Potential Maximum Retention Research Articles

The impact of rainfall value on the accuracy of the SCS-CN model: selection of model parameters

ABSTRACT The soil conservation service curve number (SCS-CN) model is a widely utilized tool for estimating runoff and relies on two empirical parameters: the CN and the ratio of initial abstraction to maximum potential retention (λ). The determination of the parameters is via the empirical method or calculations based on actual data. However, few studies address the effect of rainfall on parameter selection, and collecting runoff data for model analysis is challenging. This study, taking the Nemor River Basin in Northeast China as the research region, investigates how the combination of CN and λ impacts the model in different rainfall conditions. Using runoff plots and reanalysis product data, the study reveals that: (1) the calculated methods outperformed the empirical method, increasing the Nash efficiency coefficient from 0.34 to 0.65. (2) A higher λ value (0.2 compared to 0.02) reduces runoff and smoothes the runoff curve, which becomes less obvious with increasing CN. (3) The CN values exhibit a non-monotonic relationship with rainfall, initially decreasing before rising, highlighting the need to adjust the CN based on rainfall. Moreover, the SCS-CN model's performance with reanalysis data approximates that with actual data, confirming the viability of reanalysis datasets in this region.

Restored vegetation dominates the decrease in surface and subsurface runoff on the Loess Plateau

Understanding the runoff generation response to environmental changes at the basin scale can provide insights into the evolution of hydrological processes. The objective of this study is to detect and attribute the changes in surface and subsurface runoff in the Chinese Loess Plateau, a region with significant environmental and hydrological changes in the past several decades, to understand how and to what extent the runoff generation can be changed. Taking 13 basins (comprising 56 hydrological stations) on the Chinese Loess Plateau as the study area, we identified rainfall-runoff events and established indicators (event surface runoff coefficient, event timescale, normalized event peak discharge, baseflow, and baseflow index) to characterize the runoff generation, and further attributed the changes in runoff generation characteristics before (1961–2000) and after (2001–2019) the revegetation project. In particular, we inversely estimated the parameters of Soil Conservation Service Curve Number (SCS-CN) to analyze the initial abstraction and potential maximum retention under changing environment. Surface and subsurface runoff significantly decreased, with the surface runoff coefficient and baseflow decreasing by 34 % ± 24 % and 26 % ± 23 %, respectively. The surface runoff hydrograph became flatter with a 75 % decrease in the normalized event peak discharge and 82 % increase in the timescale of surface runoff, respectively. Normalized Difference Vegetation Index (32 %) and population (34 %) dominated the increase in the potential maximum retention, with 84 % of stations experiencing growth exceeding 100 %. The reduction in surface runoff can be primarily attributed to the enhanced soil infiltration due to revegetation and other human activities. Higher evapotranspiration resulting from revegetation was likely the dominant factor leading to the decrease in subsurface runoff. This study detailed the impacts of revegetation on surface and subsurface runoff generation, which can enhance our understanding of hydrological changes.

Rainfall-Runoff Parameter Estimation from Ungauged Flat Afforested Catchments Using the NRCS-CN Method

Of the numerous methods applied in rainfall-runoff models, the most common is the NRCS-CN method that is applied to calculate raised-water runoffs and compare them with the runoff values measured for 12 selected rainfall-runoff events. This study was conducted on three experimental forest catchments with an area ranging from 67.6 to 747 ha. Total rainfall values ranging from 22.2 to 84.1 mm were analysed. Relatively low effective rainfall values were obtained for the lowest average for catchment 1 (Pe = 0.23 mm) and the runoff coefficient (α = 0.40%) and for the highest average for catchment 3 (Pe = 1.35 mm) and an average runoff coefficient (α = 3.12%). The maximum potential retention Si value, corresponding to each pair of P-Pe events, was the effect of the catchment’s moisture and absorptive capacity conditions. The lowest retention S value was calculated for catchment 3. The highest average retention value was calculated for catchment 1, in which the lightest soils were found. The best fit of the initial loss coefficient for the majority of rainfall-runoff events occurred for the λ coefficient values of 0.05 and 0.075. At higher λ, the effective rainfall Pe was not generated. LAG times calculated using 10 methods yielded diverse values. The fit of a specific formula was largely influenced by the size of the catchment, as well as the number and type of parameters considered during model calibration. The method based on catchment width demonstrated the best fit for all catchments, with R2 ranging from 0.77 to 0.78 and RMSE from 0.52 for catchment 2 to 1.11 for catchment 1.

