Surface runoff prediction regarding LULC and climate dynamics using coupled LTM, optimized ARIMA, and GIS-based SCS-CN models in tropical region

Rizeei, HM; Pradhan, B; Saharkhiz, MA

Surface runoff prediction regarding LULC and climate dynamics using coupled LTM, optimized ARIMA, and GIS-based SCS-CN models in tropical region

Rizeei, HM

Pradhan, B

Saharkhiz, MA

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Publication Type:: Journal Article
Citation:: Arabian Journal of Geosciences, 2018, 11 (3)
Issue Date:: 2018-02-01

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Field	Value	Language
dc.contributor.author	Rizeei, HM https://orcid.org/0000-0002-7690-8440	en_US
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054	en_US
dc.contributor.author	Saharkhiz, MA	en_US
dc.date.issued	2018-02-01	en_US
dc.identifier.citation	Arabian Journal of Geosciences, 2018, 11 (3)	en_US
dc.identifier.issn	1866-7511	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130834
dc.description.abstract	© 2018, Saudi Society for Geosciences. The effects of climate and land use/land cover (LULC) dynamics have directly affected the surface runoff and flooding events. Hence, current study proposes a full-packaged model to monitor the changes in surface runoff in addition to forecast of the future surface runoff based on LULC and precipitation variations. On one hand, six different LULC classes were extracted from Spot-5 satellite image. Conjointly, land transformation model (LTM) was used to detect the LULC pixel changes from 2000 to 2010 as well as predict the 2020 ones. On the other hand, the time series-autoregressive integrated moving average (ARIMA) model was applied to forecast the amount of rainfall in 2020. The ARIMA parameters were calibrated and fitted by latest Taguchi method. To simulate the maximum probable surface runoff, distributed soil conservation service-curve number (SCS-CN) model was applied. The comparison results showed that firstly, deforestation and urbanization have been occurred upon the given time, and they are anticipated to increase as well. Secondly, the amount of rainfall has non-stationary declined since 2000 till 2015 and this trend is estimated to continue by 2020. Thirdly, due to damaging changes in LULC, the surface runoff has been also increased till 2010 and it is forecasted to gradually exceed by 2020. Generally, model calibrations and accuracy assessments have been indicated, using distributed-GIS-based SCS-CN model in combination with the LTM and ARIMA models are an efficient and reliable approach for detecting, monitoring, and forecasting surface runoff.	en_US
dc.relation.ispartof	Arabian Journal of Geosciences	en_US
dc.relation.isbasedon	10.1007/s12517-018-3397-6	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Surface runoff prediction regarding LULC and climate dynamics using coupled LTM, optimized ARIMA, and GIS-based SCS-CN models in tropical region	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	11	en_US
utslib.for	0403 Geology	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access	*
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	11	en_US

Abstract:

© 2018, Saudi Society for Geosciences. The effects of climate and land use/land cover (LULC) dynamics have directly affected the surface runoff and flooding events. Hence, current study proposes a full-packaged model to monitor the changes in surface runoff in addition to forecast of the future surface runoff based on LULC and precipitation variations. On one hand, six different LULC classes were extracted from Spot-5 satellite image. Conjointly, land transformation model (LTM) was used to detect the LULC pixel changes from 2000 to 2010 as well as predict the 2020 ones. On the other hand, the time series-autoregressive integrated moving average (ARIMA) model was applied to forecast the amount of rainfall in 2020. The ARIMA parameters were calibrated and fitted by latest Taguchi method. To simulate the maximum probable surface runoff, distributed soil conservation service-curve number (SCS-CN) model was applied. The comparison results showed that firstly, deforestation and urbanization have been occurred upon the given time, and they are anticipated to increase as well. Secondly, the amount of rainfall has non-stationary declined since 2000 till 2015 and this trend is estimated to continue by 2020. Thirdly, due to damaging changes in LULC, the surface runoff has been also increased till 2010 and it is forecasted to gradually exceed by 2020. Generally, model calibrations and accuracy assessments have been indicated, using distributed-GIS-based SCS-CN model in combination with the LTM and ARIMA models are an efficient and reliable approach for detecting, monitoring, and forecasting surface runoff.

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http://hdl.handle.net/10453/130834