Investigation of land use changes and its relationship with groundwater level (Case study: Ardabil plain)

Subject Areas : Applications in water resources management

Sayyad Asghari Saraskanroud ¹ , Ehsan Ghale ² , Elhameh Ebady ³

1 - Associate Professor, Department of Natural Geography, Faculty of Literature and Humanities, University of Mohaghegh Ardabili, Ardabil,, Iran
2 - PhD Student of Geomorphology, Department of Natural Geography, Faculty of Literature and Humanities, University of Mohaghegh Ardabili, Ardabil, Iran
3 - PhD Student of Geomorphology, Department of Natural Geography, Faculty of Literature and Humanities, University of Mohaghegh Ardabili, Ardabil, Iran

Received: 2020-01-12 Accepted : 2020-09-19 Published : 2021-03-21

Keywords: Geostatistical methods, land use, Object-oriented Classification, Groundwater,

Abstract :

Background and Objective Groundwater is the most important source of fresh water in the world. Drinking water for two billion people is supplied directly from groundwater and is used to irrigate the world's largest food supply. Improper harvesting of groundwater reservoirs has led to the fact that the amount of feeder feed is not responsive to harvesting and the groundwater level has dropped. The drop in groundwater levels has led to problems such as drying up water wells, declining river and lake discharge, lowering water quality, increasing pumping costs and water extraction and land subsidence. Awareness of water level changes is necessary to understand the status of groundwater aquifers and their optimal management. By assessing groundwater level fluctuations, it can be used to manage water resources. One of the major applications of remote sensing is the detection and determination of land use changes. Using remote sensing, it is possible to study and identify various phenomena. The aim of this study was to investigate the effect of different land use on groundwater using interpolation geostatistical methods as well as object-oriented classification methods for land use mapping. Materials and Methods Ardabil plain is a mountainous plain located in northwestern Iran and east of the Azerbaijani plateau. The plain covers an area of 990 km2 among the highlands around it and in terms of political divisions includes parts of the cities of Ardabil and Namin. The data used in this study included a Landsat 8 satellite image of the OLI surveyor for the 2015 land use map, as well as a Landsat TM 5 surveyor for the 1987 land use plan. Also, in this study, the groundwater depth data of 43 piezometer wells in Ardabil plain were used. In this research, after preparing the statistics of piezometric wells, the data reconstruction method was used to eliminate the deficiencies in the study data. Reconstruction, which was used only to correct deficiencies in the data, is an interpolation method performed by the Neural Power software (based on the artificial neural network). To normalize the data, logarithmic transformations were used in SPSS and GS+ software was used for geostatistical analysis. ENVI software was used for atmospheric and radiometric corrections and ArcGIS software was used to extract the layer map. Results and Discussion The largest area in 1987 belongs to the irrigated agricultural class with an area of 51840 hectares. The second area belongs to the rainfed agricultural class, which has the largest area with 48,790 hectares. The smallest area also belongs to the use of water with 88.65 hectares. Looking at the uses of 1394, the results showed significant differences in such a way that the use of irrigated agriculture with 10.17 hectares has increased significantly compared to 1987. After extracting the land use change map to select the best intrusion model from among the various models, all models were evaluated and only the models that were more accurate than the other models were selected. The highest average water level was recorded in 1987 for agricultural agriculture and the lowest average water level was recorded for the forest area. Considering the land use map and the groundwater level map of 1394, the above analysis is confirmed and as it is known, the highest average water level this year belongs to the use of irrigated agriculture with 20.17 meters and the lowest average recorded water level is related to the use of the forest is 11.45 meters. Compared to 1987, water use has had a decrease in water level, which has reduced the water level of dams and also reduced the volume of water in rivers and even dried up several of these rivers. After water use, one of the most interesting uses that need to be analyzed and the reason for its search is the use of irrigated agriculture. This land use has the highest water level drop in 1987 and in 2015 it has faced the highest water level drop. The reason can be attributed to the over-harvesting of groundwater for irrigated crops that need more irrigation. Due to the fact that the Rain-fed crops in the study area are mostly wheat and do not need water or needless, but the amount of groundwater level in 2015 compared to 1987 has been accompanied by a significant decline. The use of pastures in 2015 compared to 1987 has dropped significantly, which indicates the critical situation of groundwater and excessive use of these resources. Conclusion In this study, in the first step, in order to classify and then examine the changes that occurred in a certain period of time in the study area. In order to classify the relevant images, An object-oriented classification method was used in eCognition software and the relevant outputs were extracted in ArcGIS software. Evaluation of classification accuracy for 2015 has a very high accuracy so that the overall accuracy and coefficient of the extracted Kapa at the highest possible level, the overall accuracy of 100% and the coefficient of Kapa 0.99 and for the year 1987 was extracted with less accuracy and general accuracy for In 1987, 98% and the Kappa coefficient was 0.95. After extracting the land use change map to select the best intrusion model from among the various models, all models were evaluated. Due to ME and RMSE values, the curing method has higher accuracy than other methods. Among the various modes of the curing method, the Gaussian model has the highest accuracy. According to the results, the most changed use in this area has been the use of pastures in irrigated agriculture and Rain-fed agriculture. This change shows that the increase in the use of irrigated agriculture and Rain-fed agriculture in this area has been accompanied by a decrease in the use of rangelands, which indicates the destruction of pastures. According to the groundwater level map, the highest average water level was recorded in 1987 for irrigated agricultural use and the lowest average water level was recorded for the forest area. Also, the highest average water level in 2015 belongs to the use of irrigated agriculture with 20.17 meters and the lowest average recorded water level is related to forest use with 11.45 meters. One of the interesting uses that need to be analyzed and the reason for its search is the use of irrigated agriculture. This land use has the highest water level drop in 1987 and in 2015 it has faced the highest water level drop. The reason can be attributed to the over-harvesting of groundwater for irrigated crops that need more irrigation. In general, all uses in 2015 compared to 1366 have faced a decrease in water balance. As a result of these changes, farmers have made more use of groundwater resources, which has led to a drop in groundwater levels over a 28 years period in the study area. This overuse is enough to reduce the average level of the plain by 4.9 meters during the mentioned period. http://dorl.net/dor/20.1001.1.26767082.1400.12.1.5.6

