Potential Maximum Retention Research Articles

Modeling of flood generation in semi-arid catchment using a spatially distributed model: case of study Wadi Mekerra catchment (Northwest Algeria)

This paper aims to apply the MERCEDES distributed hydrological model with an event-based mode to simulate flood generation in the Wadi Mekerra basin situated northwest of Algeria. This catchment is characterized by a semi-arid climate, convective thunderstorms, and ephemeral flow. The input data are mainly the daily rainfall-runoff and numerical maps such as slope, flow direction, and land use. The comparison between calculated and measured runoff revealed that the MERCEDES performance to simulate flood generation in Wadi Mekerra basin is encouraging and satisfying which is justified by the Nash–Sutcliffe and observations standard deviation ratio criteria. However, the proposed model tends to underestimate peak runoff due to convective rainfall that may be much localized in space. Furthermore, the sensitivity analysis shows that the hydrological response in the Wadi Mekerra catchment depends strongly on the potential maximum retention parameter which is related to the land use type and varies significantly between seasons according to the vegetation cover dynamics, rainfall intensity, and drought.

Temporal variations in the potential hydrological performance of extensive green roof systems

Existing literature provides contradictory information about variation in potential green roof hydrological performance over time. This study has evaluated a long-term hydrological monitoring record from a series of extensive green roof test beds to identify long-term evolutions and sub-annual (seasonal) variations in potential hydrological performance. Monitoring of nine differently-configured extensive green roof test beds took place over a period of 6 years in Sheffield, UK.Long-term evolutions and sub-annual trends in maximum potential retention performance were identified through physical monitoring of substrate field capacity over time. An independent evaluation of temporal variations in detention performance was undertaken through the fitting of reservoir-routing model parameters. Aggregation of the resulting retention and detention variations permitted the prediction of extensive green roof hydrological performance in response to a 1-in-30-year 1-h summer design storm for Sheffield, UK, which facilitated the comparison of multi and sub-annual hydrological performance variations.Sub-annual (seasonal) variation was found to be significantly greater than long-term evolution. Potential retention performance increased by up to 12% after 5-years, whilst the maximum sub-annual variation in potential retention was 27%. For vegetated roof configurations, a 4% long-term improvement was observed for detention performance, compared to a maximum 63% sub-annual variation. Consistent long-term reductions in detention performance were observed in unvegetated roof configurations, with a non-standard expanded-clay substrate experiencing a 45% reduction in peak attenuation over 5-years. Conventional roof configurations exhibit stable long-term hydrological performance, but are nonetheless subject to sub-annual variation.

Direct runoff assessment using modified SME method in catchments in the Upper Vistula River Basin

Correct determination of direct runoff is crucial for proper and safe dimensioning of hydroengineering structures. It is commonly assessed using SCS-CN method developed in the United States. However, due to deficiencies of this method, many improvements and modifications have been proposed. In this paper, a modified Sahu–Mishra–Eldo (SME) method was introduced and tested for three catchments located in the upper Vistula basin. Modification of SME method involved a determination of maximum potential retention S based on CN parameter derived from SCS-CN method. The modified SME method yielded direct runoff values very similar to those observed in the investigated catchments. Moreover, it generated significantly smaller errors in the direct runoff estimation as compared with SCS-CN and SME methods in the analyzed catchments. This approach may be used for estimating the runoff in uncontrolled catchments.

A Modified SCS-CN Method Incorporating Storm Duration and Antecedent Soil Moisture Estimation for Runoff Prediction

