Statistical downscaling of CMIP5 outputs for projecting future changes in rainfall in the Onkaparinga catchment

Rashid, M. M.; Beecham, Simon; Chowdhury, R. K.

doi:10.1016/j.scitotenv.2015.05.024

Cited by 38 publications

(28 citation statements)

References 51 publications

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“…However, GCMs generate rainfall forecasts at relatively coarse spatial scales, in the order of a few hundred kilometres and are unable to resolve the effects of sub-grid scale features such as topography and land use [16]. These outputs cannot be directly used in catchment scale studies, which require hydroclimatic data at fine spatial resolutions [17].…”

Section: South-east Queensland Regionmentioning

confidence: 99%

“…The scale mismatch between the GCM outputs and the hydroclimatic required at the catchment level is a major obstacle in climate studies of hydrology and water resources [17]. As a solution to the scale mismatch between the GCM's outputs and the information required at the catchment scale, downscaling techniques have been developed [16,17]. However, results from using this approach applied to catchments in eastern Australia [15] demonstrate little improvement over climatology, a major limitation being the low level of skill in the monthly rainfall forecasts from the general circulation model at the course grid scale [5].…”

Section: South-east Queensland Regionmentioning

confidence: 99%

See 1 more Smart Citation

Forecasting extreme monthly rainfall events in regions of Queensland, Australia using artificial neural networks

Abbot¹,

Marohasy²

2017

Int. J. SDP

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Extreme rainfall in Queensland during December 2010 and January 2011 resulted in catastrophic flooding, causing loss of life, extensive property damage and major disruption of economic activity. Official medium-term rainfall forecasts failed to warn of the impending heavy rainfall. Since the flooding, the Australian Bureau of Meteorology has changed its method of forecast from an empirical statistical scheme to the application of a general circulation model (GCM), the Predictive Ocean and Atmospheric Model for Australia (POAMA). Our previous studies demonstrated that more skilful monthly rainfall forecasts can be achieved using artificial neural networks (ANNs). This study extends those previous investigations focussing on the capacity of the forecast methodology to differentiate between extreme rainfall events and more average conditions, up to one year in advance. Sites within two geographical regions of Queensland are examined: (i) coastal Queensland using rainfall observations from Bingera, Plane Creek and Victoria Mill; (ii) a region of south-east Queensland, using rainfall observations from 54 weather stations, extending approximately 300 km northward along the Queensland coast, from the Gold Coast to Bundaberg, and approximately 200 km inland. For both regions, the capacity to differentiate between average conditions and impending extreme rainfall events up to one year in advance is demonstrated.

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Section: South-east Queensland Regionmentioning

confidence: 99%

Section: South-east Queensland Regionmentioning

confidence: 99%

Forecasting extreme monthly rainfall events in regions of Queensland, Australia using artificial neural networks

Abbot¹,

Marohasy²

2017

Int. J. SDP

View full text Add to dashboard Cite

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“…Precipitation bias after ensemble simulation always remains, and the less the bias between ensemble outputs and observations, the more reliable the future projection based on these data [21,22]. Generally, there are two methods to correct the bias: one corrects the predictor variables before downscaling, and the other corrects the bias between downscaled precipitation and observations.…”

Section: Introductionmentioning

confidence: 99%

Future Changes of Precipitation over the Han River Basin Using NEX-GDDP Dataset and the SVR_QM Method

Chen

2019

Atmosphere

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After the release of the high-resolution downscaled National Aeronautics and Space Administration (NASA) Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset, it is worth exploiting this dataset to improve the simulation and projection of local precipitation. This study developed support vector regression (SVR) and quantile mapping (SVR_QM) ensemble and correction models on the basis of historic precipitation in the Han River basin and the 21 NEX-GDDP models. The generated SVR_QM models were applied to project changes of precipitation during the 21st century for the region. Several statistical metrics, including Pearson’s correlation coefficient (PCC), root mean squared error (RMSE), and relative bias (Rbias), were used for evaluation and comparative analyses. The results demonstrated the superior performance of SVR_QM compared with multi-layer perceptron (MLP), SVR, and random forest (RF), as well as simple model average (MME) ensemble methods and single NEX-GDDP models. PCC was up to 0.84 from 0.61–0.71 for the single NEX-GDDP models, RMSE was up to 34.02 mm from 48–51 mm, and Rbias values were almost removed. Additionally, the projected precipitation changes during the 21st century in most stations had an increasing trend under both Representative Concentration Pathway RCP4.5 and RCP8.5 emissions scenarios; the regional average precipitation during the middle (2040–2059) and late (2070–2089) 21st century increased by 3.54% and 5.12% under RCP4.5 and by 7.44% and 9.52% under RCP8.5, respectively.

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“…It is common to adopt the outputs of general circulation models (GCMs) with predefined scenarios to forecast future weather conditions and to assess their impacts on regional water resources (Arora, ; Timbal et al, ; Alkuwari et al, ; Beecham et al, ; Mahmood and Babel, ; Rashid et al, ; Tofiq and Guven, ). However, GCM outputs are represented in a grid ranging from 150–300 km in size, which is too spatially coarse for regional weather and water resource conditions, and further limits their direct regional application (Arora, ; Timbal et al, ; Beecham et al, ; Manor and Berkovic, ; Su et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…Compared to dynamical downscaling, statistical downscaling is relatively easy to implement and computationally less expensive (Wilby and Wigley, ; Wilby et al, ; Fowler et al, ; Jeong et al, ; Sachindra et al, ). Therefore, statistical downscaling with the concept of transfer functions is commonly employed in many hydrological applications (Charles et al, ; Dibike and Coulibaly, ; Fowler et al, ; Jeong et al, ; Mehrotra et al, ; Mekanik et al, ; Beecham et al, ; Rashid et al, ; Laflamme et al, ). Once the relationship is established, it can further be utilized with different future scenarios of the GCMs to forecast impacts.…”

Section: Introductionmentioning

confidence: 99%

Assessing future rainfall uncertainties of climate change in Taiwan with a bootstrapped neural network‐based downscaling model

Lin

Chuang³

et al. 2018

Water & Environment J

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To investigate the impacts of climate change on Taiwan, a downscaling model (DSM) was used due to the large grid size of general circulation models (GCMs). DSM is a data‐driven model based on the Radial Basis Function Neural Network (RBFNN). A Genetic Algorithm (GA) was adapted for parameter optimization, and the bootstrap method was employed to assess uncertainty. Two weather stations at similar latitudes but separated by mountains with altitudes of above 3000 m were selected as examples. Three GCMs were chosen for the model building and the assessment of near future (2050–2060) and far future (2080–2090) climate change impacts of three future scenarios A1B, A2 and B1. The results suggest that in the future, rainfall will tend to increase in winter but decrease in summer, with a similar average rainfall. In addition, our results suggest that in the future, typhoons might arrive later in the season.

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Statistical downscaling of CMIP5 outputs for projecting future changes in rainfall in the Onkaparinga catchment

Cited by 38 publications

References 51 publications

Forecasting extreme monthly rainfall events in regions of Queensland, Australia using artificial neural networks

Forecasting extreme monthly rainfall events in regions of Queensland, Australia using artificial neural networks

Future Changes of Precipitation over the Han River Basin Using NEX-GDDP Dataset and the SVR_QM Method

Assessing future rainfall uncertainties of climate change in Taiwan with a bootstrapped neural network‐based downscaling model

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