# 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.

## References

- Akomolafe, G.F.; Rosazlina, R. Land use and land cover changes influence the land surface temperature and vegetation in Penang Island, Peninsular Malaysia. Sci. Rep.
**2022**, 12, 21250. [Google Scholar] [CrossRef] [PubMed] - Wong, C.L.; Liew, J.; Yusop, Z.; Ismail, T.; Venneker, R.; Uhlenbrook, S. Rainfall characteristics and regionalization in Peninsular Malaysia based on a high resolution gridded data set. Water
**2016**, 8, 500. [Google Scholar] [CrossRef] [Green Version] - Baig, M.F.; Mustafa, M.R.U.; Baig, I.; Takaijudin, H.B.; Zeshan, M.T. Assessment of land use land cover changes and future predictions using CA-ANN simulation for Selangor, Malaysia. Water
**2022**, 14, 402. [Google Scholar] [CrossRef] - Abdul Rahim, N.; Zulkifli, Y. Hydrological impacts of forestry and land use activities: Malaysian and regional experience. In Paper Presented at the Seminar on Water, Forestry and Land Use Perspectives; Forest Research Institute Malaysia: Kepong, Malaysia, 1999. [Google Scholar]
- Bonell, M. Selected issues in mountain hydrology of the humid tropics. In Water: Forestry and Land Use Perspectives; IHPVI/Technical document in Hydrology; UNESCO: Paris, France, 2004; pp. 34–56. [Google Scholar]
- Bruijnzeel, L.A.; Bonell, M.; Gilmour, D.A.; Lamd, D. Conclusion: Forest water and people in the humid tropics: An emerging view. In Forest, Water and People in the Humid Tropics; Past, Present and Future Hydrological Research for Integrated Land and Water Management; Cambridge University Press: Paris, France, 2005; pp. 906–925. [Google Scholar]
- Hall, A.L. People in tropical forest: Problem or solutions? In Forest, Water and People in the Humid Tropics; Past, Present and Future Hydrological Research for Integrated Land and Water Management; Cambridge University Press: Paris, France, 2005; pp. 75–85. [Google Scholar]
- Murdiyarso, D. Water resources management policy responses to land cover change in South East Asian River basins. In Forest, Water and People in the Humid Tropics; Past, Present and Future Hydrological Research for Integrated Land and Water Management; Cambridge University Press: Paris, France, 2005; pp. 121–133. [Google Scholar]
- Calder, I.R. The Blue Revolution: Land Use and Integrated Water Resources Management; Earthscan Publications Ltd.: London, UK, 1999. [Google Scholar]
- Velaquez, A.; Duran, E.; Ramirez, I.; Mas, J.F.; Bocco, G.; Ramirez, G.; Palacio, J.L. Land use-cover change processes in highly biodiverse areas: The case of Qaxaca, Mexico. Glob. Environ. Chang.
**2003**, 13, 175–184. [Google Scholar] [CrossRef] - Yunus, A.J.M.; Nakagoshi, N.; Ibrahim, A.L. Riparian land use and land cover change analysis using GOS in Pinang River watershed, Malaysia. Tropics
**2004**, 13, 235–248. [Google Scholar] [CrossRef] - Miyamoto, M.; Parid, M.M.; Aini, Z.N.; Michinaka, T. Proximate and underlying causes of forest cover change in Peninsula Malaysia. For. Policy Econ.
**2014**, 44, 18–25. [Google Scholar] [CrossRef] [Green Version] - Hamid, W.A.; Rahman, S.B.W.A. Comparison results of forest cover mapping of Peninsular Malaysia using geospatial technology. IOP Conf. Ser. Earth Environ. Sci.
**2016**, 37, 012027. [Google Scholar] [CrossRef] [Green Version] - Kamlisa, U.K.; Renate, B.A.; Mui-How, P. Monitoring deforestation in Malaysia between 1985 and 2013: Insight from South-Western Sabah and its protected peat swamp area. Land Use Policy
**2016**, 57, 418–430. [Google Scholar] - Mohd Jaafar, W.S.W.; Maulud, K.N.A.; Kamarulzaman, A.M.M.; Raihan, A.; Md Sah, S.; Ahmad, A.; Saad, S.N.M.; Mohd Azmi, A.T.; Syukri, N.K.A.J.; Khan, W.R. The influence of deforestation on land surface temperature—A case study of Perak and Kedah, Malaysia. Forests
**2020**, 11, 670. [Google Scholar] [CrossRef] - Geography > Area > Total: Countries Compared. Available online: https://www.nationmaster.com/country-info/stats/Geography/Area/Total (accessed on 15 October 2021).
