Statistical and Type II Error Assessment of a Runoff Predictive Model in Peninsula Malaysia

: Flood related disasters continue to threaten mankind despite preventative efforts in tech-nological advancement. Since 1954, the Soil Conservation Services (SCS) Curve Number (CN 0.2 ) rainfall-runoff model has been widely used but reportedly produced inconsistent results in ﬁeld studies worldwide. As such, this article presents methodology to reassess the validity of the model and perform model calibration with inferential statistics. A closed form equation was solved to narrow previous research gap with a derived 3D runoff difference model for type II error assessment. Under this study, the SCS runoff model is statistically insigniﬁcant (alpha = 0.01) without calibration. Curve Number CN 0.2 = 72.58 for Peninsula Malaysia with a 99% conﬁdence interval range of 67 to 76. Within these CN 0.2 areas, SCS model underpredicts runoff amounts when the rainfall depth of a storm is < 70 mm. Its overprediction tendency worsens in cases involving larger storm events. For areas of 1 km 2 , it underpredicted runoff amount the most (2.4 million liters) at CN 0.2 = 67 and the rainfall depth of 55 mm while it nearly overpredicted runoff amount by 25 million liters when the storm depth reached 430 mm in Peninsula Malaysia. The SCS model must be validated with rainfall-runoff datasets prior to its adoption for runoff prediction in any part of the world. SCS practitioners are encouraged to adopt the general formulae from this article to derive assessment models and equations for their studies.


Introduction
Nearly 8.5 million casualties attributed to flood related disasters were reported between 1990 and 2020 all over the world, which is equivalent to one death every seven minutes. In the recent six decades, about 10,000 cases were reported with 1.3 million deaths and at least $3.3 trillion of financial losses. This financial loss is estimated to be an equivalent rate of almost USD$1800/s [1]. Floods are not only a nuisance to people but also impede the financial well-being, economic development, and natural and cultural heritage preservation efforts of a country. The impact is more profound amidst the COVID-19 pandemic. Uncertainties regarding different scenarios surrounding climate change also require us to safeguard agricultural production and manage water resources wisely to ensure sustainable development for the future. As such, there is an imminent need for hydrologists and modelers to reassess the rainfall-runoff model and improve the modelling approach for better applications in flood prediction.
In order to comply with the federal flood control program in 1954, the United States Department of Agriculture (USDA), Soil Conservation Services (SCS) developed a Curve Number (CN) runoff estimation procedure to implement across the nation. The hydrologic methods which were originally developed to address specific situations were adopted immediately without professional review and critics [2][3][4][5]. The work became the basic CN rainfall-runoff model: Q = Amount of runoff depth (mm) P = Depth of rainfall (mm) S = Watershed maximum water retention potential (mm) I a = Rainfall initial abstraction amount (mm) SCS also hypothesized that I a = λS = 0.2S where λ is the initial abstraction ratio coefficient and fixed at λ = 0.2 as a constant. This equation was tenuously justified with daily rainfall and runoff data. The only official documentation source is the NRCS's National Engineering Handbook, Section 4 (NEH-4) [5]. Its substitution simplifies Equation (1) into the existing SCS CN model as: if P < 0.2S, Q = 0. The SCS CN methodology has been widely accepted since its inception in 1954. It has been incorporated in various types of software, adopted by many government agencies in design and even appears in every hydrology textbook. However, studies around the world from recent decades reported that Equation (2) inconsistently under and over-predicted runoff results. Curve Number (CN) selection from the SCS handbook for a watershed runoff prediction modelling were reported as subjective and often could not represent other watershed with similar land cover [2][3][4].
Despite that, many recent studies started to develop and propose extended applications with Equation (2). Some researchers even proposed a global gridded CN concept for runoff modelling [6,7] while others incorporated land-use information in their studies and the GIS modelling technique [8][9][10][11][12]. Contrarily, some reported that the usage of CN in representing a watershed is often contradictory in describing related land cover areas [13]. Some researchers still reported difficulty to calibrate the existing model [14,15] while other studies started to incorporate soil moisture and saturation-excess concepts in their modelling approach [16][17][18][19]. US researchers [2,20] were first to conduct large scale studies on the SCS CN model by analyzing more than half a million rainfall events across 24 states in the USA and reported an optimum λ = 0.05 to achieve better runoff modelling results than Equation (2) in USA. To date, SCS practitioners do not have a systematic approach to assess the SCS CN model framework and analyze the impact on runoff prediction when the model is not calibrated.

Data and Methods
The SCS CN model (Equation (2)) has been adopted in Malaysia for runoff prediction studies and design. However, no attempt has been made to validate previous study findings by performing hydrological characteristics calibration on the SCS CN model and to derive the λ value with inferential statistics for the entirety of Peninsula Malaysia. The impact of not calibrating the SCS CN model and the blind adoption of Equation (2) for runoff predictions in Peninsula Malaysia are unknown. Therefore, this study extended study results from US researchers [2,20] to develop assessment methods of the SCS CN model for SCS practitioners.
Slightly larger than England (130,395 km 2 ), the land area of Peninsula Malaysia is 132,265 km 2 . It shares a land border with Thailand to the north and Singapore across the strait of Johor to its south. The formation of the Malaysian Department of Irrigation and Drainage (DID) in 1932 assumed all works in connection with drainage and irrigation from the Public Works Department. Flood mitigation and hydrology was made an additional responsibility of DID from 1972 onwards after the declaration of a national disaster due to Drainage (DID) in 1932 assumed all works in connection with drainage and irrigation from the Public Works Department. Flood mitigation and hydrology was made an additional responsibility of DID from 1972 onwards after the declaration of a national disaster due to severe floods in 1971. From 1986, coastal engineering has become an added function of the DID while river management became its official duty from 1990.
The Department has moved from the Ministry of Agriculture and Agro-based Industry (MOA) to Ministry of Natural Resource and Environment (NRE) on 27 March 2004. Over the years, DID took up new and expanded responsibilities. Today, the DID's duties encompass: River Basin Management and Coastal Zone, Water Resources Management and Hydrology, Flood Management and Eco-friendly Drainage projects in Malaysia.
The rainfall-runoff dataset from the DID, Hydrological Procedure no. 27 (DID HP 27) was used in this study. It is the latest official dataset published by this federal government agency that consists of 227 different storm events recorded between October 1970 to December 2000 from 41 different rural watersheds ( Figure 1) across Peninsula Malaysia. The smallest storm event had a rainfall depth of 19 mm with a measurable runoff depth of 4.8 mm while the largest recorded storm event was 420 mm with 258 mm in runoff depth [21].  Objectives of this study are: 1.
To assess the 1954 SCS assumption of: I a = 0.2S in Q = (P−I a ) 2 P−I a +S and determine its validity for runoff prediction use in Peninsula Malaysia according to the DID HP 27 dataset.

