当数据源有限时，尤其是在未测量的区域，水文建模的准确性将受到影响。由于这个问题，它不会受到任何重大关注，尤其是在潜在的水文极端事件上。因此，目标是使用集成的统计降尺度模型和地理信息系统 （SDSM-GIS） 模型分析未测量降雨站的长期预测降雨量的准确性。SDSM 被用作气候因子，通过测量和未测量站预测 Δ2030s 气候趋势的变化。已选择五个预测因子集来形成该地区的局部气候，由 NCEP（验证）和 CanESM2-RCP4.5（预测）提供。根据统计分析，控制 SDSM 以产生可靠的验证结果，具有较低的 %MAE （<23%） 和较高的 R。预计 Δ2030 年代的降雨量将减少 14%。所有 RCP 都同意长期降雨模式与历史一致，年降雨强度较低。RCP8.5 显示的降雨量变化最小。然后，这些发现用于比较控制站 （Stn 2） 月降雨量的准确性。GIS-Kriging 方法作为插值剂，成功地产生了与控制站相似的降雨趋势。准确率估计达到 84%。比较未测绘站和测绘测站之间的月预测结果中的小 %MAE 证明，集成的 SDSM-GIS 模型可以在测绘测站产生可靠的长期降雨。
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Projection the long-term ungauged rainfall using integrated Statistical Downscaling Model and Geographic Information System (SDSM-GIS) model
An accuracy in the hydrological modelling will be affected when having limited data sources especially at ungauged areas. Due to this matter, it will not receiving any significant attention especially on the potential hydrologic extremes. Thus, the objective was to analyse the accuracy of the long-term projected rainfall at ungauged rainfall station using integrated Statistical Downscaling Model and Geographic Information System (SDSM-GIS) model. The SDSM was used as a climate agent to predict the changes of the climate trend in Δ2030s by gauged and ungauged stations. There were five predictors set have been selected to form the local climate at the region which provided by NCEP (validated) and CanESM2-RCP4.5 (projected). According to the statistical analyses, the SDSM was controlled to produce reliable validated results with lesser %MAE (<23%) and higher R. The projected rainfall was suspected to decrease 14% in Δ2030s. All the RCPs agreed the long term rainfall pattern was consistent to the historical with lower annual rainfall intensity. The RCP8.5 shows the least rainfall changes. These findings then used to compare the accuracy of monthly rainfall at control station (Stn 2). The GIS-Kriging method being as an interpolation agent was successfully to produce similar rainfall trend with the control station. The accuracy was estimated to reach 84%. Comparing between ungauged and gauged stations, the small %MAE in the projected monthly results between gauged and ungauged stations as a proved the integrated SDSM-GIS model can producing a reliable long-term rainfall generation at ungauged station.
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