# Assessing the Impact of Deforestation on Decadal Runoff Estimates in Non-Homogeneous Catchments of Peninsula Malaysia

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

^{2}(roughly 2/3 of Switzerland or nearly 39 Singapores) [16] of humid primary forest area at the clearing rate of 1421 km

^{2}/year within 19 years. This had resulted in a 17% reduction of humid primary forest in Malaysia within the given time period [17]. Compared to the deforestation of an area of 13,000 km

^{2}from 1978 to 1994 [18], the forest-clearing rate in Malaysia has doubled in recent decades. Deforestation challenged the quality and quantity of water [19] and caused significant hydrological changes which included an increase in runoff [20,21].

_{0.2}) to demonstrate variations in runoff response due to agricultural land-use and seasonal changes [51]. This paper applied the SCS-CN model calibration methodology with inferential statistics that was developed by authors in a previous study [50] and demonstrated the extended application to model decadal rainfall-runoff conditions in Peninsula Malaysia. The correlation between deforestation and urbanization on runoff increment in Peninsula Malaysia was also established.

## 2. Materials and Methods

#### 2.1. Study Site and Data Collection

^{2}, is slightly larger than England (130,395 km

^{2}). It is bordered by Thailand to the north and Singapore across the strait of Johor to the south. This study utilized the calibrated SCS lump rainfall-runoff model in conjunction with the most recent rainfall-runoff dataset published by the Malaysian federal agency. The dataset, which can be located in the appendix of the Department of Irrigation and Drainage’s Hydrological Procedures no. 27 (DID HP 27), documented 227 storm events across 41 distinct catchments between 1970 and January 2000 in Peninsula Malaysia [52]. The smallest recorded storm event had a rainfall depth of 19 mm, with a measurable runoff depth of 4.8 mm, while the largest event measured 420 mm in rainfall depth and 258 mm in runoff depth.

#### 2.2. Calibration of SCS-CN Model

_{a}= Rainfall initial abstraction amount (mm)

_{a}= λS. If P < I

_{a}, Q = 0.

_{a}= λS = 0.2S, where λ represents the initial abstraction ratio coefficient, which was proposed as a constant value of 0.2. The justification for Equation (1) was based on daily rainfall and runoff data, rather than event measurements, and its only official documentation source can be found in the National Engineering Handbook, Section 4 (NEH-4) [60,61]. By substituting I

_{a}= 0.2S, Equation (1) is simplified into Equation (2):

_{0}as shown below:

_{0}): Equation (2) (λ = 0.2) is valid to model runoff estimates with DID HP 27 dataset.

- Rearrange Equation (1) into: $S=\frac{{(P-{I}_{a})}^{2}}{Q}+{I}_{a}-P$
- For each P-Q event pair, calculate the corresponding S value with the above equation under the SCS constraint that I
_{a}< P value and calculate the λ value with λ = I_{a}/S. - Conduct bootstrap, BCa (at α = 0.01 level) inferential statistical analyses (2000 samples with replacement) for the calculated λ and S datasets separately for each decadal model.
- Generate 99% confidence intervals for λ and S datasets for each decadal model.
- Test the null hypothesis (H
_{0}) by referring to the λ confidence intervals’ span and its standard deviation for each decadal model. If the λ = 0.2 value exists within the λ confidence interval, use Equation (2) to model rainfall-runoff. Otherwise, move to step 6. - Find the optimum λ and S values from BCa confidence intervals and calculate I
_{a}for each decadal model using supervised optimization technique by minimizing the overall model bias (BIAS) near to the value of zero. - Formulate the calibrated SCS model by substituting I
_{a}and S into Equation (1). - According to a group of researchers [63], when λ value other than 0.2 is detected, its corresponding S value (denoted by S
_{λ}) must be correlated to the S_{0.2}values for CN calculation. As such, correlate S_{λ}and S_{0.2}with the S general formula which was derived by a past researcher [50]: ${S}_{\lambda}=\frac{\left[P-\frac{\left(\lambda -1\right)Q}{2\lambda}\right]-\sqrt{PQ-{P}^{2}+{\left[P-\frac{\left(\lambda -1\right)Q}{2\lambda}\right]}^{2}}}{\lambda}$ - Substitute optimum λ and S
_{λ}into Equation (1) to formulate the decadal model. - Lastly, substitute ${S}_{0.2}=\frac{\mathrm{25,400}}{{\mathrm{C}\mathrm{N}}_{0.2}}-254$ into each decadal model to express Q in term of P and CN
_{0.2}.