2.
To solve the closed form mathematical equation of the "critical rainfall amount" and develop a statistically significant SCS CN model calibration methodology.

3.
To assess the impact of not calibrating the existing SCS CN runoff predictive model (Equation (2)) for runoff prediction in Peninsula Malaysia with the official rainfallrunoff dataset from DID HP 27 [21].

The Reverse Derivation of λ and S Value
In hydrology, the difference between I a and P is the effective rainfall depth (P e ) to initiate Q thus P e = P − I a . Substitute this relationship into SCS CN model (Equation (1)), it can be re-arranged to calculate the two key parameters of S and λ values according to the respective P-Q data pair [2,5,22]. Equation (1) can then be expressed as below after the substitution of P e = P − I a : rearrange Equation (3) to isolate S as: Equation (4) is subjected to the constraint where S must be a positive integer. SCS also proposed the correlation of I a = λ S thus λ can be calculated once I a and S are known by rearranging the equation as: Equation (5) is subjected to the constraint defined by SCS that S ≥ I a [5], and therefore the range of λ must be (0, 1). The upper limit for λ value is equal to 1 (where I a = S) which is hardly realized in the real world as it implies the condition of a thick canopy interception. The infiltration during early parts of the storm and surface depression storage is equal to the maximum potential retention value (S) of a watershed [5].
Past studies reported different λ values in their work for model calibration. However, the statistical assurance of those new values was hardly mentioned [4]. Latest studies in this area started to report that the modelling approach with multiple CN and I a values can reflect the heterogeneity of a watershed and the SCS CN model must be calibrated according to local rainfall-runoff data to improve the runoff prediction accuracy. Equation (2) may no longer be valid for runoff prediction modelling [23][24][25]. SCS defined I a = λS, the existence of multiple I a values implied that multiple λ and S values can be found within a watershed. These latest study results [24,25] escalate the SCS CN model calibration difficulty to another level as SCS practitioners must identify a best collective representative I a value to calibrate Equation (1). Therefore, this study proposed to use non-parametric inferential statistics as the guide to make a statistically significant selection of the two key parameters (S and λ values) to calibrate the fundamental SCS CN runoff framework (Equation (1)).
Under the SCS CN hydrological framework, the initial abstraction (I a ) amount must be less than the P value because I a must first be fulfilled to initiate runoff. Therefore, a reasonable collective representative I a value for runoff modelling must be less than the minimum P value from the entire P-Q dataset [5]. Given the P-Q dataset, an initial "I a " value which was less than the minimum P value from the dataset was chosen as the first iterative value in order to calculate the corresponding S and λ values for each P-Q data pair according to Equations (4) and (5). In the event where either constraint in Equation (4) or (5) were to be violated, the "collective representative I a " value must be reduced until every calculated λ and S values abide to their constraints for each P-Q data pair according to the SCS CN model framework [5].
The alpha value was set at a stringent level of 0.01 in this study to reduce the type I error in null assessment so that the SCS CN model will not be unnecessarily calibrated due to wrong null rejection under objective 1. It will also justify the urgent SCS CN model calibration need to the DID for runoff prediction work in Malaysia, review any past studies and projects that used Equation (2) when the null hypothesis is rejected. This study is only willing to accept 1% error chance because these DID processes are too costly to initiate by mistake.
According to the U.S. Geological Survey (USGS) Statistical methods in water resources guide, the minimum required sample size is 100 to be considered as a large dataset for water resources related study at the 0.01 alpha level [26]. As such, the DID HP 27 dataset will be sufficient for this study. Given the 227 rainfall-runoff (P-Q) data pairs from DID HP 27, corresponding λ and S values can be calculated. These 227 λ and S values will be bootstrapped independently with the Bias Corrected and Accelerated (BCa) procedure by using the IBM Predictive Analytics software (PASW) version 18.0 (commonly known as SPSS) [27]. The method neither assumes data normality nor has limitation to certain data distribution and performs random sampling with replacement in SPSS [27,28]. In this study, the Mersenne Twister seed number for random sampling generation was set at 2 million (by default) and 10 million to conduct 2000, 5000, and 10,000 sampling for the calculated λ and S dataset.
Consequently, the BCa option in SPSS was used to generate a sampling distribution and 99% confidence interval (CI) to optimize the parameter of interest such as S and λ. Additionally, it provides standard error statistics and CI for the median value, which are unavailable under most parametric tests in SPSS [27]. BCa procedure was chosen by this study for its ability to correct for skewness and bias in the bootstrap distribution [29]. When the dataset has a high positive skewness, BCa can also correct the issue that the bootstrap CI range might be too small [26]. BCa 99% CI has wider range than the 95% CI. Therefore, this study used BCa option in SPSS to generate 99% CI (instead of 95% CI) for both λ and S dataset so that the assessment of the initial claim from SCS that λ = 0.2 can be inferred from the wider BCa CI.

Supervised Numerical Optimization Analyses
Past researchers faced the dilemma of choosing between the mean and median of a dataset [2,30]. To address this issue, this study utilized an algorithm of numerical analysis guided by inferential statistics for decision making.
λ and S were optimized using Equation (1) with a supervised numerical analyses approach. To prevent the optimization algorithm from focusing on residual sum of squares (RSS) minimization only, the overall model bias (BIAS) will be minimized near to the value of zero concurrently during the parameter optimization process. This acts as a check with the BCa technique to ensure that the optimized λ and S value are not biased towards the dataset during the SCS model calibration. In the event of skewed data nature, the supervised numerical optimization would be conducted to search for an optimum value within the BCa median's confidence interval limits of the derived λ and S dataset, respectively. The optimized S value and its confidence interval range will lead to the calculation of CN value to represent the entire DID HP 27 dataset in Peninsula Malaysia (see Section 3.2). H 0 assesses the validity of Equation (2) for this study as pertained to the DID HP 27 dataset. The assessment of H 0 will be inferred from the BCa confidence interval of λ [28]. The rejection of H 0 indicates that the SCS CN model (Equation (2)) is invalid to model the dataset of this study. It requires the acceptance of H 0 to adopt Equation (2) for rainfall-runoff modelling while the rejection of H 0 will pave a way to derive a new λ value for the DID HP 27 dataset. The optimized λ and S values will be used to formulate a new calibrated runoff prediction model for Peninsula Malaysia. SCS practitioners are encouraged to validate the existing SCS CN model (Equation (2)) prior to runoff modelling adoption.

