Temperature and precipitation distributions depend on variable topography and heterogeneous landuse/landcover in the Indian Himalayan Region (IHR). It imparts a major concern for hydrological, glaciological modelling, dam structure assessment, etc. Thus, there is an inherent requirement of robust information for climate impact studies over the topographical variable and landuse heterogenous region in the Indian Himalayan Region (IHR). In particular, the importance of bias corrections become critically important over Himalayan river basins, in which model outputs with the corresponding in-situ observations are used for improving the model distribution. These improved details in present and future, as well, are important to carry out the climate change impact studies at basin scale for hydrological, glaciological, climatological studies, etc. Thus, in the present study firstly, model fields are bias corrected with the corresponding in-situ observations. And then trends in these bias corrected data is compared with the corresponding in-situ observations. These assessment of present and future changes in temperature and precipitation over Satluj River Basin (SRB) located in the western Himalayas is illustrated. Model fields are considered from a Regional climate model (REMO) from Coordinated Regional Downscaling Experiment-South Asia (CORDEX-SA) in three Representative Concentration Pathways (RCPs), i.e., 2.6, 4.5 and 8.5 W/m². These projections are bias corrected using distributed quantile mapping. The precipitation (temperature) bias correction is performed using the distributed quantile mapping on the gamma (normal) distribution. The standard trend statistics is applied for quantitative assessment. A good capture of bias correction in temperature and precipitation is illustrated. Efficient bias removal is depicted in cumulative distribution curve (CDF) at individual station. Trend analysis shows that highest rate of precipitation decrement at low altitude station (Kasol) with the rate of −6.362 mm/year in RCP 8.5. Over the SRB highest rate of temperature increment is seen at highest altitude station (Kaza) with the rate of 0.084°C/year in RCP 8.5. On an average, fall in precipitation and increase in temperature with >99% confidence level in RCP 8.5 is seen. In addition, intensity lowers in other lower RCPs. The study sums up with the efficacy of CORDEX-SA REMO model in capturing present and future change in temperature and precipitation over the SRB in western Himalayas using the bias correction.

中文翻译：

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气候变化情景下印度喜马拉雅山萨特鲁日河流域的气象趋势

温度和降水分布取决于印度喜马拉雅地区（IHR）的地形变化和土地利用/土地覆盖的异质性。它引起了水文，冰川学模型，大坝结构评估等方面的主要关注。因此，对印度喜马拉雅地区（IHR）的地形变量和土地利用异质性地区的气候影响研究存在可靠的信息内在要求。特别是，在喜马拉雅河流域，偏差校正的重要性变得至关重要，在该流域中，模型输出以及相应的原位观察值用于改善模型分布。现在和将来这些改进的细节，对于进行流域规模的气候变化影响研究对于水文，冰川学，气候学等研究也很重要。因此，在本研究中，首先，模型场被相应的偏差校正。现场观察。然后将这些偏差校正数据的趋势与相应的原位进行比较观察。说明了对喜马拉雅西部萨特鲁日河流域（SRB）温度和降水当前和未来变化的评估。在三个代表性浓度路径（RCP）中，即2.6、4.5和8.5 W / m ²的区域气候模型（REMO）来自南亚协调区域缩小实验（CORDEX-SA）考虑了模型场。。使用分布式分位数映射对这些预测进行偏差校正。使用分布式伽马（正态）分布的分位数映射来执行降水（温度）偏差校正。标准趋势统计用于定量评估。说明了温度和降水偏差校正的良好捕获。有效的偏差消除在各个站点的累积分布曲线（CDF）中进行了描述。趋势分析显示，在RCP 8.5中，低海拔站（Kasol）的降水减少率最高，为-6.362 mm /年。在SRB上方，最高海拔站（Kaza）的最高温度增量速率为RCP 8.5中的0.084°C /年。平均而言，在RCP 8.5中可以看到降水下降和温度上升，且置信度> 99％。另外，在其他较低的RCP中强度降低。该研究总结了CORDEX-SA REMO模型在利用偏倚校正来捕获喜马拉雅西部SRB上当前和未来的温度和降水变化方面的功效。