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

Liu, Jiaming; Yuan, Di; Zhang, Liping; Zou, Xia; Xing-yuan, Song

doi:10.1155/2016/7463963

Cited by 28 publications

(23 citation statements)

References 52 publications

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“…For this aim, various downscaling methods have been developed, which are mainly categorized into two types: dynamic and statistical. The latter is more common in climate change projection studies due to its ease and low cost compared with the dynamic downscaling methods (Hassan et al ., ; Liu et al ., ). This study focuses on one of the most widely used downscaling methods, which is called the statistical downscaling method or statistical downscaling model (SDSM), as a regression‐based technique (Wilby and Dawson, ).…”

Section: Introductionmentioning

confidence: 97%

Downscaling the contribution to uncertainty in climate‐change assessments: representative concentration pathway (RCP) scenarios for the South Alborz Range, Iran

Mirdashtvan

Najafinejad

Malekian

et al. 2017

Meteorological Applications

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One of the most important stages in climate-change-impact studies is uncertainty analysis, due to its great effect on both predictions and decision-making. This study presents a procedure that characterizes the changes of climatic variables for the period 2011-2040 under representative concentration pathway (RCP) scenarios and then quantifies the uncertainty linked with the downscaling process using a bootstrapping method at 95% confidence intervals in one of the most vulnerable basins, the "Karaj-Jajrud" located in the South Alborz Range, Iran. The results show that there is a consistent warming in mean air temperature time-series with different magnitudes for all the RCP scenarios in the region for 2011-2040, whereas the results indicate decreasing precipitation compared with the baseline period for all RCP scenarios in the study area. Analysing the impacts of the downscaling process uncertainty on the prediction results shows that the contribution of this uncertainty source to the prediction uncertainty is relatively high, as about 30% of the downscaled temperature and precipitation data fall inside the 95% simulation confidence intervals. Furthermore, precipitation-series uncertainty is more than the air temperature series. Climate change assessments and their uncertainty analysis can help managers to enhance preparedness and adaptation strategies in order to mitigate the consequences of natural hazards. More investigations can be done by adopting more general circulation models and other downscaling methods to compare the uncertainty that arises from each uncertainty source.

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Section: Introductionmentioning

confidence: 97%

Downscaling the contribution to uncertainty in climate‐change assessments: representative concentration pathway (RCP) scenarios for the South Alborz Range, Iran

Mirdashtvan

Najafinejad

Malekian

et al. 2017

Meteorological Applications

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“…The exclusion of influential predictors from a downscaling model can cause it to perform poorly, as important large‐scale atmospheric information may be missing in the inputs. Also, the inclusion of non‐influential predictors or inclusion of predictors which have no significant impact on a predictand can ingest noise into the model (Liu et al, ) and ultimately lead to its poor performance. Furthermore, the redundant information in the predictors can yield unnecessarily complex models which can increase the computational cost (May et al, ).…”

Section: Introductionmentioning

confidence: 99%

Cautionary note on the use of genetic programming in statistical downscaling

Sachindra

Ahmed

Shahid

et al. 2018

Intl Journal of Climatology

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The selection of inputs (predictors) to downscaling models is an important task in any statistical downscaling exercise. The selection of an appropriate set of predictors to a downscaling model enhances its generalization skills as such set of predictors can reliably explain the catchment‐scale hydroclimatic variable (predictand). Among the predictor selection procedures seen in the literature, the use of genetic programming (GP) can be regarded as a unique approach as it not only selects a set of predictors influential on the predictand but also simultaneously determines a linear or nonlinear regression relationship between the predictors and the predictand. In this short communication, the details of an investigation on the assessment of effectiveness of GP in identifying a unique optimum set of predictors influential on the predictand and its ability to generate a unique optimum predictor–predictand relationship are presented. In this investigation, downscaling models were evolved for relatively wet and dry precipitation stations pertaining to two study areas using two different sets of reanalysis data for each calendar month maintaining the same GP attributes. It was found that irrespective of the climate regime (i.e., wet and dry) and reanalysis data set used, the probability of identification of a unique optimum set of predictors influential on precipitation by GP is quite low. Therefore, it can be argued that the use of GP for the selection of a unique optimum set of predictors influential on a predictand is not effective. However, when run repetitively, GP algorithm selected certain predictors more frequently than others. Also, when run repetitively, the structure of the predictor–predictand relationships evolved by GP varied from one run to another, indicating that the physical interpretation of the predictor–predictand relationships evolved by GP in a downscaling exercise can be unreliable.

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“…The method is capable of correcting systematic errors in GCMs' output with less computational complexity and a higher feasibility [20]. There are several statistical downscaling methods, and the availability of these methods varies between different regions [21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Projection of Climate Change Scenarios in Different Temperature Zones in the Eastern Monsoon Region, China

Liu

2017

Water

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The Eastern Monsoon Region of China is sensitive to climate change because of its special location. In this study, the Automated Statistical Downscaling (ASD) tool was used to simulate and project future climate change scenarios in different temperate zones in the Eastern Monsoon Region of China. The performances of the single General Circulation Model (GCM) and the GCMs ensemble from Coupled Model Inter-comparison Project Phase 5 (CMIP5) were compared, and the capability of the ASD model was evaluated. The simple mathematical averaging ensemble of GCMs is superior to the single GCM and to the other two weighted ensembles. The ASD model was capable of presenting the temporal and spatial variation of four variables (precipitation, mean air temperature, maximum and minimum air temperature) during both the calibration and validation periods. The performance of the ASD model varied among the different temperate zones. In the simulated future scenarios, the air temperature in the four zones showed an upward trend. Except for the subtropical zone, there was a tendency for increased precipitation in both the warm temperate zone and the cold temperate and middle temperate zones. Of particular interest is that, in the subtropical zone, the precipitation will decline in the future, whereas the air temperature (especially the maximum air temperature) will increase, which may put more pressure on water resource situations in this area.

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

Cited by 28 publications

References 52 publications

Downscaling the contribution to uncertainty in climate‐change assessments: representative concentration pathway (RCP) scenarios for the South Alborz Range, Iran

Downscaling the contribution to uncertainty in climate‐change assessments: representative concentration pathway (RCP) scenarios for the South Alborz Range, Iran

Cautionary note on the use of genetic programming in statistical downscaling

Projection of Climate Change Scenarios in Different Temperature Zones in the Eastern Monsoon Region, China

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