In one of the widely used methods to estimate surface runoff - Soil Conservation Service Curve Number (SCS-CN), the antecedent moisture condition (AMC) is categorized into three AMC levels causing irrational abrupt jumps in estimated runoff. A few improved SCS-CN methods have been developed to overcome several in-built inconsistencies in the soil moisture accounting (SMA) procedure that lies behind the SCS-CN method. However, these methods still inherit the structural inconsistency in the SMA procedure. In this study, a modified SCS-CN method was proposed based on the revised SMA procedure incorporating storm duration and a physical formulation for estimating antecedent soil moisture (V0). The proposed formulation for V0 estimation has shown a high degree of applicability in simulating the temporal pattern of soil moisture in the experimental plot. The modified method was calibrated and validated using a dataset of 189 storm-runoff events from two experimental watersheds in the Chinese Loess Plateau. The results indicated that the proposed method, which boosted the model efficiencies to 88% in both calibration and validation cases, performed better than the original SCS-CN and the Singh et al. (2015) method, a modified SCS-CN method based on SMA. The proposed method was then applied to a third watershed using the tabulated CN value and the parameters of the minimum infiltration rate (fc) and coefficient (β) derived for the first two watersheds. The root mean square error between the measured and predicted runoff values was improved from 6 mm to 1 mm. Moreover, the parameter sensitivity analysis indicated that the potential maximum retention (S) parameter is the most sensitive, followed by fc. It can be concluded that the modified SCS-CN method, may predict surface runoff more accurately in the Chinese Loess Plateau.

Spatial Conservation of Ecological Landscape-Hydrologic Responses to Land Use Change in Datu Tableland

Datu Tableland is located at the land of Taichung Basin and Taichung’s Western Coastal Plain in central Taiwan. It is a large Urban Green Corridors with multi-functions of Ecological Service. It was suffered by flooding happened by human-made activities such as the development of scientific industrial park in recent years. This research collects data from different government agencies, including remote sensed data, 2009 digital land use data, soil data to establish the model of analyzing the changes of Ecological Service. Through analyzing and interpreting these data this study produce the layer of vegetation coverage and water storage capacity. At the same time, this study use arcgis database to manage and analyze the files efficiently. Dividing Datu Tableland to three subwatersheds makes the distribution of the changing of land use meaningful. It indicates landscape effects hydrological index and ecological vegetation coverage actually. This paper also estimates potential maximum retention amount in three subwatersheds. The total water storage capacity are estimated along urbanization and industrial development. The process of quantifying the impacts of LULC changes on hydrology at different scales including geomorphological and hydrological scales provides quantitative information for making decisions for land and water resource management. This paper also links landscape ecological thinking and hydrological thinking to ecological service analysis. The anticipated benefits for flood reduction by water storage facilities may be also received.

Effectiveness of basin morphometry, remote sensing, and applied geosciences on groundwater recharge potential mapping: a comparative study within a small watershed

A multidisciplinary approach using the integrated field of geosciences (e.g., geomorphology, geotectonics, geophysics, and hydrology) is established to conduct groundwater recharge potential mapping of the Kunur River Basin, India. The relative mean error (RME) calculation of the results of three applied techniques and water table data from twenty-four observation wells in the basin over the 2000–2010 period are presented. Nine subbasins were identified and ranked for the RME calculation, where the observation wells-based ranking was taken as standard order for comparison. A linear model has been developed using six factors (drainage density, surface slope, ruggedness index, lineament density, Bouguer gravity anomaly, and potential maximum water retention capacity) and a grid-wise weighted index. In a separate comparative approach, the sub-basin and grid-wise analyses have been conducted to identify the suitable spatial unit for watershed level hydrological modeling.

Physical verification of the effect of land features and antecedent moisture on runoff curve number

Using the data of a large number of observed rainfall (P)/runoff (Q) events for an agricultural experimental field located in Roorkee, Haridwar, Uttarakhand, India, the effects of land features such as slope and antecedent moisture were evaluated on the parameter curve number (CN) of the SCS-CN methodology used for runoff estimation. The plot of 5% slope yielded the largest runoff and, in turn, CN compared to the plots of 3% and 1% grades for the same soil type and land use. The derived CN values from observed P–Q data were considerably different from conventional NEH-4 table values. The optimized initial abstraction ratio (λ) values showed that the original assumption of the λ of 0.20 is unusually high. The median and mean λ values were respectively 0 and 0.034 for natural P–Q data and 0.033 and 0.108 for ordered P–Q data. The employment of a modified equation of Sharpley and Williams (1990) for deriving the slope-corrected CN-values improved runoff estimation significantly. As expected, CN (or, potential maximum retention, S) values showed a higher degree of dependence on the physically observed 1-day antecedent soil moisture (θo1) than other duration antecedent soil moisture values.

