enhanced. Similarly, using the RS & GIS-based CNs on natural watersheds of the Godavari basin and considering AMC-I, the performance of M5 was again better than M1-M4 in terms of RMSE, Mean Bias Error (mBIAS), Mean Absolute Error (MAE), and Normalized-Nash Sutcliffe Efficiency (NNSE). Interestingly, there existed a significant (p < 0.05) relationship between the in-situ water content (w) measured for the experimental plots of Roorkee and the model input variable antecedent moisture (M), offering a physical touch to the conceptual model.

Revised runoff curve number for runoff prediction in the Loess Plateau of China

AbstractThe soil conservation service (now Natural Resources Conservation Service) Curve Number (SCS‐CN), one of the most commonly used methods for surface runoff prediction. The runoff calculated by this method was very sensitive to CN values. In this study, CN values were calculated by both arithmetic mean (CN_C) and least square fit method (CN_F) using observed rainfall‐runoff data from 43 sites in the Loess Plateau region, which are considerably different from the CN2 values obtained from the USDA‐SCS handbook table (CN_T). The results showed that using CN_C instead of CN_T for each watershed produce little improvement, while replacing CN_T with CN_F improves the performance of the original SCS‐CN method, but still performs poorly in most study sites. This is mainly due to the SCS‐CN method using a constant CN value and discounting of the temporal variation in rainfall‐runoff process. Therefore, three factors—soil moisture, rainfall depth and intensity—affecting the surface runoff variability are considered to reflect the variation of CN in each watershed, and a new CN value was developed. The reliability of the proposed method was tested with data from 38 watersheds, and then applied to the remaining five typical watersheds using the optimized parameters. The results indicated that the proposed method, which boosted the model efficiencies to 81.83% and 74.23% during calibration and validation cases, respectively, performed better than the original SCS‐CN and the Shi and Wang (2020b) method, a modified SCS‐CN method based on tabulated CN value. Thus, the proposed method incorporating the influence of the temporal variability of soil moisture, rainfall depth, and intensity factors suggests an accurate runoff prediction for general applications under different hydrological and climatic conditions on the Loess Plateau region.

Improving runoff estimation by raster‐based Natural Resources Conservation Service‐Curve Number adjustment for a new initial abstraction ratio in semi‐arid climates

AbstractThe Natural Resources Conservation Service Curve Number (NRCS‐CN) is a popular rainfall‐runoff modeling method. In this study the performance of the NRCS‐CN method in runoff estimation for single storms based on a new initial abstraction ratio ( in the semi‐arid climate of Khorasan Razavi, Iran, is presented. The method utilizes public domain Geographic Information Systems (GIS) software for the Geospatial analysis and generating the CN map of the study area. CN values provided in the standard Service Curve Number‐tables (CN0.2) were found to overestimate runoff potential compared to modified tables of CN0.05. Evaluation of the performance of CN0.05 for runoff estimation was undertaken using data collected in thirty‐five rainfall‐runoff events in the Kardeh watershed. A strong correlation (R = 0.97) was found between the observed and estimated direct runoff when CN0.05 was used for the runoff estimation as well as between the observed and estimated runoff based on Nash‐Sutcliffe efficiency (0.88). Overall, runoff predictions were improved with the revised NRCS‐CN method in semi‐arid climatic settings when is set to 0.05. We provide an easy‐to‐use relationship between CN0.2 and CN0.05 that improves Runoff estimation from NRCS‐CN.

Curve number calibration for measuring impacts of land management in sub-humid Ethiopia

Study RegionWe investigate the event runoff response in six sub-catchments in the Lake Tana sub-basin, headwater of the Blue Nile basin, northwest Ethiopia. Steep and mountainous terrains surround floodplains, imposing runoff and soil erosion in the upper catchments and flooding and sedimentation at floodplains. This study was conducted in the upland runoff source catchments. Study FocusThe focus is to investigate catchment characteristics that control the event runoff response in upland catchments, and how recent land management practices may have contributed to improved hydrological conditions. Event rainfall and runoff data were obtained at five-minute time steps through automated divers and tipping bucket rain gauges and related to catchment characteristics. New Hydrological Insights for the RegionOur results show that the catchment event quickflow response was controlled by different factors of both natural and anthropogenic nature of which forest and shrubs, bund density and soil organic matter content were found to be the most important to reduce event quickflow. On the contrary, increase in cropland area caused an increase in quickflow. Through least square fitting procedure of the Natural Resources Conservation Service Curve Number method (NRCS-CN), a site specific abstraction ratio (λ) value of 0.01, rather than the commonly used 0.2 or 0.05, was found to be most appropriate for the sub-humid highlands of Ethiopia.

