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

Bibliographic Data

ID22031300
AuthorsHadis Pakdel (0000-0001-5977-1772, University of Southern Queensland), Sreeni Chadalavada (University of Southern Queensland), Md Jahangir Alam (0000-0002-1025-7067, School of Engineering, The University of Southern Queensland, Springfield Lakes, QLD 4300, Australia), Jahangir Alam (0000-0002-7935-6346, University of Southern Queensland), Dev Raj Paudyal (0000-0003-1010-2118, University of Southern Queensland), Majid Vazifedoust (0000-0002-0962-2813, University of Guilan)
Year2024
Volume13
Issue4
Pages123
Publication date2024-04-08
Open AccessYes
TypeARTICLE
VenueISPRS International Journal of Geo-Information (JOURNAL)
Journal identifiersISSN: 2220-9964 • E-ISSN: 2220-9964
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi13040123
OpenAlexW4394576822
LanguageEN
Citations received1
References cited66

The 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

Cartography · Climate change · Climatology · Drainage basin · Evapotranspiration · Geography · Land cover · Land use · Physical geography · Population · Streamflow · Surface runoff · Environmental Science · Flood Risk Assessment and Management · Hydrology and Drought Analysis · Hydrology and Watershed Management Studies · Geology

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