Abstract
Although assessment of the anticipated impacts of projected climate change is very much required for many sectors, non-availability of climate data on the local scale is a major limiting factor. In this background, statistical downscaling has a lot of scope. However, the downscaled data have to be thoroughly analysed in order to assess the uncertainty associated with it. In this study, a detailed uncertainty analysis was performed on statistically and dynamically downscaled monthly precipitation data in the Chaliyar River Basin, in Kerala, India. The mean and variance of the downscaled and observed data for each month were compared. The Wilcoxon signed-rank test, Levene’s test, Brown–Forsythe test, and the nonparametric Levene’s test were performed on the downscaled precipitation data at 5 % significance level. Results showed that the error is not significant in the case of the statistically downscaled data using predictors generated from the reanalysis data. In the case of statistically downscaled data from the predictions of the general circulation model (GCM), error in the mean is significant in some months, probably due to uncertainty in the GCM predictors, whereas the error in the variance is insignificant. For dynamically downscaled data, the error in the mean as well as the variance is not significant. Uncertainty analysis is required to be performed on the downscaled data before its use in impact assessment.
Keywords
- Artificial Neural Network Model
- General Circulation Model
- Statistical Downscaling
- Exploratory Data Analysis
- Normalise Mean Square Error
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bradley RS, Vuille M, Diaz HF, Vergara W (2006) Threats to water supplies in the tropical Andes. Science 312(5781):1755–1756
Brown MB, Forsythe AB (1974) Robust tests for equality of variances. J Am Stat Assoc 69:364–367
Cooke RM (2013) Uncertainty analysis comes to integrated assessment models for climate change…and conversely. Clim Chang 117:467–479
Crane RG, Hewitson BC (1998) Doubled CO2 precipitation changes for the Susquehanna basin: downscaling from the genesis general circulation model. Int J Climatol 18:65–76
Dibike YB, Coulibaly P (2006) Temporal neural networks for downscaling climate variability and extremes. Neural Network 19(2):135–144
Fistikoglu O, Okkan U (2011) Statistical downscaling of monthly precipitation using NCEP/NCAR reanalysis data for Tahtali River Basin in Turkey. J Hydrol Eng 16(2):157–164. doi:10.1061/(ASCE)HE.1943-5584.0000300
Fu G, Liu Z, Charles SP, Xu Z, Yao Z (2013) A score based method for assessing the performance of GCMs: A case study of southeastern Australia. J Geophys Res D 118:4154–4416
Gardner MW, Dorling SR (1998) Artificial neural networks (the multi-layer perceptron)—a review of applications in the atmospheric sciences. Atmos Environ 32:2627–2636
Hagan MT, Menhaj MB (1994) Training feed forward network with Marquardt algorithm. IEEE Trans Neural Network 5(6):989–993
Hassell D, Jones RG (1999) Simulating climatic change of the southern Asian monsoon using a nested regional climate model. Technical note no. 8, Hadley Centre, Bracknell, UK, 16 p
Haykin S (1994) Neural networks: a comprehensive foundation. MacMillan, New York
Haylock MR, Cawley GC, Harpham C, Wilby RL, Goodess CM (2006) Downscaling heavy precipitation over the United Kingdom: a comparison of dynamical and statistical methods and their future scenarios. Int J Climatol 26:1397–1415
Hewitson BC, Crane RG (1996) Climate downscaling: techniques and application. Clim Res 7:85–95
Johnson RA (2011) Miller and Freund’s probability and statistics for engineers. Pearson Prentice Hall, New Jersey
Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Leetmaa A, Reynolds R, Jenne R, Joseph D (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77(3):437–471
Khan MS, Coulibaly P, Dibike Y (2006) Uncertainty analysis of statistical downscaling methods. J Hydrol 319:357–382