Investigation of SCS-CN and its inspired modified models for runoff estimation in South Korean watersheds

Employing a dataset of 658 large storm-events from 15 South Korean watersheds (48.6–249.63 km2), this study established the initial abstraction (Ia) as 2% of the rainfall amount (obtained based on three different scenarios), instead of the originally assumed 20% of the maximum potential retention (S) in the Soil Conservation Service Curve Number (SCS–CN) model. After investigating 8 different models, including the original SCS-CN and its inspired modified models, it was found that lower values of the initial abstraction coefficient (λ) exhibited better runoff estimation than the fixed λ (=0.2) as lower λ < 0.2 was recommended by researchers and supported by this work for CN values calculated from observed storm-events. To reduce errors in runoff estimation, CNs should be calibrated using observed rainfall-runoff data from regional watersheds. The proposed model, which incorporates the newly suggested initial abstraction based on rainfall-runoff rank-order data, outperformed in 14 out of 15 watersheds. Using the optimized/calculated CN values, the proposed model ranked first (as best) based on the evaluation of three different performance indices, followed by the model of Hawkins et al. (2002), one of the models of the Mishra and Singh, and the original SCS-CN model respectively. Owing to a significant degree of agreement between the observed and calculated runoff, the proposed model is recommended for field applications in this study area.

위성토양수분과 지점강우량을 이용한 지역 선행습윤조건 분석

Soil moisture is one of the most important interests in hydrological response and the interaction between the land surface and atmosphere. Estimation of Antecedent Wetness Conditions (AWC) which is soil moisture condition prior to a rainfall in the basin should be considered for rainfall-runoff prediction. In this study, Soil Wetness Index (SWI), Antecedent Precipitation Index (API5), remotely sensed Soil Moisture (SMrs), and 5 days ground Soil Moisture (SMg5) were selected to estimate the AWC at four study area in the Korean Peninsula. The remotely sensed soil moisture data were taken from the AMSR-E soil moisture archive. The maximum potential retention (Sobs) was obtained from direct runoff and rainfall using Soil Conservation Service-Curve Number (SCS-CN) method by rainfall data of 2011 for each study area. Results showed the great correlations between the maximum potential retention and SWI with a mean correlation coefficient which is equal to –0.73. The results of time length representing the time scale of soil moisture showed a gap from region to region. It was due to the differences of soil types and the characteristics of study area. Since the remotely sensed soil moisture has been proved as reasonable hydrological variables to predict a wetness in the basin, it should be continuously monitored.

Estimation of Surface Runoff In Northeastern Missan Governorate by Using (Nrcs-Cn) Technique And Gis

In this study, an attempted was made to estimate average of annual total surface runoff in the&#x0D; northeastern Missan Governorate, south of Iraq by using Natural Resources Conservation Services&#x0D; Curve Number (NRCS – CN) empirical technique in GIS platform. Thematic layers of soil&#x0D; hydrological groups and LULC were created using ArcGIS 9.3 software. Intersection of these two&#x0D; thematic layers generated a map with new polygons representing the merged hydrological soil group&#x0D; and LULC. The appropriated CN values adapted from different sources and technical release of&#x0D; United State of Department of Agriculture (USDA) were assigned manually for each new merged&#x0D; polygon. The curve numbers for different antecedent moisture content (AMC) were calculated as 38,&#x0D; 59, 77 for dry, normal, and wet soil conditions. The associated maximum potential retention for each&#x0D; AMC were calculated as 75,87, 176.51, and 414.42 for dry, normal, and wet soil moisture conditions.&#x0D; Application of the method showed that the average annual runoff depth is 0.36 mm. This figure was&#x0D; multiplied by the area of the study area (1856) km2 to get the total average of runoff volume 7424000m3 &#x0D; which represents 0.0003 from the total annual average of rainfall. The applied method is robust&#x0D; and easy to implement and could be used to roughly estimate the surface runoff with minimal data.