The S General Formula
Equation (1) was re-arranged into a general form of S λ = f (P, Q, λ) in a previous study [4]. When λ = 0.2, the corresponding S 0.2 value leads to the derivation of conventional CN values in use by SCS practitioners. Any other λ values will result in S λ leading to the derivation of CN λ values which are different from the SCS tabulated CN values. The general S λ formula (see [4] for derivation steps) used by this study is: S λ = Total abstraction amount of any λ value (mm).

Correlation Between S λ and S 0.2
According to previous researchers, when the optimum λ value is different from the conventional value where λ = 0.2, a correlation between the newfound λ value and 0.2 must be used in order to calculate the curve number again [2,3,20]. US researchers termed the batch of curve numbers derived from any λ value other than λ = 0.2 as "conjugate curve numbers" denoted by CN λ which are different from the SCS tabulated curve numbers [2][3][4]20]. Given the P-Q dataset, S λ and S 0.2 can be calculated using Equation (6). A correlation between the S λ and S 0.2 dataset must be established before the calculation of conventional CN value (see Section 3.2). SCS practitioners must use the correlation equation between the S λ and S 0.2 to calculate the conventional CN value to avoid the mistake of using conjugate curve number in their study.

The 3D Runoff Difference Model
Using P-Q datasets from multiple watersheds or from multiple locations within a watershed, a 3D runoff difference model can be created as a collective visual representation of multiple rainfall depths to compare with different CN 0.2 scenarios. If Equation (2) fails the Null assessment, this 3D model can reflect the runoff difference between it and the new calibrated runoff model for further analyses. The model will be a guide to visualize the runoff under and over prediction zones between two models. In 1954, SCS correlated S and CN. The SI unit version of the formula is: Equation (7) was derived from the SCS assumption where λ = 0.2, and therefore it will be more appropriate to denote CN as CN 0.2 and S with S 0.2 . Substituting Equation (7) into Equation (2), the SCS model can be simplified to become: Q 0.2 = f (P, CN 0.2 ) and represented in SI form of: The general form of Equation (1) after the substitution of I a = λS for any λ value becomes: where P > λS λ , else Q λ = 0. As such, the runoff difference between SCS model (uncalibrated) and the new calibrated runoff model (with new λ) can be quantified as the difference between Equations (8) and (9) as: Q v = Runoff depth prediction difference between 2 runoff models (mm) CN 0.2 = the conventional curve number As Equation (2) was widely adopted in many countries, it is important to assess the runoff prediction difference with Equation (10). It is a general equation that can be used by SCS practitioners to determine the impact of not calibrating Equation (2) for runoff predictions under their study.
In Equation (10), Q v will be positive when the conventional SCS runoff model (Equation (2)) over-predicted runoff when compared to the calibrated new runoff equation and vice versa. If the newly derived λ < 0.2, Equation (10) is subject to the constraint where P > λS. When the new derived λ > 0.2, Equation (10) will abide to the constraint of P > 0.2S 0.2 , else Q v = 0 because there is no runoff difference as I a of the lower λ value model is yet to be fulfilled to initiate the runoff process [2,5] and produce a runoff difference between two runoff models. All in all, the smaller λ runoff model will initiate runoff ahead of the larger λ runoff model [5].

Outer Boundary Equation
Equation (2) is subject to a constraint where P > I a or P > λS λ , else Q λ = 0. The 3D runoff difference model captures the runoff difference of two different runoff models. When the I a constraint of the lower λ value model has been fulfilled, runoff will be initiated. Base on this concept, the I a constraint of the lower λ value model becomes the outer boundary of the 3D runoff difference model which also represents the runoff indifference boundary with the following general equation:

Inner Boundary Equation
The second boundary is the "Inner Boundary" of the 3D runoff difference model. This boundary separates the runoff under-prediction zone from the over-prediction zone of the SCS runoff model. The runoff difference is equal to zero at the crossover boundary, which is also known as the runoff indifference boundary. Therefore, when Q v = 0 (runoff indifference) in Equation (10), the form can be re-expressed as: Equations (11) and (12) are also general equations that can be used by SCS practitioners to analyze the 3D runoff difference model (created with Equation (10)) in their study.

Models Comparison
Runoff models are compared and benchmarked for their model predictive accuracy in this paper. Model's residual sum of squares (RSS), predictive model BIAS prediction and model efficiency index (E), also known as Nash-Sutcliffe index, were calculated with the following formulae to draw further comparison between them.
n = Total number of data pairs. Lower RSS implies a better model. Index E lies on a spectrum of minus 1.0 to 1.0 whereby index value = 1.0 shows an ideal conjectured model. In the instance where E < 0, it is inferior to utilizing an average to predict the dataset. BIAS is the overall model prediction error indicator. Zero BIAS value indicates an error free model prediction while negative value indicates the overall predictive model's under-prediction tendency and vice versa.

Asymptotic Curve Number Fitting
Other than numerical optimization technique, many researchers [31][32][33][34][35] used asymptotic CN fitting method (AFM) to determine the best representative CN for the watershed of interest with P-Q dataset (λ value remains as 0.20 under this method). Therefore, AFM will be used to benchmark against the proposed method in this article. Under AFM, CN cannot be determined for the Complacent behavior watershed, but Standard behavior watershed follows the following formula [33]: CN(P) = Fitted CN value of a specific rainfall depth CN ∞ = CN of a watershed of interest K = Fitting parameter Violent behavior watershed follows the following formula [33]: P th = Threshold Rainfall depth (mm).

Critical Rainfall Amount (P crit )
The concept of P crit was initially suggested by US researchers [2,20,22] which can only be obtained through numerical analysis solving technique or by trial and error procedure. In their work, optimum λ was reported as 0.05 and the P crit points were identified through the intersection of conjugate CN 0.05 and CN 0.2 curve on the graph in their study.
The concept of P crit was built upon the runoff indifference between 2 runoff models. When Q v = 0 (runoff indifference between two runoff models), Equation (10) becomes Equation (12). As such, this study introduces runoff difference curves which was created with numerical analysis technique as the visual presentation of Equation (12). Runoff difference curves can be plotted for specific CN 0.2 classes across multiple rainfall depth scenarios. Unlike previous research work, it combined two curves into a single curve and identify P crit at where the curve crosses the x-axis.