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.

Estimation of Runoff Using USDA SCS-Curve Number and Autoregressive Time Series Model Parameters for Kachhinda Watershed, Morena District, Madhya Pradesh, India

Hydrological modeling is an influential method for examining hydrologic systems, serving as a valuable tool in investigating these systems for both present studies involving two hydrologic runoff models viz. Soil Conservation Service Two methods, namely the Soil Conservation Service-Curve Number (SCS-CN) method and the Autoregressive Time Series model, were utilized in the Kachhinda watershed to estimate surface runoff, CN, and AMC conditions. The study area covers 600 hectares and is sited in Morena district of M.P. in Chambal division. The SCS-CN technique was employed to determine the curve number and estimate surface runoff by using the potential maximum retention. The CN of the watershed was calculated and compared to observed and estimated surface runoff, which were found to be in close agreement with each other. Additionally, an Autoregressive Time Series model was developed to establish the correlation between observed and estimated runoff, resulting in a correlation coefficient of 0.974. Different orders of AR time series models (0, 1, and 2) were tested to predict annual stream flow, and the model goodness of fit was evaluated using the Box-Pierce Portmanteau test and the Akaike Informations Principle. The Akaike Information Criterions value for the Autoregressive (1) model for runoff was found to be 0.919158, which is within the range of the values obtained for Autoregressive (0) (0.207433) and Autoregressive (2) (5.9767) models.

GIS based NRCS-CN modeling of rainfall-runoff in river Thamirabarani sub-basin, Southern India

River Thamirabarani sub-basin lies in the southern part of the Western Ghats in the Kanyakumari district of Tamil Nadu, India. The basin’s landforms are formed by swift surficial characteristics with undulated terrains composed of steep-sloped valleys, valley fills, and short-length streams with intensive flows that makes-up site-specific hydro-morphological characteristics. In this study, the GIS-based Natural Resource Conservation Service-Curve Number (NRCS-CN) model is used to assess rainfall-induced runoff by analyzing various hydrological parameters. The curve number (CN) is assigned to each hydrologic soil group (HSGs) based on the measurement of initial abstraction (Ia) and potential maximum retention (S) derived from soils, landuse/ land cover, antecedent soil moisture, etc. The resulting map shows the estimated runoff at the rate of 2.27 – 5.94 mm/m2/yr for the whole study area, whereas the higher runoff rate (4.83 – 5.94 mm/m2/yr) is noted in the upland range of the swift surficial terrains in the north and north-eastern parts that encompasses undulated structural hills, steep-sloped valleys, inselberg, and denudational hills, etc., whereas the work of swift surficial terrains and associated slope gradient of the landforms are considered to be higher runoff rate than the other parts. The moderate runoff rate (2.92 – 3.98 mm/m2/yr) is estimated in the middle parts of pediplains that consist of croplands, plantations, riverbanks, fallows, and built-up areas. Significantly, the lower runoff rate (<2.92 mm/m2/yr) sparsely occurred in the different landforms of the middle and southern parts that include pediplains, riverbanks, natural vegetative covers, valley-filled sediment deposits, etc. Overall results indicate that the higher runoff found over the swift surficial landforms in the north and north-eastern parts due to intensive flow through short-length stream orders. This study is mainly used for understanding hydro-morphological processes and their impacts on basin environments.

Assessment of runoff potential for disaster risk reduction using geospatial technology in Opa watershed, Southwestern Nigeria

Flood prediction is very important in land and water resources management. Many flood disasters could be mitigated with adequate preparedness especially in urban watershed. This study assessed the runoff potential of Opa watershed in Southwest Nigeria using remote sensing and the Soil Conservation Service or SCS curve number (CN) techniques. The 2007 NigSat image of the year 2007 was classified into different land cover classes and combined with its hydrological soil groups to determine the curve number of each sub-watershed. The sub-watershed with low curve number is considered to have lower runoff potential while the one with higher curve number is considered to have higher runoff potential. The CN was also used to estimate the potential maximum retention, (S) and potential runoff, (Q) for each sub-watershed using a rainfall event of 2-year return period in the watershed. The weighted runoff was used to determine sub-watersheds with highest and lowest runoff potential. The study showed that urban sub-watershed 9 with average CN value of 85.93 has highest weighted runoff potential (5.53 mm) while the vegetated sub-watershed 10 with average CN value of 69.46 has the lowest weighted runoff potential (0.34 mm). The study concluded that using available geospatial technology and appropriate hydrologic assessment techniques constitute an effective flood prediction method for disaster risk reduction and sustainable urban watershed management.