Lambert SJ, Boer GJ (2011) CMIP1 evaluation and intercomparison of coupled climate models. Clim Dynam 17:83–106
Levene H (1960) Contributions to probability and statistics. Stanford University Press, California
Levin RI, Rubin DS (1998) Statistics for management. Prentice Hall, New Jersey
Maraun D, Wetterhall F, Ireson AM, Chandler RE, Kendon EJ, Widmann M, Brienen S, Rust HW, Sauter T, Themeßl M, Venema VKC, Chun KP, Goodess CM, Jones RG, Onof C, Vrac M, Thiele-Eich I (2010) Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Rev Geophys 48:RG3003
McGuffie K, Henderson-Sellers A (2005) A climate modelling primer. Wiley, England
Murphy JM (1999) An evaluation of statistical and dynamical techniques for downscaling local climate. J Clim 12:2256–2284
Nordstokke DW, Zumbo BD (2010) A new nonparametric test for equal variances. Psicologica 31:401–430
Nordstokke DW, Zumbo BD, Cairns SL, Saklofske DH (2011) The operating characteristics of the nonparametric Levene test for equal variances with assessment and evaluation data. Pract Assess Res Eval 16(5)
Pearson K (1896) Mathematical contributions to the theory of evolution III regression heredity and panmixia. Philos Trans R Soc Lond 187:253–318
Rupa Kumar K, Sahai AK, Krishna Kumar K, Patwardhan SK (2006) High-resolution climate change scenarios for India for the 21st century. CurrSci 90:334–345
Simmons AJ, Burridge DM (1981) An energy and angular-momentum conserving finite-difference scheme and hybrid coordinates. Mon Weather Rev 109:758–766
Simon W, Hassell D, Hein D, Jones R, Taylor R (2004) Installing and using the Hadley Centre regional climate modelling system PRECIS. Met Office Hadley Centre, Exeter, UK
Skelly WC, Henderson-Sellers A (1996) Grid box or grid point: what type of data do GCMs deliver to climate impacts researchers? Int J Climatol 16:1079–1086
Smith I, Chandler E (2010) Refining rainfall projections for the Murray Darling Basin of south-east Australia—the effect of sampling model results based on performance. Clim Chang 102:377–393
Stainforth DA, Allen MR, Tredger ER, Smith LA (2007) Confidence, uncertainty and decision support relevance in climate predictions. Phil Trans R Soc A 365:2145–2161
Still CJ, Foster PN, Schneider SH (1999) Simulating the effects of climate change on tropical mountain cloud forests. Nature 398(6728):608–610
Viviroli D, Archer DR, Buytaert W, Fowler HJ, Greenwood GB, Hamlet AF, Huang Y, Koboltschnig G, Litaor MI, Lopez-Moreno JI, Lorentz S, Schadler B, Schreier H, Schwaiger K, Vuille M, Woods R (2011) Climate change and mountain water resources: overview and recommendations for research, management and policy. Hydrol Earth Syst Sci 15:471–504. doi:10.5194/hess-15-471-2011
Wilby RL, Fowler HJ (2011) Regional climate downscaling. In: Fung F, Lopez A, New M (ed) Modelling the impact of climate change on water resources, 1st edn. Blackwell, West Sussex, pp 34–85
Wilby RL, Charles SP, Zorita E, Timbal B, Whetton P, Mearns LO (2004) The guidelines for use of climate scenarios developed from statistical downscaling methods. Supporting material of the Intergovernmental Panel on Climate Change (IPCC), prepared on behalf of Task Group on Data and Scenario Support for Impacts and Climate Analysis (TGICA). http://ipccddc.cru.uea.ac.uk/guidelines/StatDown Guide.pdf
Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics 1:80–83
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Thampi, S.G., Chithra, N.R. (2015). Uncertainty Analysis of Statistically and Dynamically Downscaled Precipitation Data: A Study of the Chaliyar River Basin, Kerala, India. In: Shrestha, S., Anal, A., Salam, P., van der Valk, M. (eds) Managing Water Resources under Climate Uncertainty. Springer Water. Springer, Cham. https://doi.org/10.1007/978-3-319-10467-6_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-10467-6_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10466-9
Online ISBN: 978-3-319-10467-6
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)