Comparing Calculation Techniques for Effective Rainfalls Using NRCS-CN Method: Focused on Introducing Weighted Average and Slope-based CN

유역의 유효우량을 산정할 경우 NRCS-CN 방법을 이용하는 것이 보통이다. NRCN-CN 방법에서 유역의 유출특성을 나타내는 중요한 인자인 유출곡선지수(CN)은 미국 유역을 대상으로 개발되어졌기 때문에, 미국 이외의 국가에서 적용될 경우 지역의 강우-유출 특성을 반영하지 못하는 한계점을 가지고 있다. 이에 많은 연구에서 유역의 CN값을 보정하는 방법을 제시하고 있다. 본 연구에서는 우리나라에 적절한 유효우량 산정방법을 제시하기 위하여 유역의 CN값을 가중평균하는 방법과 유역의 경사도를 고려하여 CN값을 보정하는 방법을 적용하여 유효우량을 산정하고 이를 관측 유효우량과 비교 분석을 수행하였다. 본 연구 결과, 경사도를 고려하여 CN값을 보정할 경우 유효우량이 전반적으로 크게 산정되었으며, 유효우량의 지속시간 또한 길어지는 것으로 나타났다. 유효우량의 산정방법에 따른 통계학적 오차 분석을 수행한 결과, 경사도로 보정한 CN값을 적용한 가중평균 유효우량 산정방법이 관측 직접유출량과 높은 정확성을 가지는 것으로 나타났다. 또한, 관측 자료를 이용하여 추정한 초기손실량과 GIS 자료를 이용하여 추정한 최대잠재보유수량과의 관계를 분석한 결과, NRCS-CN 방법에서 적용되고 있는 선형가정은 실제 강우-유출 관계와 큰 차이를 가지고 있는 것으로 나타났다. The NRCS-CN method is generally used to estimate effective rainfalls in a basin. However, since the curve number which plays a critical role in the NRCS-CN method was originally developed for US watersheds, it is limited to be directly applied to other basins outside the United States. Therefore various modifications have been suggested to revise the NRCS-CN for specific watershed condition. This study introduced the weighted average method and the slope-based CN to estimate effective rainfalls available for Korean watersheds and compared with the observed direct runoff. The overall results achieved from this study indicated that the adjusted slope-based CN considerably increases effective rainfalls in general and makes the duration of effective storm longer. Based on the statistical error analysis performed for various modifications of NRCS-CN, the weighted average method with the adjusted slope-based CN has highest precision with the observed direct runoff. In addition, after analyzing the relation between the initial loss estimated from rainfall-runoff observations and the potential maximum retention from GIS-based data, it turns out that the assumption of linear relationship between the initial loss and the potential maximum retention is not available for Korean watersheds.

Integration of HEC-RAS and geographical information system (GIS) in the hydrological study of peak flow response to deforestation on a small watershed in Malaysia

Land use change is one of the most serious environmental and hydrological problems nowadays. It has become a global issue including Malaysia due to the greenhouse effect. Land use changes cause a significant adjustment of the hydrological balance of a watershed mainly by eliminating evapotranspiration, redistributing and modifying the amount of rainfall that reach the ground. A 20 years historical data were used to represent a geographical and hydrological data for a Kinta watershed, in the state of Perak. The geographical data were processed using geographical information system (GIS) to show the land use change in the study area. The hydrological data were analyzed through regression analysis, flow duration curve, and soil conservation service (SCS) method for analyzing runoff for land use change. The regression analyses have found significant increase of r2 value from 3.1% between year 1990 to 1996, 7.5% between year 1997 to 2000, and 13.2% between the years of 2001 to 2006. The flow duration curve has selected streamflow data event on December 28 to 29, 1996 on the Kinta River at outlet of Kinta watershed result which has shown volume of streamflow that was 2.209 × 106 m3. Meanwhile the direct runoff was 8.979 mm from volume divided by area of watershed 246 km2. The I• index calculated was 28.144 mm/h and I•Δt was 28.144 mm. Land use analysis shown result for runoff depth and S, potential max retention followed by CN for each year. The result showed increase of runoff depth and decrease of S, potential maximum retention. These findings have proved that the changes in land use in terms of the increasing deforestation activities or increasing new development will give a crucial impact to the hydrology response. Key words: Land use change, flow duration curve, soil conservation service (SCS) method, runoff depth, geographical information system (GIS).

