ASSESSING THE EFFECTS OF LAND USE CONVERSION ON RUNOFF AND NUTRIENT LOAD
DOI:
https://doi.org/10.11113/mjce.v30.16069Keywords:
Nutrient, agricultural land, total nitrogen (TN), total phosphorus (TP), HSPF.Abstract
An agricultural land with intensive cultivation, large catchments with extended rivers and agricultural population of high-density are the primary reasons for higher pollutant loads in freshwater. However, there are problems in pursuing nutrient losses since several parameters, such as variability in soils and climate are associated with heavy rainfall, especially in tropic regions; plant management, limited resources, and insufficient technical support are not consistent in every crop management. Changes in agricultural practices and unmonitored point sources discharge from watershed, have led to algal bloom in abundance, and thus generated eutrophication at the downstream. The complex watershed processes and forecasting the effects of land use change on water quality can be determined by using tools of watershed models. The Hydrological Simulation Programme-FORTRAN (HSPF) uses lumped parameters, continuous model to predict the long-term evaluation, and deterministic for simulating the water quality and quantity process that occur at the watershed. Pervious land segments (PERLND), impervious land segments (IMPLND), and channel reach (RCHRES) modules were used to determine the general water quality and quantity on Johor watershed. Based on calibration and validation, the HSPF model was capable of simulating different runoff seasons. An increment of 60% in agricultural land had increased the annual mean total phosphorus (TP) load and total nitrogen (TN) load by 3.82% and 5.34%, respectively. A 2-fold increase in agricultural land would result in an approximately 2-fold increase in the quantity of annual TN and TP loads. Between TN and TP loads, TP load has potentially increased more than TN load during the dry, wet, and base-flow years. Upon the long-term of water quality and quantity simulation, this study provides essential knowledge for a method-based runoff and nutrient management plan for the Johor watershed.References
Amirreza, S., Haw, Y., Kathleen M.B. Boomer., Latif Kalin., Xuyong Li., and Donald E.Weller. (2017). Using multiple watershed models to assess the water quality impacts of alternate land development scenarios for a small community. Catena 150 (2017) 87–99.
Ali Najah., Ahmed Elshafie., Othman A. Karim., and Othman Jaffar. (2009). Prediction of Johor River Water Quality Parameters Using Artificial Neural Networks. European Journal of Scientific Research. Vol.28 No.3 (2009), 422-435.
Bicknell,B.R., Imnoff JC, Jr., Jobes TH., and Donigian AS. (2001). Hydrological Simulation Program-FORTRAN (HSPF) version 12, User’s Manual. Prepared for AQUA TERRA Consultants Mountain View, Califronia in cooperation with Water Resources Discipline, U.S. Geological Survey Reston, Virginia, USA.
Borah and M. Bera. (2004). Watershed−Scale Hydrologic and Nonpoint−Source Pollution Models: Review Of Applications. Vol. 47(3): 789−803 2004. Transactions of the ASAE, American Society of Agricultural Engineers ISSN 0001−2351.
Box GEP and Jenkins GM. (1970). Time series analysis, forecasting and control. San Francisco, CA: Holden-Bay Inc.
Chibanga R., Berlamont J., and Vandewalle J. (2001). Artificial neural networks in hydrological watershed modelling: surface flow contribution from the ungaged parts of a catchment. In: Proceedings of the 13th International Conference on Tools with Artificial Intelligence, 7-9 November 2001, Dallas, Texas, USA.
Chen.M, Boyle EA., Lee J-M., Nurhati I, Zurbrick C., Switzer AD., and Carrasco G. (2016). Lead isotope exchange between dissolved and fluvial particulate matter: a laboratory study from the Johor River estuary. Phil. Trans. R. Soc. A 374: 20160054.
Eugene Ng, Y.J., Yap, C.K., Zakaria, M.P., and Tan, S.G. (2013). Assessment of heavy metal pollution in the straits of johore by using transplanted caged mussel, Perna viridis. Pertanika J. Sci. Technol. 21, 75–96.
Hui Xie., Lei Chen., and Zhenyao Shen. (2015). Assessment of Agricultural Best Management Practices Using Models: Current Issues and Future Perspectives. Water 2015, 7, 1088-1108; doi:10.3390/w7031088.
