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Evaluation of soil moisture data products over Indian region and analysis of spatio-temporal characteristics with respect to monsoon rainfall

机译:印度地区土壤水分数据产品评估及季风降雨时空特征分析

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Soil moisture (SM) is an essential climate variable of greater relevance in the monsoon scenario, hence validation and understanding of its spatio-temporal variability over the Indian region is of high significance. In the present study, five SM products are evaluated against in situ SM measurements conducted by India Meteorological Department and the selected data product is used for spatio-temporal characterization of SM in relation to monsoon rainfall. The data products evaluated are: European Space Agency's merged satellite SM, Modern-Era Retrospective analysis for Research and Applications (MERRA) Land SM, ECMWF's ERA interim SM, Climate Forecast System Reanalysis SM, and Global Land Data Assimilation System Noah Land Surface Model SM. Comparisons show that seasonal SM patterns in all products generally follow the characteristics of rainfall, even though there are certain differences in details. The statistical estimates indicate fairly good agreement between in situ and the five products, with some variations among them and over the homogeneous rainfall regions. On comparison, MERRA SM is found appropriate for further analyses on spatio-temporal characteristics, which are then carried out with the 20 year (1993-2012) SM data. Stability analyses revealed SM patterns indicative of relative SM variability as well as persistence. The spatial stability analysis depicts dry and wet patterns and their seasonal variations over different geographical locations in relation to all India spatial average. Large temporal variations are found over central, western and northern Indian regions caused by large intraseasonal variability in rainfall. In brief, intraseasonal and interannual soil moisture variations broadly follow the rainfall pattern, with long-term influences attributed to SM memory effects. The soil moisture persistence and dominant scales of variability are explored with autocorrelation and wavelet transform techniques. Seasonal persistence is large over regions receiving excessive rainfall, while drought years are noted to have large intraseasonal SM persistence compared to that of surplus year. Wavelet decomposition demonstrates that low periodicity (2-10 days) SM modes match that of rainfall during initial periods of monsoon. The occurrence of significant higher periodicity modes are dominant in drought years compared to surplus, which also vary with geographical locations. (C) 2016 Elsevier B.V. All rights reserved.
机译:在季风情景中,土壤湿度(SM)是至关重要的气候变量,因此,验证和了解其在印度地区的时空变化具有重要意义。在本研究中,针对印度气象局进行的原位SM测量评估了五种SM产品,并将所选数据产品用于与季风降雨有关的SM时空表征。评估的数据产品包括:欧洲航天局的合并卫星SM,研究和应用的现代时代回顾分析(MERRA)陆地SM,ECMWF的ERA临时SM,气候预测系统再分析SM和全球土地数据同化系统Noah地表模型SM 。比较表明,即使在细节上存在某些差异,所有产品中的季节性SM模式通常都遵循降雨的特征。统计估计值表明,就地与这五种产品之间有很好的一致性,其中五种产品之间以及整个降雨均匀地区都有一定的差异。相比之下,发现MERRA SM适合进一步分析时空特征,然后使用20年(1993-2012)SM数据进行分析。稳定性分析表明,SM模式指示相对SM变异性和持久性。空间稳定性分析描述了相对于所有印度空间平均值而言,不同地理位置的干湿模式及其季节性变化。在印度中部,西部和北部地区发现了较大的时间变化,这是由于降雨的季节内变化较大所致。简而言之,季节内和年际土壤水分的变化大致遵循降雨模式,长期影响归因于SM记忆效应。利用自相关和小波变换技术研究了土壤水分的持久性和变异的主要尺度。在降雨过多的地区,季节性持久性很大,而与干旱年份相比,干旱年份的季节内SM持久性较大。小波分解表明,低周期(2-10天)SM模式与季风初期的降雨模式相匹配。与干旱过剩相比,干旱期间显着较高的周期性模式占主导地位,干旱也随地理位置而变化。 (C)2016 Elsevier B.V.保留所有权利。

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