Initial abstraction and curve numbers for semiarid watersheds in Southeastern Arizona

The Soil Conservation Service (SCS) curve number (CN) estimates of direct runoff from rainfall for semiarid catchments can be inaccurate. Investigation of the Walnut Gulch Experimental Watershed (WGEW) (Southeastern Arizona) and its ten nested catchments determined that the inaccuracy is due to the original SCS ratio (λ) of 0.2 between initial abstraction and maximum potential retention. Sensitivity analyses indicate that runoff estimation can be very sensitive to the initial abstraction ratio, especially for relatively low rainfall amount and for watersheds covered by deep, coarse, and porous soil, conditions that dominate many semiarid watersheds worldwide. Changing the ratio of initial abstraction to the maximum potential retention to optimal values ranging from 0.01 to 0.53 for different Walnut Gulch catchments improved runoff estimates. The greater the channel area and the finer the soil, the smaller the initial abstraction ratio is. The variation of the initial abstraction ratio for the WGEW is due to the variation of maximum potential retention and initial abstraction, which are channel area and soil-dependent parameters. The greater the channel area, the higher the maximum potential retention S is, and the coarser the soil, the larger the initial abstraction Ia is. In addition, the effect of initial abstraction ratio on runoff estimation increases with decreasing CN. Thus, impacts of initial abstraction ratio on runoff estimation should be considered, especially for semiarid watersheds where the CN is usually low. Copyright © 2012 John Wiley & Sons, Ltd.

RS and Geographical Information System–Based Evaluation of Distributed and Composite Curve Number Techniques

AbstractThis paper compares the composite and distributed curve number (CN) techniques in computation of runoff from a hypothetical watershed of 100×100 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 (Ia) 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 Ia=0.3S than that for Ia=0.2S;...

Development of a thresholds approach for real‐time flash flood prediction in complex geomorphological river basins

AbstractFlash floods represent one of the deadliest and costliest natural disasters worldwide. The hydrological analysis of a flash flood event contributes in the understanding of the runoff creation process. This study presents the analysis of some flash flood events that took place in a complex geomorphological Mediterranean River basin. The objective of the present work is to develop the thresholds for a real‐time flash flood forecasting model in a complex geomorphological watershed, based on high‐frequency data from strategically located hydrological and meteorological telemetric stations. These stations provide hourly real‐time data which were used to determine hydrological and meteorological parameters. The main characteristics of various hydrographs specified in this study were the runoff coefficients, lag time, time to peak, and the maximum potential retention. The estimation of these hydrometeorological parameters provides the necessary information in order to successfully manage flash floods events. Especially, the time to peak is the most significant hydrological parameter that affects the response time of an oncoming flash flood event. A study of the above parameters is essential for the specification of thresholds which are related to the geomorphological characteristics of the river basin, the rainfall accumulation of an event, the rainfall intensity, the threshold runoff through karstic area, the season during which the rainfall takes place and the time intervals between the rainstorms that affect the soil moisture conditions. All these factors are combined into a real‐time‐threshold flash flood prediction model. Historical flash flood events at the downstream are also used for the validation of the model. An application of the proposed model is presented for the Koiliaris River basin in Crete, Greece. Copyright © 2011 John Wiley &amp; Sons, Ltd.

Improved SCS-CN–Inspired Model

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 (λ) 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 Sabs (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.

Improving Curve Number Based Storm Runoff Estimates Using Soil Moisture Proxies

Advances in data dissemination and the availability of new remote sensing datasets present both opportunities and challenges for hydrologists in improving flood forecasting systems. The current study investigates the improvement in SCS curve number (CN)-based storm runoff estimates obtained after inclusion of various soil moisture proxies based on additional data on precipitation, baseflow, and soil moisture. A dataset (1980-2007) comprising 186 Australian catchments (ranging from 51 to 1979 km 2 in size) was used. In order to investigate the value of a particular proxy, the observed S (potential maximum retention) was compared to values obtained with different soil moisture proxies using linear regression. An antecedent precipitation index (API) based on gauged precipitation using a decay parameter proved most valuable in improving storm runoff estimates, stressing the importance of high quality precipitation data. An antecedent baseflow index (ABFI) also performed well. Proxies based on remote sensing (TRMM and AMSR-E) gave promising results, particularly when considering the expected arrival of higher accuracy data from upcoming satellites. The five-day API performed poorly. The inclusion of soil moisture proxies resulted in mean modeled versus observed correlation coefficients around 0.75 for almost all proxies. The greatest improvement in runoff estimates was observed in drier catchments with low Enhanced Vegetation Index (EVI) and topographical slope (all intercorrelated parameters). The present results suggest the usefulness of incorporating remotely sensed proxies for soil moisture and catchment wetness in flood forecasting systems.

