Quantifying Spatiotemporal Change in Landuse and Land Cover and Accessing Water Quality: A Case Study of Missouri Watershed James Sub-Region, North Dakota
Spatial causal effects on water quality are essential in identification of vulnerable watersheds. Modelling landuse variables is an effective method of projecting localized impairment. This study presents an integrated index, designed to gauge the ability of an 8-digit Hydrologic Unit Code watershed in its ability to produce clean water. This index, IAPCW, can be successfully applied on a geospatial platform. In this study we utilized IAPCW to address forest cover dynamics of an impaired watershed, that is, Missouri Watershed James Sub-region in North Dakota. Specific parametric functions were analysed and combined within a customized GIS interface to provide a multi-faceted structured technique to derive IAPCW. These included ambient forest cover, housing density, agricultural land, soil erodibility and road density; it can be lucidly ascertained that where a prevailing forest cover undergoes conversion processes, the secondary effect may spur an exponential increase in water treatment costs. These parameters when projected statistically validated temporal and spatial relations of landuse/land cover dynamics to nutrient concentrations especially those that would be noted at the mouth of the watershed. In this study, we found that the levels of Total Dissolved Solids (TDS) were much higher for the years 2014 to 2016 with a discernible increased alkalizing effect within the watershed. When IAPCW was compared to Annualized Agricultural Nonpoint Source (AnnAGNPS), the spatial distribution generated by the AnnAGNPS study showed that fallow areas produced significant amounts of sediment loads from the sub-watershed. These same locations in this study generated a low IAPCW.
References
[1]
Mohammadi, M., Sheikh, V. and Saddodin, A. (2013) Development and Application of the GFHM Distributed Hydrologic Model for Flood Hydrograph Modelling. Case Study: The Jafarabad Watershed, Golestan Province, Iran.
[2]
Sekar, I. and Randhir, T.O. (2007) Spatial Assessment of Conjunctive Water Harvesting Potentialin Watershed Systems. Journal of Hydrology, 334, 39-52.
https:/doi.org/10.1016/j.jhydrol.2006.09.024
[3]
Pelorosso, R., Leone, A. and Boccia, L. (2009) Land cover and Land Use Change in the Italian Central Apennines: A Comparison of Assessment Methods. Applied Geography, 29, 35-48. https:/doi.org/10.1016/j.apgeog.2008.07.003
[4]
Jansen, L.J. and Di Gregorio, A. (2002) Parametric land Cover and Land-Use Classifications as Tools for Environmental Change Detection. Agriculture, Ecosystems & Environment, 91, 89-100. https:/doi.org/10.1016/S0167-8809(01)00243-2
[5]
Bender, O., Boehmer, H.J., Jens, D. and Schumacher, K.P. (2005) Analysis of Land-Use Change in Asector of Upper Franconia (Bavaria, Germany) since 1850 Using Land Register Records. Landscape Ecology, 20, 149-163. https:/doi.org/10.1007/s10980-003-1506-7
[6]
Mendoza, M.E., Granados, E.L., Geneletti, D., Pérez-Salicrup, D.R. and Salinas, V. (2011) Analysing Land Cover and Land Use Change Processes at Watershed Level: A Multitemporal Study in the Lake Cuitzeo Watershed, Mexico (1975-2003). Applied Geography, 31, 237-250. https:/doi.org/10.1016/j.apgeog.2010.05.010
[7]
Xiao, J., Shen, Y., Ge, J., Tateishi, R., Tang, C., Liang, Y. and Huang, Z. (2006) Evaluating Urban Expansion and Land Use Change in Shijiazhuang, China, by Using GIS and Remote Sensing. Landscape and Urban Planning, 75, 69-80.
https:/doi.org/10.1016/j.landurbplan.2004.12.005
[8]
Turner, M.G., O’Neill, R.V., Gardner, R.H. and Milne, B.T. (1989) Effects of Changing Spatial Scale on the Analysis of Landscape Pattern. Landscape Ecology, 3, 153-162.
https:/doi.org/10.1007/BF00131534
[9]
Jensen, J.R. (2009) Remote Sensing of the Environment: An Earth Resource Perspective, 2nd Edition, Pearson Education India, New Delhi, 613 p.