#### 2.3. IMERG Satellite Rainfall Trend Analysis

## 3. Results and Discussion

#### 3.1. The Optimum λ and S of Decadal Models

_{0}) at the alpha = 0.01 level. Equation (2) was found to be statistically invalid and, thus, cannot be utilized to model runoff conditions in Peninsula Malaysia for the M70, M80, and M90 decadal datasets. The rejection of H

_{0}necessitates the search for a new, optimal value of λ to develop a new rainfall-runoff prediction model.

_{λ}for the M70, M80, and M90 decadal datasets are presented in Table 4, Table 5 and Table 6. The normality of the S

_{λ}dataset was tested using SPSS for all three decadal groups, and found to be normally distributed. Therefore, the optimal S

_{λ}value was searched for within the range of the mean confidence intervals. There intervals are [117.083, 187.008] for M70 dataset (Table 4), [141.892, 231.088] for M80 dataset (Table 5), and [131.989, 192.939] for M90 dataset (Table 6).

_{λ}values for the M70, M80, and M90 decadal datasets using a supervised optimization technique are presented in Table 7. The product of the optimal λ and S

_{λ}values gives the representative initial abstraction value for each dataset, which can be calculated as I

_{a}= λS

_{λ}.

#### 3.2. The Decadal Rainfall-Runoff Models

_{a}and S

_{λ}values from Table 7 into Equation (1). Equations (3)–(5) were then formulated to model the decadal rainfall-runoff conditions in Peninsula Malaysia. To further analyse decadal runoff trend across multiple rainfall depths and CN

_{0.2}scenarios in Peninsula Malaysia, Equations (3)–(5) need to be re-expressed in terms of CN

_{0.2}to benefit SCS practitioners as they are more familiar with the use of curve number [50].

_{λ}and S

_{0.2}for each decadal dataset, the general S

_{λ}formula (step 8 in methodology Section 2.2) can be used with the optimum λ values. Through SPSS, this study identified statistically significant power function correlation between S

_{λ}and S

_{0.2}for the M70, M80, and M90 decadal datasets, which is consistent with previous research findings [66,67,68]. The final equations are listed in Table 9.

_{λ}to S

_{0.2}, enabling SCS practitioners to use a rainfall-runoff model with CN

_{0.2}, which they are more familiar with. Furthermore, by establishing a correlation between the newly derived S

_{λ}and S

_{0.2}, Equations (3)–(5) were modified to be expressed in CN

_{0.2}terms, facilitating decadal trend analyses with CN

_{0.2}.

_{0.2}by substituting S

_{λ}in Equation (1) with Equations (6)–(8), as well as the SCS-CN formula (Step 10 in methodology Section 2.2). By doing so, the decadal runoff predictive models can be re-expressed as shown in Appendix B. The resulting alternate representations for decadal runoff predictive models in Peninsula Malaysia are presented in Table 10 in term of CN

_{0.2}.

#### 3.3. The Decadal Runoff Trend Analyses

_{0.2}scenarios, facilitating the analysis of runoff changes. The DID HP 27 dataset contains the lowest and highest recorded rainfall depths, ranging from 20 mm to 430 mm across CN

_{0.2}classes from 46 to 94. Runoff difference tables can then be calculated between any two decadal models.

_{0.2}classes mentioned above. This positive correlation indicates an upward trend in inter-decadal runoff, which can be visually represented in Figure 1, Figure 2 and Figure 3. To assess the magnitude of this upward trend in each inter-decadal scenario and CN

_{0.2}class (ranging from 46 to 94) according to rainfall depths from 20 mm to 430 mm, Sen slopes and its collective inferential statistics were calculated. The Sen slopes and inferential statistics of all CN

_{0.2}classes were then analysed collectively for each inter-decadal scenario at the alpha = 0.01 level, and the results are tabulated in Table 11, Table 12 and Table 13.

_{0.2}classes from 46 to 94 can be estimated to be 1.21 mm between M80 and M90 (i.e., 0.0121 × 100 mm).