References:

Abdullah AYM, Masrur A, Adnan MSG, Baky MAA, Hassan QK, Dewan A. 2019. Spatio-Temporal Patterns of Land Use/Land Cover Change in the Heterogeneous Coastal Region of Bangladesh between 1990 and 2017. Remote Sensing, 11(7): 790. doi:https://doi.org/10.3390/rs11070790.

Abijith D, Subbarayan S, Leelambar S, Jesudasan Jacinth J, Thiyagarajan S, Parthasarathy KSS. 2020. GIS-based multi-criteria analysis for identification of potential groundwater recharge zones - a case study from Ponnaniyaru watershed, Tamil Nadu, India. HydroResearch, 3: 1-14. doi:https://doi.org/10.1016/j.hydres.2020.02.002.

Adhikari RK, Mohanasundaram S, Sangam S. 2020. Impacts of land-use changes on the groundwater recharge in the Ho Chi Minh city, Vietnam. Environmental Research, 185: 109440. doi:https://doi.org/10.1016/j.envres.2020.109440.

Albhaisi M, Brendonck L, Batelaan O. 2013. Predicted impacts of land use change on groundwater recharge of the upper Berg catchment, South Africa. Water SA, 39(2): 211-220. doi:https://doi.org/10.4314/wsa.v39i2.4.

Asghari Saraskanrood S, DolatShahi z. 2018. Investigating the amounts of solutes and chemical elements found in the sources Drinking water in Khorramabad city. Journal of Applied researches in Geographical Sciences, 18(50): 141-154. doi:https://doi.org/10.29252/jgs.18.50.141. (In Persian).

Chowdhury A. 2016. Assessment of spatial groundwater level variations using geo-statistics and GIS in Haringhata Block, Nadia District, West Bengal. International Journal of Research in Engineering and Technology, 5(5): 276-280.

Dams J, Woldeamlak S, Batelaan O. 2007. Forecasting land-use change and its impact on the groundwater system of the Kleine Nete catchment, Belgium. Hydrology and Earth System Sciences Discussions, 4(6): 4265-4295.

Di Piazza A, Lo Conti F, Noto L, Viola F, La Loggia G. 2011. Comparative analysis of different techniques for spatial interpolation of rainfall data to create a serially complete monthly time series of precipitation for Sicily, Italy. International Journal of Applied Earth Observation and Geoinformation, 13(3): 396-408. doi:https://doi.org/10.1016/j.jag.2011.01.005.

Esfandyary F, Gharachorlu M, Ebadi E. 2018. Assessment and estimation the spatial variation of groundwater level by various interpolation methods in Sarab plain. Geography and Development Iranian Journal, 16(51): 65-80. doi:https://doi.org/10.22111/gdij.2018.3860. (In Persian).

