scholarly journals Comparison of Three Statistical Downscaling Methods and Ensemble Downscaling Method Based on Bayesian Model Averaging in Upper Hanjiang River Basin, China

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Jiaming Liu ◽  
Di Yuan ◽  
Liping Zhang ◽  
Xia Zou ◽  
Xingyuan Song
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Statistical Downscaling ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Support Vector ◽  
Simulation Accuracy ◽  
Downscaling Method ◽  
Hanjiang River

Many downscaling techniques have been developed in the past few years for projection of station-scale hydrological variables from large-scale atmospheric variables to assess the hydrological impacts of climate change. To improve the simulation accuracy of downscaling methods, the Bayesian Model Averaging (BMA) method combined with three statistical downscaling methods, which are support vector machine (SVM), BCC/RCG-Weather Generators (BCC/RCG-WG), and Statistics Downscaling Model (SDSM), is proposed in this study, based on the statistical relationship between the larger scale climate predictors and observed precipitation in upper Hanjiang River Basin (HRB). The statistical analysis of three performance criteria (the Nash-Sutcliffe coefficient of efficiency, the coefficient of correlation, and the relative error) shows that the performance of ensemble downscaling method based on BMA for rainfall is better than that of each single statistical downscaling method. Moreover, the performance for the runoff modelled by the SWAT rainfall-runoff model using the downscaled daily rainfall by four methods is also compared, and the ensemble downscaling method has better simulation accuracy. The ensemble downscaling technology based on BMA can provide scientific basis for the study of runoff response to climate change.

Comparisons of Machine Learning Methods of Statistical Downscaling Method: Case Studies of Daily Climate Anomalies in Thailand

2021 ◽  
Author(s):  
Kanawut Chattrairat ◽  
Waranyu Wongseree ◽  
Adisorn Leelasantitham
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Performance Criteria ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Support Vector ◽  
Seasonal Temperature ◽  
Statistical Downscaling Method ◽  
Downscaling Method

The climate change which is essential for daily life and especially agriculture has been forecasted by global climate models (GCMs) in the past few years. Statistical downscaling method (SD) has been used to improve the GCMs and enables the projection of local climate. Many pieces of research have studied climate change in case of individually seasonal temperature and precipitation for simulation; however, regional difference has not been included in the calculation. In this research, four fundamental SDs, linear regression (LR), Gaussian process (GP), support vector machine (SVM) and deep learning (DL), are studied for daily maximum temperature (TMAX), daily minimum temperature (TMIN), and precipitation (PRCP) based on the statistical relationship between the larger-scale climate predictors and predictands in Thailand. Additionally, the data sets of climate variables from over 45 weather stations overall in Thailand are used to calculate in this calculation. The statistical analysis of two performance criteria (correlation and root mean square error (RMSE)) shows that the DL provides the best performance for simulation. The TMAX and TMIN were calculated and gave a similar trend for all models. PRCP results found that in the North and South are adequate and poor performance due to high and low precipitation, respectively. We illustrate that DL is one of the suitable models for the climate change problem.

scholarly journals Coupling a Markov Chain and Support Vector Machine for At-Site Downscaling of Daily Precipitation

2017 ◽  
Vol 18 (9) ◽  
pp. 2385-2406 ◽  
Author(s):  
Yu-Kun Hou ◽  
Hua Chen ◽  
Chong-Yu Xu ◽  
Jie Chen ◽  
Sheng-Lian Guo
Keyword(s):  
Climate Change ◽  
Markov Chain ◽  
Statistical Downscaling ◽  
Daily Precipitation ◽  
Global Climate Models ◽  
Support Vector ◽  
Statistical Downscaling Method ◽  
Calibration And Validation ◽  
Downscaling Method ◽  
Calibration Methods