_{0.2}range of 46 to 70 to assess the runoff changes across lower CN

_{0.2}classes, which correspond to rural and forested catchments. This was done to obtain a more accurate estimate of the inter-decadal runoff increment conditions in these areas. The results showed that the runoff incremental trend between M80 and M90 of CN

_{0.2}(46 to 70) had a Sen slope value of 0.0190 (p = 0.01, 99% confidence interval from 0.01595 to 0.02216). The Sen slope value between M70 and M80 was 0.0075 (p = 0.01, 99% confidence interval from 0.00606 to 0.00898), and between M70 and M90, it was 0.0276 (p = 0.01, 99% confidence interval from 0.02262 to 0.03239). For example, the expected runoff increment from rainfall of 100 mm across CN

_{0.2}classes from 46 to 70 was estimated to be 1.90 mm between M80 and M90. The study found that runoff increments were significant (p = 0.01) between all inter-decadal scenarios and were more apparent in forested and rural areas (highlighted area in Figure 4).

_{0.2}groups, which are associated with forested catchments, are particularly affected. These study outcomes are in line with previous studies [67,68,69,70]. Inter-decadal runoff differences are more pronounced under high rainfall depths. The mean runoff of different decades across different CN

_{0.2}classes was calculated and compiled, as shown in Figure 5. M90 had the highest runoff, while M70 had the lowest. Greater percentage changes in mean runoff were observed in lower CN

_{0.2}classes (forested catchments) compared to higher CN

_{0.2}classes (urban area). The largest mean runoff incremental percentage was 12.6% (6.6 mm) from M70 to M90 at CN

_{0.2}= 46, while the smallest change was 0.1% (0.1 mm) from M80 to M90 at CN

_{0.2}= 94.

#### 3.4. The Impact of CN_{0.2} Variation on Runoff

_{0.2}value is often calibrated to match observed runoff dataset in modelling practice. Researchers observed that a variation of ±10% in CN

_{0.2}might lead to ±50% runoff variation [72] while [73] it was reported that even 1% increase in CN

_{0.2}with rainfall depth of 175 mm had caused 2.03% increase in runoff. References [73,74] concluded that CN

_{0.2}variations will have a larger impact on runoff than other parameters in Equation (1).

_{0.2}tweaking becomes a convenient way to calibrate and validate hydrological models. However, other studies reported that CN

_{0.2}value of a catchment was unstable and decreased when rainfall increased [74,75,76]. The error and sensitivity analysis results by some researchers stated that CN

_{0.2}variations would induce a larger impact on runoff calculation with inherent error rather than rainfall depth variations [72,74,77]. CN

_{0.2}tweaking might achieve or improve temporal hydrological modelling accuracy through the trial-and-error technique, but the practicality of the end result was often uncertain and lack of statistical justification.

_{0.2}variation with the DID HP 27 dataset. According to [78], the practical CN values were likely to be within the range of 40 to 98. The optimum best collective CN

_{0.2}was 71 for the entire DID HP 27 dataset, thus, CN

_{0.2}variation up to 40% was chosen to cover the range of CN

_{0.2}from 43 to 99 and rainfall from 20 mm to 430 mm. CN

_{0.2}upscaling induced larger runoff change than downscaling while both effects were largely felt at rainfall depths below 100 mm. On average, runoff would reduce by 37% when CN

_{0.2}was downscaled up to 40% between 20 mm and 430 mm. On the other hand, the average runoff increased by 306% when CN

_{0.2}was upscaled up to 40%. The average runoff for both scenarios was almost identical when rainfall depths were limited to higher rainfall depths (100 mm to 430 mm). The average runoff reduced by 34% when CN

_{0.2}was downscaled up to 40%, while average runoff increased by 35% when CN

_{0.2}was upscaled to the same range. Varying the CN

_{0.2}value by ±10% resulted in an average runoff change of 40%, which is consistent with the findings reported in [72]. Similarly, upscaling the CN

_{0.2}value by 1% with a rainfall depth of 175 mm caused a 2% increase in runoff, which matches the range reported by [73]. Sen slope analyses showed that in both CN

_{0.2}upscaling and downscaling scenario, runoff reduction and incremental rates reduced toward the high rainfall depths but increased according to the CN

_{0.2}variation percentage. Lower rainfall depths (20 to 100 mm) had higher runoff variation percentages than higher rainfall depths (100 to 430 mm), as reported by previous studies [67,68,69,70]. Figure 6 and Figure 7 present the overview of the impact of CN

_{0.2}variation on runoff with equations to estimate the percentage change in runoff.