- Malaysia. Available online: https://www.globalforestwatch.org/ (accessed on 15 October 2021).
- Department of Forestry, Malaysia. Annual forestry Report 1997; Ministry of Science Technology and the Environment: Kuala Lumpur, Malaysia, 1998. [Google Scholar]
- Ngah, M.S.Y.C.; Othman, Z. Impact of land development on water quantity and water quality in Peninsular Malaysia. Malaysian J. Environ. Manag.
**2011**, 12, 113–120. [Google Scholar] - Hamilton, L.S.; King, P.N. Tropical Forested Watershed: Hydrologic and Soils Responses to Major Uses of Conversion; Wesview Press Inc.: Boulder, CO, USA, 1983. [Google Scholar]
- Hamilton, L.S.; Pearce, A.J. What Are the Soil and Water Benefits of Planting Trees in Developing Countries Watershed? Sustainable Resources Development in The Third World; Westview Press: Boulder, CO, USA, 1987. [Google Scholar]
- Hutjes, R.W.A.; Wierda, A.; Veen, A.W.L. Rainfall interception in the Tai Forest, Ivory Coast: Application of two simulation model to a humid tropical system. J. Hydrol.
**1990**, 114, 259–275. [Google Scholar] [CrossRef] - Sinun, W.; Wong, W.M.; Douglas, I.; Spencer, T. Throughfall, stemflow, overland flow and throughflow in the Ulu Segama rain forest, Sabah, Malaysia. Phil. Tran. R. Soc.
**1992**, 335, 389–395. [Google Scholar] - Asdak, C.; Jarvis, P.G.; Gardingen, P.V.; Fraser, A. Rainfall interception loss in unlogged and logged forest areas of Central Kalimantan, Indonesia. J. Hydrol.
**1998**, 206, 237–244. [Google Scholar] [CrossRef] [Green Version] - Calder, I.R. The influence of land use on water yield in upland areas of the UK. J. Hydrol.
**1986**, 88, 201–212. [Google Scholar] [CrossRef] - Abdul Rahim, N.; Saifuddin, S.; Zulkifli, Y. Water balance and hydrological characteristics of forested watersheds in Peninsular Malaysia. In Proceedings of the 2nd International Study Conference on GEWEX in Asia and GAME, Pattaya, Thailand, 6–10 March 1995. [Google Scholar]
- Abdul Rahim, N.; Baharuddin, K. Hydrologic Regime of Dipterocarp Forest Catchments in Peninsular Malaysia; Hydrology Workshop; Universiti Kebangsaan Malaysia: Kota Kinabalu, Malaysia, 1986. [Google Scholar]
- Abdul Rahim, N. Water yield changes after forest conversion to agricultural land use in Peninsular Malaysia. J. Trop. Sci.
**1988**, 1, 67–84. [Google Scholar] - Zulkifli, Y. Effect of selective logging methods on dissolved nutrient export in Berembun Watershed, Peninsular Malaysia. In Proceedings of the Regional Seminar on Tropical Forest Hydrology, Kuala Lumpur, Malaysia, 4–9 September 1989. [Google Scholar]
- Bruijnzeel, L.A. Hydrology of Moist Tropical Forests and Effects of Conversion: A State of Knowledge Review; UNESCO IHP Tropic Programme; UNESCO: Paris, France, 1990. [Google Scholar]
- Bruijnzeel, L.A. Land use and hydrology in warm humid region: Where do we stand? IAHS
**1993**, 216, 1–34. [Google Scholar] - Bonell, M. Progress in the understanding of runoff generation dynamics in forest. J. Hydrol.
**1993**, 150, 217–275. [Google Scholar] [CrossRef] - Bonell, M. Tropical Forest hydrology and the role of UNESCO International Hydrological programme. HESS
**1999**, 3, 451–461. [Google Scholar] [CrossRef] - Malmer, A. Hydrological effects and nutrient losses of forest plantation establishment on tropical rainforest land in Sabah, Malaysia. Water Resour. Res.
**1996**, 32, 2213–2220. [Google Scholar] [CrossRef] - Zhang, P.; Shao, G.; Zhao, G.; Le Master, D.C.; Parker, G.R.; Dunning, J.B., Jr.; Li, Q. China’s forest policy for the 21th century. Science
**2000**, 288, 2135–2136. [Google Scholar] [CrossRef] [Green Version] - Sharma, K.P.; Vorosmarty, C.J.; Moore, B. Sensitivity of the Himalayan hydrology to land-use and climate changes. Clim. Chang.
