A perfect prognosis approach for daily precipitation series in consideration of space–time correlation structure

Abstract

Downscaling techniques are the required tools to link the global climate model outputs provided at a coarse grid resolution to finer scale surface variables appropriate for climate change impact studies. Besides the at-site temporal persistence, the downscaled variables have to satisfy the spatial dependence naturally observed between the climate variables at different locations. Furthermore, the precipitation spatial intermittency should be fulfilled. Because of the complexity in describing these properties, they are often ignored, which can affect the effectiveness of the hydrologic process modeling. This study is a continuation of the work by Khalili and Nguyen (Clim Dyn 49(7–8):2261–2278. https://doi.org/10.1007/s00382-016-3443-6, 2017) regarding the multi-site statistical downscaling of daily precipitation series. Different approach of multi-site statistical downscaling based on the concept of the spatial autocorrelation is presented in this paper. This approach has proven to give effective results for multi-site multivariate statistical downscaling of daily extreme temperature time series (Khalili et al. in Int J Climatol 33:15–32. https://doi.org/10.1002/joc.3402, 2013). However, more challenges are presented by the precipitation variable because of the high spatio-temporal variability and intermittency. The proposed approach consists of logistic and multiple regression models, linking the global climate predictors to the precipitation occurrences and amounts respectively, and using the spatial autocorrelation concept to reproduce the spatial dependence observed between the precipitation series at different sites. An empirical technique has also been involved in this approach in order to fulfill the precipitation intermittency property. The proposed approach was performed using observed daily precipitation data from ten weather stations located in the southwest region of Quebec and southeast region of Ontario in Canada, and climate predictors from the NCEP/NCAR (National Centers for Environmental Prediction/National Centre for Atmospheric Research) reanalysis dataset. The results have proven the ability of the proposed approach to adequately reproduce the observed precipitation occurrence and amount characteristics, temporal and spatial dependence, spatial intermittency and temporal variability.

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Acknowledgements

The authors acknowledge the Data Access Integration (DAI, see http://loki.qc.ec.gc.ca/DAI/) Team for providing the data. The DAI data download gateway is made possible through collaboration among the Global Environmental and Climate Change Centre (GEC3), the Adaptation and Impacts Research Division (AIRD) of Environment Canada, and the Drought Research Initiative (DRI). Also, the authors acknowledge the financial support from the Natural Sciences and Engineering Research Council of Canada (Special Research Opportunity Program) for the project entitled “Probabilistic assessment of regional changes in climate variability and extremes”, and the “Fond Québécois de Recherche sur la Nature et les Technologies”.

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Correspondence to Malika Khalili.

Appendix

Appendix

See Table 4 and Figs. 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29 and 30.

Table 4 The daily NCEP-CGCM3 and NCEP-HADCM3 atmospheric predictors available at each NCEP-CGCM3 and NCEP-HADCM3 grid point respectively
Fig. 17
figure17

Monthly box plots of the consecutive dry days (CDD) from the observed data and multi-site SD model (MSDM) using NCEP-CGCM3 (MSDM-NCEP-CGCM3) and NCEP-HADCM3 (MSDM-NCEP-HADCM3) for a the calibration period: 1961–1985 and b the validation period: 1986–2000. The band in the middle of each box corresponds to the median value, the boxes to the inter-quartile range (IQR), and the whiskers to the \(1.5 \times IQR\). Outliers are represented by the crosses. Each box plot represents the daily time series obtained at all stations aggregated over the 25-year calibration and 15-year validation periods

Fig. 18
figure18

Same as Fig. 17 but for the simple daily intensity index (SDII)

Fig. 19
figure19

Same as Fig. 17 but for the 99th percentile of precipitation (PREC99p)

Fig. 20
figure20

Same as Fig. 17 but for the maximum 3-days precipitation total (R3 days)

Fig. 21
figure21

Same as Fig. 17 but for the daily precipitation mean (Mean) at St-Jerome station

Fig. 22
figure22

Same as Fig. 17 but for the daily precipitation standard deviation (STD) at Drummondville station

Fig. 23
figure23

Same as Fig. 17 but for the percentage of wet days (PRCP1) at Montreal/INT A station

Fig. 24
figure24

Interannual anomalies of the consecutive dry days (CDD) from the observed data and multi-site SD model (MSDM) using NCEP-CGCM3 (MSDM-NCEP-CGCM3) and NCEP-HADCM3 (MSDM-NCEP-HADCM3), over a the calibration period: 1961–1985 and b the validation period: 1986–2000

Fig. 25
figure25

Same as Fig. 24 but for the simple daily intensity index (SDII)

Fig. 26
figure26

Same as Fig. 24 but for the 99th percentile of precipitation (PREC99p)

Fig. 27
figure27

Same as Fig. 24 but for the maximum 3-days precipitation total (R3 days)

Fig. 28
figure28

Same as Fig. 24 but for the daily precipitation mean (Mean) at Oka station

Fig. 29
figure29

Same as Fig. 24 but for the daily precipitation standard deviation (STD) at Morrisburg station

Fig. 30
figure30

Same as Fig. 24 but for the percentage of wet days (PRCP1) at Ottawa CDA station

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Khalili, M., Nguyen, VTV. A perfect prognosis approach for daily precipitation series in consideration of space–time correlation structure. Stoch Environ Res Risk Assess 32, 3333–3364 (2018). https://doi.org/10.1007/s00477-018-1625-y

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Keywords

  • Climate change
  • Precipitation
  • Statistical downscaling
  • Regression model
  • Spatial autocorrelation
  • Multi-site stochastic simulation