A comparison of multi-site daily rainfall downscaling techniques under Australian conditions

Frost, Andrew; Charles, Stephen P.; Timbal, Bertrand; Chiew, Francis H. S.; Mehrotra, R.; Nguyen, Kim Chi; Chandler, Richard E.; McGregor, John L.; Fu, Guobin; Kirono, Dewi; Fernandez, E.; Kent, David M.

doi:10.1016/j.jhydrol.2011.06.021

Cited by 98 publications

(106 citation statements)

References 37 publications

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“…General circulation models (GCMs) are a common used tool for the assessment of climate change, but they currently remain relatively coarse in resolution and so unable to resolve subgrid-scale features such as topography, clouds, and land use (Grotch and MacCracken 1991;Fowler et al 2007). In particular, GCM outputs are inadequate for capturing rainfall spatial-temporal variability, which is required for hydrological modeling (Frost et al 2011). Downscaling is thus often used to bridge the scale mismatch gap between the GCM and regional hydrological impacts studies (Maraun et al 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, comparisons of different methods are very important to understand under which conditions these methods can be applied. Comparisons of different statistical downscaling methods for precipitation have been conducted in many countries and regions (Dibike and Coulibaly 2005;Khan et al 2006;Timbal et al 2008a;Tryhorn and DeGaetano 2010;Liu et al 2011;Raje and Mujumdar 2011;Frost et al 2011). However, few studies have considered the intermittency structure and daily to monthly spatial correlation of rainfall.…”

Section: Introductionmentioning

confidence: 99%

“…However, few studies have considered the intermittency structure and daily to monthly spatial correlation of rainfall. Moreover, it is also unclear whether the total monthly, seasonal, and site-to-site variations, as required for hydrologic modeling, can be adequately reproduced by these models (Frost et al 2011).…”

Section: Introductionmentioning

confidence: 99%

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A comparison of three multi-site statistical downscaling models for daily rainfall in the North China Plain

Liu

et al. 2012

Theor Appl Climatol

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Three statistical downscaling methods (conditional resampling statistical downscaling model: CR-SDSM, the generalised linear model for daily climate time series: GLIMCLIM, and the non-homogeneous hidden Markov model: NHMM) for multi-site daily rainfall were evaluated and compared in the North China Plain (NCP). The comparison focused on a range of statistics important for hydrological studies including rainfall amount, extreme rainfall, intra-annual variability, and spatial coherency. The results showed that no single model performed well over all statistics/timescales, suggesting that the user should chose appropriate methods after assessing their advantages and limitations when applying downscaling methods for particular purposes. Specifically, the CR-SDSM provided relatively robust results for annual/monthly statistics and extreme characteristics, but exhibited weakness for some daily statistics, such as daily rainfall amount, dry-spell length, and annual wet/ dry days. GLIMCLIM performed well for annual dry/ wet days, dry/wet spell length, and spatial coherency, but slightly overestimated the daily rainfall. Additionally, NHMM performed better for daily rainfall and annual wet/dry days, but slightly underestimated dry/wet spell length and overestimated the daily extremes. The results of this study could be applied when investigating climate change impact on hydrology and water availability for the NCP, which suffers from intense water shortages due to climate change and human activities in recent years.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A comparison of three multi-site statistical downscaling models for daily rainfall in the North China Plain

Liu

et al. 2012

Theor Appl Climatol

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“…Purely statistical methods, though, offer ease of use, even lower computational requirements, and simplicity, which has been shown to be an advantageous attribute for downscaling hydrologic data [36][37][38].…”

Section: Downscaling Grace Datamentioning

confidence: 99%

Downscaling GRACE Remote Sensing Datasets to High-Resolution Groundwater Storage Change Maps of California’s Central Valley

2018

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NASA's Gravity Recovery and Climate Experiment (GRACE) has already proven to be a powerful data source for regional groundwater assessments in many areas around the world. However, the applicability of GRACE data products to more localized studies and their utility to water management authorities have been constrained by their limited spatial resolution (~200,000 km 2 ). Researchers have begun to address these shortcomings with data assimilation approaches that integrate GRACE-derived total water storage estimates into complex regional models, producing higher-resolution estimates of hydrologic variables (~2500 km 2 ). Here we take those approaches one step further by developing an empirically based model capable of downscaling GRACE data to a high-resolution (~16 km 2 ) dataset of groundwater storage changes over a portion of California's Central Valley. The model utilizes an artificial neural network to generate a series of high-resolution maps of groundwater storage change from 2002 to 2010 using GRACE estimates of variations in total water storage and a series of widely available hydrologic variables (PRISM precipitation and temperature data, digital elevation model (DEM)-derived slope, and Natural Resources Conservation Service (NRCS) soil type). The neural network downscaling model is able to accurately reproduce local groundwater behavior, with acceptable Nash-Sutcliffe efficiency (NSE) values for calibration and validation (ranging from 0.2445 to 0.9577 and 0.0391 to 0.7511, respectively). Ultimately, the model generates maps of local groundwater storage change at a 100-fold higher resolution than GRACE gridded data products without the use of computationally intensive physical models. The model's simulated maps have the potential for application to local groundwater management initiatives in the region.

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“…Although there is a large body of literatures where an intercomparison of different downscaling methods has been made (Mehrotra et al 2004;Diaz-Nieto and Wilby 2005;Frost et al 2011;Liu et al 2012), very few of these studies have compared downscaling methods from the point of data usage ways. Here, we present two statistical downscaling methods and compare them to give the optimal one for China.…”

Section: Introductionmentioning

confidence: 99%

A comparison of two downscaling methods for precipitation in China

et al. 2015

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In most cases, climate change projections from General Circulation Models (GCM) and Regional Climate Models cannot be directly applied to climate change impact studies, and downscaling is, therefore, needed. A large number of statistical downscaling methods exist, but no clear recommendations exist of which methods are more appropriate, depending on the application. This paper compares two different statistical downscaling methods, Pre sim1 and Pre sim2 , using the Coupled Model Intercomparison Project Phase 5 (CMIP5) datasets and station observations. Both methods include two steps, but the major difference between them is how the CMIP5 dataset and the station data used. The downscaled precipitation data are validated with observations through China and Jiangxi province from 1976 to 2005. Results show that GCMs cannot be used directly in climate change impact studies. In China, the second method Pre sim2 , which establishes regression model based on the station data, has a tendency to overestimate or underestimate the real values. The accuracy of Pre sim1 is much better than Pre sim2 based on mean absolute error, mean relative error and root mean square error. Pre sim1 fuses the mode data and station data effectively. Results also show the importance of the meteorological station data in the process of residual modification.

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A comparison of multi-site daily rainfall downscaling techniques under Australian conditions

Cited by 98 publications

References 37 publications

A comparison of three multi-site statistical downscaling models for daily rainfall in the North China Plain

A comparison of three multi-site statistical downscaling models for daily rainfall in the North China Plain

Downscaling GRACE Remote Sensing Datasets to High-Resolution Groundwater Storage Change Maps of California’s Central Valley

A comparison of two downscaling methods for precipitation in China

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