**2000**, 47, 117–139. [Google Scholar] [CrossRef] - Lazaro, T.R. Urban Hydrology: A Multidisciplinary Perspective; Revised Edition; Technomic Publishing Company, Inc.: Lancaster, UK, 1990. [Google Scholar]
- High Deforestation Rates in Malaysian States Hit by Flooding. Available online: https://news.mongabay.com/2015/01/high-deforestation-rates-in-malaysian-states-hit-by-flooding/ (accessed on 15 October 2021).
- Bosch, J.M.; Hewlett, J.D. A review of catchment experiments to determine the effect of vegetation changes on water yield and evapotranspiration. J. Hydrol.
**1982**, 55, 3–23. [Google Scholar] [CrossRef] - Brown, A.G.; Nambiar, E.K.S.; Cossalter, C. Plantations for the tropics: Their role, extent and nature. In Management of Soil, Nutrients and Water in the Tropical Plantation Forests; Nambiar, E.K.S., Brown, A.G., Eds.; Australian Centre for International Agricultural Research: Canberra, Australia; pp. 1–23.
- Sidle, R.C.; Sasaki, S.; Otsuki, M.; Noguchi, S.; Abdul Rahim, N. Sediment pathways in a tropical forest: Effects of logging roads and skid trails. Hydrol. Proc.
**2004**, 18, 703–720. [Google Scholar] [CrossRef] - Adnan, N.A.; Atkinson, P.M.; Yusoff, Z.M.; Rasam, A.R.A. Climate variability and anthropogenic impacts on a semi-distributed monsoon catchment runoff simulations. In Proceedings of the International Colloquium on Signal Processing & Its Applications, CSPA, Kuala Lumpur, Malaysia, 7–9 March 2014; pp. 178–183. [Google Scholar]
- Jourgholami, M.; Karami, S.; Tavankar, F.; Monaco, A.L.; Picchio, R. Effects of slope gradient on runoff and sediment yield on machine-induced compacted soil in temperate forests. Forests
**2021**, 12, 49. [Google Scholar] [CrossRef] - Hu, P.; Cai, T.; Sui, F.; Duan, L.; Man, X.; Cui, X. Response of runoff to extreme land use change in the permafrost region of Northeastern China. Forests
**2021**, 12, 1021. [Google Scholar] [CrossRef] - Liu, X.Z.; Kang, S.Z.; Liu, L.D.; Zhang, X.P. SCS model based on geographic information and its application to simulate rainfall-runoff relationship at typical small watershed level in Loess Plateau. Tran. CSAE
**2005**, 21, 93–97. [Google Scholar] - Baltas, E.A.; Dervos, N.A.; Mimikou, M.A. Technical note: Determination of the SCS initial abstraction ratio in an experimental watershed in Greece. HESS
**2007**, 11, 1825–1829. [Google Scholar] [CrossRef] [Green Version] - Mansor, S.; Saadatkhah, N.; Khuzaimah, Z. Regional modelling of rainfall-induced runoff using hydrological model by incorporating plant cover effects: Case study in Kelantan, Malaysia. Nat. Hazards
**2018**, 93, 739–764. [Google Scholar] [CrossRef] - Sahin, V.; Hall, M.J. The effects of afforestation and deforestation on water yields. J. Hydrol.
**1996**, 178, 293–309. [Google Scholar] [CrossRef] - Ling, L.; Yusop, Z.; Yap, W.S.; Tan, W.L.; Chow, M.F.; Ling, J.L. A calibrated, watershed-specific SCS-CN method: Application to Wangjiaqiao watershed in the three gorges area, China. Water
**2019**, 12, 60. [Google Scholar] [CrossRef] [Green Version] - Ling, L.; Yusop, Z.; Ling, J.L. Statistical and Type II Error Assessment of a Runoff Predictive Model in Peninsula Malaysia. Mathematics
**2021**, 9, 812. [Google Scholar] [CrossRef] - Hawkins, R.H.; Ward, T.J.; Woodward, D.E.; Vanmullem, J.A. Progress Report: ASCE Task Committee on Curve Number Hydrology. Managing Watersheds for Human and Natural Impacts; ASCE: New York, NY, USA, 2005; pp. 1–12. [Google Scholar]
- DID, Hydrological Procedure No. 27. Design Flood Hydrograph Estimation for Rural Catchments in Peninsula Malaysia. JPS, DID, Kuala Lumpur. 2010. Available online: https://www.water.gov.my/jps/resources/PDF/Hydrology%20Publication/Hydrological_Procedure_No_27_(HP_27).pdf (accessed on 19 October 2021).
