Modeling the Impact of Watershed Physical Attributes on Surface Water Quality and Uncertainty Assessment Using the Monte Carlo Simulation Approach

Document Type : Original Article

Authors

1 N, Department of Environmental Science, Faculty of Natural Resources, University of Tehran, Karaj, Iran

2 Department of Environmental Science, Faculty of Natural Resources, University of Tehran, Karaj, Iran

3 Department of Watershed Science and Management, Faculty of Natural Resources, University of Tehran, Karaj, Iraد

Abstract

Modeling of the relationship between physical characteristics of a catchment and water quality parameters plays a significant role in unified watershed management. In addition, in the process of modeling there is always an inevitable level of uncertainty caused by error in the input data, parameters and the structure of the model. Therefore, quantifying the uncertainty in the output of the model in order to reach certain forecasts in modeling is essential. In this research, water quality data, land use/cover, land suitability map and geological map of hydrometric stations located in the western part of the Caspian Sea were used. The modeling was done using Multiple Linear Regression Stepwise Method, while the model uncertainty analysis was examined using Monte Carlo simulation. Based on the results of the correlation and regression analyses, it could be concluded that all these parameters have a strong positive correlation with a variety of human activities such as agriculture and urban development. This indicated that an increase in agricultural land or a decline in forest areas will ultimately lead to a decline in water quality. The results of the Monte Carlo simulation shows that although some models have high R-squared values, the possibility of generation of negative outputs (Pr

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Ahearn DS, Sheibley RW, Dahlgren RA, Anderson M, Johnson J, Tate KW (2005) Land use and land cover influence on water quality in the last free-flowing river draining the western Sierra Nevada, California. Journal of Hydrology 313(3):234-47
Amiri BJ, Nakane K (2009) Comparative prediction of stream water total nitrogen from land cover using artificial neural network and multiple linear regression. Polish Journal of Environmental Studies 18(2):151-60
Amiri BJ, Nakane K (2006) Modeling the relationship between land cover and river water quality in the Yamaguchi prefecture of Japan. Journal of Ecology and Environment 29(4):343-52
Amiri B, Sudheer K, Fohrer N (2012) Linkage between in-stream total phosphorus and land cover in Chugoku district, Japan: an ANN approach. Journal of Hydrology and Hydromechanics 60(1):33-44
Amiri BJ, Fohrer N, Cullmann J, Hörmann G, Müller F, Adamowski J (2016) Regionalization of tank model using landscape metrics of catchments. Water Resources Management 30(14):5065-5085
Arheimer B, Liden R (2000) Nitrogen and phosphorus concentrations from agricultural catchments-influence of spatial and temporal variables. Journal of Hydrology 227(1-4):140-159
Atkinson SF, Johnson DR, Venables BJ, Slye JL, Kennedy JR, Dyer SD, Price BB, Ciarlo M, Stanton K, Sanderson H, Nielsen A (2009) Use of watershed factors to predict consumer surfactant risk, water quality, and habitat quality in the upper Trinity River, Texas. Science of the Total Environment 407(13):4028-4037
Baker A (2005) Land use and water quality. Encyclopedia of Hydrological Sciences, 3456p
Beven K, Binley A (1992) The future of distributed models: model calibration and uncertainty prediction. Hydrological Processes 6(3):279-98
Bozorg Haddad O, Seifollahi-Aghmiuni S (2013) An introduction to uncertainty analysis in water resources systems. University of Tehran, 217p (in Persian)
Burgman M (2005) Risks and decisions for conservation and environmental management. Cambridge University Press, 504p
Chessman BC, Townsend SA (2010) Differing effects of catchment land use on water chemistry explain contrasting behaviour of a diatom index in tropical northern and temperate southern Australia. Ecological Indicators 10(3):620-626
Fatehi I, Amiri BJ, Alizadeh A, Adamowski J (2015) Modeling the relationship between catchment attributes and in-stream water quality. Water Resources Management 29(14):5055-5072
Haidary A, Amiri BJ, Adamowski J, Fohrer N, Nakane K (2013) Assessing the impacts of four land use types on the water quality of wetlands in Japan. Water Resources Management 27(7):2217-2229
Hartmann J, Moosdorf N, Lauerwald R, Hinderer M, West AJ (2014) Global chemical weathering and associated P-release- The role of lithology, temperature and soil properties. Chemical Geology 363:45-163
Kiesel J, Schmalz B, Fohrer N (2009) SEPAL–a simple GIS-based tool to estimate sediment pathways in lowland catchments. Advances in Geosciences 21:25-32
Kitanidis PK, Bras RL (1980) Real‐time forecasting with a conceptual hydrologic model: 2. Applications and results. Water Resources Research 16(6):1034-1044
Mirzaei M, Solgi E, Salman Mahini A (2017) Effect of land use on phosphorous, nitrogen, dissolved solids, and suspended solids concentrations and its presentation in GIS (Case study: Zayandehroud Basin). Iran-Water Resources Research 13:191-198 (In Persian)
Pang Y, Xiang S, Chu ZS, Xue LQ, Ye BB (2015) Relationship between agricultural land and water quality of inflow river in Erhai lake basin. Huan Jing Ke Xue=Huanjing Kexue 36(11):4005-4012
Parris K (2011) Impact of agriculture on water pollution in OECD countries: Recent trends and future prospects. International Journal of Water Resources Development 27(1):33-52
Power M, McCarty LS (2006) Environmental risk management decision-making in a societal context. Human and Ecological Risk Assessment 12(1):18-27
Sangani MH, Amiri BJ, Shabani AA, Sakieh Y, Ashrafi S (2015) Modeling relationships between catchment attributes and river water quality in southern catchments of the Caspian Sea. Environmental Science and Pollution Research 22(7):4985-5002
Shafiei M, Ghahraman B, Saghafian B, Davary K, Vazifedoust M (2014) Calibration and uncertainty assessment of SWAP model using GLUE. Water Research on Agriculture 28(25):909-917 (In Persian)
Solgi E, Sheikhzadeh H (2016) Technical note; study of water quality of aras river using physico-chemical variables. Iran-Water Resources Research 12:207-213 (In Persian)
Yang X, Jin W (2010) GIS-based spatial regression and prediction of water quality in river networks: a case study in Iowa. Journal of Environmental Management 91(10):1943-1951
Zampella RA, Procopio NA, Lathrop RG, Dow CL (2007) Relationship of land‐use/land‐cover patterns and surface‐water quality in the Mullica River Basin. JAWRA Journal of the American Water Resources Association 43(3):594-604