Effect of Hydrologic Soil Groups on Runoff Generation in Reservoir Catchments in Northern Ghana

The study was carried out in northern Ghana to estimate surface runoff in nine (9) reservoir catchments for a period of 20 years (1999–2018) using the United States Department of Agriculture Natural Resource Conservation Service-Curve Number (USDA-NRCS-CN) method. The key input parameters of the method were hydrologic soil group, antecedent moisture condition, rainfall amount, landuse/landcover, weighted curve number and potential maximum soil moisture retention. The annual rainfall within the reservoir catchments was found to vary between 617.2 and 1,382.3 mm. The estimated annual runoff depths ranged from 68.5 ± 7.5 to 98.0 ± 13.3 mm and the percentage annual rainfall converted into runoff varied between 9.8 ± 0.7 and 13.7 ± 0.9 %. Hydrologic soil groups accounted for about 67.2 % and 62.5 % of the variation in runoff depth and percentage runoff respectively, in the reservoir catchments. Catchment size accounted for only 2.7 % and 3.9 % of the variation in runoff depth and the percentage of the amount of rainfall converted into runoff respectively, in the reservoir catchments. Therefore, hydrologic soil groups should be used by reservoir managers as a primary indicator of the potential runoff generation in reservoir catchments.

Development and performance evaluation of SCS-CN based hybrid model.

In this study, a hybrid approach has been used to increase the predictive efficiency of the SCS-CN model. A recently proposed Ajmal model (developed after randomized configuration) that ignored initial abstraction and maximum potential retention has been given the conceptual framework of the SCS-CN model and a new outcome-based hybrid model (Miv) was formulated. A total of 78 watersheds (7817 events) were used for calibration and the remaining 36 watersheds (3967 events) for validation to develop this hybrid model. The numerical value of hybrid model parameters Lc, λ and S were calibrated using calibration dataset and a simple non-linear one-parameter model has been developed. The performance of the Ajmal (Miii) and hybrid model (Miv) was compared with the original SCS-CN method (λ = 0.2 as Mi and λ = 0.05 as Mii). The performance of models was compared by using four statistical error indices i.e. RMSE, NSE, PBIAS, and n(t) and applying ranking and grading system (RGS). The mean RMSE, NSE, PBIAS, and n(t) values were found superior for Miv (5.60 mm, 0.71, 6.97%, 1.15) model followed by Miii (5.98 mm, 0.65, 16.52%, 1.01), Mii (6.27 mm, 0.61, 20%, 0.90) and Mi (6.98 mm, 0.46, 24.2%, 0.72) model for tested watersheds. The hybrid model (Miv) exhibited consistently well performance for all size watersheds. On the basis of the agreement between watershed runoff coefficient (C) and calibrated model parameter (Lc or CN), R2 value was found relatively higher for hybrid model (Miv) than other models.

Estimation of Runoff from River Asa Watershed Using SCS Curve Number and Geographic Information System (GIS)

The study aims to determine the runoff depth using Soil Conservation Service Curve Number (SCS-CN) method in Geographical Information System (GIS) environment. For River Asa Watershed, the SCS Curve Number method has been adopted for estimating the runoff depth using Rainfall data from 1987-2018. Land use and change cover map were used for the classification of soil type, in order to determine the Hydrological Soil Group (HSG) using ArcGIS. The runoff was estimated from the rainfall runoff equation. The Antecedent Moisture Condition (AMC), potential maximum retention(s) and initial abstraction were computed. Thematic maps such as Soil and Land Use / Land cover and have been used in conjunction with hydrological data for determining hydrological soil Group (HSG) and Curve Number (CN) for land used and change cover classes over the watershed. The values of Hydrological Soil Group, Curve Number and Annual runoff depth varied (6.28-914.22) km2, (65-100) and (16.50-144.89) × 106 m3. The study shows that the high runoff depth was observed in Hydrological soil group (HSG), when compared with curve number (CN), this is due to dense vegetation cover.

