Assessing the Influence of Land Use and Land Cover (LULC) Changes on Hydrological Response of the Timah-Tasoh Reservoir

Changing patterns in land use and land cover (LULC) is potentially due to development pressure. Due to the increase in LULC change, it alters natural ecosystems and changing the composition of the natural ecosystem services. This study provides evidence to support the hypothesis that LULC can influence both inflow and discharge. The objectives of the study were to measure the impact of LULC change on inflow of the Timah-Tasoh reservoir. The study area land cover was reclassified into five categories which included agriculture, built-up areas, forest, vacant land, and water bodies. Based on the effect of LULC on hydrological response, it is observed that the catchment is not sensitive to changes in LULC. Two mains of LULC scenarios were used including deforestation and afforestation. With an increase in CN to 89.26 (deforestation), the inflow rate increases by 2% (342.20 m3/s) compared to the present inflow rate. Meanwhile, the inflow rate will decrease to 0.21% (334.80 m3/s) if the CN value is 80.02 (afforestation). Thus, by using this study’s results, a better method and parameters compatible with another hydrological modelling can be developed for future study.


Introduction
Changes in land use are a consequence of human actions in the natural environment and are related to economic, population, technological and environmental changes.The impact of LULC alteration within a catchment depends on the extent of human modifications to the natural land cover [1].Unfortunately, the increased land use changes already impact the water quality and quantity.As the world's population, urbanisation and climate change continue to grow, it is expected to become increasingly limited.According to [2], the relationship between land use change and water quality and quantity has been analyses.Increasing city demand, changes in dietary habits, marketing arrangements, political influence and labour issues will change water consumption.Urbanisation is the movement of people from rural areas to cities and towns, which allows them to grow.This generally contributes to the growth of commercial, social, and residential properties.Due to urbanisation, flash floods can occur due to increased water rainfall, and the reservoir cannot accommodate the amount of rainwater that falls [3].These efforts eventually cause urbanisation difficulties.Even though it is crucial in hydrologic design, the overall influence of urbanisation on how flows of different frequencies could change over time is not clearly known.Furthermore, uncontrolled development and drainage management issues may also increase peak discharge during widespread deforestation.However, no study has been performed on the effects over the catchment of the Timah-Tasoh reservoir.
By increasing impervious surfaces, urbanisation can alter hydrological regimes in watersheds.Impervious surface areas increase due to the replacement of agricultural and forest land.As the impermeable surfaces increase, a watershed's hydrology becomes more dynamic and unstable [4].Thus, estimating reservoir inflow is critical for balancing water resource development and economic development.Inflow observation data cannot be obtained directly in many reservoirs due to a lack of hydrological measurement stations, particularly in areas where hydrological measurement has been underdeveloped [5].Due to urbanisation, there have been a lot of natural disasters, especially those related to the catchment area, which has impacted LULC, especially in floods.A lack of awareness of the subsystems of a watershed might result in watershed management techniques that are unsuccessful and unsustainable [6].
This paper aims to measure the impact of LULC change on the inflow of the Timah-Tasoh reservoir based on the future scenarios.In following sections, the data and materials are presented, followed by results, discussions and conclusion.

Description of study area
The study area focus of this study is the upper part of the sub-catchments in the Timah-Tasoh reservoir as shown in Figures 1a and 1b.Timah-Tasoh reservoir is the largest reservoir in Perlis, Malaysia which lies approximately 13 km 2 north of Kangar town near the Thailand border.With an average surface of 13.33 km 2 and a storage capacity of 40 million m 3 , the reservoir has a storage capacity.Timah-Tasoh reservoir is used for flood mitigation in addition to its other functions, which include water supply, irrigation, and recreational activities.Water resources in a catchment can be impacted by changes such as climate change, LULC and socio-economics factors.In fact, the flow discharge can change to greater and lower due to the changes in LULC.With the increasing urbanisation, the inflow can be increase, while the afforestation decreases inflow of a catchment [7].Most LULC changes can be attributed to a decision that was taken by farmers or farm managers.In addition, the area surrounding the reservoir and its upper catchment includes mainly agriculture, with plantation of rubber, paddy, sugar cane and others which also can affect the inflow discharge [8].