Melone F., Barbetta S, Diomede T.(2005). Review and selection of hydrological models – Integration of hydrological models and meteorological inputs. Contract No. 12. Gennaio, Italy.
Mou Leong Tan., Darren L. Ficklin., Ab Latif Ibrahim., and Zulkifli Yusop. (2014a). Impacts and uncertainties of climate change on streamflow of the Johor River Basin, Malaysia using a CMIP5 General Circulation Model ensemble. Journal of Water and Climate Change. IWA Publishing.676-695.
Mou Leong Tan., Ab Latif Ibrahim., Zulkifli Yusop., Zheng Duan., and Lloyd Ling. (2014b). Impacts of land-use and climate variability on hydrological components in the Johor River basin, Malaysia, Hydrological Sciences Journal, DOI:10.1080/02626667.2014.967246.
Nathan William Johnson (2005). ArcGIS and HSPF Model Development. Master of Science in Engineering thesis. The University of Texas at Austin. August 2005.
Nguyen Hong Quan and Günter Meon (2015). Nutrient Dynamics During Flood Events in Tropical Catchments: A Case Study in Southern Vietnam. Clean – Soil, Air, Water 2015, 43 (5), 652–661.
Oogathoo S. (2006). Runoff simulation in the Canagagigue Creek watershed using the MIKE SHE model (Master of Science). Montreal, Canada: McGill University.
Ouyang, Y., Leininger, T.D., and Moran, M. (2013). Impacts of reforestation upon sediment load and water outflow in the Lower Yazoo River Watershed, Mississippi. Ecol. Eng.61, 394-406.
Ouyang, Y., Leininger, T.D., and Moran, M. (2015). Estimating effects of reforestation on nitrogen and phosphorus load reductions in the Lower Yazoo River Watershed, Mississippi. Ecol. Eng.61, 449-456.
Philips, J.R. (1957). The Theory of Infiltration: The Infiltration Equation and Its Solution, Soil Science, 83:345-375.
P.B. Duda., P.R. Hummel., A.S. Donigian Jr., and J.C Imhoff. (2012). BASINS/HSPF: Model use, calibration, and validation. American Society of Agricultural and Biological Engineers Vol. 55(4): 1523-1547 Razi, M.A.M., Ariffin, J., Tahir, W and Arish, N.A.M (2010). Flood Estimation Studies using Hydrologic Modeling System (HEC-HMS) for Johor River, Malaysia. Journal of Applied Sciences, 10. 930-939
Saleh and Du (2004). Evaluation of SWAT and HSPF within basins program for the Upper North Bosque River watershed in Central Texas. American Society of Agricultural Engineers ISSN 0001−2351. Vol. 47(4): 1039−1049.
Syed I. Ahmed., Amanjot Singh., Ramesh Rudra., and Bahram Gharabaghi. (2014). Comparison of CANWET and HSPF for water budget and water quality modelling in rural Ontario. Water Quality Research Journal of Canada. 49.1.53-71
USEPA (2000). BASINS Technical Note 6: Estimating hydrology and hydraulic parameters for HSPF. EPA-823-R-00-012, July 2000. Valizadeh, N., Ahmed, E., Majid, M., Hadi, G., Muhammad, M., and Othman, J. (2014). Accuracy Enhancement for Forecasting Water Levels of Reservoirs and River Streams Using a Multiple-Input-Pattern Fuzzification Approach. The Scientific World Journal, 9
WMO. (1994) Guide to hydrological practices. WMO No. 168. 5th Ed.World Meteorological Organization, Geneva.
Yurekli K., and Kurunc A. (2005). Testing the residuals of an ARIMA model on the Cekerek stream watershed in Turkey. Turkish J Eng Environ Sci; 29: 61-74. Yin, Y., Jiang, S., Pers, C., Yang, X., Liu, Q., Yuan, J., and Zheng, Z. (2016). Assessment of the Spatial and Temporal Variations of Water Quality for Agricultural Lands with Crop Rotation in China by Using a HYPE Model. International Journal of Environmental Research and Public Health, 13(3), 336.
