A soil temperature dataset based on random forest in the Three River Source Region.

IF 6.9 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2025-05-27 DOI:10.1038/s41597-025-04910-3

Xiaoqing Tan, Siqiong Luo, Hongmei Li, Zhuoqun Li, Qingxue Dong

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Abstract

Changes in soil temperature (ST) in the Three River Source Region (TRSR) significantly influence regional climate, ecology, and hydrological processes. However, existing models and reanalysis data exhibit considerable deviations in ST due to limitations in physical processes and parameterization schemes. To address this issue, we developed a new ST dataset using the Random Forest method (RFST), integrating observed ST data with relevant gridded datasets. RFST provides monthly ST data at nine layers with a spatial resolution of 0.01° × 0.01° from 1982 to 2015. Validation against two soil observation networks and six meteorological stations shows that the Nash-Sutcliffe Efficiency (NSE) of RFST exceeds 0.7 at all depths. Compared to ERA5 and CRA40, RFST corrects the cold bias, improves NSE, and reduces RMSE from 4 °C-8 °C to 1 °C-2 °C. RFST not only corrects the underestimation of ST and its warming rate but also aligns more closely with observed values for surface freezing and thawing indices as well as soil freeze-thaw periods, providing a more accurate representation of soil thermal conditions in the TRSR.

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基于随机森林的三江源区土壤温度数据集。

三江源区土壤温度变化对区域气候、生态和水文过程具有重要影响。然而，由于物理过程和参数化方案的限制，现有模型和再分析数据在温度上显示出相当大的偏差。为了解决这个问题，我们使用随机森林方法（RFST）开发了一个新的ST数据集，将观测到的ST数据与相关的网格数据集整合在一起。RFST提供1982 - 2015年9层逐月温度数据，空间分辨率为0.01°× 0.01°。对2个土壤观测网和6个气象站的验证表明，RFST在所有深度的NSE均超过0.7。与ERA5和CRA40相比，RFST纠正了冷偏置，提高了NSE，并将RMSE从4°C-8°C降低到1°C-2°C。RFST不仅修正了对温度及其升温速率的低估，而且与地表冻融指数和土壤冻融期的观测值更接近，从而更准确地反映了TRSR中土壤热条件。

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.