[10]
Madurapperuma, B., Rozario, P., Oduor, P. and Kotchman, L. (2015) Land-Use and Land-Cover Change Detection in Pipestem Creek Watershed, North Dakota. International Journal of Geomatics and Geosciences, 5, 416-426.
[11]
Osborne, L.L. and Wiley, M.J. (1988) Empirical Relationships between Land Use/Cover and Stream Water Quality in an Agricultural Watershed. Journal of Environmental Management, 26, 9-27.
[12]
Soranno, P.A., Hubler, S.L., Carpenter, S.R. and Lathrop, R.C. (1996) Phosphorus Loads to Surface Waters: A Simple Model to Account for spatial Pattern of Land Use. Ecological Applications, 6, 865-878. https:/doi.org/10.2307/2269490
[13]
Leh, M., Bajwa, S. and Chaubey, I. (2013) Impact of Land Use Change on Erosion Risk: An Integrated Remote Sensing, Geographic Information System and Modeling Methodology. Land Degradation & Development, 24, 409-421. https:/doi.org/10.1002/ldr.1137
[14]
Chin, A. (2006) Urban Transformation of River Landscapes in a Global Context. Geomorphology, 79, 460-487. https:/doi.org/10.1016/j.geomorph.2006.06.033
[15]
Fei, Z., Tiyip, T., Kung, H. and Jianli, D. (2010) The Change of Land Use/Cover and Characteristics of Landscape Pattern in Arid Areas Oasis: An Application in Jinghe, Xinjiang. Geo-Spatial Information Science, 13, 174-185. https:/doi.org/10.1007/s11806-010-0322-x
[16]
Iqbal, M.F. and Khan, I.A. (2014) Spatiotemporal Land Use Land Cover Change Analysis and Erosion Risk Mapping of Azad Jammu and Kashmir, Pakistan. The Egyptian Journal of Remote Sensing and Space Science, 17, 209-229. https:/doi.org/10.1016/j.ejrs.2014.09.004
[17]
Ceballos-Silva, A. and Lopez-Blanco, J. (2003) Delineation of Suitable Areas for Crops Using a Multi-Criteria Evaluation Approach and Land Use/Cover Mapping: A Case Study in Central Mexico. Agricultural Systems, 77, 117-136.
https:/doi.org/10.1016/S0308-521X(02)00103-8
[18]
Kollias, V.J. and Kalivas, D.P. (1998) The Enhancement of a Commercial Geographical Information System (ARC/INFO) with Fuzzy Processing Capabilities for the Evaluation of Land Resources. Computers and Electronics in Agriculture, 20, 79-95.
https:/doi.org/10.1016/S0168-1699(98)00010-6
[19]
Chen, Y.C., Lien, H.P. and Tzeng, G.H. (2010) Measures and Evaluation for Environment Watershed Plans Using a Novel Hybrid MCDM Model. Expert Systems with Applications, 37, 926-938. https:/doi.org/10.1016/j.eswa.2009.04.068
[20]
Davis, M.L. and Masten, S.J. (2004) Principles of Environmental Engineering and Science. McGraw-Hill, New York, 704.
[21]
Ritter, W.F. and Shirmohammadi, A. (Eds.) (2000) Agricultural Nonpoint Source Pollution: Watershed Management and Hydrology. CRC Press, Boca Raton.
[22]
NRCS (2007) SSURGO Data Layers.
http://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm
[23]
Anderson, J.R. (1976) A Land Use and Land Cover Classification System for Use with Remote Sensor Data. Vol. 964, US Government Printing Office.
[24]
Barnes, M., Todd, A., Lilja, R.W. and Barten, P. (2009) Forests, Water and People: Drinking Water Supply and Forest Lands in the Northeast and Midwest United States.
[25]
Station, M.A.E. (1998) Recommended Chemical Soil Test Procedures for the North Central Region. North Central Regional Research Publication No. 221.
[26]
Pfaff, J.D. (1993) Method 300.0 Determination of Inorganic Anions by Ion Chromatography. US Environmental Protection Agency, Office of Research and Development, Environmental Monitoring Systems Laboratory.