Application of Risk Analysis in the Screening of Flood Disaster Hot Spots and Adaptation Strategies

In recent years, Taiwan has established a sound flood control foundation in terms of river management. Due to climate change and land development, surface runoff has increased. In addition, the functions of flood control engineering facilities have their limits. Surface runoff cannot be fully absorbed by rivers, and frequent floods still occur in some areas. According to the characteristics of water flowing along the terrain to low-lying land, the terrain features can be used to find out the hot areas prone to flooding and the appropriate location of flood storage space for improving flooding. On the basis of the natural terrain environment, the disaster risk framework is used to manage environmental complexity, and to carry out research on flood warning and governance decision-making systems, so that human beings can coexist with the uncertainty of flood risk. In this study, the Zhuoshuixi Basin was used as the sample area, the SCS-CN method was used to analyze the excess runoff, and the risk concept was used to establish a flood evaluation model. In addition, through the changes in land use, the SCS-CN method estimates the difference of potential maximum retention, quantifies the variation of excess rainfall in each watershed division, and uses the digital elevation model to calculate the depression site to analyze the relationship between the difference of potential maximum retention and the depression space of the watershed. The results show that the adaptation strategy for high-risk flooded areas should be strengthened, and areas with large water storage space and a small potential maximum retention difference can be the best location for offsite compensation.

Modified NRCS Abstraction Method for Flood Hydrograph Generation

The Natural Resources Conservation Service (NRCS) abstraction method is based on two main assumptions. The first is that the ratio of actual water retention to maximum potential retention is equal to the ratio of actual surface runoff to potential surface runoff. The second assumption is that the initial abstraction for the watershed is 20% of the maximum potential retention. This study shows that both assumptions violate continuity principles and proposes a modification that renders an elementary relationship accounting for all abstraction forms by dividing them into a variable and constant components. Consequently, the surface runoff computation becomes dependent on the soil initial moisture content and implicitly influenced by the initial abstraction, while retaining the advantage of the subjective selection of curve number from an extensive database from which the NRCS method has gained popularity. A time of concentration model is proposed to extend the computation for flood hydrograph generation. A numerical example is provided to demonstrate the computation and results and to show how the surface runoff variation pattern behaves for the NRCS and modified methods when either a variable or constant abstraction component dominates.

Sensitivity analysis of a simplified precipitation-runoff model to estimate water availability in Southern Portuguese watersheds

The water availability estimation in large regions is a relevant procedure to define broad water resources management policies but may prove difficult due to the lack of data and uncertainty to related regional hydrological and hydrogeological characterization. BALSEQ, a daily sequential water budget model, was applied in a set of twenty-two watersheds in southern Portugal, aiming to understand the possible relations between the model parameters and watershed characteristics that may allow assembling calibration functions for non-monitored watersheds. A sensitivity analysis was conducted by comparing BALSEQ results with measured surface flow, focusing specifically on the fraction of the potential maximum retention (φ) and the maximum amount of water available in the soil for evapotranspiration (AGUT) parameters and the underlying hydrogeological conceptual model that ultimately controls the surface-groundwater interactions. The overall results did not allow to identify clear relations that permit extrapolation to other regions without data as the sensitivity analysis procedures returned similar results for wide intervals of parameters for the majority of watersheds. The results confirmed that the groundwater discharge is an important component for the total measured surface flow and that the φ parameter should not be overlooked when calculating direct runoff. Poor adjustments between the model results and measured flow were observed in watersheds with a low Surface flow – Rainfall ratio.

A Modified Distributed CN-VSA Method for Mapping of the Seasonally Variable Source Areas

Many watershed models employ the Soil Conservation Service Curve Number (SCS-CN) approach for runoff simulation based on soil and land use information. These models implicitly assume that runoff is generated by the Hortonian process and; therefore, cannot correctly account for the effects of topography, variable source area (VSA) and/or soil moisture distribution in a watershed. This paper presents a new distributed CN-VSA method that is based on the SCS-CN approach to estimate runoff amount and uses the topographic wetness index (TWI) to distribute the runoff-generating areas within the watershed spatially. The size of the saturated-watershed areas and their spatial locations are simulated by assuming an average annual value of potential maximum retention. However, the literature indicates significant seasonal variation in potential maximum retention which can considerably effect water balance and amount of nonpoint source pollution. This paper focuses on developing a modified distributed CN-VSA method that accounts for the seasonal changes in the potential maximum retention. The results indicate that the modified distributed CN-VSA approach is better than distributed CN-VSA to simulate runoff amount and spatial distribution of runoff-generating areas. Overall, the study results are significant for improved understanding of hydrological response of watershed where seasonal factors describe the potential maximum retention, and, thus, saturation excess runoff generation in the watershed.