#### 3.5. The Impact of Deforestation and Urbanization on Runoff in Peninsula Malaysia

_{v}%) across different CN

_{0.2}classes in Peninsula Malaysia. During the period between M70 and M90 in Peninsula Malaysia, the mean excess (incremental) runoff volume difference for CN

_{0.2}classes ranging from 46 to 70 was calculated to be 6.8 mm, equivalent to 6.8 million litres per square kilometre. This corresponds to a 10.2% increase in excess runoff, and it occurred simultaneously with a 25.5% decrease in forest area. These findings provide insights into the hydrological impacts of deforestation on non-homogeneous catchments. In general, inter-decadal mean runoff differences were more pronounced in forested and rural catchments (lower CN

_{0.2}classes) than urban areas. Inter-decadal runoff difference between M70 and M90 is significantly greater than runoff difference between M70 and M80 (Figure 9).

^{2}

_{adj}= 0.964, SE = 0.175, p < 0.012

Year | Urban Population (Millions) | Forest Area (Millions Hectare) |
---|---|---|

1970 | 2.03 | 8.01 |

1980 | 4.81 | 6.35 |

1990 | 7.97 | 6.27 |

2000 | 12.26 | 5.97 |

#### 3.6. Decadal λ and I_{a}

_{a}) values (Table 7). Over time, the optimal λ and I

_{a}values for each decade were found to decrease, indicating changes in land cover resulting from deforestation and urbanization that impact runoff conditions in rural catchments. The decreasing trend in λ leads to a corresponding increase in runoff over time in Peninsula Malaysia.

_{0.2}with one batch of runoff data and validate the final results against another batch to determine the optimum CN

_{0.2}value for modelling a combined dataset. However, this study highlights concerns with this practice due to land-use and land-cover changes in Peninsula Malaysia, which directly affect catchment runoff conditions over time.

_{0.2}classes from M70 to M90 due to changes in land use. Therefore, SCS practitioners must be cautious and aware that blindly accepting the λ value as 0.2 is not advisable, and it is strongly recommended to derive a regional-specific λ value. Although an optimum λ value of 0.051 was used in a previous study [50] to model the entire dataset with a Nash-Sutcliffe value of 0.92, it differed significantly from the optimum λ values of different decades. Hence, runoff predictive models formulated with different optimum λ values will yield differences in runoff predictions.

#### 3.7. Rainfall Trend Analyses

^{2}, is vulnerable to flood disaster, affecting almost 4.82 million people, equivalent to 22% of the total population [89]. In 2014, the states of Johor, Kelantan, Pahang, Perak, and Terengganu in Peninsula Malaysia, which were severely affected by floods, also recorded high rates of forest loss [38].

## 4. Conclusions

- The use of the conventional SCS-CN runoff model will commit type II error in this study to predict runoff conditions of different study periods. It must be pre-justified with statistics and calibrated prior to adoption for any runoff prediction. It is also not recommended to conduct calibration and validation on the entire DID HP 27 dataset of this study as each demarcated decade was represented with its unique and statistically significant runoff predictive model. Calibration and validation methodology based on the conventional SCS-CN runoff model fail to quantify accurate runoff conditions spanning across different time periods with significant land-use and cover change.
- CN adjustment practice to formulate a hydrological model can have a large inherent error as small adjustments on the curve number can lead to large variation in the runoff. Given sufficient sample size, SCS-CN runoff model should be calibrated and formulated according to its unique optimum λ values to represent rainfall-runoff conditions of different time periods. In this study, when CN value was varied ± 10%, the average runoff changed by 40%. This study found a significant increase in runoff across all CN
_{0.2}classes in Peninsula Malaysia due to changes in land use, emphasizing the importance of deriving a regional-specific λ value and cautioning that different optimum λ values for different decades will yield differences in runoff predictions. - This study emphasizes the significance of accounting for regional and decadal-specific rainfall-runoff conditions to estimate runoff in non-homogeneous catchments effectively. The calibrated SCS-CN model using data from different decades showed a remarkable ability to accurately estimate runoff amounts, even in non-homogeneous catchments. The models achieved a strong ability to estimate runoff amounts, attaining a Nash-Sutcliffe Index ranging from 0.907 to 0.958, even in non-homogeneous catchments.
- Calibrated SCS decadal (lump) runoff models show significant decadal runoff uptrend which coincides with the overall deforestation rate in Peninsula Malaysia. The presented methodology may become more apparent with regional specific deforestation rate and its corresponding rainfall-runoff dataset. The reduction of forest area by 25.5% in Peninsula Malaysia between 1970 and 2000 was found to be directly proportional to an increase in excess runoff volume of 10.2%. In general, inter-decadal mean runoff differences were more pronounced in forested and rural catchments (lower CN classes) than urban areas.
- NASA’s Giovanni system was used to generate 20 years of annual rainfall maps while monthly rainfall data (2001 to 2020) was also extracted for trend analysis and short-term forecast. This study found no significant uptrend in the rainfall within the period, and the occurrence of flood and landslide incidents can likely be attributed to land-use changes in Peninsula Malaysia.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Appendix A

