There is imperative need of robust basin-scale data for climate impact studies over the topographically varying and landuse heterogenous river basins in the Indian Himalayan Region (IHR). Even finer resolution regional climate models’ (RCMs) information is elusive for these purposes. Based on available model fields and corresponding in-situ observed fields, bias correction for precipitation over Upper Ganga River Basin (UGRB) and temperature over Satluj River Basin (SRB) is demonstrated. These chosen river basins are in central and western Himalayas, respectively. Model precipitation (temperature) field from RegCM4.7 (REMO) and corresponding observed precipitation (temperature) field from nine (eight) stations of UGRB (SRB) are considered. Empirical quantile mapping (inverse function method) method is used. It is seen that each model has a distinct systematic bias relating to both precipitation and temperature means with respect to their corresponding observed means. Applying bias correction methods to the model fields resulted in reducing these mean biases and other errors. These findings illustrate handling and improving the model fields for hydrology, glaciology studies, etc.

对于印度喜马拉雅地区 (IHR) 地形变化和土地利用异质流域的气候影响研究，迫切需要可靠的流域尺度数据。对于这些目的，即使分辨率更高的区域气候模型 (RCM) 信息也难以捉摸。基于可用的模型字段和相应的原位对观测场、恒河上游流域 (UGRB) 降水和 Satluj 河流域 (SRB) 上的温度进行了偏差校正。这些选定的流域分别位于喜马拉雅山脉的中部和西部。考虑了来自RegCM4.7（REMO）的模型降水（温度）场和来自UGRB（SRB）九（八）个站点的相应观测降水（温度）场。使用经验分位数映射（反函数法）方法。可以看出，相对于相应的观测平均值，每个模型在降水和温度平均值方面都有明显的系统偏差。将偏差校正方法应用于模型字段可减少这些平均偏差和其他误差。这些发现说明了处理和改进水文学、冰川学研究等的模型领域。