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Spatiotemporal Relationship Linking Land Use/Land Cover with Groundwater Level

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Groundwater

Part of the book series: Water Science and Technology Library ((WSTL,volume 76))

Abstract

Land use and land cover changes have been undergoing at a rapid rate universally. Anthropogenic activities where human intervention is involved is been considered as a major driver for land use changes. This type of advancement in land activity has caused depletion of natural resources more so groundwater table. There is a great need for assessment of groundwater profile at local level. Groundwater is maximally harnessed for many water use purposes in Dakshina Kannada district, Karnataka. This study aims to analyze and quantify the effect of land use/land cover (LU/LC) transformations along a time scale with the fluctuating groundwater levels. Groundwater level information of the year 2003 and 2013 from observation wells and satellite imagery from Landsat with ETM 7 and OLI sensors were used. Kriging, a geostatistical method of interpolation, used well data as points taken at different locations (29 wells) all over the district and created a continuous surface using interpolation with the estimate of error. LULC map of 2003 and 2013 was derived from classification of TM images using supervised, parametric maximum likelihood classifier. Area is broadly categorized into four classes, namely vegetation, urban areas, water, and other category. Accuracy assessment of this classification yielded kappa statistics of greater than 0.8 for both the images and overall accuracy greater than 90%. Further, relationship between LULC and groundwater level is inferred with the help of 1 by 1 km grid. Rainfall and stream network were used in ascertaining the sensitive areas in terms of groundwater that hold a hydrogeological importance. It was inferred from the study that the groundwater depletion to the extent of 2 m has been evident in the urban areas with an increase in built-up greater than 25 acres per 250 acres. These fluctuations are evident in the northwest and south regions of the district compared to the other areas. Moreover, these hydrogeologically sensitive areas for recharge need protection from further development activities.

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References

  • Barabas N, Goovaerts P (2004) Comparison of geostatistical algorithms for completing groundwater monitoring well timeseries using data of a nearby river. Geostat Environ Appl 199–210

    Google Scholar 

  • Burrough PA (2001) GIS and geostatistics: essential partners for spatial analysis. Environ Ecol Stat 8(4):361–377

    Article  Google Scholar 

  • Central Ground Water Board (2015). http://www.cgwb.gov.in/aboutcgwb.htmlaccessed. 30 Dec 2015

  • Dams J, Woldeamlak ST, Batelaan O (2008) Predicting land-use change and its impact on the groundwater system of the KleineNete catchment, Belgium. Hydrol Earth Syst Sci 12:1369–1385

    Article  Google Scholar 

  • Foster S, Cherlet J (2014) Global water partnership—the links between land use and groundwater. A GWP perspectives paper

    Google Scholar 

  • Goovaerts P (1999) Ordinary cokriging revisited. Math Geol 30(1):21–43

    Article  Google Scholar 

  • Goovaerts P (2008) How can geostatistics be tailored to the analysis of environmental health data? VIII Int Geostat Congr 147–156

    Google Scholar 

  • Goovearts P, Journel AG (1995) Integrating soil map information in modelling the spatial variation of continuous soil properties. Eur J Soil Sci 46:397–414

    Article  Google Scholar 

  • Goovearts P, Avery D, Alfred F, David G, Brenda G, James LE, Peter A (2008) Geostatistical modeling of the spatial distribution of soil dioxins in the vicinity of an incinerator. Theory and application to Midland, Michigan. Environ Sci Technol 30(20):3648–3654

    Article  Google Scholar 

  • Groundwater Resources (2016) NIH. http://www.nih.ernet.in/rbis/india_information/groundwater.htmaccessed. 1 Jan 2016

  • Kumar V (2007) Optimal contour mapping of groundwater levels using Universal kriging—a case study. Hydrol Sci J des Sci Hydrologiques 52(5):1038–1050

    Google Scholar 

  • Langroodi SH, Masoum MG, Nasiri H, Javi ST (2015) Spatial and temporal variability analysis of groundwater quantity to land-use/land-cover change in the Khanmirza agricultural plain in Iran. Arab J Geosci 8:8385–8397

    Article  Google Scholar 

  • Li S, Charles C, Degre A (2011) Geostatistical interpolation of daily rainfall at catchment scale: the use of several variogram models in the Ourthe and Ambleve catchments, Belgium. Hydrol Earth Syst Sci 15:2259–2274

    Article  Google Scholar 

  • Mishra N, Khare D, Gupta KK, Shukla R (2014) Impact of land use change on groundwater—a review. Adv Water Resour Prot (AWRP) 2:28–41

    Google Scholar 

  • Mukherjee P, Singh CK, Mukherjee S (2012) Delineation of groundwater potential zones in arid region of India—a remote sensing and GIS approach. Water Resour Manage 26:2643–2672

    Google Scholar 

  • Ross A, Martinez-Santos P (2010) The challenge of groundwater governance: case studies from Spain and Australia. Reg Environ Change 10(4):299–310

    Article  Google Scholar 

  • Saito H, Goovaerts P (2000) Geostatistical interpolation of positively skewed and censored data in a dioxin-contaminated site. Environ Sci Technol 34:4228–4235

    Article  Google Scholar 

  • Saito H, Sean A, Mckenna Goovaerts P (2005) Accounting for geophysical information in geostatistical characterization of unexploded ordnance (UXO) sites. Environ Ecol Stat 12:7–25

    Google Scholar 

  • Scanlon BR, Reedy RC, Tonestrom David AS, Prudicz Dav Id E, Dennehy KF (2005) Impact of land use and land cover change on groundwater recharge and quality in the southwestern US. Global Change Biol 11:1577–1593

    Google Scholar 

  • Srivastava PK, Gupta M, Mukherjee S (2012) Mapping spatial distribution of pollutants in groundwater of a tropical area of India, using remote sensing and GIS. Appl Geomatics, Springer 4:21–32

    Article  Google Scholar 

  • Waco KE, Taylor W (2010) The influence of groundwater withdrawal and land use changes on brook charr (Salvelinusfontinalis) thermal habitat in two cold water tributaries in Michigan, U.S.A. Hydrobiologia 650:01–116

    Google Scholar 

  • Zhang YK, Schilling KE (2006) Effects of land cover on water table, soil moisture, evapotranspiration, and groundwater recharge: a field observation and analysis. J Hydrol 319:328–338

    Article  Google Scholar 

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Correspondence to M. Prajwal .

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Bhat, V., Prajwal, M., Shetty, A., Srivastava, A., Bhosale, R. (2018). Spatiotemporal Relationship Linking Land Use/Land Cover with Groundwater Level. In: Singh, V., Yadav, S., Yadava, R. (eds) Groundwater. Water Science and Technology Library, vol 76. Springer, Singapore. https://doi.org/10.1007/978-981-10-5789-2_4

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