**Table A1.**Monthly rainfall in Peninsula Malaysia from 2001 to 2009 [64].

Year | Month | Rainfall (mm/Month) | Year | Month | Rainfall (mm/Month) | Year | Month | Rainfall (mm/Month) |
---|---|---|---|---|---|---|---|---|

2001 | 1 | 326 | 2004 | 1 | 206 | 2007 | 1 | 323 |

2 | 117 | 2 | 80 | 2 | 92 | |||

3 | 224 | 3 | 185 | 3 | 179 | |||

4 | 231 | 4 | 174 | 4 | 210 | |||

5 | 162 | 5 | 178 | 5 | 211 | |||

6 | 132 | 6 | 127 | 6 | 191 | |||

7 | 132 | 7 | 211 | 7 | 226 | |||

8 | 163 | 8 | 163 | 8 | 183 | |||

9 | 214 | 9 | 259 | 9 | 221 | |||

10 | 314 | 10 | 374 | 10 | 332 | |||

11 | 285 | 11 | 285 | 11 | 229 | |||

12 | 367 | 12 | 270 | 12 | 452 | |||

2002 | 1 | 114 | 2005 | 1 | 94 | 2008 | 1 | 223 |

2 | 58 | 2 | 73 | 2 | 160 | |||

3 | 129 | 3 | 142 | 3 | 278 | |||

4 | 223 | 4 | 155 | 4 | 236 | |||

5 | 200 | 5 | 230 | 5 | 169 | |||

6 | 144 | 6 | 152 | 6 | 185 | |||

7 | 159 | 7 | 183 | 7 | 210 | |||

8 | 191 | 8 | 172 | 8 | 242 | |||

9 | 216 | 9 | 195 | 9 | 220 | |||

10 | 217 | 10 | 318 | 10 | 310 | |||

11 | 298 | 11 | 401 | 11 | 443 | |||

12 | 330 | 12 | 406 | 12 | 370 | |||

2003 | 1 | 263 | 2006 | 1 | 194 | 2009 | 1 | 248 |

2 | 122 | 2 | 244 | 2 | 107 | |||

3 | 193 | 3 | 142 | 3 | 330 | |||

4 | 204 | 4 | 208 | 4 | 208 | |||

5 | 14 | 5 | 243 | 5 | 221 | |||

6 | 181 | 6 | 201 | 6 | 120 | |||

7 | 209 | 7 | 167 | 7 | 162 | |||

8 | 220 | 8 | 160 | 8 | 232 | |||

9 | 205 | 9 | 210 | 9 | 209 | |||

10 | 370 | 10 | 242 | 10 | 257 | |||

11 | 345 | 11 | 310 | 11 | 407 | |||

12 | 369 | 12 | 372 | 12 | 335 |

**Table A2.**Monthly rainfall in Peninsula Malaysia from 2010 to 2018 [64].

Year | Month | Rainfall (mm/Month) | Year | Month | Rainfall (mm/Month) | Year | Month | Rainfall (mm/Month) |
---|---|---|---|---|---|---|---|---|