The Closed Form Equation of Critical Rainfall Amount (P crit )
Through algebraic manipulation, this study successfully rearranged Equation (10) and solved the general closed-form equation of P crit in terms of CN 0.2 when Q v = 0. The breakthrough has also proven to be able to solve for P crit value precisely of any pairing runoff models and replace the trial and error procedure used by previous researchers [2,20,22]. SCS practitioners can derive the P crit equation for their study with proposed method in this article (see Section 3.10).

Critical Curve Number (CN crit )
With a similar concept (based upon Equation (12)) as the critical rainfall amount (P crit ), this study also introduces "critical curve number(s)" (CN crit ) to supplement the use of P crit . Under a specific rainfall scenario, critical curve number value(s) can also be identified from the points where Q v = 0 between 2 runoff models. Unlike the success of the P crit closed-form equation derivation, the effort to realize the closed-form equation of CN crit in term of P is still unfruitful to date. Therefore, the numerical analysis technique was applied to estimate CN crit value(s) with visual aid from the runoff difference curves graph. Runoff difference curves methodology as Section 3.9 covered can be adopted to show that Equation (2) or Equation (8) will under-predict runoff amount in any curve number areas below the critical curve number value and vice versa.

Soft Computing and Data Mining of the 3D Model
In general, Equation (10) represents the runoff prediction errors of Equation (2) under multiple P and CN 0.2 scenarios but it is difficult to visualize the quantified effect by looking at Equation (10) and solve for the global maxima and minima in order to represent the worst under and over runoff prediction amounts between two runoff models.
Based on the rainfall depth range of the dataset [21], a numerical table can be compiled with Equation (10) through the substitution of different P, CN 0.2 scenarios and the λ value to quantify runoff depth prediction difference between two runoff models in a table. A 3D model can also be constructed with the collective information from the table (Section 3.7). With the visual aid of a 3D runoff difference model, it is possible to extract all minimum and maximum runoff prediction difference amount and represent them with statistically significant equations. The minimum under-prediction difference amount equation represents the worst under-design case incurred by Equation (2) and vice versa.

The Reverse Derivation of Optimum λ and S for Peninsula Malaysia
In all, 227 λ and S values were calculated according to corresponding rainfall-runoff (P-Q) data pairs. The calculated λ dataset was checked for normality in SPSS with Kolmogorov-Smirnov and Shapiro-Wilk test statistics, both tests concluded the λ dataset to be nonnormal (p < 0.001). Nearly 95% (214 out of the 227) storm events calculated λ value below 0.2 while none was equal to 0.2 as proposed by SCS.
According to Section 2.1, as defined by the SCS [5], the "collective representative I a " was reduced to 5.9 mm to fulfil both constraints of Equations (4) and (5) for the entire dataset of DID HP 27 [21]. 227 calculated λ and S values were independently used for 2000, 5000, and 10,000 random samplings prior to CI generations and cross checking (This study found that the CI upper and lower limits only differ at the fourth decimal places with 2000, 5000, and 10,000 random samplings while there were no difference between the use of 2 million (by default) and 10 million Mersenne Twister seed numbers for random sampling generation) in SPSS. The inferential statistics of the derived λ and S values are tabulated in Tables 1 and 2.  Table 1, neither the mean nor the median BCa λ's 99% CI include the λ value of 0.2 (In comparison, the BCa 95% mean and the median CI for λ span across smaller range (0.036, 0.084)). Therefore, H 0 can be rejected at alpha = 0.01 level. As such, Equation (2) is statistically insignificant (not even significant at alpha = 0.05) and cannot be used to predict runoff conditions in this study. λ dataset is skewed (skewness of 5.125 in Table 1) thus the search of the optimum collective representative λ value via numerical optimization technique focusses on median λ's confidence interval [0.034, 0.051].
On the other hand, data distribution of the S dataset is somewhat skewed with a skewness of 1.624 ( Table 2). The definition of skewness is non-uniform, some guidelines suggested skewness value less than 3.0 to be considered as normal while some set a more stringent limit at 1.0. To avoid the ambiguity of skewness determination, the search of the optimum S value was widened to include the lowest and the highest confidence interval limit of both mean and median values (118.125, 196.332) on S [2,30]. The optimum λ value was recognized as 0.051 (rounded) while 150.46 mm was the optimum S value in formulating the best runoff predictive model (based on Equation (1)) according to the entire dataset of DID HP 27 with an overall predictive model's BIAS near to zero. The collective representation of the I a for the entire dataset was found from the product of the optimum λ and S and therefore, the best collective representative value of I a to model the entire dataset in Peninsula Malaysia is 8.3 mm from this study.
As mentioned in Section 2.1 and 2.2, BCa technique produced confidence intervals (Tables 1 and 2) for the optimization of λ and S value to calibrate the SCS CN model. It also generated a range of λ and S value to enable the calculation of multiple I a and CN values which is in line with the latest research development in this area [23][24][25]. Other than the best collective representative I a value, SCS practitioners who use the proposed method in this article have an option to compare other possible I a values with other research results in future.

The Correlation between S λ and S 0.2 for Peninsula Malaysia
The derivation of S λ formula (Equation (6)) proved mathematically that even with the same P-Q dataset, as λ varies, the corresponding total abstraction amount (S) varies as well and therefore, the corresponding CN value will change also. As such, it is more appropriate to re-represent Equation (7) in general form as: CN λ = Curve number of any λ value (dimensionless) S λ = Total abstraction amount of any λ value (mm) Given the P-Q dataset and λ value, the corresponding CN λ can be derived from Equation (18). When λ = 0.2, its corresponding S 0.2 value gives rise to deriving the conventional curve number compiled by SCS. To differentiate the conventional SCS CN, the notation of "CN 0.2 " is used in the remaining of this paper. When λ = 0.2, its corresponding S λ value derives "Conjugate Curve Number" (CN λ ) [2,20,22]. As the optimum λ value = 0.051, the correlation between S 0.051 and S 0.2 was identified with SPSS for this study as:

Conjugate Curve Numbers (CN λ ) for Peninsula Malaysia
Given the P-Q data pairs from DID HP 27, conjugate curve number values (CN λ ) of each storm event can be calculated with aforementioned equations in the following steps: Since the optimum λ value obtained was 0.051, Equation (18) (20) proves that conjugate curve number (CN λ ) is not the same as the conventional curve number CN 0.2 which was derived using Equation (7). Thus, it is inappropriate to use any conjugate curve number (CN λ ) with Equation (2) in any rainfall-runoff modelling work.