- Forestry Department Peninsular Malaysia. Forestry Statistics Peninsular Malaysia 1971–1978; Forestry Department Peninsular Malaysia: Kuala Lumpur, Malaysia, 1979. [Google Scholar]
- Forestry Department Peninsular Malaysia. Forestry Statistics Peninsular Malaysia 1993; Forestry Department Peninsular Malaysia: Kuala Lumpur, Malaysia, 1994. [Google Scholar]
- Forestry Department Peninsular Malaysia. Forestry Statistics Peninsular Malaysia 1995; Forestry Department Peninsular Malaysia: Kuala Lumpur, Malaysia, 1996. [Google Scholar]
- Forestry Department Peninsular Malaysia. Forestry Statistics Peninsular Malaysia 2000; Forestry Department Peninsular Malaysia: Kuala Lumpur, Malaysia, 2001. [Google Scholar]
- Forestry Department Peninsular Malaysia. Forestry Statistics Peninsular Malaysia 2005; Forestry Department Peninsular Malaysia: Kuala Lumpur, Malaysia, 2006. [Google Scholar]
- Forestry Department Peninsular Malaysia. Forestry Statistics Peninsular Malaysia 2009; Forestry Department Peninsular Malaysia: Kuala Lumpur, Malaysia, 2010. [Google Scholar]
- Forestry Department Peninsular Malaysia. Forestry Statistics Peninsular Malaysia 2010; Forestry Department Peninsular Malaysia: Kuala Lumpur, Malaysia, 2011. [Google Scholar]
- Hawkins, R.H. Curve Number Method: Time to Think Anew? J. Hydrol. Eng.
**2014**, 19, 1059. [Google Scholar] [CrossRef] - Davidsen, S.; Löwe, R.; Ravn, N.H.; Jensen, L.N.; Arnbjerg-Nielsen, K. Initial conditions of urban permeable surfaces in rainfall-runoff models using Horton’s infiltration. Water Sci. Technol.
**2017**, 77, 662–669. [Google Scholar] [CrossRef] [PubMed] - Downloading IBM SPSS Statistics 26. Available online: https://www.ibm.com/support/pages/downloading-ibm-spss-statistics-26 (accessed on 15 October 2021).
- Hawkins, R.H.; Ward, T.J.; Woodward, D.E.; Van Mullem, J. Curve Number Hydrology: State of the Practice; ASCE: Reston, VA, USA, 2009. [Google Scholar]
- Huffman, G.J.; Stocker, E.F.; Bolvin, D.T.; Nelkin, E.J.; Tan, J. GPM IMERG Final Precipitation L3 1 Month 0.1 Degree x 0.1 Degree V06; Goddard Earth Sciences Data and Information Services Center (GES DISC): Greenbelt, MD, USA, 2019. [Google Scholar]
- Giovanni. Available online: https://giovanni.gsfc.nasa.gov/giovanni/ (accessed on 15 October 2021).
- Jiang, R.Y. Investigation of Runoff Curve Number Initial Abstraction Ratio. Master’s Thesis, The University of Arizona, Tucson, AZ, USA, 2001. [Google Scholar]
- Woodward, D.E.; Hawkins, R.H.; Jiang, R.; Hjelmfelt, A.T.; Van Mullem, J.A.; Quan, Q.D. Runoff curve number method: Examination of the initial abstraction ratio. In Proceedings of the World Water and Environmental Resources Congress, Philadelphia, PA, USA, 23–26 June 2003; pp. 1–10. [Google Scholar]
- Hawkins, R.H.; Ward, T.J.; Woodward, D.E.; Van Mullem, J.A. Continuing Evolution of Rainfall-Runoff and the Curve Number Precedent. In Proceedings of the 2nd Joint Federal Interagency Conference Proceeding, Las Vegas, NV, USA, 27 June–1 July 2010; pp. 1–12. [Google Scholar]
- Ponce, V.M.; Hawkins, R.H. Runoff Curve Number: Has it Reached Maturity? J. Hydrol.
**1996**, 1, 11–19. [Google Scholar] [CrossRef] - Hawkins, R.H.; Khojeini, A.V. Initial abstraction and loss in the curve number method. In Proceedings of the Arizona State Hydrological Society Proceedings, Las Vegas, NV, USA, 15–17 April 2000; pp. 115–119. [Google Scholar]
- Kim, N.W.; Lee, J.; Lee, J.E. SWAT application to estimate design runoff curve number for South Korean conditions. Hydrol. Proc.
**2010**, 24, 2156–2170. [Google Scholar] [CrossRef] - Boughton, W.C. A Review of the USDA SCS Curve Number Method. Aust. J. Soil Res.
**1989**, 27, 511–523. [Google Scholar] [CrossRef] - McCuen, R.H. Approach to confidence interval estimation for curve numbers. J. Hydrol.
