ASSESSING THE HYDROLOGIC EFFECTS OF URBANIZATION THROUGH NUMERICAL MODELING USING SWAT: A CASE STUDY OF LAGUNA DE BAY BASIN
Laguna de Bay, having a surface area of about 900 km<sup>2</sup> is the largest freshwater lake in the Philippines, and is the most important water body in Metro Manila with its variety of uses ranging from aquaculture, irrigation, water supply and flood control. Due to its available res...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Copernicus Publications
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/25cd5b39611d42b8bd7e1396f7f15d2c |
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| Sumario: | Laguna de Bay, having a surface area of about 900 km<sup>2</sup> is the largest freshwater lake in the Philippines, and is the most important water body in Metro Manila with its variety of uses ranging from aquaculture, irrigation, water supply and flood control. Due to its available resources and strategic location, over extraction, land conversion, and urbanization, have resulted in massive changes in the lake's watershed. The objective of this study is to simulate the impact of land cover change, particularly urbanization, on the hydrology of Laguna de Bay watershed. By hypothetically converting brushland to urban areas and using ArcSWAT to simulate the effects of urbanization, discharges and water balances were assessed. The long-term hydrologic simulations showed an annual increase of 20.6 m<sup>3</sup>/s (68%) in surface runoff and a 12.8 m<sup>3</sup>/s (26%) decrease in groundwater recharge for the entire watershed as urban areas increase. The mean seasonal flows were 75. m<sup>3</sup>/s during the dry season and 149.4 m<sup>3</sup>/s during the wet season for the original land cover, and 70.2 m<sup>3</sup>/s and 154.1 m<sup>3</sup>/s for the urbanized land cover, during the dry and wet seasons, respectively. Water percolating into the aquifers beneath the ground were also lessened by 13.6 m<sup>3</sup>/s (23%). The calibration of Marikina subbasin resulted to a satisfactory percent bias (PBIAS), Nash-Sutcliffe (NSE), and the ratio of the root-mean-square error to the standard deviation of measured data (RSR). Other subbasins resulted in a relatively lower performance rating due to limited available monitoring stations within the basin. |
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