Statistical downscaling of general-circulation-model- simulated average monthly air temperature to the beginning of flowering of the dandelion (Taraxacum officinale) in Slovenia

2001 ◽  
Vol 46 (1) ◽  
pp. 22-32 ◽  
Author(s):  
Klemen Bergant ◽  
Lučka Kajfež-Bogataj ◽  
Zalika Črepinšek
Keyword(s):  
General Circulation Model ◽  
Air Temperature ◽  
Statistical Downscaling ◽  
General Circulation ◽  
Circulation Model ◽  
Taraxacum Officinale ◽  
Beginning Of Flowering

Statistical downscaling of general circulation model outputs to precipitation, evaporation and temperature using a key station approach

2016 ◽  
Vol 7 (4) ◽  
pp. 683-707
Author(s):  
D. A. Sachindra ◽  
F. Huang ◽  
A. Barton ◽  
B. J. C. Perera
Keyword(s):  
General Circulation Model ◽  
Statistical Downscaling ◽  
General Circulation ◽  
Emission Scenario ◽  
Circulation Model ◽  
Reanalysis Data ◽  
Maximum Temperature ◽  
Monthly Precipitation ◽  
Regression Equations ◽  
Highly Correlated

Using a key station approach, statistical downscaling of monthly general circulation model outputs to monthly precipitation, evaporation, minimum temperature and maximum temperature at 17 observation stations located in Victoria, Australia was performed. Using the observations of each predictand, over the period 1950–2010, correlations among all stations were computed. For each predictand, the station which showed the highest number of correlations above 0.80 with other stations was selected as the first key station. The stations that were highly correlated with that key station were considered as the member stations of the first cluster. By employing this same procedure on the remaining stations, the next key station was found. This procedure was performed until all stations were segregated into clusters. Thereafter, using the observations of each predictand, regression equations (inter-station regression relationships) were developed between the key stations and the member stations for each calendar month. The downscaling models at the key stations were developed using reanalysis data as inputs to them. The outputs of HadCM3 pertaining to A2 emission scenario were introduced to these downscaling models to produce projections of the predictands over the period 2000–2099. Then the outputs of these downscaling models were introduced to the inter-station regression relationships to produce projections of predictands at all member stations.

Statistical downscaling of general circulation model outputs to catchment scale hydroclimatic variables: issues, challenges and possible solutions

2014 ◽  
Vol 5 (4) ◽  
pp. 496-525 ◽  
Author(s):  
D. A. Sachindra ◽  
F. Huang ◽  
A. Barton ◽  
B. J. C. Perera
Keyword(s):  
General Circulation Model ◽  
Statistical Downscaling ◽  
General Circulation ◽  
Circulation Model ◽  
Potential Method ◽  
Mathematical Representation ◽  
Physical Processes ◽  
Catchment Scale ◽  
Scenario Uncertainty ◽  
Hydroclimatic Variables

The aim of this paper is to discuss the issues and challenges associated with statistical downscaling of general circulation model (GCM) outputs to hydroclimatic variables at catchment scale and also to discuss potential solutions to address these issues and challenges. Outputs of GCMs (predictors of statistical downscaling models) suffer a considerable degree of uncertainty, mainly due to the lack of theoretical robustness caused by the limited understanding of various physical processes of the atmosphere and the incomplete mathematical representation of those processes in GCMs. The presence of several future GHG emission scenarios with equal likelihood of occurrence leads to scenario uncertainty. Outputs of a downscaling study are dependent on the quality and the length of the record of field observations, as statistical downscaling models are calibrated and validated against these observations of the hydroclimatic variables (predictands of statistical downscaling models). The downscaled results vary from one statistical downscaling technique to another due to different representations of the predictor–predictand relationships. Also different techniques used in selecting the predictors for statistical downscaling models influence the model outputs. Although statistical downscaling faces these issues, it is still considered as a potential method of predicting the catchment scale hydroclimatology from GCM outputs.

scholarly journals Statistical downscaling of climate data to estimate streamflow in a semi-arid catchment

2012 ◽  
Vol 9 (4) ◽  
pp. 4869-4918 ◽  
Author(s):  
S. Samadi ◽  
G. J. Carbone ◽  
M. Mahdavi ◽  
F. Sharifi ◽  
M. R. Bihamta
Keyword(s):  
General Circulation Model ◽  
Statistical Downscaling ◽  
General Circulation ◽  
Large Scale ◽  
Atmospheric Precipitation ◽  
Circulation Model ◽  
Climate Data ◽  
Catchment Scale ◽  
Artificial Neural Network Ann ◽  
Semi Arid

Abstract. Linear and non-linear statistical 'downscaling' study is done to relate large-scale climate information from a general circulation model (GCM) to local-scale river flows in west Iran. This study aims to investigate and evaluate the more promising downscaling techniques, and provides a through inter comparison study using the Karkheh catchment as an experimental site in a semi arid region for the years of 2040 to 2069. A hybrid conceptual hydrological model was used in conjunction with modeled outcomes from a General Circulation Model (GCM), HadCM3, along with two downscaling techniques, Statistical Downscaling Model (SDSM) and Artificial Neural Network (ANN), to determine how future streamflow may change in a semi arid catchment. The results show that the choice of a downscaling algorithm having a significant impact on the streamflow estimations for a semi-arid catchment, which are mainly, influenced, respectively, by atmospheric precipitation and temperature projections. According to the SDSM and ANN projections, daily temperature will increase up to +0.58° (+3.90%) and +0.48° (+3.48%) and daily precipitation will decrease up to −0.1mm (−2.56%) and −0.4 mm (−2.82%) respectively. Moreover streamflow changes corresponding to downscaled future projections presented a reduction in mean annual flow of −3.7 m3 s−1 and −9.47 m3 s−1 using SDSM and ANN outputs respectively. The results suggest a significant decrease of streamflow in both downscaling projections, particularly in winter. The discussion considers the performance of each statistical method for downscaling future flow at catchment scale as well as the relationship between atmospheric processes and flow variability and changes.

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