Prediction of design flood discharge by statistical downscaling and General Circulation Models

Tofiq, F.A.; Güven, Aytaç

doi:10.1016/j.jhydrol.2014.06.028

Cited by 39 publications

(23 citation statements)

References 13 publications

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“…General Circulation Models (GCMs) are the main tools used for projection of global climate into the future [1], and they are based on the mathematical representations of physics of the atmosphere, ocean, ice caps and land surface processes [2]. For projecting the global climate into the future, GCMs are forced with various scenarios of future greenhouse gas (GHG) emissions.…”

Section: Introductionmentioning

confidence: 99%

Statistical Downscaling of General Circulation Model Outputs to Precipitation Accounting for Non-Stationarities in Predictor-Predictand Relationships

Sachindra

Perera

2016

PLoS ONE

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This paper presents a novel approach to incorporate the non-stationarities characterised in the GCM outputs, into the Predictor-Predictand Relationships (PPRs) in statistical downscaling models. In this approach, a series of 42 PPRs based on multi-linear regression (MLR) technique were determined for each calendar month using a 20-year moving window moved at a 1-year time step on the predictor data obtained from the NCEP/NCAR reanalysis data archive and observations of precipitation at 3 stations located in Victoria, Australia, for the period 1950–2010. Then the relationships between the constants and coefficients in the PPRs and the statistics of reanalysis data of predictors were determined for the period 1950–2010, for each calendar month. Thereafter, using these relationships with the statistics of the past data of HadCM3 GCM pertaining to the predictors, new PPRs were derived for the periods 1950–69, 1970–89 and 1990–99 for each station. This process yielded a non-stationary downscaling model consisting of a PPR per calendar month for each of the above three periods for each station. The non-stationarities in the climate are characterised by the long-term changes in the statistics of the climate variables and above process enabled relating the non-stationarities in the climate to the PPRs. These new PPRs were then used with the past data of HadCM3, to reproduce the observed precipitation. It was found that the non-stationary MLR based downscaling model was able to produce more accurate simulations of observed precipitation more often than conventional stationary downscaling models developed with MLR and Genetic Programming (GP).

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Section: Introductionmentioning

confidence: 99%

Statistical Downscaling of General Circulation Model Outputs to Precipitation Accounting for Non-Stationarities in Predictor-Predictand Relationships

Sachindra

Perera

2016

PLoS ONE

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“…The second set of data is predicted daily inflow from statistical downscaling of General Circulation Models (GCMs) which was obtained from the authors previous studies. This data set is based on downscaling the discharges from daily weather variables without involving the rainfall runoff models (Tofiq and Guven, 2014). The data set covers the predicted daily inflow for 100 years (2001-2100) based on A2 scenario of Canadian Global Climate Model version 3 (CGCM3).…”

Section: Methodsmentioning

confidence: 99%

“…According to the Intergovernmental Panel on Climate Change (IPCC), average worldwide surface temperature and precipitation are increased noticeably in the twentieth century with an increase in the frequency of severe precipitation events. Meanwhile the mean sea level rose 10-20 cm worldwide (Tofiq and Guven, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Potential changes in inflow design flood under future climate projections for Darbandikhan Dam

Tofiq

Güven

2015

Journal of Hydrology

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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%

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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Prediction of design flood discharge by statistical downscaling and General Circulation Models

Cited by 39 publications

References 13 publications

Statistical Downscaling of General Circulation Model Outputs to Precipitation Accounting for Non-Stationarities in Predictor-Predictand Relationships

Statistical Downscaling of General Circulation Model Outputs to Precipitation Accounting for Non-Stationarities in Predictor-Predictand Relationships

Potential changes in inflow design flood under future climate projections for Darbandikhan Dam

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

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