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

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

Journal of Water and Climate Change ◽

10.2166/wcc.2014.056 ◽

2014 ◽

Vol 5 (4) ◽

pp. 496-525 ◽

Cited By ~ 9

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.

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Conjunction of wavelet-entropy and SOM clustering for multi-GCM statistical downscaling

Hydrology Research ◽

10.2166/nh.2018.169 ◽

2018 ◽

Vol 50 (1) ◽

pp. 1-23 ◽

Cited By ~ 9

Author(s):

Aida Hosseini Baghanam ◽

Vahid Nourani ◽

Mohammad-Ali Keynejad ◽

Hassan Taghipour ◽

Mohammad-Taghi Alami

Keyword(s):

Statistical Downscaling ◽

General Circulation ◽

Large Scale ◽

General Circulation Models ◽

Reanalysis Data ◽

Self Organizing Map ◽

Climate Data ◽

Wavelet Entropy ◽

Som Clustering ◽

Artificial Neural Network Ann

Abstract Important issues in statistical downscaling of general circulation models (GCMs) is to select dominant large-scale climate data (predictors). This study developed a predictor screening framework, which integrates wavelet-entropy (WE) and self-organizing map (SOM) to downscale station rainfall. WEs were computed as the representatives of predictors and fed into the SOM to cluster the predictors. SOM-based clustering of predictors according to WEs could lead to physically meaningful selection of the dominant predictors. Then, artificial neural network (ANN) as the statistical downscaling method was developed. To assess the advantages of different GCMs, multi-GCM ensemble approach was used by Can-ESM2, BNU-ESM, and INM-CM4 GCMs. Moreover, NCEP reanalysis data were used to calibrate downscaling model as well for comparison purposes. The calibration, validation, and projection of the proposed model were performed during January 1951 to December 1991, January 1992 to December 2005 and January 2017 to December 2100, respectively. The proposed data screening model could reduce the dimensionality of data and select appropriate predictors for generalizing future rainfall. Results showed better performance of ANN than multiple linear regression (MLR) model. The projection results yielded 29% and 21% decrease of rainfall at the study area for 2017–2050 under RCPs 4.5 and 8.5, respectively.

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Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-4-3413-2007 ◽

2007 ◽

Vol 4 (5) ◽

pp. 3413-3440 ◽

Cited By ~ 18

Author(s):

E. P. Maurer ◽

H. G. Hidalgo

Keyword(s):

Statistical Downscaling ◽

General Circulation ◽

Large Scale ◽

Impact Analysis ◽

Climate Model ◽

Daily Precipitation ◽

Circulation Model ◽

Reanalysis Data ◽

Temperature Extremes ◽

Daily Data

Abstract. Downscaling of climate model data is essential to most impact analysis. We compare two methods of statistical downscaling to produce continuous, gridded time series of precipitation and surface air temperature at a 1/8-degree (approximately 140 km² per grid cell) resolution over the western U.S. We use NCEP/NCAR Reanalysis data from 1950–1999 as a surrogate General Circulation Model (GCM). The two methods included are constructed analogues (CA) and a bias correction and spatial downscaling (BCSD), both of which have been shown to be skillful in different settings, and BCSD has been used extensively in hydrologic impact analysis. Both methods use the coarse scale Reanalysis fields of precipitation and temperature as predictors of the corresponding fine scale fields. CA downscales daily large-scale data directly and BCSD downscales monthly data, with a random resampling technique to generate daily values. The methods produce comparable skill in producing downscaled, gridded fields of precipitation and temperatures at a monthly and seasonal level. For daily precipitation, both methods exhibit some skill in reproducing both observed wet and dry extremes and the difference between the methods is not significant, reflecting the general low skill in daily precipitation variability in the reanalysis data. For low temperature extremes, the CA method produces greater downscaling skill than BCSD for fall and winter seasons. For high temperature extremes, CA demonstrates higher skill than BCSD in summer. We find that the choice of most appropriate downscaling technique depends on the variables, seasons, and regions of interest, on the availability of daily data, and whether the day to day correspondence of weather from the GCM needs to be reproduced for some applications. The ability to produce skillful downscaled daily data depends primarily on the ability of the climate model to show daily skill.

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