Influence of hydrological runoff and catchment characteristics on accumulation floatable litter load in gross pollutant trap (GPT)

The accumulation of floatable litter load captured in gross pollutant trap (GPT) due to the hydrological runoff has become challenging due to rapid development and alteration of land use changes. The application of soil conservation service curve number (SCS-CN) method has been widely used to estimate the direct runoff from the given rainfall especially when there is no runoff measured within the catchment. The results found that there was enormous variation on floatable litter load between different months and individual GPT. There was a high accumulation of floatable litter load on August 2015 with 495 kg/month and at K17 with 510 kg. Both of these conditions was significantly influenced by rainfall depth, hydrological parameters such as initial abstraction, potential maximum retention, and average weighted curve number (CNaw) values from different types of land use within their respective catchment area for individual GPT. Thus, the outcomes from this study can be very useful and beneficial for related government agencies and various stakeholders in ameliorating this critical and challenging environmental issue in terms of operation and management; technical operation; the selection of GPT prior to installation in other areas including the design, costs and maintenance.

Surface runoff estimation of Sind river basin using integrated SCS-CN and GIS techniques

Rainfall and runoff are the significant hydrologic component in the water resources assessment. Numerous methods are available to estimate runoff from rainfall; however, the SCS-CN method remains the most popular and frequently used method as runoff curve number (CN) is a crucial factor of the SCS-CN method and depends on land use/land cover (LULC), soil type, and antecedent soil moisture (AMC). Besides this, various parameters, such as Hydrological Soil Characteristics (HSG), precipitation (P), Potential Maximum Retention (PMR), Antecedent Moisture Condition (AMC), Weighted Curve Number (WCN), are the mandatory inputs to the SCS-CN model. In the results, the daily runoff from the Sind river basin for ten years, i.e., 2005 to 2014, has been used. As a result, the average annual surface runoff calculated for the Sind river basin is 133.71 mm. The total average volume of runoff is 35.04 × 10 8 m 3, representing 17.21% of the total average annual rainfall.

Application of the SCS–CN Method to the Hancheon Basin on the Volcanic Jeju Island, Korea

This study investigates three issues regarding the application of the SCS–CN (Soil Conservation Service–Curve Number) method to a basin on the volcanic Jeju Island, Korea. The first issue is the possible relation between the initial abstraction and the maximum potential retention. The second is the determination of the maximum potential retention, which is also closely related to the estimation of CN. The third issue is the effect of the antecedent soil moisture condition (AMC) on the initial abstraction, maximum potential retention and CN. All of these issues are dealt with based on the analysis of several rainfall events observed in the Hancheon basin, a typical basin on Jeju Island. In summary, the results are that, firstly, estimates of initial abstraction, ratio λ, maximum potential retention, and CN were all found to be consistent with the SCS–CN model structure. That is, CN and the maximum potential retention showed a strong negative correlation, and the ratio λ and the maximum potential retention also showed a rather weak negative correlation. On the other hand, a significant positive correlation was found between CN and the ratio λ. Second, in the case where the accumulated number of days is four or five, the effect of antecedent precipitation amount is clear. The antecedent five-day rainfall amount for the AMC-III condition is higher than 400 mm, compared to the AMC-I condition of less than 100 mm. Third, an inverse proportional relationship is found between the AMC and the maximum potential retention. On the other hand, a clear linear proportional relation is found between the AMC and CN. Finally, the maximum potential retention for the Hancheon basin is around 200 mm, with the corresponding CN being around 65. The ratio between the initial abstraction and the maximum potential retention is around 0.3. Even though these results are derived by analyzing a limited number of rainfall events, they are believed to properly consider the soil characteristics of Jeju Island.

Estimation of Initial Abstraction for Hydrological Modeling Based on Global Land Data Assimilation System–Simulated Datasets

AbstractInitial abstraction (Ia) is a sensitive parameter in hydrological models, and its value directly determines the amount of runoff. Ia, which is influenced by many factors related to antecedent watershed condition (AWC), is difficult to estimate due to lack of observed data. In the Soil Conservation Service curve number (SCS-CN) method, it is often assumed that Ia is 0.2 times the potential maximum retention S. Yet this assumption has frequently been questioned. In this paper, Ia/S and factors potentially influencing Ia were collected from rainfall–runoff events. Soil moisture and evaporation data were extracted from GLDAS-Noah datasets to represent AWC. Based on the driving factors of Ia, identified using the Pearson correlation coefficient and maximal information coefficient, artificial neural network (ANN)-estimated Ia was applied to simulate the selected flood events in the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) model. The results indicated that Ia/S varies over different events and different watersheds. Over 75% of the Ia/S values are less than 0.2 in the two study areas. The driving factors affecting Ia vary over different watersheds, and the antecedent precipitation index appears to be the most influential factor. Flood simulation by the HEC-HMS model using statistical Ia gives the best fitness, whereas applying ANN-estimated Ia outperforms the simulation with median Ia/S. For over 60% of the flood events, ANN-estimated Ia provided better fitness in flood peak and depth, with an average Nash–Sutcliffe efficiency coefficient of 0.76 compared to 0.71 for median Ia/S. The proposed ANN-estimated Ia is physically based and can be applied without calibration, saving time in constructing hydrological models.

