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SiCLIMA:阿拉贡(西班牙东北部)的高分辨率水文气候和温度数据集。

SiCLIMA: High-resolution hydroclimate and temperature dataset for Aragón (northeast Spain).

作者信息

Serrano-Notivoli Roberto, Saz Miguel Ángel, Longares Luis Alberto, de Luis Martín

机构信息

Departamento de Geografía y Ordenación del Territorio. Instituto Universitario de Investigación en Ciencias Ambientales de Aragón (IUCA), Universidad de Zaragoza, Pedro Cerbuna, 12, 50009 Zaragoza, Spain.

出版信息

Data Brief. 2024 Aug 24;56:110876. doi: 10.1016/j.dib.2024.110876. eCollection 2024 Oct.

Abstract

A new high-resolution climatic gridded dataset was built for Aragón (northeast Spain) using a large collection of daily precipitation and temperature observations from more than 3000 weather stations. The grid covers, at the unprecedented spatial resolution of 0.25 km, daily maximum and minimum temperatures and precipitation in the 1950-2020 period. The complex orography (from 70 to 3,400 m.a.s.l.) of the medium-sized region (∼48,000 km) required a climate modelling method based on a spatially-dense weather monitoring network and local predictors. The 3-step workflow for grid creation consisted of: 1) a comprehensive quality control of raw observations, based on a spatial comparison with nearest data; 2) a climate reconstruction based on the creation of reference values, through multivariate linear regressions, for every day and location, based on the observed climate and terrain-based environmental variables; and 3) the prediction of precipitation and temperature values in a regular 500 × 500 m grid, based on the reconstructed data series. The resulting dataset improves the spatial representativity of climate and allows for a detailed analyses at landscape scale not only in climate studies but also in related disciplines such as hydrology or biogeography, amongst others.

摘要

利用来自3000多个气象站的大量日降水量和气温观测数据,为西班牙东北部的阿拉贡地区构建了一个新的高分辨率气候网格化数据集。该网格以0.25千米这一前所未有的空间分辨率,覆盖了1950 - 2020年期间的日最高气温、最低气温和降水量。这个中型区域(约48000平方千米)复杂的地形(海拔从70米到3400米)需要一种基于空间密集型气象监测网络和局部预测因子的气候建模方法。创建网格的三步工作流程包括:1)基于与最近数据的空间比较,对原始观测数据进行全面质量控制;2)通过多元线性回归,基于观测到的气候和基于地形的环境变量,为每一天和每个位置创建参考值,从而进行气候重建;3)基于重建的数据序列,在500×500米的规则网格中预测降水量和气温值。所得数据集提高了气候的空间代表性,不仅在气候研究中,而且在水文或生物地理学等相关学科中,都能在景观尺度上进行详细分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e4f8/11402527/e7fbc686b491/gr1.jpg

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