A Multi-Framework of Google Earth Engine and GEV for Spatial Analysis of Extremes in Non-Stationary Condition in Southeast Queensland, Australia
Article
Article Title | A Multi-Framework of Google Earth Engine and GEV for Spatial Analysis of Extremes in Non-Stationary Condition in Southeast Queensland, Australia |
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ERA Journal ID | 200858 |
Article Category | Article |
Authors | Pakdel, Hadis, Paudyal, Dev Raj, Chadalavada, Sreeni, Alam, Md Jahangir and Vazifedoust, Majid |
Journal Title | ISPRS International Journal of Geo-Information |
Journal Citation | 12 (9) |
Article Number | 370 |
Number of Pages | 25 |
Year | 2023 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 2220-9964 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/ijgi12090370 |
Web Address (URL) | https://www.mdpi.com/2220-9964/12/9/370 |
Abstract | The frequency and severity of extremes, including extreme precipitation events, extreme evapotranspiration and extreme water storage deficit events, are changing. Thus, the necessity for developing a framework that estimates non-stationary conditions is urgent. The aim of this paper is to develop a framework using the geeSEBAL platform, Generalised Extreme Value (GEV) models and spatiotemporal analysis techniques that incorporate the physical system in terms of cause and effect. Firstly, the geeSEBAL platform has enabled the estimation of actual evapotranspiration (ETa) with an unprecedented level of spatial-temporal resolution. Following this, the Non-stationary Extreme Value Analysis (NEVA) approach employs the Bayesian method using a Differential Evolution Markov Chain technique to calculate the frequency and magnitude of extreme values across the parameter space. Station and global climate datasets have been used to analyse the spatial and temporal variation of rainfall, reference evapotranspiration (ETo), ETa and water storage (WS) variables in the Lockyer Valley located in Southeast Queensland (SEQ), Australia. Frequency analysis of rainfall, ETa, and water storage deficit for 14 stations were performed using a GEV distribution under stationary and non-stationary assumptions. Comparing the ETa, ETo and ERA5 rainfall with station data showed reasonable agreement as follows: Pearson correlation of 0.59–0.75 for ETa, RMSE of 45.23–58.56 mm for ETa, Pearson correlation of 0.96–0.97 for ETo, RMSE of 73.13–87.73 mm for ETo and Pearson correlation of 0.87–0.92 for rainfall and RMSE of 37.53–57.10 mm for rainfall. The lower and upper uncertainty bounds between stationary and non-stationary conditions for rainfall station data of Gatton varied from 550.98 mm (stationary) to 624.97 mm (non-stationary), and for ERA5 rainfall datasets, 441.30 mm (stationary) to 450.77 mm (non-stationary). The results demonstrate that global climate datasets underestimate the difference between stationary and non-stationary conditions by 9.47 mm compared to results of 73.99 mm derived from station data. Similarly, the results demonstrate less variation between stationary and non-stationary conditions in water storage, followed by a sharp variation in rainfall and moderate variation in evapotranspiration. The findings of this study indicate that neglecting the non-stationary condition in some hydrometeorological variables can lead to underestimating their amounts. This framework can be applied to any geographical area for estimating extreme conditions, providing valuable insights for infrastructure planning and design, risk assessment and disaster management. |
Keywords | : climate extremes; Google Earth Engine; geeSEBAL; non-stationary; GEV distribution |
Related Output | |
Is part of | Extreme climate variability and impacts of future climate change on the streamflow in the southeast Queensland, Australia |
Article Publishing Charge (APC) Amount Paid | 1700 |
Article Publishing Charge (APC) Funding | Researcher |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 401302. Geospatial information systems and geospatial data modelling |
400513. Water resources engineering | |
370704. Surface water hydrology | |
Public Notes | This article is part of a UniSQ Thesis by publication. See Related Output. |
Byline Affiliations | School of Engineering |
School of Surveying and Built Environment | |
Murray-Darling Basin Authority, Australia | |
University of Guilan, Iran |
https://research.usq.edu.au/item/z1163/a-multi-framework-of-google-earth-engine-and-gev-for-spatial-analysis-of-extremes-in-non-stationary-condition-in-southeast-queensland-australia
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