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An evaluation of how downscaled climate data represents historical precipitation characteristics beyond the means and variances

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dc.contributor.author Kusangaya, S
dc.contributor.author Warburton Toucher, ML
dc.contributor.author Archer, Emma RM
dc.contributor.author Jewitt, GPW
dc.date.accessioned 2017-09-27T12:37:55Z
dc.date.available 2017-09-27T12:37:55Z
dc.date.issued 2016-09
dc.identifier.citation Kusangaya, S., Warburton Toucher, M.L., Archer, E.R.M., et al. 2016. An evaluation of how downscaled climate data represents historical precipitation characteristics beyond the means and variances. Global and Planetary Change, vol. 144: 129-141 en_US
dc.identifier.issn 0921-8181
dc.identifier.uri doi.org/10.1016/j.gloplacha.2016.07.014
dc.identifier.uri http://www.sciencedirect.com/science/article/pii/S0921818116301229
dc.identifier.uri https://www.researchgate.net/publication/305679755_An_evaluation_of_how_downscaled_climate_data_represents_historical_precipitation_characteristics_beyond_the_means_and_variances
dc.identifier.uri http://hdl.handle.net/10204/9609
dc.description Copyright: 2016 Elsevier. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, kindly consult the publisher's website. en_US
dc.description.abstract Precipitation is the main driver of the hydrological cycle. For climate change impact analysis, use of downscaled precipitation, amongst other factors, determines accuracy of modelled runoff. Precipitation is, however, considerably more difficult to model than temperature, largely due to its high spatial and temporal variability and its nonlinear nature. Due to such qualities of precipitation, a key challenge for water resources management is thus how to incorporate potentially significant but highly uncertain precipitation characteristics when modelling potential changes in climate for water resources management in order to support local management decisions. Research undertaken here was aimed at evaluating how downscaled climate data represented the underlying historical precipitation characteristics beyond the means and variances. Using the uMngeni Catchment in KwaZulu-Natal, South Africa as a case study, the occurrence of rainfall, rainfall threshold events and wet dry sequence was analysed for current climate (1961–1999). The number of rain days with daily rainfall > 1 mm, > 5 mm, > 10 mm, > 20 mm and > 40 mm for each of the 10 selected climate models was, compared to the number of rain days at 15 rain stations. Results from graphical and statistical analysis indicated that on a monthly basis rain days are over estimated for all climate models. Seasonally, the number of rain days were overestimated in autumn and winter and underestimated in summer and spring. The overall conclusion was that despite the advancement in downscaling and the improved spatial scale for a better representation of the climate variables, such as rainfall for use in hydrological impact studies, downscaled rainfall data still does not simulate well some important rainfall characteristics, such as number of rain days and wet-dry sequences. This is particularly critical, since, whilst for climatologists, means and variances might be simulated well in downscaled GCMs, for hydrologists, downscaled climate data still needs to represent the underlying historical precipitation properties, such as consecutive wet days, number of rain days and their seasonal and monthly distribution during the downscaling process. This then calls for an improvement in the downscaling process by incorporating rainfall drivers such as cyclones, so as to capture better, these rainfall characteristics important to hydrologists. en_US
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.relation.ispartofseries Worklist;18350
dc.subject Downscaled climate models en_US
dc.subject Rainfall uncertainty en_US
dc.subject Climate change en_US
dc.subject Hydrology en_US
dc.title An evaluation of how downscaled climate data represents historical precipitation characteristics beyond the means and variances en_US
dc.type Article en_US
dc.identifier.apacitation Kusangaya, S., Warburton Toucher, M., Archer, E. R., & Jewitt, G. (2016). An evaluation of how downscaled climate data represents historical precipitation characteristics beyond the means and variances. http://hdl.handle.net/10204/9609 en_ZA
dc.identifier.chicagocitation Kusangaya, S, ML Warburton Toucher, Emma RM Archer, and GPW Jewitt "An evaluation of how downscaled climate data represents historical precipitation characteristics beyond the means and variances." (2016) http://hdl.handle.net/10204/9609 en_ZA
dc.identifier.vancouvercitation Kusangaya S, Warburton Toucher M, Archer ER, Jewitt G. An evaluation of how downscaled climate data represents historical precipitation characteristics beyond the means and variances. 2016; http://hdl.handle.net/10204/9609. en_ZA
dc.identifier.ris TY - Article AU - Kusangaya, S AU - Warburton Toucher, ML AU - Archer, Emma RM AU - Jewitt, GPW AB - Precipitation is the main driver of the hydrological cycle. For climate change impact analysis, use of downscaled precipitation, amongst other factors, determines accuracy of modelled runoff. Precipitation is, however, considerably more difficult to model than temperature, largely due to its high spatial and temporal variability and its nonlinear nature. Due to such qualities of precipitation, a key challenge for water resources management is thus how to incorporate potentially significant but highly uncertain precipitation characteristics when modelling potential changes in climate for water resources management in order to support local management decisions. Research undertaken here was aimed at evaluating how downscaled climate data represented the underlying historical precipitation characteristics beyond the means and variances. Using the uMngeni Catchment in KwaZulu-Natal, South Africa as a case study, the occurrence of rainfall, rainfall threshold events and wet dry sequence was analysed for current climate (1961–1999). The number of rain days with daily rainfall > 1 mm, > 5 mm, > 10 mm, > 20 mm and > 40 mm for each of the 10 selected climate models was, compared to the number of rain days at 15 rain stations. Results from graphical and statistical analysis indicated that on a monthly basis rain days are over estimated for all climate models. Seasonally, the number of rain days were overestimated in autumn and winter and underestimated in summer and spring. The overall conclusion was that despite the advancement in downscaling and the improved spatial scale for a better representation of the climate variables, such as rainfall for use in hydrological impact studies, downscaled rainfall data still does not simulate well some important rainfall characteristics, such as number of rain days and wet-dry sequences. This is particularly critical, since, whilst for climatologists, means and variances might be simulated well in downscaled GCMs, for hydrologists, downscaled climate data still needs to represent the underlying historical precipitation properties, such as consecutive wet days, number of rain days and their seasonal and monthly distribution during the downscaling process. This then calls for an improvement in the downscaling process by incorporating rainfall drivers such as cyclones, so as to capture better, these rainfall characteristics important to hydrologists. DA - 2016-09 DB - ResearchSpace DP - CSIR KW - Downscaled climate models KW - Rainfall uncertainty KW - Climate change KW - Hydrology LK - https://researchspace.csir.co.za PY - 2016 SM - 0921-8181 T1 - An evaluation of how downscaled climate data represents historical precipitation characteristics beyond the means and variances TI - An evaluation of how downscaled climate data represents historical precipitation characteristics beyond the means and variances UR - http://hdl.handle.net/10204/9609 ER - en_ZA


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