Assessing impact of urbanisation on surface runoff using vegetation-impervious surface-soil (V-I-S) fraction and NRCS curve number (CN) model

The progressive decrease in natural surfaces to create space for urban dwellers with the consequential changes caused to hydrological regime of an area is taking a toll on environmental resources. The Natural Resources Conservation Service (NRCS) Curve Number (CN) model (earlier known as Soil Conservation Service Curve Number, SCS-CN) is used in the present study for assessing temporal variations in surface runoff in upper catchment of Rispana River, Dehradun city, India. Also, the composite CN is calculated using vegetation-impervious surface-soil (V-I-S) fraction derived from spectral normalisation and linear spectral mixture analysis method while assigning initial CN for hydrologic soil-cover complex for five years during 1991 to 2018. The results indicated a steady rise in impervious surface fraction and consequent surface runoff (8.25%) from 1991 to 2018, while urban green space fraction has shown depleting trends in north and central part of the city. The level of imperviousness highlighted an over 60% of central part of the city as impervious, which gradually decreases outwards from the city. A positive correlation is observed between surface runoff and ISA (R2 varies between 0.62 and 0.89, p < 0.05), whereas inverse relationship is observed with vegetative cover in all sub-basins (R2 varies between 0.61 and 0.91, p < 0.05). The study demonstrates the potential of NRCS-CN model with inputs drawn from V-I-S fraction for simulating surface runoff and understanding the urban flood risk. The work would be useful for the urban dwellers, administrators and planners for better planning and sustainable development of urban areas.

Decadal Urban Land Use/Land Cover Changes and Its Impact on Surface Runoff Potential for the Dhaka City and Surroundings Using Remote Sensing

Rapid urban growth processes give rise to impervious surfaces and are regarded as the primary cause of urban flooding or waterlogging in urban areas. The high rate of urbanization has caused waterlogging and urban flooding in many parts of Dhaka city. Therefore, the study is undertaken to quantify the changes in land use/land cover (LULC) and urban runoff extent based on the Natural Resources Conservation Service (NRCS) Curve Number (CN) during 1978–2018. The five-decadal LULC has been analyzed using three-generation Landsat time-series data considering six different classes, namely agriculture, built-up, wetland, open land, green spaces, and water bodies for the years 1978, 1988, 1998, 2007, and 2018. Significant changes in LULC for the study area from 1978–2018 are observed as 13.1%, 4.8%, and 7.8% reduction in agricultural land, green spaces, and water bodies, respectively, and a 22.1% increase in the built-up area is estimated. Within Dhaka city, 14.6%, 16.0%, and 12.3% reduction in agricultural land, green spaces, and water bodies, respectively, and a radical increase of 41.9% in built-up area are reckoned. The decadal runoff assessment has been carried out using the NRCS-CN method, considering an extreme rainfall event of 341 mm/day (13 September 2004). The catchment area under very high runoff category is observed as 159.5 km2 (1978) and 318.3 km2 (2018), whereas, for Dhaka city, the setting is dynamic as the area under the very high runoff category has increased from 74.24 km2 (24.44%) to 174.23 km2 (57.36%) in years 1978 and 2018, respectively, and, mostly, the very high runoff potential areas correspond to the dense built-up surfaces.

Urbanization impacts on flash urban floods in Bodrum Province, Turkey

ABSTRACT Stormwater, which cannot penetrate the soil surface due to the impermeable layer created by urbanization, inevitably causes floods. Therefore, the change in urban areas and calculation of flood size are of great importance in urban flood management. In this study, five basins are examined which have been experiencing floods caused by increasing urbanization in Bodrum, one of Turkey’s most important tourism centres. Firstly, the effect of urbanization changes on the flood size was calculated, according to satellite images from 1984, 2010, 2011 and 2019, using the Natural Resources Conservation Service curve number method. Then, the multi-criteria decision-making model was used to create flood-risk maps of basins. As a result of the study, it was determined that the increase in urbanization in the period from 1984 to 2019 caused the flood flows to increase as well as the size of the areas at risk to increase.

