Variability of Extreme Climate Events and Prediction of Land Cover Change and Future Climate Change Effects on the Streamflow in Southeast Queensland, Australia

Article

Pakdel, Hadis, Chadalavada, Sreeni, Alam, Md Jahangir, Paudyal, Dev Raj and Vazifedoust, Majid. 2024. "Variability of Extreme Climate Events and Prediction of Land Cover Change and Future Climate Change Effects on the Streamflow in Southeast Queensland, Australia." ISPRS International Journal of Geo-Information. 13 (4). https://doi.org/10.3390/ijgi13040123
Article Title

Variability of Extreme Climate Events and Prediction of Land Cover Change and Future Climate Change Effects on the Streamflow in Southeast Queensland, Australia

ERA Journal ID200858
Article CategoryArticle
AuthorsPakdel, Hadis, Chadalavada, Sreeni, Alam, Md Jahangir, Paudyal, Dev Raj and Vazifedoust, Majid
Journal TitleISPRS International Journal of Geo-Information
Journal Citation13 (4)
Article Number123
Number of Pages23
Year2024
PublisherMDPI AG
Place of PublicationSwitzerland
ISSN2220-9964
Digital Object Identifier (DOI)https://doi.org/10.3390/ijgi13040123
Web Address (URL)https://www.mdpi.com/2220-9964/13/4/123
AbstractThe severity and frequency of extremes are changing; thus, it is becoming necessary to evaluate the impacts of land cover changes and urbanisation along with climate change. A framework of the Generalised Extreme Value (GEV) method, Google Earth Engine (GEE), and land cover patterns’ classification including Random Forest (RF) and Support Vector Machine (SVM) can be useful for streamflow impact analysis. For this study, we developed a unique framework consisting of a hydrological model in line with the Process-informed Nonstationary Extreme Value Analysis (ProNEVA) GEV model and an ensemble of General Circulation Models (GCMs), mapping land cover patterns using classification methods within the GEE platform. We applied these methods in Southeast Queensland (SEQ) to analyse the maximum instantaneous floods in non-stationary catchment conditions, considering the physical system in terms of cause and effect. Independent variables (DEM, population, slope, roads, and distance from roads) and an integrated RF, SVM methodology were utilised as spatial maps to predict their influences on land cover changes for the near and far future. The results indicated that physical factors significantly influence the layout of landscapes. First, the values of projected evapotranspiration and rainfall were extracted from the multi-model ensemble to investigate the eight GCMs under two climate change scenarios (RCP4.5 and RCP8.5). The AWBM hydrological model was calibrated with daily streamflow and applied to generate historical runoff for 1990–2010. Runoff was projected under two scenarios for eight GCMs and by incorporating the percentage of each land cover into the hydrological model for two horizons (2020–2065 and 2066–2085). Following that, the ProNEVA model was used to calculate the frequency and magnitude of runoff extremes across the parameter space. The maximum peak flood differences under the RCP4.5 and RCP8.5 scenarios were 16.90% and 15.18%, respectively. The outcomes of this study suggested that neglecting the non-stationary assumption in flood frequency can lead to underestimating the amounts that can lead to more risks for the related hydraulic structures. This framework is adaptable to various geographical regions to estimate extreme conditions, offering valuable insights for infrastructure design, planning, risk assessment, and the sustainable management of future water resources in the context of long-term water management plans.
Keywordsclimate change; hydrological extreme; non-station; land cover change; GEV distribution; Google Earth Engine
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Is part ofExtreme climate variability and impacts of future climate change on the streamflow in the southeast Queensland, Australia
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020410402. Environmental assessment and monitoring
Public Notes

This article is part of a UniSQ Thesis by publication. See Related Output.

Byline AffiliationsSchool of Engineering
Murray-Darling Basin Authority, Australia
School of Surveying and Built Environment
University of Guilan, Iran
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Paudyal, Dev Raj, McDougall, Kevin and Apan, Armando. 2011. "Assessing spatial information access, use and sharing for catchment management in Australia." Hock, Barbara (ed.) 2011 Surveying and Spatial Sciences Conference: Innovation in Action: Working Smarter (SSSC 2011). Wellington, New Zealand 21 - 25 Nov 2011 Rotorua, New Zealand.
Spatial information sharing for catchment management in Australia
Paudyal, Dev Raj. 2012. Spatial information sharing for catchment management in Australia. PhD Thesis Doctor of Philosophy. University of Southern Queensland.
Pro-poor land management in developing countries: a case study in Nepal and India
Paudyal, Dev Raj. 2011. Pro-poor land management in developing countries: a case study in Nepal and India. Saarbrüchen, Germany. VDM Verlag Dr. Muller.
Spatial data infrastructure convergence: building spatial data infrastructure bridges to address climate change
Paudyal, Dev Raj, McDougall, Kevin and Apan, Armando. 2011. "Spatial data infrastructure convergence: building spatial data infrastructure bridges to address climate change." Joshi, P. K. and Singh, T. P. (ed.) Geoinformatics for climate change studies. New Delhi, India. TERI Press. pp. 377-392
Pro-poor land management in developing countries: a case study in Nepal and India
Paudyal, Dev Raj. 2008. Pro-poor land management in developing countries: a case study in Nepal and India. Copenhagen, Denmark. International Federation of Surveyors (FIG).
A regional collaborative network to improve spatial information sharing in Australia
Paudyal, Dev Raj, McDougall, Kevin and Apan, Armando. 2010. "A regional collaborative network to improve spatial information sharing in Australia." 12th Global Spatial Data Infrastructure Association World Conference (GSDI 12). Singapore 19 - 22 Oct 2010 Melbourne, Australia.
Spatial data infrastructure for pro-poor land management
Paudyal, Dev Raj and McDougall, Kevin. 2010. "Spatial data infrastructure for pro-poor land management." Staiger, Rudolf (ed.) 24th International Federation of Surveyors International Congress (FIG 2010): Facing the Challenges - Building the Capacity. Sydney, Australia 11 - 16 Apr 2010 Sydney, Australia.
Spatial data infrastructure for catchment management: a case study of Condamine Catchment, Australia
Paudyal, Dev Raj, McDougall, Kevin and Apan, Armando. 2009. "Spatial data infrastructure for catchment management: a case study of Condamine Catchment, Australia." Indian Cartographer. 28, pp. 439-444.
Building SDI bridges for catchment management
Paudyal, Dev Raj, McDougall, Kevin and Apan, Armando. 2009. "Building SDI bridges for catchment management." van Loenen, B., Besemer, J. W. J. and Zevenbergen, J. A. (ed.) SDI convergence: research, emerging trends, and critical assessment. Delft, Netherlands. Netherlands Geodetic Commission. pp. 265-279
Building spatial data infrastructure to support sustainable catchment management
Paudyal, Dev Raj and McDougall, Kevin. 2008. "Building spatial data infrastructure to support sustainable catchment management." McDougall, Kevin (ed.) Queensland Spatial Conference 2008. Surfers Paradise, Australia 17 - 19 Jul 2008 Brisbane, Australia.