Malika Khalili

Stochastic Multisite Generation of Daily Precipitation Data Using Spatial Autocorrelation

Abstract There are a number of stochastic models that simulate weather data required for various water resources applications in hydrology, agriculture, ecosystem, and climate change studies. However, many of them ignore the dependence between station locations exhibited by the observed meteorological time series. This paper proposes a multisite generation approach of daily precipitation data based on the concept of spatial autocorrelation. This theory refers to spatial dependence between observations with respect to their geographical adjacency. In hydrometeorology, spatial autocorrelation can be computed to describe daily dependence between the weather stations through the use of a spatial weight matrix, which defines the degree of significance of the weather stations surrounding each observation. The methodology is based on the use of the spatial moving average process to generate spatially autocorrelated random numbers that will be used in a stochastic weather generator. The resulting precipitation pr...

An efficient statistical approach to multi-site downscaling of daily precipitation series in the context of climate change

Abstract Global Climate Models (GCMs) have been extensively used in many climate change impact studies. However, the coarser resolution of these GCM outputs is not adequate to assess the potential effects of climate change on local scale. Downscaling techniques have thus been proposed to resolve this problem either by dynamical or statistical approaches. The statistical downscaling (SD) methods are widely privileged because of their simplicity of implementation and use. However, many of them ignore the observed spatial dependence between different locations, which significantly affects the impact study results. An improved multi-site SD approach is thus presented in this paper to downscaling of daily precipitation at many sites concurrently. This approach is based on a combination of multiple regression models for rainfall occurrences and amounts and the Singular Value Decomposition technique, which models the stochastic components of these regression models to preserve accurately the space–time statistical properties of the daily precipitation. Furthermore, this method was able to describe adequately the intermittency property of the precipitation processes. The proposed approach has been assessed using 10 rain gauges located in the southwest region of Quebec and southeast region of Ontario in Canada, and climate predictors from the National Centers for Environmental Prediction/National Centre for Atmospheric Research re-analysis data set. The results have indicated the ability of the proposed approach to reproduce accurately multiple observed statistical properties of the precipitation occurrences and amounts, the at-site temporal persistence, the spatial dependence between sites and the temporal variability and spatial intermittency of the precipitation processes.

On the Use of Multi Site Generated Meteorological Input Data for Realistic Hydrological Modeling in the Context of Climate Change Impact Studies

Weather generators have been used successfully for a wide array of applications such as hydrology, agriculture, environmental studies and recently climate change studies. Unfortunately, most weather models ignored spatial dependence exhibited by weather series at multiple sites because of climatic phenomena, which extend over a region rather than a station location and constrain the observations in a given place to be correlated to those in the surrounding area. The multi-site generation approach was then developed and has been successfully applied to precipitation occurrences and amounts. In this paper, the proposed multi-site generation approach will be used to simulate minimum and maximum temperature data. It analyzes patterns in space and investigates the dependence of weather data at multiple locations. It aims at reproducing daily spatial autocorrelations in the synthetic time series that are identical to those observed. The Peribonca River Basin in the Canadian province of Quebec was used and the results are generally satisfactory. Moreover, this multi-site approach has an important repercussion on the hydrological model compared to the uni-site approach. In order to evaluate the effects of climate changes on the Peribonca river basin hydrology, the parameters of the weather generator will be modified.

Efficient watershed modeling using a multi-site weather generator for meteorological data

The multi-site generation of precipitation data is developed using a Richardson (1981) WGEN-type weather generator. This approach is based on spatial autocorrelation to analyze patterns in space and investigate the dependence of weather data at multiple locations. Reproducing the dependence between meteorological data at several stations should make the hydrological model results more realistic. The Chute du diable watershed and surrounding area located in the province of Quebec, Canada was used to test the proposed approach. Daily spatial autocorrelations between precipitation occurrences and amounts were successfully reproduced as well as total monthly precipitation and monthly numbers of rainy days. A hydrological model has been used to quantify the natural inflow process. As envisaged, the multi-site generation of weather data produced more practical natural inflow hydrographs, compared to those obtained using a uni-site weather generator.

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

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.

Multisite Statistical Downscaling of Daily Precipitation Processes

The main objective of the present study is to develop an efficient statistical downscaling (SD) approach for simulating simultaneously and concurrently daily precipitation series at many sites. The proposed approach consists of a combination of two distinct multiple regression models to represent the linkage between global climate predictors and and the probability of local daily rainfall occurrences and the daily rainfall amounts, and the singular value decomposition (SVD) technique to represent the observed statistical properties of the stochastic component of the proposed combined model. The feasibility of the suggested multisite downscaling method was assessed using observed daily precipitation data available at ten weather stations located in the southwest region of Quebec and southeast region of Ontario in Canada and the climate predictors estimated from the National Centre for Environmental Prediction (NCEP) re-analysis data set for the period from 1961 to 2000. It was found that the proposed SD approach was able to describe accurately various precipitation characteristics, including their spatial and temporal variations as well as their inter-annual anomalies. In particular, it has been shown that the proposed procedure was quite efficient in the simulation of daily precipitation series for many sites because of the effective computation of its SVD component.