Abstract Statistical downscaling is useful for managing scale and resolution problems in outputs from global climate models (GCMs) for climate change impact studies. To improve downscaling of precipitation occurrence, this study proposes a revised regression-based statistical downscaling method that couples a support vector classifier (SVC) and first-order two-state Markov chain to generate the occurrence and a support vector regression (SVR) to simulate the amount. The proposed method is compared to the Statistical Downscaling Model (SDSM) for reproducing the temporal and quantitative distribution of observed precipitation using 10 meteorological indicators. Two types of calibration and validation methods were compared. The first method used sequential split sampling of calibration and validation periods, while the second used odd years for calibration and even years for validation. The proposed coupled approach outperformed the other methods in downscaling daily precipitation in all study periods using both calibration methods. Using odd years for calibration and even years for validation can reduce the influence of possible climate change–induced nonstationary data series. The study shows that it is necessary to combine different types of precipitation state classifiers with a method of regression or distribution to improve the performance of traditional statistical downscaling. These methods were applied to simulate future precipitation change from 2031 to 2100 with the CMIP5 climate variables. The results indicated increasing tendencies in both mean and maximum future precipitation predicted using all the downscaling methods evaluated. However, the proposed method is an at-site statistical downscaling method, and therefore this method will need to be modified for extension into a multisite domain.

scholarly journals Comparison of different statistical downscaling models and future projection of areal mean precipitation of a river basin under climate change effect

2021 ◽  
Author(s):  
A. Guven ◽  
A. Pala
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Gene Expression Programming ◽  
Circulation Model ◽  
Support Vector ◽  
Group Method ◽  
Monthly Precipitation ◽  
Weather Factors

Abstract Investigation of the hydrological impacts of climate change at the local scale requires the use of a statistical downscaling technique. In order to use the output of a Global Circulation Model (GCM) model, downscaling technique is used. In this study, statistical downscaling of monthly areal mean precipitation of Göksun River basin in Turkey was carried out using the Group Method of Data Handling (GMDH), Support Vector Machines (SVM) and Gene-expression Programming (GEP) techniques. Large-scale weather factors are used for a basin with monthly areal mean precipitation (PM) record from 1971 to 2000 for training and testing periods. The R2-value for precipitation in SVM, GEP and GMDH models are 0.62, 0.59, and 0.6 respectively, for testing periods. The results showed that SVM has the best model performance than the other proposed downscaling models, however, AIC values showed the GEP model has the lowest AIC value. The simulated results for CGCM3 A1B and A2 scenarios show a similarity in their average precipitation prediction. Generally, both scenarios anticipate a decrease in the average monthly precipitation during the simulated periods. Therefore, the results of future projections show that the mean precipitation might decrease during the period of 2021–2100.

scholarly journals Multi-Model Grand Ensemble Hydrologic Forecasting in the Fu River Basin Using Bayesian Model Averaging

Water ◽  
2017 ◽  
Vol 9 (2) ◽  
pp. 74 ◽  
Author(s):  
Bo Qu ◽  
Xingnan Zhang ◽  
Florian Pappenberger ◽  
Tao Zhang ◽  
Yuanhao Fang
Keyword(s):  
River Basin ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging

Probabilistic climate change predictions applying Bayesian model averaging

2007 ◽  
Vol 365 (1857) ◽  
pp. 2103-2116 ◽  
Author(s):  
Seung-Ki Min ◽  
Daniel Simonis ◽  
Andreas Hense
Keyword(s):  
Climate Change ◽  
Bayesian Model ◽  
Bayes Factor ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Surface Air Temperature ◽  
First Century ◽  
Intergovernmental Panel ◽  
Twenty First Century ◽  
Global Mean