NRCS-CN Estimation from Onsite and Remote Sensing Data for Management of a Reservoir in the Eastern Pyrenees

Onsite and Earth observation (EO) data are used for the calibration of the Natural Resources Conservation Service curve number (NRCS-CN) value in a hydrological simulation model. The model was developed for La Muga catchment (eastern Pyrenees) highly vulnerable to flood and drought episodes. It is an integral part of a regional reservoir management tool, which aims at minimizing the flood risk while maximizing the preservation of water storage. The CN values were optimized for five recorded events for the model to match the observed hydrographs at the reservoir when supported with the measured rainfall intensities. This study also investigates the possibilities of using antecedent moisture conditions (AMC) retrieved from satellite data to inform the selection of the NRCS-CN losses parameter. A good correlation was found between the calibrated CN values and the AMC obtained from satellite data. This correlation highlights the interest in using EO data to update NRCS-CN estimates. This advances in hydrologic-hydraulic coupled modeling combined with new remote sensing datasets present valuable opportunities and potential benefits for flood risk management and water resources preservation.

Assessment of modified curve number method by evaluation of runoff depth in Sulak basin-Iraq

Traditional hydrological analysis used maps and ground survey as tools to obtain the basic parameters of the watershed. However, for large catchment area the above criteria are both costly and time consuming. In this research, a hydrological model modified natural resources conservation service curve number (NRCS-CN) and geographical information system GIS technique are used together to obtain the runoff depth for Sulak catchment area that located in the northern western part of Iraq. The basin divided into three sub-basins, runoff has been estimate for each sub-basin by three approaches. The research detect that the slope parameter affects runoff estimation significantly also its found that for each sub-watershed runoff varies drastically from sub-watershed to sub-watershed of Sulak basin. The analysis of variance test showed that there was significant difference between each curve number value for each sub-watershed, accordingly runoff calculated by Williams method gives valves above the average comparing with other methods. Total runoff estimated by three approaches for entire catchment area which shows that around 51% of total runoff is generated in months January and February, That because of heavy rainfall and high soil moisture content (antecedent moisture content AMC-III) during that period.

Estimation of surface runoff using NRCS curve number in some areas in northwest coast, Egypt

The sustainable agricultural development in the northwest coast of Egypt suffers constantly from the effects of surface runoff. Moreover, there is an urgent need by decision makers to know the effects of runoff. So the aim of this work is to integrate remote sensing and field data and the natural resource conservation service curve number model (NRCS-CN).using geographic information systems (GIS) for spatial evaluation of surface runoff .CN approach to assessment the effect of patio-temporal variations of different soil types as well as potential climate change impact on surface runoff. DEM was used to describe the effects of slope variables on water retention and surface runoff volumes. In addition the results reflects that the magnitude of surface runoff is associated with CN values using NRCS-CN model . The average of water retention ranging between 2.5 to 3.9m the results illustrated that the highest value of runoff is distinguished around the urban area and its surrounding where it ranged between 138 - 199 mm. The results show an increase in the amount of surface runoff to 199 mm when rainfall increases 200 mm / year. The north of the area may be exposed to erosion hazards more than the south and a change in the soil quality may occur in addition to the environmental imbalance in the region.

Quantitative assessment of surface runoff at arid region: a case study in the Middle of Nile Delta

BackgroundEvaluation of surface runoff is an essential factor in the precision water and soil conservation management through their main extreme impacts on soil properties. The natural resource conservation service curve number model (NRCS-CN) model is used to estimate the magnitude of runoff. Collected topographic data is used to explain the effects of slope variation on water retention and surface runoff. Twenty-eight soil profiles are prepared in Nile delta, Egypt to cover different geomorphic units and hydrological soil groups in the study area.ResultsThe results revealed that the highest value of surface runoff was distinguished close to the urban area and ranges between 40 and 50 mm. In urban areas, the surfaces are paved and there are no infiltration of water. Consequently, the runoff water directly flows to the storm channels. Runoff values ranging between 30 and 40 mm occurred at the north of the study area. The sloping surface and the nature of the clay soil contributed to generate more runoff than do lowland areas.ConclusionThe study presented and tested the hydric runoff estimation based-model on the integrating of hydric balance parameters. The GIS tools analyze and compose these parameters to perform an indirect method for the quantity of water that results in direct surface runoff flow. This method helps to gain clear imaging of the surface runoff risks in the study area.