The 3D Runoff Difference Model for Peninsula Malaysia
According to the discussions from Sections 2.4 and 2.5, the S amount is specific to its corresponding λ value. The optimum λ value = 0.051 to model runoff conditions for the DID HP 27 dataset thus by substituting λ with 0.051 into Equation (9) yields a calibrated rainfall-runoff predictive model on Equation (1) in the form of: The substitution of Equations (19) and (7) Equation (21) It subjects to the constraint P > 50.8 100 CN 0.2 − 1 else Q v = 0. Equation (8) or Equation (2) represents the un-calibrated SCS CN model. The runoff depth prediction differences between Equations (8) and (21) were collectively quantified by Equation (22) of which the 3D runoff difference model (Section 3.7 and Figure 2) was constructed with. Equation (22) also quantifies type II errors from Equation (2) (existing SCS model) if it is not calibrated for runoff prediction in Peninsula Malaysia.

Outer Boundary Equation
As per Section 2.7, the calibrated new λ value (0.051) is less than 0.2; thus, its model's constraint can be adopted to represent the runoff indifference boundary where runoff has not been initiated. Therefore, Equation (22) Equation (21)  Equation (8) or (2) represents the un-calibrated SCS CN model. The runoff depth prediction differences between Equations (8) and (21) were collectively quantified by Equation (22) of which the 3D runoff difference model (Section 3.7 and Figure 2) was constructed with. Equation (22) also quantifies type II errors from Equation (2)

Outer Boundary Equation
As per Section 2.7, the calibrated new λ value (0.051) is less than 0.2; thus, its model's constraint can be adopted to represent the runoff indifference boundary where runoff has not been initiated. Therefore, Equation (22)  else Qv = 0. Equation (19) can be substituted into 11 to preserve the conventional curve number (CN0.2) through following the steps.
Substitute λ with 0.051, Equations (7) and (19) into Equation (11) Equation (23) is the runoff indifference boundary equation between two runoff models. It is otherwise recognized as the "Outer Boundary" equation of the 3D runoff difference model (Figure 2a,b).

Inner Boundary Equation
When Qv = 0 in Equation (22)  Substitute λ with 0.051, Equations (7) and (19) into Equation (11) Equation (23) is the runoff indifference boundary equation between two runoff models. It is otherwise recognized as the "Outer Boundary" equation of the 3D runoff difference model (Figure 2a,b).

Inner Boundary Equation
When Q v = 0 in Equation (22) Equation (24) is also known as the "Inner Boundary" equation of the 3D runoff difference model for Peninsula Malaysia that demarcates the runoff under-prediction and over-prediction zones between two runoff models in this study.

The Construction of the 3D Runoff Difference Model
DID HP 27 dataset consist of 227 storm events ranging from 19 mm to 420 mm. In order to analyze and quantify the runoff prediction depth difference between Equation (2) (or Equation (8)) and 21 under multiple rainfall and CN 0.2 scenarios, rainfall depth (P) ranging from 10 mm to 430 mm across different CN 0.2 values (from 26 to 98) were entered into Equation (22) to calculate the runoff depth prediction difference that can be found in Figure 3. Those tabulated values are runoff prediction errors (or type II errors) from Equation (2) which are in line with previous studies that reported more profound error in forested watersheds represented by CN 0.2 values < 60 [2,20,22] Similarly, for Peninsula Malaysia, both runoff under and over prediction errors worsen when the value of CN0.2 reduces (Figure 3).
Equation (24) is also known as the "Inner Boundary" equation of the 3D runoff difference model for Peninsula Malaysia that demarcates the runoff under-prediction and over-prediction zones between two runoff models in this study.

The Construction of the 3D Runoff Difference Model
DID HP 27 dataset consist of 227 storm events ranging from 19 mm to 420 mm. In order to analyze and quantify the runoff prediction depth difference between Equation (2) (or Equation (8)) and 21 under multiple rainfall and CN0.2 scenarios, rainfall depth (P) ranging from 10 mm to 430 mm across different CN0.2 values (from 26 to 98) were entered into Equation (22) to calculate the runoff depth prediction difference that can be found in Figure 3. Those tabulated values are runoff prediction errors (or type II errors) from Equation (2) which are in line with previous studies that reported more profound error in forested watersheds represented by CN0.2 values < 60 [2,20,22] Similarly, for Peninsula Malaysia, both runoff under and over prediction errors worsen when the value of CN0.2 reduces ( Figure 3).  Figure 3 can also be presented as a 3D model as seen in Figure 2a,b. Equations (23) and (24) represent boundary lines as indicated on the 3D model, respectively. SCS practitioners can refer to Figure 3 to perform runoff prediction correction on Equation (2).
For areas in Peninsula Malaysia with CN0.2 value from 67 to 76 (marked by the dash line), the existing SCS model underpredicts runoff amount as indicated in red zone when rainfall depth of a storm is < 70 or 85 mm. SCS model tends to overpredict runoff amount after 85 mm and its overprediction tendency worsens toward larger storm events as indicated in white zone. Without model calibration, the SCS model worst runoff underprediction within these areas happens at CN0.2 = 67 area at rainfall depth of 55 mm, the model where I a has not been fulfilled yet to initiate any runoff amount. Collectively, Figure 3 can also be presented as a 3D model as seen in Figure 2a,b. Equations (23) and (24) represent boundary lines as indicated on the 3D model, respectively. SCS practitioners can refer to Figure 3 to perform runoff prediction correction on Equation (2).
For areas in Peninsula Malaysia with CN 0.2 value from 67 to 76 (marked by the dash line), the existing SCS model underpredicts runoff amount as indicated in red zone when rainfall depth of a storm is < 70 or 85 mm. SCS model tends to overpredict runoff amount after 85 mm and its overprediction tendency worsens toward larger storm events as indicated in white zone. Without model calibration, the SCS model worst runoff underprediction within these areas happens at CN 0.2 = 67 area at rainfall depth of 55 mm, the model underpredicted runoff amount by 2.4 million liters in 1 km 2 area while it nearly overpredicted runoff amount by 25 million liters when the storm depth reaches 430 mm in Peninsula Malaysia. Blind adoption of the existing SCS CN model is likely to over-predict runoff amount when the rainfall depth of a storm event is larger than 85 mm in Peninsula Malaysia. As such, any past study or engineering projects based upon the return period concept of rainfall amount below 70 mm might be under-designed.