**2002**, 1, 43–48. [Google Scholar] [CrossRef] - Hawkins, R.H. Improved Prediction of Storm Runoff in Mountain Watersheds. J. Irrig. Drain. Eng. ASCE
**1973**, 99, 519–523. [Google Scholar] [CrossRef] - Zevenbergen, A.T. Runoff Curve Numbers for Rangelands from Rangeland from Landsat Data; Technical Rep, HL85-1; U.S. Department of Agriculture Research Service, Hydraulic Laboratory: Beltsville, MD, USA, 1985. [Google Scholar]
- Sneller, J.A. Computation of Runoff Curve Numbers for Rangelands from Landsat Data; Technical Rep, HL85-2; U.S. Department of Agriculture Research Service, Hydraulic Laboratory: Beltsville, MD, USA, 1985. [Google Scholar]
- Hawkins, R.H. Asymptotic determination of runoff curve numbers from data. J. Irrig. Drain. Eng. ASCE
**1993**, 119, 334–345. [Google Scholar] [CrossRef] - Van Mullen, J.A. Runoff and peak discharges using Green–Ampt infiltration model. J. Hydr. Eng. Div.-ASCE
**1991**, 117, 354–370. [Google Scholar] [CrossRef] - Official Website Forestry Department of Peninsular Malaysia. Available online: https://www.forestry.gov.my/en/2016-06-07-02-53-46/2016-06-07-03-12-29 (accessed on 15 October 2021).
- Cheng, M.H. The Impact of Ethnicity on the Regional Economic Development in Malaysia. Master’s Thesis, Hiroshima University, Hiroshima, Japan, 2011. [Google Scholar]
- Department of Statistics, Malaysia. General Report of the Population Census of Malaysia 1970; Department of Statistics, Malaysia: Kuala Lumpur, Malaysia, 1977; Volume 1. [Google Scholar]
- Department of Statistics, Malaysia. General Report of the Population Census of Malaysia 1991; Department of Statistics, Malaysia: Kuala Lumpur, Malaysia, 1995; Volume 1. [Google Scholar]
- Department of Statistics, Malaysia. Preliminary Count Report for Urban and Rural Areas; Department of Statistics, Malaysia: Kuala Lumpur, Malaysia, 2001. [Google Scholar]
- Department of Statistics, Malaysia. Population Distribution and Basic Demographic Characteristics; Department of Statistics, Malaysia: Kuala Lumpur, Malaysia, 2001. [Google Scholar]
- Department of Statistics, Malaysia. Migration Survey Report Malaysia 2007; Department of Statistics, Malaysia: Putrajaya, Malaysia, 2009. [Google Scholar]
- Department of Statistics, Malaysia. Preliminary Count Report 2010; Department of Statistics, Malaysia: Putrajaya, Putrajaya, Malaysia, 2010. [Google Scholar]
- Department of Statistics, Malaysia. Population Distribution by Local Authority Areas and Mukims; Department of Statistics, Malaysia: Putrajaya, Malaysia, 2011. [Google Scholar]
- Iya, S.G.D.; Gasim, M.B.; Toriman, M.E.; Abdullahi, M.G. Floods in Malaysia: Historical reviews, causes, effects and mitigations approach. Int. J. Interdiscip. Res. Innov.
**2014**, 2, 59–65. [Google Scholar] - Malaysia Flood List. Available online: https://floodlist.com/tag/malaysia (accessed on 18 October 2021).
- When Forest can no Longer Prevent Floods. Available online: https://themalaysianreserve.com/2021/01/11/when-forest-can-no-longer-prevent-floods/ (accessed on 18 October 2021).
- Deforestation the Cause of Flood. Available online: https://www.thestar.com.my/opinion/letters/2014/11/08/deforestation-the-cause-of-flood (accessed on 18 October 2021).
- Deforestation Likely Behind Deadly Sabah Mudslide, Says Expert. Available online: https://www.nst.com.my/news/2017/04/227539/deforestation-likely-behind-deadly-sabah-mudslide-says-expert (accessed on 18 October 2021).
- Akter, A.; Mohd Noor, M.J.M.; Goto, M.; Khanam, S.; Parvez, A.; Rasheduzzaman, M. Landslide disaster in Malaysia: An overview. Int. J. Innov. Res.
**2019**, 8, 292–302. [Google Scholar] [CrossRef] - Majid, N.A.; Taha, M.R.; Selamat, S.N. Historical landslide events in Malaysia 1993–2019. Indian J. Sci. Technol.
**2020**, 13, 3387–3399. [Google Scholar] [CrossRef]

**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