Statistical downscaling of rainfall: a non-stationary and multi-resolution approach

Rashid, M. M.; Beecham, Simon; Chowdhury, R. K.

doi:10.1007/s00704-015-1465-3

Cited by 32 publications

(18 citation statements)

References 37 publications

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“…The potential predictors vary spatiotemporally as the relationships between the large-scale atmospheric conditions and the catchment scale climate can vary over time and space [24]. In order to identify the potential predictors, initially, for each calendar month, the NCEP/NCAR reanalysis data of probable predictors pertaining to the 42 grid points (shown in Fig 3) and observed precipitation data at Halls Gap, Birchip and Swan Hill for the period 1950–2010 were split into three 20-year time slices in the chronological order.…”

Section: Methodsmentioning

confidence: 99%

“…To the date, there are only a few studies that have investigated potential approaches for handling non-stationarities in the PPRs in statistical downscaling [22]. In these studies, potential approaches for handling non-stationarities in the PPRs in statistical downscaling have been developed: considering the changes in the occurrence frequency of modes of natural variability of climate as an indicator of changes in the climate [23, 19], using a moving window approach [22] and employing wavelet transformation [24]. …”

Section: Introductionmentioning

confidence: 99%

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Statistical Downscaling of General Circulation Model Outputs to Precipitation Accounting for Non-Stationarities in Predictor-Predictand Relationships

Sachindra

Perera

2016

PLoS ONE

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This paper presents a novel approach to incorporate the non-stationarities characterised in the GCM outputs, into the Predictor-Predictand Relationships (PPRs) in statistical downscaling models. In this approach, a series of 42 PPRs based on multi-linear regression (MLR) technique were determined for each calendar month using a 20-year moving window moved at a 1-year time step on the predictor data obtained from the NCEP/NCAR reanalysis data archive and observations of precipitation at 3 stations located in Victoria, Australia, for the period 1950–2010. Then the relationships between the constants and coefficients in the PPRs and the statistics of reanalysis data of predictors were determined for the period 1950–2010, for each calendar month. Thereafter, using these relationships with the statistics of the past data of HadCM3 GCM pertaining to the predictors, new PPRs were derived for the periods 1950–69, 1970–89 and 1990–99 for each station. This process yielded a non-stationary downscaling model consisting of a PPR per calendar month for each of the above three periods for each station. The non-stationarities in the climate are characterised by the long-term changes in the statistics of the climate variables and above process enabled relating the non-stationarities in the climate to the PPRs. These new PPRs were then used with the past data of HadCM3, to reproduce the observed precipitation. It was found that the non-stationary MLR based downscaling model was able to produce more accurate simulations of observed precipitation more often than conventional stationary downscaling models developed with MLR and Genetic Programming (GP).

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Statistical Downscaling of General Circulation Model Outputs to Precipitation Accounting for Non-Stationarities in Predictor-Predictand Relationships

Sachindra

Perera

2016

PLoS ONE

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“…WTs are a useful statistical technique that allows time series to be decomposed into time and frequency domains simultaneously (Torrence & Compo, ). Wavelets have been widely used in hydroclimatology for a range of applications including the downscaling of rainfall (Rashid et al, ), analysis of rainfall variability in Australia (Westra & Sharma, ), assessment of GCM skill in simulating persistence (Johnson et al, ), and identification of trends in hydroclimatic variables (Nalley et al, ; Rashid et al, ). The WT can be performed in discrete mode, and the reconstruction of the signal from the wavelet coefficient is done using the inverse filtration of the WT.…”

Section: Methodsmentioning

confidence: 99%

Identifying Sustained Drought Anomalies in Hydrological Records: A Wavelet Approach

Rashid

Johnson

2018

JGR Atmospheres

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Identification of climatic drivers that simulate the variability of year to multiyear sustained rainfall anomalies is important for water resource management. This study provides a comprehensive investigation of the relationship of drought and climatic variables filtered to represent specific frequency bands using a wavelet transform. The Standardized Precipitation Index (SPI) is used to represent drought periods and wet anomalies over Australia. Strong evidence is found that the year to multiyear SPI extremes are strongly related to the low‐frequency signal present in climate variables. Interannual variability (period of 3.73 and 7.4 years) of climate variables are associated with drought at the annual scale, whereas multiyear droughts are strongly influenced by the variability at interdecadal frequencies (period of 14.9 years). The strength of these relationships is found to vary with climate variables and regions. While significant negative correlation between SPI and surface air temperature at interannual and interdecadal frequencies exists over the eastern Australia, this relationship is positive for parts of Western Australia. Regional analysis for 13 river basins over Australia indicates that wavelet decomposed low frequencies of climate variables perform better at predicting drought and wet periods than the unfiltered climate variables. Identifying filtered climatic predictors has significant potential in reconstructing drought records in past climates as well as simulating likely drought for future climates. Additionally, the selection of filtered climatic predictor variables enables an assessment of the improvements needed in climate model simulations so as to improve our ability to better simulate drought or sustained wet extremes.

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“…The third assumption is practically questionable under a changing climate scenario (Ghosh and Mujumdar, 2008). It is also confirmed from the recent literature that the relationship between the causal variables and the target variable vary over time, that is, non-stationary (Duan et al, 2012;Hertig and Jacobeit, 2013;Rashid et al, 2016;Merkenschlager et al, 2017). Raje and Mujumdar (2010) discussed the sources of such non-stationarity in the downscaling.…”

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confidence: 87%

Development of a time‐varying downscaling model considering non‐stationarity using a Bayesian approach

Pichuka

Maity

2018

Intl Journal of Climatology

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Stationarity in the relationship between causal variables and target variables is the fundamental assumption of statistical downscaling models. However, we hypothesize that this assumption may not be valid in a changing climate. This study develops a downscaling technique in which the relationship between causal and target variables is considered to be time-varying rather than static. The proposed time-varying downscaling model (TVDM) is utilized to downscale monthly precipitation over India to 0.25 × 0.25 gridded scale using the large-scale outputs from multiple general circulation models (GCMs), namely the Hadley Centre Coupled Model version 3 (HadCM3), coupled Hadley Centre Global Environmental Model version 2-Earth System model (HadGEM2-ES) and Canadian Earth System Model version 2 (CanESM2). Observed precipitation data are obtained from the India Meteorological Department (IMD), Pune. For future projection, the temporal evolution of each of the TVDM parameters is investigated using its deterministic (trend and periodicity) and stochastic components. TVDM is found to outperform the most commonly used statistical downscaling model (SDSM) and regional climate model (RCM) output at all the locations. The Regional Climate Model version 4 (RegCM4) precipitation data (RCM outputs) are obtained from the Coordinated Regional Climate Downscaling Experiment (CORDEX) data portal supplied by Indian Institute of Tropical Meteorology (IITM), Pune. The proposed model (TVDM) differs from the existing stationarity assumption-based approaches in updating the relationship between causal and target variables over time. It is understood that parameter uncertainty is the major issue in consideration of non-stationarity. Still, the TVDM is found to be very useful in the context of climate change due to its time-varying component.

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Statistical downscaling of rainfall: a non-stationary and multi-resolution approach

Cited by 32 publications

References 37 publications

Statistical Downscaling of General Circulation Model Outputs to Precipitation Accounting for Non-Stationarities in Predictor-Predictand Relationships

Statistical Downscaling of General Circulation Model Outputs to Precipitation Accounting for Non-Stationarities in Predictor-Predictand Relationships

Identifying Sustained Drought Anomalies in Hydrological Records: A Wavelet Approach

Development of a time‐varying downscaling model considering non‐stationarity using a Bayesian approach

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