Soft Computing and Data Mining of the 3D Runoff Difference Model
Even though the 3D runoff difference model can be expressed using the closed form Equation (22), it is not easy to obtain the minimum (global minima) or maximum (global maxima) runoff depth difference equations. However, with the 3D runoff difference model as a visual aid accompanied by soft computing techniques, the data mining of this vital information becomes attainable.
The minimum and maximum runoff depth prediction errors across multiple P and CN 0.2 scenarios between the two runoff models can be extracted from Figure 3. The statistically significant equations can then be determined using the SPSS to formulate the worst under and over-estimated runoff prediction error equations from Equation (2) or Equation (8) against Equation (21).
The data mining process extracts all the minimum and maximum runoff prediction differences (bold numbers, highlighted in red and yellow color, respectively in Figure 4) according to each rainfall depth scenarios (in row). underpredicted runoff amount by 2.4 million liters in 1 km 2 area while it nearly overpredicted runoff amount by 25 million liters when the storm depth reaches 430 mm in Peninsula Malaysia. Blind adoption of the existing SCS CN model is likely to over-predict runoff amount when the rainfall depth of a storm event is larger than 85 mm in Peninsula Malaysia. As such, any past study or engineering projects based upon the return period concept of rainfall amount below 70 mm might be under-designed.

Soft Computing and Data Mining of the 3D Runoff Difference Model
Even though the 3D runoff difference model can be expressed using the closed form Equation (22), it is not easy to obtain the minimum (global minima) or maximum (global maxima) runoff depth difference equations. However, with the 3D runoff difference model as a visual aid accompanied by soft computing techniques, the data mining of this vital information becomes attainable.
The minimum and maximum runoff depth prediction errors across multiple P and CN0.2 scenarios between the two runoff models can be extracted from Figure 3. The statistically significant equations can then be determined using the SPSS to formulate the worst under and over-estimated runoff prediction error equations from Equations (2) or (8) against Equation (21).
The data mining process extracts all the minimum and maximum runoff prediction differences (bold numbers, highlighted in red and yellow color, respectively in Figure 4) according to each rainfall depth scenarios (in row). Two statistically significant and best correlation equations were identified through SPSS regression modelling as: Min Qv = 5.14 x 10 -5 P 2 − 0.052 P − 0.222 (25) Max Qv = 5.14 x 10 -5 P 2 + 0.045 P − 0.734 (26) where Min Qv represents worse under-predicted runoff scenarios while Max Qv represents the maximum over-predicted runoff scenarios. Equation (25) has an R 2 -adj of 0.999, standard error of 0.037 and p < 0.001 while Equation (26) has an R 2 -adj of 0.999, standard Two statistically significant and best correlation equations were identified through SPSS regression modelling as: Min Qv = 5.14 × 10 −5 P 2 − 0.052 P − 0.222 (25) Max Qv = 5.14 × 10 −5 P 2 + 0.045 P − 0.734 (26) where Min Q v represents worse under-predicted runoff scenarios while Max Q v represents the maximum over-predicted runoff scenarios. Equation (25) has an R 2 -adj of 0.999, standard error of 0.037 and p < 0.001 while Equation (26) has an R 2 -adj of 0.999, standard error of 0.191 and p < 0.001. Given a specific rainfall depth, the worst under-estimated and over-estimated runoff prediction errors of Equation (2) or Equation (8) due to a specific rainfall depth can be estimated by Equations (25) and (26), respectively. It is also possible to employ soft computing technique to derive similar runoff prediction error equations in term of curve number. From Figure 5, the minimum and maximum runoff prediction differences can be extracted as per their respective curve number (in column) which induced the runoff difference (bold numbers, highlighted in red and yellow color, respectively in Figure 5).
Two statistically significant and best correlation equations from SPSS regression modelling results are: where Min Q v , Max Q v and CN 0.2 have been defined earlier. Equation (27) has an R 2 -adj of 0.992, standard error of 0.242 and p < 0.001 while Equation (28) has an R 2 -adj of 0.999, standard error of 0.255 and p < 0.001. Given a specific curve number, the worst underestimated and over-estimated runoff prediction errors of Equation (2) or Equation (8) due to a specific CN 0.2 area can be estimated with Equations (27) and (28), respectively.
error of 0.191 and p < 0.001. Given a specific rainfall depth, the worst under-estimated and over-estimated runoff prediction errors of Equation (2) or (8) due to a specific rainfall depth can be estimated by Equations (25) and (26), respectively. It is also possible to employ soft computing technique to derive similar runoff prediction error equations in term of curve number. From Figure 5, the minimum and maximum runoff prediction differences can be extracted as per their respective curve number (in column) which induced the runoff difference (bold numbers, highlighted in red and yellow color, respectively in Figure 5).
Two statistically significant and best correlation equations from SPSS regression modelling results are: Max Qv = 2.2 × 10 −4 CN0.2 3 − 0.061 CN0.2 2 + 4.77 CN0.2 − 86.519 (28) where Min Qv, Max Qv and CN0.2 have been defined earlier. Equation (27) has an R 2 -adj of 0.992, standard error of 0.242 and p < 0.001 while Equation (28) has an R 2 -adj of 0.999, standard error of 0.255 and p < 0.001. Given a specific curve number, the worst underestimated and over-estimated runoff prediction errors of Equation (2) or (8) due to a specific CN0.2 area can be estimated with Equations (27) and (28), respectively. The dash line on the 3D model in the valley of the red zone is described by Equations (25) and (27) while Equations (26) and (28) represent the dash line found on the ridge of the 3D runoff difference model (see Figure 2a). SCS practitioners can adopt Equations (25)- (28) to estimate the worst-case runoff prediction errors of Equation (2) when compared to the newly found λ (0.051) model in Peninsula Malaysia. On the other hand, regional or watershed specific equations can also be established by SCS practitioners for their study as proposed.

Runoff Difference Curves of the Critical Rainfall Amount
This study introduced runoff difference curves which were created with numerical analysis technique to visually present Equation (22) and to identify Pcrit. Runoff difference The dash line on the 3D model in the valley of the red zone is described by Equations (25) and (27) while Equations (26) and (28) represent the dash line found on the ridge of the 3D runoff difference model (see Figure 2a). SCS practitioners can adopt Equations (25)- (28) to estimate the worst-case runoff prediction errors of Equation (2) when compared to the newly found λ (0.051) model in Peninsula Malaysia. On the other hand, regional or watershed specific equations can also be established by SCS practitioners for their study as proposed.