Assimilation of Observed Soil Moisture Data in Storm Rainfall-Runoff Modeling

Estimation of antecedent wetness conditions is one of the most important aspects of storm rainfall-runoff modeling. This study investigated the use of observations of near-surface soil moisture carried out in a small experimental plot to estimate wetness conditions of five nested catchments, from 13 to 137km2 in area, in Central Italy, including the plot itself. In particular, the relationship between the observed degree of saturation, θe, and the potential maximum retention parameter, S, of the soil conservation service-curve number (SCS-CN) method for abstraction was investigated using 15 rainfall-runoff events (ten for calibration and five for verification) that occurred in the period 2002–2005. Two antecedent precipitation indices (API) and one base flow index (BFI) were also considered for the estimation of wetness conditions. When interpreting S as the mean soil water deficit of the catchment, an inverse linear relationship with θe was found with the coefficient of determination decreasing with catchment area, but still significant for the largest catchment. On the contrary, the reliability of regression increased with catchment area when BFI was employed. Both API indices led to poor results for all investigated catchments. The accuracy of the modified SCS-CN method, i.e., incorporating θe for the estimation of S, coupled with a geomorphological unit hydrograph transfer function, was tested in simulating the catchment response. Assimilating the observed soil moisture in the rainfall-runoff model, both the runoff volume and the peak discharge were well predicted with average Nash–Sutcliffe efficiency greater than 90% in the verification phase.

Research on the SCS-CN initial abstraction ratio using rainfall-runoff event analysis in the Three Gorges Area, China

The Soil Conservation Service Curve Number (SCS-CN) method is widely used for predicting direct runoff from rainfall. The ratio of initial abstraction ([a) to maximum potential retention (S) was assumed in its original development to be equal to 0.2 in SCS-CN method. The constant initial abstraction ratio is the most ambiguous assumption and requires considerable refinement. The objectives of this study were (1) to determine the initial abstraction ratio, in an experimental watershed in the Three Gorges Area of China, by analyzing measured rainfall-runoff events; (2) to compare the performance of the traditional and modified la/S values with observed rainfall-runoff data. The dataset consisted of 6 years of rainfall and runoff measurements from the experimental watershed. The results indicated that the la/S values, using event rainfall-runoff data, varied from 0.010 to 0.154, with a median of 0.048. The average initial abstraction ratio of the watershed was equal to 0.053. The standard SCS-CN method underestimates large runoff events, yielded a slope of the regression line of 0.559 and an intercept of 0.301. The modified la/S value was about 0.05 that better predicted runoff depths with an R(2) of 0.804 and a linear regression slope of 0.834. It also improved model efficiency coefficient (E) to 0.768 compared with 0.482 for traditional la/S value. This la/S-adjusted SCS-CN method appears to be better appropriate for runoff prediction in the Three Gorges Area of China. (C) 2008 Elsevier B.V. All rights reserved.

Estimation of the Runoff Curve Number via Direct Rainfall Simulator Measurements in the State of Iowa, USA

This study was the first to provide detailed methodological steps to estimate in-situ runoff curve number (CN) for selected agricultural fields in the State of Iowa via rainfall simulators. Representative fields in six counties were chosen to identify the effects of the following variables on runoff CN: rainfall intensity, soil type, soil moisture condition, tillage practice, and residue cover. The study also re-evaluated the range of the existing CN values for the different hydrologic soil groups in Iowa, and revised the equations describing the CN method to consider variables such as residue cover and soil moisture in a more detailed manner than the existing USDA method. The findings of this investigation showed that rainfall simulators are useful instruments for estimating in-situ runoff CN because rainfall intensity was adjustable during an experimental run. Further, the simulators eliminate the need of natural storm events. The range of the estimated CN values in summer agreed well (deviation less than 6%) with the reported CN values. However, the range of the estimated CN values in fall was generally less the reported CN values (deviation of about 40%) due to the high residue levels found in the fields after harvest. The effects of tillage practice and crop type were insignificant compared to residue cover and soil moisture. The study has also shown that the initial abstraction Ia is not linearly proportional to the potential maximum retention S, which agrees with the available literature.