Estimation of potential runoff in Wadi Damad watershed, Jazan Province, southwestern Saudi Arabia

This study aims to compute the potential runoff in the mountain Wadi Ḑamâd watershed located in the South Western of Saudi Arabia, using Soil Conservation Service-Curve Number (SCS-CN) method based on the maximum daily rainfall recorded during 47 years from 1970 to 2016 at the stations of Ḑamâd, Ayban, Wadi Ḑamâd and Jabal Fayfa, Malaki, and Al Aridhah. The SCS-CN method depends on land use, land cover, and soil complex characteristics of a watershed, and it is the hydrological model widely used for estimating runoff using curve number (CN). In the present study, SCS-CN method is used for estimating the potential runoff in the mountain drainage basin of Wadi Ḑamâd extends on area of 1084.0 km2. The CN was obtained at every studied station using the sheet 78 of the land resources map 1:500,000, and it is 62 at Ḑamâd, 66 at Ayban and Jabal Fayfa, and 70 at Wadi Ḑamâd, Malaki, and Al Aridhah. The potential maximum retention corresponding to these CN was 141.2 mm, 117.2 mm, and 108.9 mm. Therefore, the initial abstraction corresponding to the potential maximum retention is respectively 28.2 mm, 23.4 mm, 21.8 mm, and 23.4 mm. The results of this study reveal that the average of yearly rainfall varies from 171.3 to 538.1 mm, and the maximum daily rainfall is ranged between 37.4 and 77.5 mm at Malaki and Ḑamâd respectively. In the opposite, the excess rainfall and the direct runoff vary systematically with the rainfall. The maximum excess rainfall is ranged between 60.6 mm at Jabal Fayfa and 103.2 mm at Wadi Ḑamâd. However, the direct maximum runoff rises respectively from 20.6 to 50.2 mm.

Application of Modified SME-CN Method for Predicting Event Runoff and Peak Discharge from a Drained Forest Watershed on the North Carolina Atlantic Coastal Plain

HighlightsProposed new method to assess subsurface and surface runoff from drained forested watershed.Factor Fp in the graphical peak discharge method is less in forested wetland watershed than recommended by USDA.Modified graphical peak discharge method correctly approximated observed peak discharges.Abstract. The NRCS curve number (CN) method is a widely used event-based model for estimating runoff using readily available watershed parameters and rainfall data from upland agricultural catchments. However, there is limited literature on application of the CN method in drained forest systems. This study proposes an application of the modified Sahu-Mishra-Eldho (SME) CN method developed and tested in earlier studies. In this study, the SME method was further modified by redefining the maximum potential retention to assess subsurface drainage and surface runoff, which are parts of total outflow, separately for a pine forest watershed with a high water table soil drained by ditches spaced 100 m apart in coastal North Carolina. Assuming that the measured outflow from the drained watershed was dominated by subsurface drainage, computed event outflow using the modified SME-CN (MSME-CN) model showed good agreement with the observed outflow data (without extreme rainfall events) for the study watershed, yielding a Nash-Sutcliffe coefficient of 0.97, R2 = 0.97, and RMSE = 3.46 mm. Linking the direct runoff from the MSME model into the SCS graphical peak discharge method (GPDM) also improved event peak flow estimates compared to those from the GPDM using SCS-CN based outflow, with calculated RMSE of 11.93 and 31.35 L s-1 and modeling efficiency (EF) of 0.79 and -0.45, respectively. In addition, based on analysis, the wetland factor (Fp) of 0.72 recommended in the GPDM was found to be very large and unsuitable for the study watershed with its high retention capacity. The authors suggest multi-site-year validation of the MSME-CN model, which is sensitive to input parameters such as PET5, P, CN, and a, to gain more confidence in it and the associated GPDM. Keywords: Peak discharge, Pine forest, Poorly drained soil, Potential retention, Subsurface runoff, Surface runoff.