Digital Elevation Model (DEM) and land use classification
The DEM is an important component in determining the topography of the catchment area, as shown in Figure 2a.The data was obtained by downloading it from the website of the USGS (https://earthexplorer.usgs.gov/).For the sub-catchments in this study area, a 30 m resolution DEM was used.The delineation procedure was used for the Timah-Tasoh reservoir, which was then split into several sub-catchments for more precise analysis.Furthermore, the modification of study area was modified by using ArcGIS.
In a study by [8], the land surrounding the reservoir and its upstream catchments is primarily agricultural, with plantations of rubber, paddy, sugar cane, and timber being the most common crops grown.Moreover, due to the fact that land clearing activities in Padang Besar, Perlis have an impact on the water discharge of the Tasoh River, it was determined that urbanisation and some infrastructural development were observed to take place in the catchment area [9].
Land cover sets for 2000 to 2021 were downloaded from USGS and obtained from the Landsat 8 satellite image.Landsat satellite image dates were chosen based on image quality, especially with little or low cloud cover.The two primary methods for classifying data obtained through remote sensing are unsupervised and supervised classifications.In this study, supervised classification was performed, and existing topographical maps were utilised to ease this process.Furthermore, in previous research by [10], supervised classification is the most common method for quantitatively analysing remote sensing picture data.Figure 2b shows that the study area land cover was reclassified into five categories which included agriculture, built-up areas, forest, vacant land (natural), and water bodies.

Land use and land cover change scenario
Agriculture, built-up areas, forest, vacant land and water bodies were the five categories considered when analysing the land cover.For future scenarios, it is divided into two scenarios.Scenarios 1 and 2 are about deforestation and afforestation, named low, medium and high, respectively.Scenario 1 is also assumed to convert forests to built-up and agriculture, considered deforestation and classified into Extreme 1, Extreme 2, and Extreme 3.Each CN was estimated by decreasing or increasing the area of land use.Table 1 shows the future land use scenarios modified from [11].

Estimation of peak discharge
This study uses the HEC-HMS model to assess the peak discharge corresponding to the LULC scenarios.
The model is well-established and widely used to simulate the hydrological response [12].The development of the HEC-HMS model for the catchments can be referred to [13].

SCS-CN calculation
The SCS CN was determined by using the land use category.The current analysis used the 2001 until 2010 land use (calibration), and no change was made to any land use types except for agriculture, builtup and forest.According to Table 2, a new CN was generated for each scenario.
In the future scenario, deforestation increased the CN value of all sub-catchments, most noticeably in Extreme 3, then in Extreme 1 and finally in Extreme 2. Therefore, when forests were transformed into agriculture and built-up (Extreme 1), the CN was higher than when the forests were only turned into agricultural (Extreme 2).The results indicated that, among the three most severe scenarios, the conversion of forest to built-up generates the highest CN (Extreme 3) compared to the combination of agricultural and built-up.
In this study, afforestation has been proposed as one of the future scenarios because afforestation has been shown to lessen the severity of natural disasters like floods, landslides and drought on human populations through enhancing hydrological processes like infiltration can improve hydrological processes such as infiltration [14].This scenario describes that the CN values decrease than other scenarios.Averagely, high afforestation has the lowest CN value (80.02) with a 60% forest increase, 40% reduction in agriculture and 20% built-up land, while the CN value for medium and low afforestation were 80.34 and 80.71, respectively.These CN values for each scenario suggest that deforestation will lead to higher discharges, while afforestation will lead to lower discharges compared to the present study.

LULC map based on scenario conditions
The distribution of land use scenarios area is shown in Figure 3.The area of the different land use scenarios was analysed by using ArcGIS.Land use in the study area shows a significant change for each future scenario.The present areas for the forest, agriculture and built-up lands were 85.13 km 2 , 61.22 km 2 and 25.13 km 2 , respectively.According to the deforestation, the forest area decreased to 68.07 km 2 , 46.81 km 2 and 31.79 km 2 for low, medium and high.For agriculture, the area is slightly increased for the scenario while the build-up land was constant as the previous for the low and increased for the medium (34.73 km 2 ) and high (42.92km 2 ).The conversion of forest into deforestation was the most notable change when the forest was 0.0 km 2 for this future scenario.For Extreme 1 and Extreme 2, the agriculture area is higher than Extreme 3, while the built-up area for Extreme 3 is higher than Extremes 1 and 2 due to the conversion of forest to built-up land.
For the afforestation, For the afforestation scenario, forest dominated the land use, with the area accounting for 97.67 km 2 to 114.27 km 2 .The result is in good agreement with [15] also stated that forest is the highest area exceeding 70% of the total for land use change.Agriculture was reduced from 61.22 km 2 for 48.68 km 2 to 36.26 km 2 , while built-up was decreased from 25.13 km 2 to 20.95 km 2 .