2010 | 1 | 151 | 2013 | 1 | 204 | 2016 | 1 | 137 |

2 | 73 | 2 | 289 | 2 | 124 | |||

3 | 147 | 3 | 88 | 3 | 57 | |||

4 | 206 | 4 | 188 | 4 | 77 | |||

5 | 232 | 5 | 191 | 5 | 237 | |||

6 | 231 | 6 | 126 | 6 | 185 | |||

7 | 213 | 7 | 159 | 7 | 182 | |||

8 | 181 | 8 | 171 | 8 | 162 | |||

9 | 202 | 9 | 230 | 9 | 215 | |||

10 | 218 | 10 | 315 | 10 | 275 | |||

11 | 333 | 11 | 287 | 11 | 305 | |||

12 | 363 | 12 | 432 | 12 | 370 | |||

2011 | 1 | 327 | 2014 | 1 | 166 | 2017 | 1 | 408 |

2 | 50 | 2 | 23 | 2 | 149 | |||

3 | 368 | 3 | 90 | 3 | 203 | |||

4 | 158 | 4 | 153 | 4 | 259 | |||

5 | 193 | 5 | 264 | 5 | 245 | |||

6 | 151 | 6 | 136 | 6 | 156 | |||

7 | 114 | 7 | 150 | 7 | 169 | |||

8 | 198 | 8 | 217 | 8 | 248 | |||

9 | 220 | 9 | 179 | 9 | 281 | |||

10 | 388 | 10 | 292 | 10 | 247 | |||

11 | 384 | 11 | 355 | 11 | 433 | |||

12 | 392 | 12 | 643 | 12 | 271 | |||

2012 | 1 | 238 | 2015 | 1 | 134 | 2018 | 1 | 442 |

2 | 146 | 2 | 71 | 2 | 74 | |||

3 | 262 | 3 | 11 | 3 | 140 | |||

4 | 230 | 4 | 201 | 4 | 164 | |||

5 | 239 | 5 | 181 | 5 | 236 | |||

6 | 97 | 6 | 163 | 6 | 172 | |||

7 | 152 | 7 | 133 | 7 | 149 | |||

8 | 187 | 8 | 242 | 8 | 123 | |||

9 | 239 | 9 | 210 | 9 | 218 | |||

10 | 254 | 10 | 247 | 10 | 330 | |||

11 | 260 | 11 | 340 | 11 | 279 | |||

12 | 492 | 12 | 226 | 12 | 360 |

**Table A3.**Monthly rainfall in Peninsula Malaysia from 2019 to 2020 [64].

Year | Month | Rainfall (mm/Month) |
---|---|---|

2019 | 1 | 164 |

2 | 68 | |

3 | 92 | |

4 | 167 | |

5 | 236 | |

6 | 210 | |

7 | 108 | |

8 | 148 | |

9 | 149 | |

10 | 320 | |

11 | 274 | |

12 | 261 | |

2020 | 1 | 108 |

2 | 121 | |

3 | 110 | |

4 | 262 | |

5 | 241 | |

6 | 249 | |

7 | 275 | |

8 | 142 | |

9 | 246 | |

10 | 213 | |

11 | 401 | |

12 | 360 |

## Appendix B

_{0.049}in above will yield:

_{0.2}= (25,400/CN

_{0.2}) − 254 into S

_{0.2}in above to obtain:

_{0.049}

_{0.049}= 0

_{0.2}= Conventional SCS tabulated curve number

_{0.049}= Runoff depth (mm) of λ = 0.049 for M70 dataset.

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**Figure 1.**Decadal runoff difference of Equations (9) and (10) for selected CN

_{0.2}values to reflect the runoff change between M70 and M80 under different rainfall depths.

**Figure 2.**Decadal runoff difference of Equations (9) and (11) for selected CN

_{0.2}values to reflect the runoff change between M70 and M90 under different rainfall depths.

**Figure 3.**Decadal runoff difference of Equations (10) and (11) for selected CN

_{0.2}values to reflect the runoff change between M80 and M90 under different rainfall depths.

**Figure 4.**The Sen Slope for decadal runoff increment for CN

_{0.2}classes between M70 to M90. Sen slopes and inferential statistics were used to analyse the collective inter-decadal runoff increment conditions. Forested and rural areas are highlighted at lower CN

_{0.2}area from 46 to 70. The estimated percentage of rainfall depths by Sen slope calculation were compared between all scenarios to contrast the inter-decadal runoff incremental percentage.

**Figure 6.**Effects of upscaling and downscaling of CN

_{0.2}on runoff. CN

_{0.2}upscaling caused runoff incremental change and vice versa. Note: CN

_{0.2}upscaling data points refer to primary axis. CN

_{0.2}variations start from CN

_{0.2}= 71, variation range (43 to 99) across rainfall depth range from 25 mm to 425 mm. The blank circle and triangular data point are benchmark points of CN

_{0.2}± 10%, the indicated 19.1% and 61% are runoff reduction and incremental due to CN

_{0.2}variations.

**Figure 7.**Response of runoff change to variation of CN

_{0.2}. Runoff change % data points are the averaged runoff change due to CN

_{0.2}upscaling and downscaling of a specific variation %. The blank data point shows the average runoff change % due to CN

_{0.2}± 10% variation.

**Figure 9.**Mean inter-decadal runoff incremental % across different CN

_{0.2}classes (46 to 70) between 1970 (M70) and 2000 (M90). Note: The graph was created with decadal runoff models and Malaysia Department of Forestry data to coincide with the total forest area loss within the same period. On average, runoff volume for CN

_{0.2}classes ranging from 46 to 70 increased by 10.2% in Peninsular Malaysia while forest area reduced by 25.5% from 1970 to 2000.

