Evaluation of two statistical downscaling models for daily precipitation over an arid basin in China

Liu, Zhaofei; Xu, Zongxue; Charles, Stephen P.; Fu, Guobin; Liu, Liu

doi:10.1002/joc.2211

Cited by 81 publications

(72 citation statements)

References 45 publications

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“…Suitable predictors should be informative, and the relationship between the predictors and predictands should be stationary [13]. Informative predictors can be identified using statistical measures, such as the Pearson, Spearmen, and Kendall correlation analysis [9], CCA [14], maximum covariance analysis (MCA) [15], partial correlation (PAR) [16][17][18], and principal component analysis (PCA) [19,20]. Interactive model fitting approaches are also used in predictor selection [21].…”

Section: Introductionmentioning

confidence: 99%

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Statistical Downscaling of Temperature with the Random Forest Model

Pang

Yue

Zhao

et al. 2017

Advances in Meteorology

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The issues with downscaling the outputs of a global climate model (GCM) to a regional scale that are appropriate to hydrological impact studies are investigated using the random forest (RF) model, which has been shown to be superior for large dataset analysis and variable importance evaluation. The RF is proposed for downscaling daily mean temperature in the Pearl River basin in southern China. Four downscaling models were developed and validated by using the observed temperature series from 61 national stations and large-scale predictor variables derived from the National Center for Environmental Prediction-National Center for Atmospheric Research reanalysis dataset. The proposed RF downscaling model was compared to multiple linear regression, artificial neural network, and support vector machine models. Principal component analysis (PCA) and partial correlation analysis (PAR) were used in the predictor selection for the other models for a comprehensive study. It was shown that the model efficiency of the RF model was higher than that of the other models according to five selected criteria. By evaluating the predictor importance, the RF could choose the best predictor combination without using PCA and PAR. The results indicate that the RF is a feasible tool for the statistical downscaling of temperature.

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Section: Introductionmentioning

confidence: 99%

“…[35,36]. The Pearl River basin ( Figure 1) is located in tropical and subtropical climate zones, the annual temperature is [14][15][16][17][18][19][20][21][22] ∘ C, and the annual precipitation is 1200-2200 mm. The distribution of precipitation is gradually reduced from east to west.…”

Section: Introductionmentioning

confidence: 99%

Statistical Downscaling of Temperature with the Random Forest Model

Pang

Yue

Zhao

et al. 2017

Advances in Meteorology

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“…The XDS method was found to perform best, followed by the bias correction and spatial disaggregation and quantile regression neural networks methods. Liu et al (2011) compared the nonhomogeneous hidden Markov model and the statistical downscaling model SDSM in terms of downscaling precipitation. Both models performed similar in simulating dry-and wet-spell length, while the nonhomogeneous hidden Markov model showed better skill in modeling the wet-day precipitation amount.…”

Section: Introductionmentioning

confidence: 99%

Simulating future precipitation extremes in a complex Alpine catchment

Dobler

Bürger

Stötter

2013

Nat. Hazards Earth Syst. Sci.

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Abstract. The objectives of the present investigation are (i) to study the effects of climate change on precipitation extremes and (ii) to assess the uncertainty in the climate projections. The investigation is performed on the Lech catchment, located in the Northern Limestone Alps. In order to estimate the uncertainty in the climate projections, two statistical downscaling models as well as a number of global and regional climate models were considered. The downscaling models applied are the Expanded Downscaling (XDS) technique and the Long Ashton Research Station Weather Generator (LARS-WG). The XDS model, which is driven by analyzed or simulated large-scale synoptic fields, has been calibrated using ECMWF-interim reanalysis data and local station data. LARS-WG is controlled through stochastic parameters representing local precipitation variability, which are calibrated from station data only. Changes in precipitation mean and variability as simulated by climate models were then used to perturb the parameters of LARS-WG in order to generate climate change scenarios. In our study we use climate simulations based on the A1B emission scenario. The results show that both downscaling models perform well in reproducing observed precipitation extremes. In general, the results demonstrate that the projections are highly variable. The choice of both the GCM and the downscaling method are found to be essential sources of uncertainty. For spring and autumn, a slight tendency toward an increase in the intensity of future precipitation extremes is obtained, as a number of simulations show statistically significant increases in the intensity of 90th and 99th percentiles of precipitation on wet days as well as the 5- and 20-yr return values.

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“…The nonhomogeneous hidden Markov model (NHMM), a stochastic down-scaling model, can downscale rainfall fields in both spatial and temporal dimensions by linking stationscale daily rainfall to large-scale atmospheric predictors. The model has been widely utilized in many downscaling studies and proved to be a promising approach to downscale precipitation at multiple locations (Hughes and Guttorp, 1994;Bates et al, 1998;Hughes et al, 1999;Robertson et al, 2004;Liu et al, 2011). Given the effectiveness of the NHMM at simulating the statistics of precipitation at both spatial and temporal scales, it is used in this study to downscale daily rainfall at 32 stations during the wheat and maize seasons for the 20th century, with interpolated regional-averaged reanalysis precipitation as input, and for 21st century, the input for 20th century scaled by the precipitation changes from GCM outputs.…”

Section: Introductionmentioning

confidence: 99%

Scenario development for estimating potential climate change impacts on crop production in the North China Plain

Chen

Greene

Robertson

et al. 2013

Intl Journal of Climatology

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ABSTRACT:It is important to investigate potential changes in temperature, precipitation and solar radiation for assessing the impacts of future climate change on agricultural production for specific regions. In this study, climate scenarios of precipitation, temperature and solar radiation for the North China Plain (NCP) were constructed in terms of stochastic daily weather sequences. A nonhomogeneous hidden Markov model (NHMM) was used to downscale daily precipitation projections at 32 stations during winter wheat and summer maize growing seasons for a baseline and a 21st century (2080-2099) A1B scenario, using selected general circulation models (GCMs). A climatological seasonal cycle of regional-averaged daily reanalysis precipitation was used as input to the down-scaling for the baseline simulation; this input was then scaled by the precipitation changes from GCM projections to generate down-scaled stochastic simulations of precipitation in the 21st century. Temperature was generated using a weakly stationary generating process, conditional on precipitation occurrence, with 21st century additive changes taken from the GCMs at the regional scale. Three hypotheses about changes in solar radiation (−20%, 0% and 20%) were made considering the large uncertainty in its future change. The down-scaled simulations exhibit station increases in the mean daily rainfall of 13.9-69.7% in the scenarios driven by the GCM with the projected largest and multi-model mean precipitation increase for the wheat season, with changes of 0.4-29.9% for the maize season. In the scenario driven by the GCM with the largest projected precipitation decrease, the simulated rainfall decreases at all stations, with changes ranging from −24.6 to −0.1% for the wheat and maize seasons, respectively. Temperature increases by about 3.7• C for the wheat season and 3.6• C for the maize season.

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Evaluation of two statistical downscaling models for daily precipitation over an arid basin in China

Cited by 81 publications

References 45 publications

Statistical Downscaling of Temperature with the Random Forest Model

Statistical Downscaling of Temperature with the Random Forest Model

Simulating future precipitation extremes in a complex Alpine catchment

Scenario development for estimating potential climate change impacts on crop production in the North China Plain

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