Impacts of LULC on discharge
The results related to discharges for the different scenarios are shown in Table 3 for deforestation and afforestation, respectively.Based on the result, and as expected, the peak discharge decreases with the weighted CN value for afforestation while the rest of the scenarios increase.By referring to changes in discharges for deforestation for medium, the peak discharge are 209.20 m 3 /s, 13.70 m 3 /s, 7.00 m 3 /s and 108.2 m 3 /s for J13, J14, J15 and J16, respectively.While in high, as indicated by the highest CN, on average, for this scenario (85.19), the peak discharges are the same as the medium.It probably has no change due to insignificant changes.Each discharge is increased between 1.20 m 3 /s to 1.60 m 3 /s for J13 and J16.However, for the low, peak discharge in J13 and J16 change from 207.8 m 3 /s to 206.50 m 3 /s and 107.0 m 3 /s to 108.10 m 3 /s with the 1.30 m 3 /s decrease and 1.10 m 3 /s increase, respectively.While the other junctions did not changes and were the same as the present study peak discharges.
For deforestation scenario, it can be seen that Extreme 3 has the highest discharge compared to the other scenarios, on total (342.20 m 3 /s), especially in J13 (210.60 m 3 /s) and (110.90 m 3 /s) in J16 compared to the present study.The discharge during these junctions increased from 2.80 m 3 /s to 3.90 m 3 /s.Based on the result, the biggest factor in the rise in discharges was transforming 100% of forested land into completely urbanised land.There will likely be less infiltration due to the combination of less forest cover areas and an increase in the proportion of impermeable land, which is a significant contributor to an increase in discharge [16].Additionally, discharge in Extreme 1 (341.80m 3 /s), which converts the forest into 50% agriculture and 50% built-up, shows a greater increase compared to Extreme 2 (340.90 m 3 /s), which converts the forest to 100% agriculture.Consequently, comparable results were observed in this study, indicating that upstream deforestation may lead to higher peak discharge [17].
In comparison with the present study and afforestation scenario, the discharge decreased for each low (335.20 m 3 /s), medium (335.0 m 3 /s) and high (334.90m 3 /s) between 0.80 m 3 /s to 1.40 m 3 /s.From this result, it was observed the larger decrease in discharge for high in J16 is 107.80 m 3 /s.In contrast, it indicates that afforestation changes in land cover lead to a continued lessening of discharge.The expansion of the forest brought the most significant reduction in peak discharge.[18] also found a similar trend for a decrease in discharge for afforestation which is lower in CN value.
However, in this study, the peak discharge of each scenario of J14 (13.70 m 3 /s) and J15 (7.0 m 3 /s) did not experience any immediate change and was maintained as the peak discharge for the present study.It may be because each J14 and J15 are only linked to one sub-catchment, B13 and B16, respectively.The area of B13 is 6.81 km 2 while B16 is 3.39 km 2 , which is considered a small catchment compared to the J16 and J13, in which both junctions are connected to several large catchments with a big area (Figure 1b).Therefore, there are significant increases and decreases for each junction in J14 and J15.

Conclusion
In conclusion, deforestation and afforestation have different effects on the inflow simulation of the reservoir.Deforestation increases the CN value for all sub-catchments, with the greatest of CN being produced when forests are converted to built-up areas.When forests are cleared to make way for agriculture and built-up development, infiltration rates decrease, and peak discharge rates increase.Still, the opposite is true when forests are replanted and the peak discharge increase.However, in this study, LULC is observed to be less sensitive to the changes in the catchment processes.With an increase in CN to 89.26 (deforestation), the inflow rate increased by 2% (342.20 m 3 /s) compared to the the present inflow rate.Meanwhile, the inflow rate will decrease by 0.21% (334.80 m 3 /s) if the CN value is 80.02 (afforestation).Thus, it can be concluded that afforestation reduces the inflow rate, while deforestation increases the rate.Therefore, using this study's results, a better method and parameters compatible with another hydrological modelling can be developed for future study.

Figure 1 .
Figure 1.a) Sub-catchments in the upper catchment and b) basin model for the Timah-Tasoh reservoir

Figure 2 .
Figure 2. a) DEM for Perlis sub-catchment and b) land use classification for study area

Table 2 .
SCS-CN for land use scenario for each sub-catchment

Table 3 .
Peak discharge for each scenarios (all values in m 3 /s)