DOI: 10.5194/hess-24-2963-2020
论文题名: Bias in dynamically downscaled rainfall characteristics for hydroclimatic projections
作者: Potter N.J. ; Chiew F.H.S. ; Charles S.P. ; Fu G. ; Zheng H. ; Zhang L.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2020
卷: 24, 期: 6 起始页码: 2963
结束页码: 2979
语种: 英语
Scopus关键词: Climate change
; Electric power system interconnection
; Hydrology
; Mapping
; Probability
; Rain
; Runoff
; Time series
; Bias-correction methods
; Dynamical downscaling
; Hydrological modelling
; Rainfall characteristics
; Regional climate modelling
; Regional water resources
; Resource availability
; Transition probabilities
; Climate models
; autocorrelation
; climate modeling
; downscaling
; future prospect
; global climate
; hydrological modeling
; hydrometeorology
; rainfall
; runoff
; water availability
; water planning
; Australia
; Australian Capital Territory
; New South Wales
; Victoria [Australia]
英文摘要: Dynamical downscaling of future projections of global climate model outputs can provide useful information about plausible and possible changes to water resource availability, for which there is increasing demand in regional water resource planning processes. By explicitly modelling climate processes within and across global climate model grid cells for a region, dynamical downscaling can provide higher-resolution hydroclimate projections and independent (from historical time series), physically plausible future rainfall time series for hydrological modelling applications. However, since rainfall is not typically constrained to observations by these methods, there is often a need for bias correction before use in hydrological modelling. Many bias-correction methods (such as scaling, empirical and distributional mapping) have been proposed in the literature, but methods that treat daily amounts only (and not sequencing) can result in residual biases in certain rainfall characteristics, which flow through to biases and problems with subsequently modelled runoff. We apply quantile-quantile mapping to rainfall dynamically downscaled by the NSW and ACT Regional Climate Modelling (NARCliM) Project in the state of Victoria, Australia, and examine the effect of this on (i) biases both before and after bias correction in different rainfall metrics, (ii) change signals in metrics in comparison to the bias and (iii) the effect of bias correction on wet-wet and dry-dry transition probabilities. After bias correction, persistence of wet states is under-correlated (i.e. more random than observations), and this results in a significant bias (underestimation) of runoff using hydrological models calibrated on historical data. A novel representation of quantile-quantile mapping is developed based on lag-one transition probabilities of dry and wet states, and we use this to explain residual biases in transition probabilities. Representing quantile-quantile mapping in this way demonstrates that any quantile mapping bias-correction method is unable to correct the underestimation of autocorrelation of rainfall sequencing, which suggests that new methods are needed to properly bias-correct dynamical downscaling rainfall outputs. © Author(s) 2020. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/162669
Appears in Collections: 气候变化与战略
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作者单位: Potter, N.J., CSIRO Land and Water, Canberra, ACT 2601, Australia; Chiew, F.H.S., CSIRO Land and Water, Canberra, ACT 2601, Australia; Charles, S.P., CSIRO Land and Water, Floreat, WA 6148, Australia; Fu, G., CSIRO Land and Water, Floreat, WA 6148, Australia; Zheng, H., CSIRO Land and Water, Canberra, ACT 2601, Australia; Zhang, L., CSIRO Land and Water, Canberra, ACT 2601, Australia
Recommended Citation:
Potter N.J.,Chiew F.H.S.,Charles S.P.,et al. Bias in dynamically downscaled rainfall characteristics for hydroclimatic projections[J]. Hydrology and Earth System Sciences,2020-01-01,24(6)