**Figure 10.**Monthly rainfall trend in Peninsula Malaysia from 2001 to 2020 (divided into 5-year interval).

**Figure 11.**Monthly rainfall time series forecasting model for Peninsula Malaysia using Expert Modeler. Modelled period: 2001–2020 (N = 240, see Appendix A). Forecasted period: 2021–2022 (N = 24).

λ | Statistics | Bootstrap, BCa 99% | |||
---|---|---|---|---|---|

Bias | Std. Error | Confidence Intervals | |||

Lower | Upper | ||||

Skewness | 3.815 | ||||

Kurtosis | 19.768 | ||||

Mean | 0.098 | 0.0003 | 0.014 | 0.069 | 0.142 |

Median | 0.065 | 0.0006 | 0.00294 | 0.049 | 0.089 |

λ | Statistics | Bootstrap, BCa 99% | |||
---|---|---|---|---|---|

Bias | Std. Error | Confidence Intervals | |||

Lower | Upper | ||||

Skewness | 3.217 | ||||

Kurtosis | 13.028 | ||||

Mean | 0.095 | 0.0002 | 0.014 | 0.066 | 0.135 |

Median | 0.047 | 0.0022 | 0.007 | 0.034 | 0.064 |

λ | Statistics | Bootstrap, BCa 99% | |||
---|---|---|---|---|---|

Bias | Std. Error | Confidence Intervals | |||

Lower | Upper | ||||

Skewness | 5.393 | ||||

Kurtosis | 34.674 | ||||

Mean | 0.076 | 0.00005 | 0.013 | 0.051 | 0.115 |

Median | 0.042 | 0.00183 | 0.007 | 0.031 | 0.063 |

S_{λ} | Statistics | Bootstrap, BCa 99% | |||
---|---|---|---|---|---|

Bias | Std. Error | Confidence Intervals | |||

Lower | Upper | ||||

Skewness | 1.298 | ||||

Kurtosis | 0.975 | ||||

Mean | 151.592 | −0.482 | 14.954 | 117.083 | 187.008 |

Median | 123.615 | −0.166 | 11.367 | 91.255 | 157.815 |

S_{λ} | Statistics | Bootstrap, BCa 99% | |||
---|---|---|---|---|---|

Bias | Std. Error | Confidence Intervals | |||

Lower | Upper | ||||

Skewness | 1.794 | ||||

Kurtosis | 5.201 | ||||

Mean | 180.994 | −0.339 | 14.954 | 141.892 | 231.088 |

Median | 147.950 | −3.921 | 19.112 | 113.890 | 183.670 |

S_{λ} | Statistics | Bootstrap, BCa 99% | |||
---|---|---|---|---|---|

Bias | Std. Error | Confidence Intervals | |||

Lower | Upper | ||||

Skewness | 1.132 | ||||

Kurtosis | 1.407 | ||||

Mean | 161.827 | 0.221 | 11.934 | 131.989 | 192.939 |

Median | 142.610 | −2.566 | 21.298 | 95.236 | 189.506 |

Dataset | Optimal λ | Optimal S_{λ} | I_{a} = λS_{λ} |
---|---|---|---|

M70 | 0.049 | 160 mm | 7.904 mm |

M80 | 0.034 | 190 mm | 6.431 mm |

M90 | 0.031 | 160 mm | 4.956 mm |

Dataset | Runoff Predictive Model | Nash-Sutcliffe Index | Equation Number |
---|---|---|---|

M70 | $Q=\frac{{\left(P-7.904\right)}^{2}}{P-152.096}$ | 0.958 | (3) |

M80 | $Q=\frac{{\left(P-6.431\right)}^{2}}{P-183.569}$ | 0.910 | (4) |

M90 | $Q=\frac{{\left(P-4.956\right)}^{2}}{P-155.044}$ | 0.907 | (5) |

Dataset | Correlation Equation | Adjusted R-Squared | Standard Error of Estimate | Equation Number |
---|---|---|---|---|