Flash flood susceptibility modeling using geo-morphometric and hydrological approaches in Panjkora Basin, Eastern Hindu Kush, Pakistan

This main objective of this study is flash flood susceptibility modeling using geo-morphometric and hydrological approaches in Panjkora Basin, Eastern Hindu Kush, Pakistan. In the study region, flash flood is one of the horrific and recurrent hydro-meteorological disasters causing damages to human life, their properties, and infrastructure. Watershed modeling approach is implemented to delineate Panjkora Basin, its sub-basins, and extract drainage network by utilizing Advance Space borne Thermal Emission and Reflection Radiometer Global Digital Elevation Model as an input data in geographic information system environment. A total of 30 sub-basins were delineated using threshold of 25 km2. The geo-morphometric parameters of each sub-basin were computed by applying Hortonian, Schumm, and Strahler Geo-morphological laws. The value of each parameter was normalized and aggregated into geo-morphometric ranking number depicting the degree of flash flood susceptibility. Surface run-off depth of each sub-basin is estimated by applying Natural Resource Conservation Service Curve Number hydrological model. Both models outputs were integrated by implementing weighted overlay analysis technique and susceptibility map is obtained. The resultant map was analyzed and zonated into very high, high, moderate, low, and very low flash flood susceptibility zones. These zones were spread over an area of 1441 km2 (27%), 1950 km2 (36.5%), 1252 km2 (23.4%), 604 km2 (11.3%), and 98 km2 (1.8%), respectively. Spatially, the very high susceptible zone is located in the upstream areas, characterized by snow covered peaks, steep gradient (> 30°), and high drainage density (> 1.7 km/km2), and geologically dominated by igneous and metamorphic lithological units. Analysis indicated that flash flood susceptibility is directly increases with increasing surface run-off and geo-morphometric ranking number. A new model is developed to geo-visualize the spatial pattern of flash flood susceptibility. Accuracy of the model is assessed using global positioning system-based primary data regarding past-flood damages and flood marks. The study results can facilitate Disaster Management Authorities and flood dealing line agencies to initiate location-specific flood-risk reduction strategies in highly susceptible areas of Panjkora Basin. Similarly, this methodological approach can be adapted for any highland river system.

Hydrological Analysis of a Dyke Pumping Station for the Purpose of Improving Its Functioning Conditions

This paper presents a methodology of calculating the water transfer capacity of a dyke pumping station in flood wave conditions in order to improve its functioning, especially with regards to the safety of the areas being drained. The exemplary analysis was carried out for a pumping station situated on a small right-bank tributary of the Odra River in the southwest part of Poland, which, due to the inadequate capacity of its pumps, extensively flooded the surrounding areas in May and June 2010. Hydrological analyses were conducted in order to determine the rate of the designed and control flows using a spatial regression equation, and as a comparison, the rainfall-runoff method was also used. The corresponding flood-wave hydrographs were also determined, which included total precipitation using the German Association For Water Resources and Land Improvement (DVWK) method, effective precipitation using the Natural Resource Conservation Service curve number (NRCS-CN) method, as well as hypothetical waves using the instantaneous unit hydrograph (IUH) method. Flood-wave routing was carried out and alternative solutions for both the output of the required pumps and the retarding reservoir capacity were highlighted on this basis. The paper presents the possibility of a correct pump capacity selection, and in turn, the size of the pumping station retarding reservoir that results from this selection. This will enable pumping station exploitation costs or maintenance costs of the retarding reservoir to be considerably reduced.

