印度 CMIP6 GCM 日降雨量的降尺度算法

IF 1.3 4区 地球科学 Q3 GEOSCIENCES, MULTIDISCIPLINARY
Rajendra Raj, Degavath Vinod, Amai Mahesha
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引用次数: 0

摘要

全球气候模型(GCMs)是确定气候系统如何反应的精密工具。然而,全球气候模型的输出分辨率较低,不适合流域尺度的建模。全球气候模型需要在地方/流域尺度上进行缩减,以确定气候变化对水文响应的影响。本研究试图利用人工神经网络 (ANN)、变化因子 (CF)、K-近邻 (KNN) 和多元线性回归 (MLR),评估各种大尺度预测因子如何有效地再现印度 35 个不同地点的地方尺度降雨。预测因子的选择基于相关值。作为潜在的预测因子,选择了气温、地电位高度、风速分量和特定平均海平面气压下的相对湿度。比较了四种不同的降尺度方法对各种统计数据的再现情况,如选定地点的平均值、标准偏差、矩阵-矩阵图、累积分布函数以及选定站点日降雨量 PDF 的核密度估计。在几乎所有站点,CF 方法都优于其他方法(R2 = 0.92-0.99,RMSE = 1.37-28.88 毫米,NSE = -16.55-0.99)。这也与 IMD 数据的概率分布模式非常相似。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Downscaling algorithms for CMIP6 GCM daily rainfall over India

Downscaling algorithms for CMIP6 GCM daily rainfall over India

The global climate models (GCMs) are sophisticated tools for determining how the climate system will respond. However, the output of GCMs has a coarse resolution, which is unsuitable for basin-level modelling. Global climate models need to be downscaled at a local/basin scale to determine the impacts of climate change on hydrological responses. The present study attempted to evaluate how effectively various large-scale predictors could reproduce local-scale rain in 35 different locations in India using artificial neural networks (ANN), change-factors (CF), K-nearest neighbour (KNN), and multiple linear regression (MLR). The selection of predictors is made based on the correlation value. As potential predictors, air temperature, geo-potential height, wind velocity component, and relative humidity at specific mean sea-level pressure are selected. The comparison of four different downscaling methods concerning the reproduction of various statistics such as mean, standard deviation at chosen locations, quantile–quantile plots, cumulative distribution function, and kernel density estimation of the PDFs of daily rainfall for selected stations is examined. The CF approach outperforms the other methods at almost all sites (R2 = 0.92–0.99, RMSE = 1.37–28.88 mm, and NSE = –16.55–0.99). This also closely resembles the probability distribution pattern of IMD data.

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来源期刊
Journal of Earth System Science
Journal of Earth System Science Earth and Planetary Sciences-General Earth and Planetary Sciences
CiteScore
3.20
自引率
5.30%
发文量
226
期刊介绍: The Journal of Earth System Science, an International Journal, was earlier a part of the Proceedings of the Indian Academy of Sciences – Section A begun in 1934, and later split in 1978 into theme journals. This journal was published as Proceedings – Earth and Planetary Sciences since 1978, and in 2005 was renamed ‘Journal of Earth System Science’. The journal is highly inter-disciplinary and publishes scholarly research – new data, ideas, and conceptual advances – in Earth System Science. The focus is on the evolution of the Earth as a system: manuscripts describing changes of anthropogenic origin in a limited region are not considered unless they go beyond describing the changes to include an analysis of earth-system processes. The journal''s scope includes the solid earth (geosphere), the atmosphere, the hydrosphere (including cryosphere), and the biosphere; it also addresses related aspects of planetary and space sciences. Contributions pertaining to the Indian sub- continent and the surrounding Indian-Ocean region are particularly welcome. Given that a large number of manuscripts report either observations or model results for a limited domain, manuscripts intended for publication in JESS are expected to fulfill at least one of the following three criteria. The data should be of relevance and should be of statistically significant size and from a region from where such data are sparse. If the data are from a well-sampled region, the data size should be considerable and advance our knowledge of the region. A model study is carried out to explain observations reported either in the same manuscript or in the literature. The analysis, whether of data or with models, is novel and the inferences advance the current knowledge.
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