Güler C, Mehmet Ali K, Reşit Nabi K. 2013. Assessment of groundwater vulnerability to nonpoint source pollution in a Mediterranean coastal zone (Mersin, Turkey) under conflicting land use practices. Ocean & Coastal Management, 71: 141-152. doi:https://doi.org/10.1016/j.ocecoaman.2012.10.010.

Güner S, Tüfekçioğlu A, Gülenay S, Küçük M. 2010. Land-use type and slope position effects on soil respiration in black locust plantations in Artvin, Turkey. African Journal of Agricultural Research, 5(8): 719-731. doi:https://hdl.handle.net/11494/542.

Johnson B, Tateishi R, Kobayashi T. 2012. Remote sensing of fractional green vegetation cover using spatially-interpolated endmembers. Remote Sensing, 4(9): 2619-2634. doi:https://doi.org/10.3390/rs4092619.

Jones D, Norm J, James G, Jim N. 2015. A cloud-based MODFLOW service for aquifer management decision support. Computers & Geosciences, 78: 81-87. doi:https://doi.org/10.1016/j.cageo.2015.02.014.

Kachhwala T. 1985. Temporal monitoring of forest land for change detection and forest cover mapping through satellite remote sensing. In: Proceedings of the 6th Asian Conference on Remote Sensing. Hyderabad, 1985. 21-26 November pp 77-83.

Khazaz L, Oulidi HJ, El Moutaki S, Ghafiri A. 2015. Comparing and Evaluating Probabilistic and Deterministic Spatial Interpolation Methods for Groundwater Level of Haouz in Morocco. Journal of Geographic Information System, 7(06): 631. doi:https://doi.org/10.4236/jgis.2015.76051.

Kumar P, Dasgupta R, Johnson BA, Saraswat C, Basu M, Kefi M, Mishra BK. 2019. Effect of land use changes on water quality in an ephemeral coastal plain: Khambhat City, Gujarat, India. Water, 11(4): 724. doi:https://doi.org/10.3390/w11040724.

Lamichhane S, Narendra Man S. 2019. Alteration of groundwater recharge areas due to land use/cover change in Kathmandu Valley, Nepal. Journal of Hydrology: Regional Studies, 26: 100635. doi:https://doi.org/10.1016/j.ejrh.2019.100635.

Lu D, Weng Q. 2007. A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5): 823-870. doi:https://doi.org/10.1080/01431160600746456.

Owuor SO, Butterbach-Bahl K, Guzha AC, Rufino MC, Pelster DE, Díaz-Pinés E, Breuer L. 2016. Groundwater recharge rates and surface runoff response to land use and land cover changes in semi-arid environments. Ecological Processes, 5(1): 16. doi:10.1186/s13717-016-0060-6.

Pijanowski CB, Daniel GB, Bradley AS, Gaurav AM. 2002. Using neural networks and GIS to forecast land use changes: a Land Transformation Model. Computers, Environment and Urban Systems, 26(6): 553-575. doi:https://doi.org/10.1016/S0198-9715(01)00015-1.

Piri H, Bameri A. 2014. Investigating the quantity variation trend of ground water table using geostatistics and GIS (Case study: Sirjan Plain). Journal of RS and GIS for Natural Resources, 5(1): 29-44. (In Persian).

Rai SC, Kumari P. 2012. Assessment of groundwater contamination from land-use/cover change in rural-urban fringe of national capital territory of Delhi (India). Scientific Annals of" Alexandru Ioan Cuza" University of Iasi-Geography series, 58(1): 31-46. https://nbn-resolving.org/urn:nbn:de:0168-ssoar-319594.

Ranjan P, Gupta AD, Kazama S, Sawamoto M. 2007. Assessment of aquifer-land use composite vulnerability in Walawe River Basin, Sri Lanka. Asian Journal of Water, Environment and Pollution, 4(2): 1-10.

Robertson W, Russeland B, Cherry J. 1996. Attenuationn of Nitrate in Acquitted Sediments of Southern Ontario. Journal of Hydrology, 180: 267-281.

Rogan J, Chen D. 2004. Remote sensing technology for mapping and monitoring land-cover and land-use change. Progress in Planning, 61(4): 301-325.

Thomas A, John T. 2006. Modelling of recharge and pollutant fluxes to urban groundwaters. Science of The Total Environment, 360(1): 158-179. doi:https://doi.org/10.1016/j.scitotenv.2005.08.050.

Zhang Z, Li A, Lei G, Bian J, Wu B. 2014. Change detection of remote sensing images based on multi-scale segmentation and decision tree algorithm over mountainous area: a case study in Panxi region, Sichuan province. Acta Ecologica Sinica, 34(24): 7222-7232.