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Precipitation pattern in the Western Himalayas revealed by four datasets

机译:四个数据集揭示的西喜马拉雅山脉的降水模式

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Data scarcity is the biggest problem for scientific research related to hydrology and climate studies in the Great Himalayas region. High-quality precipitation data are difficult to obtain due to a sparse network, cold climate and high heterogeneity in topography. In this paper, we examine four datasets in northern India of the Western Himalayas: interpolated gridded data based on gauge observations (IMD, 1 ° × 1 ° , and APHRODITE, 0.25 ° × 0.25 ° ), reanalysis data (ERA-Interim, 0.75 ° × 0.75 ° ) and high-resolution simulation by a regional climate model (WRF, 0.15 ° × 0.15 ° ). The four datasets show a similar spatial pattern and temporal variation during the period 1981–2007, though the absolute values vary significantly (497–819?mm?year sup?1/sup ). The differences are particularly large in July and August at the windward slopes and high-elevation areas. Overall, the datasets show that the summer is getting wetter and the winter is getting drier, though most of the trends in monthly precipitation are not significant. Trend analysis of summer and winter precipitation at every grids confirms the changes. Wetter summers will result in more and bigger floods in the downstream areas. Warmer and drier winters will result in less glacier accumulation. All the datasets show consistency in the period 1981–2007 and can give a spatial overview of the precipitation in the region. Comparing with the Bhuntar gauge data, the WRF dataset gives the best estimates of extreme precipitation. To conclude, we recommend the APHRODITE dataset and the WRF dataset for hydrological studies for their improved spatial variation which match the scale of hydrological processes as well as accuracy in extreme precipitation for flood simulation.
机译:数据稀缺是大喜马拉雅山地区与水文学和气候研究有关的科学研究的最大问题。由于稀疏的网络,寒冷的气候以及地形的高度异质性,很难获得高质量的降水数据。在本文中,我们研究了印度北部喜马拉雅山脉的四个数据集:基于量表观测值(IMD,1°×1°和APHRODITE,0.25°×0.25°)的内插网格数据,重新分析数据(ERA-Interim,0.75) °×0.75°)和通过区域气候模型(WRF,0.15°×0.15°)进行的高分辨率模拟。尽管绝对值有显着差异(497-819?mm?year ?1 ),但四个数据集在1981-2007年期间显示出相似的空间格局和时间变化。在7月和8月,迎风坡和高海拔地区的差异特别大。总体而言,数据集显示,尽管大部分月降水量趋势并不显着,但夏季越来越湿,冬季越来越干。每个网格的夏季和冬季降水趋势分析证实了这一变化。夏季潮湿将导致下游地区洪水泛滥。冬天越干燥,越少冰川积聚。所有数据集都显示了1981-2007年期间的一致性,并且可以对该区域的降水提供空间概况。与Bhuntar轨距数据相比,WRF数据集提供了极端降水的最佳估计。总而言之,我们建议使用APHRODITE数据集和WRF数据集进行水文研究,因为它们具有改进的空间变异性,与水文过程的规模以及极端降水的精度相匹配,可以进行洪水模拟。

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