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Integration of statistical and spatial methods for distributing precipitation in tropical areas

机译：统计和空间方法的结合，用于在热带地区分配降水

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摘要

The high spatial resolution of precipitation distribution is a major concern for experts in environmental research and planning. This paper establishes a combination of multivariate regression algorithm and spatial analysis to predict distribution of precipitation, considering the four topographical factors of altitude, slope, aspect and location. Annual average and seasonal rainfall data were collected in nine rain gauges in Ulu Kinta catchment in East Malaysia from 1974 to 2010. To examine records and fill gaps from long-term rain gauges, homogeneity analysis was performed using the double-mass curve method. Estimated missing rainfall data were also tested using index gauges from network rainfall stations. Multivariate regression analysis was conducted to propose an empirical equation for the study area. Topographical factors were considered from a 90 m resolution digital elevation model. The multivariate regression model was found to clarify 74% of spatial variability of precipitation on annual average and 78% during wet season. However, the correlation coefficient for the dry season decreased sharply to 63%. By using the kriging interpolation method, the estimated annual average improved to 78.4%; the average improved to 65.2 and 80.3% in the dry and wet seasons, respectively. This confirms the efficiency and significance of the model and its potential for use in other tropical catchments.
机译：降水分布的高空间分辨率是环境研究和规划专家的主要关注点。本文考虑了海拔，坡度，坡向和位置这四个地形因素，建立了多元回归算法和空间分析相结合的降水预测方法。 1974年至2010年，在东马来西亚乌鲁金塔流域的9个雨量器中收集了年度平均和季节性降雨数据。为检查记录并填补长期雨量器的缺口，使用双质量曲线法进行了均一性分析。还使用来自网络降雨站的指标仪测试了估计的降雨缺失数据。进行多元回归分析以提出研究区域的经验方程。从90 m分辨率数字高程模型中考虑了地形因素。多元回归模型被发现可以使年平均降水量的空间变异性达到74％，而在雨季期间则可以达到78％。但是，旱季的相关系数急剧下降至63％。通过使用克里格插值法，估计的年平均增长率提高到了78.4％。在旱季和雨季，平均水平分别提高到65.2％和80.3％。这证实了该模型的效率和重要性以及其在其他热带流域的应用潜力。

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