Abstract
This study presents an exhaustive evaluation of the performance of three statistical downscaling techniques for generating daily rainfall occurrences at 22 rainfall stations in the upper Ping river basin (UPRB), Thailand. The three downscaling techniques considered are the modified Markov model (MMM), a stochastic model, and two variants of regression models, statistical models, one with single relationship for all days of the year (RegressionYrly) and the other with individual relationships for each of the 366 days (Regression366). A stepwise regression is applied to identify the significant atmospheric (ATM) variables to be used as predictors in the downscaling models. Aggregated wetness state indicators (WIs), representing the recent past wetness state for the previous 30, 90 or 365 days, are also considered as additional potential predictors since they have been effectively used to represent the low-frequency variability in the downscaled sequences. Grouping of ATM and all possible combinations of WI is used to form eight predictor sets comprising ATM, ATM-WI30, ATM-WI90, ATM-WI365, ATM-WI30&90, ATM-WI30&365, ATM-WI90&365 and ATM-WI30&90&365. These eight predictor sets were used to run the three downscaling techniques to create 24 combination cases. These cases were first applied at each station individually (single site simulation) and thereafter collectively at all sites (multisite simulations) following multisite downscaling models leading to 48 combination cases in total that were run and evaluated. The downscaling models were calibrated using atmospheric variables from the National Centers for Environmental Prediction (NCEP) reanalysis database and validated using representative General Circulation Models (GCM) data. Identification of meaningful predictors to be used in downscaling, calibration and setting up of downscaling models, running all 48 possible predictor combinations and a thorough evaluation of results required considerable efforts and knowledge of the research area. The validation results show that the use of WIs remarkably improves the accuracy of downscaling models in terms of simulation of standard deviations of annual, monthly and seasonal wet days. By comparing the overall performance of the three downscaling techniques keeping common sets of predictors, MMM provides the best results of the simulated wet and dry spells as well as the standard deviation of monthly, seasonal and annual wet days. These findings are consistent across both single site and multisite simulations. Overall, the MMM multisite model with ATM and wetness indicators provides the best results. Upon evaluating the combinations of ATM and sets of wetness indicators, ATM-WI30&90 and ATM-WI30&365 were found to perform well during calibration in reproducing the overall rainfall occurrence statistics while ATM-WI30&365 was found to significantly improve the accuracy of monthly wet spells over the region. However, these models perform poorly during validation at annual time scale. The use of multi-dimension bias correction approaches is recommended for future research.
Highlights
The coarse resolution of General Circulation Models (GCMs) offers some limitations while assessing the impacts of climate change on hydrological processes as they often occur at a fine catchment scale
As the selection of a downscaling model is area as well as location specific, a comparison of downscaling approaches helps us to know the performance of a model in comparison to other available alternatives and to determine why or when a model performs well at a local scale. The outcome of such studies helps to improve our understanding of the spatial and temporal scale of the atmosphere–surface environment relationship over the study region. Keeping these issues in mind, we present here an exhaustive comparison of two commonly used downscaling approaches, modified Markov model (MMM) and the linear regression model, in simulating daily rainfall occurrences at 22 rainfall stations located in the upper Ping river basin (UPRB)
The results reveal whether the MMM models can be adopted over simple regression approaches to enhance model performance
Summary
The coarse resolution of General Circulation Models (GCMs) offers some limitations while assessing the impacts of climate change on hydrological processes as they often occur at a fine catchment scale. This study suggested to select the same predictors to form the regression models for simulating rainfall occurrence and amount for all stations to clarify this question These results show that bias correction is essential to correct the biases in raw climate model output before using them in climate change impact studies. The outcome of such studies helps to improve our understanding of the spatial and temporal scale of the atmosphere–surface environment relationship over the study region Keeping these issues in mind, we present here an exhaustive comparison of two commonly used downscaling approaches, MMM and the linear regression model, in simulating daily rainfall occurrences at 22 rainfall stations located in the UPRB. The results reveal whether the MMM models can be adopted over simple regression approaches to enhance model performance
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