[27]
Martin, T.D., Brockhoff, C.A., Creed, J.T. and Long, S.E. (1992) Determination of Metals and Trace Elements in Water and Wastes by Inductively Coupled Plasma-Atomic Emission Spectrometry. In: Smoley, KC., Eds., Methods for the Determination of Metals in Environmental Samples, CRC Press Inc., Boca Raton, 33-91.
[28]
American Public Health Association, American Water Works Association, Water Pollution Control Federation and Water Environment Federation (1915) Standard Methods for the Examination of Water and Wastewater. Vol. 2.
[29]
Brown, E., Skougstad, M.W. and Fishman, M.J. (1970) Methods for Collection and Analysis of Water Samples for Dissolved Minerals and Gases. No. 05-A1, US Government Printing Office.
[30]
Weidner, E. and Todd, A. (2011) From the Forest to the Faucet: Drinking Water and Forests in the US. Methods Paper, USDA Forest Service, Washington DC.
[31]
Xia, Y., Ti, C., She, D. and Yan, X. (2016) Linking River Nutrient Concentrations to Land Use and Rainfall in a Paddy Agriculture-Urban Area Gradient Watershed in Southeast China. Science of the Total Environment, 566-567, 1094-1105.
https:/doi.org/10.1016/j.scitotenv.2016.05.134
[32]
Chislock, M.F., Doster, E., Zitomer, R.A. and Wilson, A.E. (2013) Eutrophication: Causes, Consequences, and Controls in Aquatic Ecosystems. Nature Education Knowledge, 4, 10.
[33]
Carpenter, S.R., Caraco, N.F., Correll, D.L., Howarth, R.W., Sharpley, A.N. and Smith, V.H. (1998) Nonpoint Pollution of Surface Waters with Phosphorus and Nitrogen. Ecological Applications, 8, 559-568.
https://doi.org/10.1890/1051-0761(1998)008[0559:NPOSWW]2.0.CO;2
[34]
Regulations, S.D.W. (2012) Guidance for Nuisance Chemicals. EPA.gov.
[35]
DeZuane, J. (1997) Handbook of Drinking Water Quality. John Wiley & Sons, Hoboken.
[36]
Clark, M.L. and Mason, J.P. (2006) Water-Quality Characteristics, Including Sodium-Adsorption Ratios, for Four Sites in the Powder River Drainage Basin, Wyoming and Montana, Water Years 2001-2004. No. 2006-5113.
[37]
Ayers, R.S. and Westcot, D.W. (1985) Water Quality for Agriculture. Vol. 29, Food and Agriculture Organization of the United Nations, Rome.
[38]
Hosono, T., Nakano, T., Igeta, A., Tayasu, I., Tanaka, T. and Yachi, S. (2007) Impact of Fertilizer on a Small Watershed of Lake Biwa: Use of Sulfur and Strontium Isotopes in Environmental Diagnosis. Science of the Total Environment, 384, 342-354.
https:/doi.org/10.1016/j.scitotenv.2007.05.033
[39]
Ouyang, W., Jiao, W., Li, X., Giubilato, E. and Critto, A. (2016) Long-Term Agricultural Non-Point Source Pollution Loading Dynamics and Correlation with Outlet Sediment Geochemistry. Journal of Hydrology, 540, 379-385.
https:/doi.org/10.1016/j.jhydrol.2016.06.043
[40]
NRCS (2005) SSURGO Data Layers.
https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/?cid=nrcs142p2_053627
[41]
Pease, L.M., Oduor, P. and Padmanabhan, G. (2010) Estimating Sediment, Nitrogen, and Phosphorous Loads from the Pipestem Creek Watershed, North Dakota, Using AnnAGNPS. Computers & Geosciences, 36, 282-291. https:/doi.org/10.1016/j.cageo.2009.07.004
[42]
Yu, X., Bhatt, G., Duffy, C. and Shi, Y. (2013) Parameterization for Distributed Watershed Modeling Using National Data and Evolutionary Algorithm. Computers & Geosciences, 58, 80-90. https:/doi.org/10.1016/j.cageo.2013.04.025