Sources of uncertainty in the NRCS CN model: Recognition and solutions

AbstractThe Natural Resources Conservation Service Curve Number model is one of the most recognizable procedures in the field of rainfall–run‐off estimation. It has been widely applied for different purposes in hydrological models. In spite of its widespread use, some uncertainties have not even clarified and must be examined for its proper application. Particularly, choosing the most representative rainfall–run‐off events, and the coefficient λ that relates the parameters of the model (curve number CN and initial abstraction Ia). In this research, an advanced analysis is developed to evaluate the influence of λ for a set of representative watersheds of the Agricultural Research Service of the United Stated Department of Agriculture. They are characterized by different soil properties, land uses, and climatic conditions. Finally, 2 novel methodologies for the selection of the most representative rainfall–run‐off events and for the adaptation of coefficient λ are included, based on the pattern of rainfall distribution.

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.

Runoff Impacts and LID Techniques for Mansionization-Based Stormwater Effects in Fairfax County, Virginia

AbstractThis case study uses the Natural Resources Conservation Service (NRCS) curve number method to quantify the increase in stormwater runoff volume from infill residential redevelopment, or mansionization, in a 0.13-km2 residential subwatershed of Fairfax County, Virginia. Analysis of 10 redeveloped lots in the subwatershed showed an average increase in impervious cover from 8 to 28% per parcel after redevelopment, resulting in an average increase in runoff volume of 18% per parcel for the 10-year, 24-h storm. From 1997 to 2009, the total impervious cover in the subwatershed increased from 18 to 25%, resulting in a calculated 5.6% total increase in runoff volume. Low-impact development (LID) techniques were modeled as retrofits in the subwatershed to mitigate the increase in runoff volume. Measures modeled include bioretention basins, infiltration trenches, amended soils, permeable pavement, and cisterns. Results indicate that placing bioretention basins or infiltration trenches on 0.5% of the subwate...

Adjusting NRCS Curve Number for Rainfall Durations Less Than 24 Hours

The primary use of the Natural Resources Conservation Service (NRCS) curve number (CN) is to compute total storm runoff based on total rainfall. The method was originally created to determine the mean daily depth of runoff during flood producing events on small agricultural watersheds. CN values were determined using daily rainfall and runoff data. Practically, it did not rain for 24 hours during many, perhaps most, of the events, but since the data were recorded as daily rainfall, 24 hours became the implicit duration for values input to the curve number runoff model. NRCS references do not specifically state the CN applies only to the 24-hour storm. Even so, it may be inferred from what is published that the standard CN applies to the 24 hour duration storm. Many methods and computer models used for the analysis and design of stormwater management systems incorporate the NRCS CN method. Because some designs and performance evaluations are based on rainfalls with durations less than 24 hours, there is the need for a method to modify CN values for shorter duration events. It goes against basic hydrologic principles if the same CN is used for storms of all durations. Not yet formally published, the NRCS recently developed a procedure to modify CN values for rainfall durations less than 24 hours. With encouragement from the NRCS, introducing that method to the engineering community is the goal for this paper. The impact of adjusted CN values was demonstrated by calculations comparing runoff depths computed with standard and duration modified CN values for rainfalls of 1, 2, 3, 6, 12, and 24 hour duration. The standard CN significantly under-predicted runoff depths compared to the duration modified CN values. The differences increased with shorter duration storms. The impact of adjusted CN values also was demonstrated during a forensic assessment of the performance of a stormwater detention pond in a residential subdivision. The pond was designed compliant with regulations to limit the post-development peak discharge rate at or below the pre-development peak runoff rate for 2- and 10-year frequency 24-hour design storm events. Even though the pond design met regulatory standards for 24-hour design storms, downstream flooding and sediment problems frequently occurred during short duration events. As part of the forensic study, runoff hydrographs were simulated for pre-development, construction phase, and post-development land use conditions for rainfalls of 1, 2, 3, 6, 12, and 24 hour duration. The simulation results for post development conditions showed successful pond performance for the 24-hour rainfall. However, the peak outflow rates for storms with durations less than 24-hours were greater than the 24-hour pre-development peak runoff rate. The simulation results emphasize pond design calculations and decisions should include pond performance for events with duration less than 24 hours and should use duration modified CN values. It is recommended controlling regulations specify design events such as the 2- and 10-year 24-hour rainfalls, but include a mandatory check of other events, such as the 1, 2, 3, 6 and 12 hour events. Prudent and ethical practice suggests pond design be upgraded for the critical rainfall event.