Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River Basin, Thailand

Abstract

Abstract. Global Climate Models (GCMs) precipitation scenarios are often characterized by biases and coarse resolution that limit their direct application for basin level hydrological modeling. Bias-correction and spatial disaggregation methods are employed to improve the quality of ECHAM4/OPYC SRES A2 and B2 precipitation for the Ping River Basin in Thailand. Bias-correction method, based on gamma-gamma transformation, is applied to improve the frequency and amount of raw GCM precipitation at the grid nodes. Spatial disaggregation model parameters (β,σ2), based on multiplicative random cascade theory, are estimated using Mandelbrot-Kahane-Peyriere (MKP) function at q=1 for each month. Bias-correction method exhibits ability of reducing biases from the frequency and amount when compared with the computed frequency and amount at grid nodes based on spatially interpolated observed rainfall data. Spatial disaggregation model satisfactorily reproduces the observed trend and variation of average rainfall amount except during heavy rainfall events with certain degree of spatial and temporal variations. Finally, the hydrologic model, HEC-HMS, is applied to simulate the observed runoff for upper Ping River Basin based on the modified GCM precipitation scenarios and the raw GCM precipitation. Precipitation scenario developed with bias-correction and disaggregation provides an improved reproduction of basin level runoff observations.

Impacts of climate change and land-use scenarios on Margaritifera margaritifera, an environmental indicator and endangered species

“…When the aim is to forecast hydrological processes (present case), numerical simulations of climatic variables cannot be used without some form of data processing to remove the existing biases (Christensen et al, 2008;Sharma et al, 2007). So, the errors coming with the HadRM3 simulations had to be corrected in this study.…”

Towards Adaptation to Climate Change: Water for Rice in the Coastal Wetlands of Doñana, Southern Spain

“…The use of RCMs is an important tool for evaluating water management under future climate change scenarios (Varis et al 2004). Nonetheless, it is well known that the output of the RCMs cannot be used directly if there is no procedure that eliminates the existing bias (Sharma et al 2007). For this reason, in order to analyse the effect of climate change on water availability for irrigation in a regulated system, here we generate climate change projections based on the bias-corrected runoff alternatives (following Gonzalez-Zeas et al 2012).…”

Bias Correction for RCM Predictions of Precipitation and Temperature in the Chaliyar River Basin

“…Also, the biases in the output subsequently influence other hydrologic processes like evapotranspiration, runoff, snow accumulation and melt [18][19][20][21]. Some form of pre-processing is necessary to remove biases present in the computed climate output fields before they can be used for impact assessment studies [22][23][24].…”