This study explores the sensitivity of probabilistic predictions of the twenty-first century surface air temperature (SAT) changes to different multi-model averaging methods using available simulations from the Intergovernmental Panel on Climate Change fourth assessment report. A way of observationally constrained prediction is provided by training multi-model simulations for the second half of the twentieth century with respect to long-term components. The Bayesian model averaging (BMA) produces weighted probability density functions (PDFs) and we compare two methods of estimating weighting factors: Bayes factor and expectation–maximization algorithm. It is shown that Bayesian-weighted PDFs for the global mean SAT changes are characterized by multi-modal structures from the middle of the twenty-first century onward, which are not clearly seen in arithmetic ensemble mean (AEM). This occurs because BMA tends to select a few high-skilled models and down-weight the others. Additionally, Bayesian results exhibit larger means and broader PDFs in the global mean predictions than the unweighted AEM. Multi-modality is more pronounced in the continental analysis using 30-year mean (2070–2099) SATs while there is only a little effect of Bayesian weighting on the 5–95% range. These results indicate that this approach to observationally constrained probabilistic predictions can be highly sensitive to the method of training, particularly for the later half of the twenty-first century, and that a more comprehensive approach combining different regions and/or variables is required.

scholarly journals Application of Bayesian Model Averaging in the Reconstruction of Past Climate Change Using PMIP3/CMIP5 Multimodel Ensemble Simulations

2015 ◽  
Vol 29 (1) ◽  
pp. 175-189 ◽  
Author(s):  
M. Fang ◽  
X. Li
Keyword(s):  
Climate Change ◽  
Bayesian Model ◽  
Climate Models ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Multimodel Ensemble ◽  
Model Simulations ◽  
Ensemble Simulations ◽  
Intercomparison Project ◽  
Past Climate Change

Abstract Climate change simulations based on climate models are inevitably uncertain. This uncertainty typically stems from parametric and structural uncertainties in climate models as well as climate forcings. However, combining model simulations with instrumental observations using appropriate statistical methods is an effective approach for describing this uncertainty. In this study, the authors applied Bayesian model averaging (BMA), a statistical postprocessing method, to an ensemble of climate model simulations from the Paleoclimate Modelling Intercomparison Project phase 3 (PMIP3) and phase 5 of the Coupled Model Intercomparison Project (CMIP5). Uncertainties, weights, and variances of individual model simulations were estimated from a training period using the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis dataset. The results presented here demonstrate that the BMA method is successful and attains a positive performance in this study. These results show that the selected proxy-based reconstructions and simulations are consistent with BMA estimates regarding climate variability in the past 1000 years, though differences can be found for some periods. The authors conclude that BMA is an effective tool for describing uncertainties associated with individual model simulations, as it accounts for the diverse capabilities of different models and generates a more credible range of past climate change over a relatively long-term period based on multimodel ensemble simulations and training data.

scholarly journals Modeling saltwater intrusion using an integrated Bayesian model averaging method in the Pearl River Delta

2019 ◽  
Vol 21 (6) ◽  
pp. 1147-1162 ◽  
Author(s):  
Kairong Lin ◽  
Pengyu Lu ◽  
Chong-Yu Xu ◽  
Xuan Yu ◽  
Tian Lan ◽  
...  
Keyword(s):  
Bayesian Model ◽  
Pearl River Delta ◽  
Saltwater Intrusion ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
River Delta ◽  
Pearl River ◽  
Support Vector ◽  
The Pearl River Delta ◽  
The Pearl River

Abstract The reverse flow of seawater causes salinity in inland waterways and threatens water resources of the coastal population. In the Pearl River Delta, saltwater intrusion has resulted in a water crisis. In this study, we proposed a tailored approach to chlorinity prediction at complex delta systems like the Pearl River Delta. We identified the delayed predictors prior to optimization based on the maximal information coefficient (MIC) and Pearson's correlation coefficient (r). To achieve an ensemble simulation, a Bayesian model averaging (BMA) method was applied to integrate temporally sensitive empirical model predictions given by random forest (RF), support vector machine (SVM), and Elman neural network (ENN). The results showed that: (a) The ENN performed the worst among the three; (b) The BMA approach outperformed the individual models (i.e., RF, ENN, and SVM) in terms of Nash–Sutcliffe efficiency (NSE), and the percentage of bias (Pbias). The BMA weights reflect the model performance and the correlation of the predictions given by its ensemble models. (c) Our variable selection method resulted in a stronger model with greater interpretability.

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