Quantification Prediction Soil Losses in Nakhon Ratchasima, Thailand

Authors

  • Phatsakrit Kongkhiaw School of Environmental Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand
  • Chatpet Yossapol School of Environmental Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand
  • Netnapid Tantemsapya School of Environmental Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand
  • Chau Ngoc Tran School of Environmental Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand
  • Sirilak Tanang The Center for Scientific and Technological Equipment, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand

DOI:

https://doi.org/10.14456/nujst.2021.28

Keywords:

Land use change, Soil loss method, USLE

Abstract

Soil loss, accelerated by human activity, has become one of the world's most serious environmental problems because it poses significant threat to natural resources and the environment. This research focuses on soil loss in the future for sustainable land use planning for 9 watersheds over 20 years in Nakhon Ratchasima, Thailand. The integrated CA-Markov with GIS was applied to forecast 9 types of land use changes to analyze for soil conservation factors as of International Land Loss Equation (USLE). The land use change maps in 2019, 2023, 2027, 2031, and 2035 had been projected for land use predictions from 2011 and 2015 databases. The validation for the correctness of the production land use change map has 91.46% accuracy. The soil loss analysis of targeted study targets were divided into soil loss for the 9 watersheds and covers the overall provinces to determine the proportion of sediment generated in each watershed encountered the risk. The simulation results showed that the soil loss for the entire province may reach the highest soil loss at 329,271 T·km-2·y-1 with the mean at 2,929.06 T·km-2·y-1. The amount of soil loss in 2035 will be 551.26 x 106 T·y-1. In terms of soil loss of all 9 water watersheds, LTK was found the highest soil loss with 238,606 T·km-2·y-1. The average soil loss is estimated at 135.33 x 106 T·y-1, accounting for 25% of the total area. In comparison among the watershed areas, the amount of soil loss in LPP with highest soil loss rate per area at 51,100.51 ton per area per year. The future land use maps can be used to assess soil loss and can serve as an early warning system. The determination of policies to prevent future environmental problems is critical to controlling improper changes or adversely affecting the local environment.

References

Biggelaar, C. D., Lal, R., Wiebe, K., & Breneman, V. (2003). The global impact of soil erosion on productivity: I: Absolute and relative erosion-induced yield losses. Journal of Advances in Agronomy, 81, 1–48.

Christos, G. K., Panos, P., & Ioannis, Z. G. (2014): A classification of water erosion models according to their geospatial characteristics. International Journal of Digital Earth, 7(3), 229-250. DOI:10.1080/ 17538947.2012.671380

Courage, K., Masamu, A., & Bongo, A. (2009). Rural sustainability under threat in Zimbabwe-simulation of future land use/cover changes in the Bindura district based on the Markov-cellular automata model. Applied Geography, 29, 435–447.

Gitas, I. Z., Douros, K., Minakou, C., Silleos, G. N., & Karydas, C. G. (2009). Multi temporal soil erosion risk assessment in N. Chalkidiki using a modified USLE raster model. EARSel eproceedings, 8, 40–52.

Knijff, J. M., Jones, R. J. A., & Montanarella, L. (2000). Soil erosion risk assessment in Europe. Retrieved from https://www.unisdr.org/files/1581_ereurnew2.pdf

Land Development Department. (2000). Soil Erosion in Thailand. Retrieved from https://www.ldd.go.th/FAO/z_th/th.htm#change

Massanit, P. (2015). Determination of sediment using modified universal soil loss equation with geospatial models in Upper Ping river basin. Chiang Mai University, Chiang Mai.

Meteorological department. (2019). Monthly Rainfall in Thailand (mm) 30 year. Bangkok: Ministry of information and communication technology.

Morgan, R. P. C., & Nearing, M. A. (2011). Handbook of erosion modelling. UK: Blackwell.

Parveen, R., & Kumar, U. (2012). Integrated approach of universal soil loss equation (USLE) and geographical information system (GIS) for soil loss risk assessment in upper south Koel Basin, Jharkhand. Journal of Geographic Information System, 4(6), 588–596.

Patil, R. J., & Sharma, S. K. (2013). Remote Sensing and GIS based modelling of crop/cover management factor (C) of USLE in Shakker river watershed. Proceedings of International Conference on Chemical, Agricultural and Medical Sciences (CAMS-2013) 29-30 December 2013 (pp. 1–4). Kuala Lumpur, Malaysia.

Pavit, S. (2007). Geoinformatic application for analysis of soil erosion on reservoir sedimentation ratio in am Phra ploeng in changwat Nakhonratchasima. Bangkok: Srinakharinwirot University.

Pisit, K. (2011). Soil loss determination using USLE, MMF, and RMMF model in Nam chun sub-watershed, Lom sak district, Phetchabun province. Bangkok: Environmental science, College of Environment. Kasetsart university.

Pollution Control Department. (2019). Thailand state of pollution report. Retrieved from http://www.pcd.go.th/public/Publications/print_report.cfm?task=annual2562

Prem, R., So, K., Chaiwat, E., & Luminda G. (2019). Evaluation of cost and benefit of sediment based on landslide and erosion models. Catena, 173, 194-206.

Rapeepong, L. (2014). Assessment of soil loss in the Upper Ping River Basin. Bangkok: Department of Water Resources Engineering, Kasetsart university.

Stone, R. P., & Hilborn, D. (2012). Universal soil loss equation (USLE) factsheets. Retrieved from http://www.omafra.gov.on.ca/english/engineer/facts/12-051.htm

Shabani, F., Kumar, L., & Esmaeili, A. (2014). Improvement to the prediction of the USLE K factor. Journal of Geomorphology, 201(1), 229–234.

Teerawate, L. (2014). Prediction of Lamtakong watershed land use in 2024 with CA-MARKOV model. Journal of Social Sciences Srinakharinwirot University, 17, 94-113.

Tharapong, P. (2010). Potential assessment of nonpoint source pollution on surface water quality in Upper Lam Phra Phloeng watershed using geospatial models.Nakhon Ratchasima: School of Remote Sensing, Suranaree University of Technology.

The National Statistical Office of Thailand. (2019). Ministry of Digital Economy and Society. Bangkok: Thailand.

Geological Survey (USGS). (2018). USGS Land Cover and DEM (Online). Retrieved from https://landcover.usgs.gov/usgslandcover.php

Wischmeier, W. H., & Smith, D. D. (1978). Predicting rainfall erosion losses: A guide to conservation planning. Agricultural Research Service Handbook No. 537USDA. Retrieved from https://data.globalchange.gov/report/usda-aghandbook-537

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Published

2021-02-10

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Research Articles