M70 | ${S}_{0.049}=1.184{{S}_{0.2}}^{1.081}$ | 0.939 | 0.134 | (6) |

M80 | ${S}_{0.034}=1.107{{S}_{0.2}}^{1.094}$ | 0.910 | 0.201 | (7) |

M90 | ${S}_{0.031}=1.179{{S}_{0.2}}^{1.069}$ | 0.907 | 0.165 | (8) |

Dataset | Runoff Predictive Model | Equation Number |
---|---|---|

M70 | ${Q}_{0.049}=\frac{{\left[P-23.077{\left(\frac{100}{{\mathrm{C}\mathrm{N}}_{0.2}}-1\right)}^{1.081}\right]}^{2}}{\left[P+447.876{\left(\frac{100}{{\mathrm{C}\mathrm{N}}_{0.2}}-1\right)}^{1.081}\right]}$ | (9) |

M80 | ${Q}_{0.034}=\frac{{\left[P-15.992{\left(\frac{100}{{\mathrm{C}\mathrm{N}}_{0.2}}-1\right)}^{1.094}\right]}^{2}}{\left[P+456.589{\left(\frac{100}{{\mathrm{C}\mathrm{N}}_{0.2}}-1\right)}^{1.094}\right]}$ | (10) |

M90 | ${Q}_{0.031}=\frac{{\left[P-13.618{\left(\frac{100}{{\mathrm{C}\mathrm{N}}_{0.2}}-1\right)}^{1.069}\right]}^{2}}{\left[P+425.963{\left(\frac{100}{{\mathrm{C}\mathrm{N}}_{0.2}}-1\right)}^{1.069}\right]}$ | (11) |

**Table 11.**Inferential statistics of Sen Slopes for inter decadal runoff difference between M80 and M90.

Sen Slopes M80 to M90 | Statistics | Bootstrap, BCa 99% | |||
---|---|---|---|---|---|

Bias | Std. Error | Confidence Intervals | |||

Lower | Upper | ||||

Skewness | −0.022 | ||||

Kurtosis | −1.512 | ||||

Mean | 0.0121 | −0.00002 | 0.00225 | 0.00701 | 0.01720 |

Median | 0.0127 | −0.00029 | 0.00422 | 0.00439 | 0.02119 |

Std. Deviation | 0.0087 | −0.00039 | 0.00103 | 0.00588 | 0.01032 |

Range | 0.0247 |

**Table 12.**Inferential statistics of Sen Slopes for inter decadal runoff difference between M70 and M80.

Sen Slopes M70 to M80 | Statistics | Bootstrap, BCa 99% | |||
---|---|---|---|---|---|

Bias | Std. Error | Confidence Intervals | |||

Lower | Upper | ||||

Skewness | 0.272 | ||||

Kurtosis | −0.940 | ||||

Mean | 0.0051 | −0.00001 | 0.00081 | 0.00313 | 0.00713 |

Median | 0.0048 | 0.00008 | 0.00126 | 0.00230 | 0.00824 |

Std. Deviation | 0.0031 | −0.00015 | 0.00046 | 0.00178 | 0.00396 |

Range | 0.1000 |

**Table 13.**Inferential statistics of Sen Slopes for inter decadal runoff difference between M70 and M90.

Sen Slopes M70 to M90 | Statistics | Bootstrap, BCa 99% | |||
---|---|---|---|---|---|

Bias | Std. Error | Confidence Intervals | |||

Lower | Upper | ||||

Skewness | 0.065 | ||||

Kurtosis | −1.473 | ||||

Mean | 0.0178 | −0.00003 | 0.00322 | 0.01049 | 0.02506 |

Median | 0.0175 | 0.00015 | 0.00589 | 0.00712 | 0.03125 |

Std. Deviation | 0.0124 | −0.00057 | 0.00149 | 0.00823 | 0.01479 |

Range | 0.0353 |

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**MDPI and ACS Style**

Khor, J.F.; Lim, S.; Ling, V.L.; Ling, L.
Assessing the Impact of Deforestation on Decadal Runoff Estimates in Non-Homogeneous Catchments of Peninsula Malaysia. *Water* **2023**, *15*, 1162.
https://doi.org/10.3390/w15061162

**AMA Style**

Khor JF, Lim S, Ling VL, Ling L.
Assessing the Impact of Deforestation on Decadal Runoff Estimates in Non-Homogeneous Catchments of Peninsula Malaysia. *Water*. 2023; 15(6):1162.
https://doi.org/10.3390/w15061162

**Chicago/Turabian Style**

Khor, Jen Feng, Steven Lim, Vania Lois Ling, and Lloyd Ling.
2023. "Assessing the Impact of Deforestation on Decadal Runoff Estimates in Non-Homogeneous Catchments of Peninsula Malaysia" *Water* 15, no. 6: 1162.
https://doi.org/10.3390/w15061162