Runoff Difference Curves of the Critical Rainfall Amount
This study introduced runoff difference curves which were created with numerical analysis technique to visually present Equation (22) and to identify P crit . Runoff difference curves graph combines two runoff curves (of conjugate curve numbers) into a single runoff difference curve to represent the concept of 2 previous studies [2,20,22] in another view. The graph can be plotted for specific CN 0.2 classes across multiple rainfall depth scenarios to show P crit at where the curve crosses x-axis ( Figure 6). curves graph combines two runoff curves (of conjugate curve numbers) into a single runoff difference curve to represent the concept of 2 previous studies [2,20,22] in another view. The graph can be plotted for specific CN0.2 classes across multiple rainfall depth scenarios to show Pcrit at where the curve crosses x-axis ( Figure 6). Runoff difference curve can be used as a visual aid to identify the Pcrit amount where the curve intersects the x-axis (when Qv = 0). Possible true solution(s) as initial guess(es) of the trial and error process from the curve can be visually identified rather than guessing an arbitrary starting point for numerical solution as proposed by previous researchers [2,20]. Equation (22) is a quadratic model that yields two potential Pcrit solutions. Figure 6 illustrates the use of runoff difference curves to identify the "critical rainfall amount" (Pcrit) of several CN0.2 scenarios. For example, at CN0.2 = 46 (dash line curve), Pcrit is approximately 40 mm and 205 mm (eyeballed from the graph, Pcrit points are marked by solid downwards arrow where the curve intersects the x-axis, implying that Qv is near to 0). However, the Ia amount has not been initiated for rainfall less than 40 mm according to Figure 3 and therefore, only 205 mm was used as the original trial and error estimate to satisfy Equation (22) and solve for the final solution of Pcrit of CN0.2 = 46.
Runoff difference curve provides a brief overview and shows that Equation (2) will under-predict runoff amount at CN0.2 area of 46 with any rainfall depths below the Pcrit value (around 205 mm) and becomes an over-prediction thereafter. A non-linear underdesign risk is therefore exhibited in the curve, with a peak of approximately 115 mm in rainfall depth (shown as dotted downwards arrow). Runoff difference curve provides additional insight of the worst under-estimated and over-estimated runoff prediction errors due to Equation (2) of specific rainfall depth which can be estimated with Equations (25) and (26), respectively. In conclusion, for CN 0.2 = 46, Equation (2) under predicts runoff amount from any rainfall depth >25.6 mm until 199.6 mm (P crit ) and over predicts runoff amount for any rainfall depths >199.6 mm when compared to Equation (21).
Runoff difference curve can be used as a visual aid to identify the P crit amount where the curve intersects the x-axis (when Q v = 0). Possible true solution(s) as initial guess(es) of the trial and error process from the curve can be visually identified rather than guessing an arbitrary starting point for numerical solution as proposed by previous researchers [2,20]. Equation (22) is a quadratic model that yields two potential P crit solutions. Figure 6 illustrates the use of runoff difference curves to identify the "critical rainfall amount" (P crit ) of several CN 0.2 scenarios. For example, at CN 0.2 = 46 (dash line curve), P crit is approximately 40 mm and 205 mm (eyeballed from the graph, P crit points are marked by solid downwards arrow where the curve intersects the x-axis, implying that Q v is near to 0). However, the I a amount has not been initiated for rainfall less than 40 mm according to Figure 3 and therefore, only 205 mm was used as the original trial and error estimate to satisfy Equation (22) and solve for the final solution of P crit of CN 0.2 = 46.
Runoff difference curve provides a brief overview and shows that Equation (2) will under-predict runoff amount at CN 0.2 area of 46 with any rainfall depths below the P crit value (around 205 mm) and becomes an over-prediction thereafter. A non-linear underdesign risk is therefore exhibited in the curve, with a peak of approximately 115 mm in rainfall depth (shown as dotted downwards arrow). Runoff difference curve provides additional insight of the worst under-estimated and over-estimated runoff prediction errors due to Equation (2) of specific rainfall depth which can be estimated with Equations (25) and (26), respectively.

The Critical Rainfall Amount (P crit ) Closed Form Equation
Through completing the square technique, this study has successfully used Equation (22) to obtain the closed form equation of P crit in terms of CN 0.2 . The closed form equation can be applied to solve for the P crit in any pairing runoff models with any λ values. The equation can calculate the P crit amount precisely and replace the trial and error procedure mentioned in Sections 2.11 and 2.12. SCS practitioners can refer to the proposed method in this article to derive the specific P crit equation for their studies.
The derivation of the closed form equation of the critical rainfall depth (P crit ) from this study is shown below. From Equation (22) When Q v = 0 (Runoff indifferent between 2 models), substitute A and B and solve for P (P crit ). [ After grouping and simplifying, P (P crit ) can be solved via quadratic form as below: P crit = Critical rainfall depth (mm) CN 0.2 = Conventional curve number of a watershed Equation (29) is a quadratic model that yields two potential P crit solutions. The outer boundary (Equation (23)) can be used as checkpoint to determine if the lower P crit value is a valid solution because any rainfall depths beyond the outer boundary will start to yield runoff difference between the two models after fulfilling the I a requirement. The lower P crit value is usually discarded due to its proximity to (or less than) the outer boundary.
If the P crit value < the P value of Equation (23) (outer boundary equation), the I a is yet to be fulfilled thus it is impossible to have any runoff or runoff difference amount. Runoff difference curves graph is also an effective visual aid to supplement the P crit closed-form equation (refer to Figure 6 example).
Results from several derived formulae were compiled in Table 3 to provide another quick overview of the P crit for Peninsula Malaysia across multiple CN 0.2 scenarios. According to the DID HP 27 dataset, the lowest calculated CN 0.2 is 48.8; hence, column A tabulates CN 0.2 range from 47 to 99 to cover the entire possible CN 0.2 scenario in Peninsula Malaysia. Column B and D were calculated using Equation (6), column C used Equation (20) and column E used Equation (29). Column F calculated CN 0.2 percentage change into CN 0.051 .
Column A and E can be used to construct another P crit overview curve across multiple CN 0.2 scenarios (Figure 7) with a statistically significant equation regressed via SPSS as: P crit = −245.4 ln(CN 0.2 ) + 1132.6 (30) Equation (30) has an R 2 -adj of 0.997, standard error of 3.047 and p < 0.001. Given CN 0.2 value of a watershed, the corresponding P crit value can be estimated with Equation (30). Equation (2) under predicts runoff amount at any rainfall depths below the P crit overview curve in Figure 7 and vice versa. Figure 7 is also in line with the research outcome reported by [2] that Equation (2) had the tendency to under-estimate runoff amount in rural and forested watersheds as CN 0.2 decreases.  [2,20,22]. It is noteworthy to mention that US researchers used inches in their dataset; hence, Equation (18)    Using the same concept as presented in ?? and Section 3.10, the closed form P crit can also be derived to verify previous study results where the optimum λ value was identified as 0.05 in the USA. The correlation between S λ and S 0.2 is best represented by S 0.05 = 1.33S 0.2 1.15 [2,20,22]. It is noteworthy to mention that US researchers used inches in their dataset; hence, Equation (18) (CN formula, SI version) needs to be converted and CN λ = 1000 S λ +10 should be used instead. The closed form P crit equation can be derived with the same method as proposed in Section 3.10 to verify their published P crit (inches) values (Table 4) in USA [2,22]. The closed form P crit equation verified all P crit values in Table 4 except for CN 0.2 = 50** and 65*. For CN 0.2 = 50**, the calculated P crit using the closed form equation method is 5.33 inches (instead of 5.35 inches)**. The variance to the published value is about 0.5 mm. However, for CN 0.2 = 65*, the calculated P crit is 3.52 inches (instead of 4.51 inches)*, which is much lower than the published value by about 25 mm.
Verification of Table 4 Pcrit values prove that the P crit closed form equation can be used to calculate the exact P crit value for any comparing SCS CN models for SCS practitioners. The success in the closed form equation derivation narrows the study gap from previous work. It can be adopted to replace the trial and error technique used by previous researchers [2,20,22].

