two statistical downscaling models, the non-homogeneous hidden markov model (nhmm) and the statistical down–scaling model (sdsm) were used to generate future scenarios of both mean and extremes in the tarim river basin,which were based on nine combined scenarios including three general circulation models (gcms) (csiro30, echam5,and gfdl21) predictor sets and three special report on emission sce...

Two statistical downscaling models, the non-homogeneous hidden Markov model (NHMM) and the Statistical Down–Scaling Model (SDSM) were used to generate future scenarios of both mean and extremes in the Tarim River basin,which were based on nine combined scenarios including three general circulation models (GCMs) (CSIRO30, ECHAM5,and GFDL21) predictor sets and three special report on emission sce...

A two-stage methodology is developed to obtain future projections of daily relative humidity in a river basin for climate change scenarios. In the first stage, Support Vector Machine (SVM) models are developed to downscale nine sets of predictor variables (large-scale atmospheric variables) for Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (SRES) (A1B, A2, B1, ...

A variety of methods are available to estimate future solar radiation (SR) scenarios at spatial scales that are appropriate for local climate change impact assessment. However, there are no clear guidelines available in the literature to decide which methodologies are most suitable for different applications. Three methodologies to guide the estimation of SR are discussed in this study, namely:...

By applying a two-step statistical downscaling technique to four climate models under different future emission scenarios, we produced future projections of the daily precipitation and the maximum and minimum temperatures over the Spanish region of Aragón. The reliability of the downscaling technique was assessed by a verification process involving the comparison of the downscaled reanalysis da...

In this paper, downscaling models are developed using a Linear Multiple Regression (LMR) and Artificial Neural Networks (ANNs) for obtaining projections of mean monthly precipitation to lake-basin scale in an arid region in India. The effectiveness of these techniques is demonstrated through application to downscale the predictand (precipitation) for the Pichola lake region in Rajasthan state i...

based on spi and rdi indices, changes in droughts in the semi-arid areas of west golestan province was assessed in the gis environment by incorporating data from hashemabad synoptic station and 11 climatic stations. after evaluating the ability of lars-wg5 in the simulation period (1984-2010), downscaling of hadcm3, ipcm, and gfc models was done as a group under scenarios of a1b, a2 and b1 to e...

assessing the potential impacts of 21st-century climate change on species distributions and ecological processes requires climate scenarios with sufficient spatial resolution. in this study we projected future changes in maximum temperature and minimum temperature under cmip3 sres and cmip5 rcps scenarios with two station-based datasets (arazkoseh and nodeh) of the eastern golestan province. ch...

The study aims to assess climate change impacts on streamflow drought in a catchment upstream of Tseng-Wen Reservoir which is the main water supplier in southern Taiwan. A singular-value-decomposition statistical downscaling method and a stochastic weather generator were applied to six different general circulation models for rainfall and temperature downscaling. Two emission climate change sce...