Critical Curve Number (CN crit )
Equation (29) will yield two possible CN crit solutions (when Q v = 0 in Equation (22)). Although it is possible for those CN crit values to exist, all values must be verified. Potential CN crit solution(s) as the initial guess(es) to the trial and error process to satisfy Equation (22) can be identified when visually aided by runoff difference curves.
For an example, when rainfall = 100 mm (dash line curve in Figure 8), potential CN crit value is about 66 (marked by bold solid down arrows where the curve intersects with the x-axis or Q v = 0). Other possible CN crit value were discarded because the dash line curve intersects the x-axis at the left end at CN 0.2 around 22 and 99 on the right end, those values remain as a theoretical CN 0.2 value only.

Asymptotic Curve Number of Peninsula Malaysia
According to the AFM (Section 2.10), the DID HP 27 dataset resembles the standard behavior pattern (Figure 9) and thus Equation (16) was adopted to derive CN ∞ as the best representative CN 0.2 value for the dataset. Through least square fitting method under AFM, the fitting parameter k was identified to be 40.79 and CN ∞ = 67.77. When rounded to the closest positive integer, CN ∞ = CN 0.2 = 68.
CNcrit solution(s) as the initial guess(es) to the trial and error process to satisfy Equation (22) can be identified when visually aided by runoff difference curves.
For an example, when rainfall = 100 mm (dash line curve in Figure 8), potential CNcrit value is about 66 (marked by bold solid down arrows where the curve intersects with the x-axis or Qv = 0). Other possible CNcrit value were discarded because the dash line curve intersects the x-axis at the left end at CN0.2 around 22 and 99 on the right end, those values remain as a theoretical CN0.2 value only.  (8) and (21). CNcrit is the point that the runoff difference curve intersects the x-axis, marked by circle with solid down arrows lines. The dotted down arrow lines estimate the rainfall depth of maximum "under and over-design" risk for P = 100 mm, respectively. Note: when rainfall = 100 mm (dash line curve), runoff difference curve also suggests that the return period design base on rainfall depth of 100 mm is likely to cause under-design risk (negative Qv) in watersheds where CN0.2 value(s) is (are) <66, meanwhile incurring over-design risk (positive Qv) in CN0.2 values >66. Estimated worst under-design risk (marked with dotted down arrows) occurs around CN0.2 = 42 while the worst over-design risk at about 86. The worst under and over-estimated runoff prediction errors due to Equation (2) of those CN0.2 area can be estimated with Equations (27) and (28), respectively.

Asymptotic Curve Number of Peninsula Malaysia
According to the AFM (Section 2.10), the DID HP 27 dataset resembles the standard behavior pattern ( Figure 9) and thus Equation (16) was adopted to derive CN∞ as the best representative CN0.2 value for the dataset. Through least square fitting method under AFM, the fitting parameter k was identified to be 40.79 and CN∞ = 67.77. When rounded to the closest positive integer, CN∞ = CN0.2 = 68.  (8) and (21). CN crit is the point that the runoff difference curve intersects the x-axis, marked by circle with solid down arrows lines. The dotted down arrow lines estimate the rainfall depth of maximum "under and over-design" risk for P = 100 mm, respectively. Note: when rainfall = 100 mm (dash line curve), runoff difference curve also suggests that the return period design base on rainfall depth of 100 mm is likely to cause under-design risk (negative Q v ) in watersheds where CN 0.2 value(s) is (are) <66, meanwhile incurring over-design risk (positive Q v ) in CN 0.2 values >66. Estimated worst under-design risk (marked with dotted down arrows) occurs around CN 0.2 = 42 while the worst over-design risk at about 86. The worst under and over-estimated runoff prediction errors due to Equation (2) of those CN 0.2 area can be estimated with Equations (27) and (28), respectively. The AFM CN∞ result is in proximity to the equivalent CN0.2 value of 72.58 which was derived in Section 3.2, whereas CN∞ = 68 also falls within the 99% CN0.2 confidence interval of this study. This proves that the proposed SCS CN model calibration methodology in this article is capable to produce results that are in line with other method introduced by previous study.
Using Equation (18), the calculated S0.2 value of the AFM CN is 120.78 mm and Ia = 0.20 × 120.78 mm = 24.16 mm. These numbers are used in formulating the SCS runoff model with Equation (1) for benchmarking (Table 5). Table 5. Asymptotic CN fitting method (AFM) and new λ runoff model's residual analyses comparison with descriptive and inferential statistics at alpha = 0.01 level.

AFM Model
New λ Model The AFM CN∞ result is in proximity to the equivalent CN 0.2 value of 72.58 which was derived in Section 3.2, whereas CN ∞ = 68 also falls within the 99% CN 0.2 confidence interval of this study. This proves that the proposed SCS CN model calibration methodology in this article is capable to produce results that are in line with other method introduced by previous study.
Using Equation (18), the calculated S 0.2 value of the AFM CN is 120.78 mm and I a = 0.20 × 120.78 mm = 24.16 mm. These numbers are used in formulating the SCS runoff model with Equation (1) for benchmarking (Table 5).