Evaluating rainfall estimates derived from soil moisture using soil hydraulic properties over the Korean Peninsula

IF 6.3 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology Pub Date : 2025-09-17 DOI:10.1016/j.jhydrol.2025.134267

Doyoung Kim , Seulchan Lee , Seongkeun Cho , Daeha Kim , Minha Choi

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引用次数: 0

Abstract

Rainfall is an essential element within hydrological systems, serving as the primary source of moisture on the land surface. Recent climate change-induced extreme weather events have increased the spatiotemporal variability of rainfall, highlighting the demand for diverse rainfall monitoring methods. This study used the SM2RAIN algorithm with soil physical properties (infiltration rate and soil moisture nonlinearity coefficient) to generate a rainfall dataset for the Korean Peninsula. By incorporating soil physical properties into SM2RAIN, the SM2RAIN-Soil Moisture Active and Passive (SM2RAIN-SMAP) dataset was created from SMAP L4 soil moisture. The accuracy of SM2RAIN-SMAP was compared with the Global Precipitation Mission Integrated Multi-satellitE Retrievals (GPM-IMERG) and SM2RAIN-Advanced SCATterometer (SM2RAIN-ASCAT). Rainfall estimated using soil physical property parameters showed good agreement with point-scale rainfall observations with a Spearman rank correlation (Rs) of 0.7. A comparison between SM2RAIN-SMAP and SM2RAIN-ASCAT revealed that the highest correlation occurred in spring (0.77), with an average correlation of 0.65 across all seasons. An analysis of rainfall estimation performance by the land cover type revealed that SM2RAIN-SMAP performed better in croplands, whereas SM2RAIN-ASCAT showed superior performance in forests. The performance difference was attributable to the influence of vegetation interception effects and systematic errors in soil moisture products, which vary depending on sensors. The proposed approach used relatively simple calculations to improve the accuracy of rainfall monitoring and has the potential to provide diverse and reliable rainfall data for hydrometeorological research and disaster management in monsoon regions. This physics-based approach offers an alternative to the traditional empirical calibration methods used in SM2RAIN.

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利用朝鲜半岛土壤水力特性评估由土壤湿度得出的降雨量估算值

降雨是水文系统中的一个基本要素，是陆地表面水分的主要来源。近年来气候变化引起的极端天气事件增加了降雨的时空变异性，凸显了对降雨监测方法多样化的需求。本研究利用SM2RAIN算法结合土壤物理特性（入渗率和土壤水分非线性系数）生成朝鲜半岛降雨数据集。通过将土壤物理性质纳入SM2RAIN，以SMAP L4土壤水分为基础，建立SM2RAIN-土壤水分主动和被动数据集（SM2RAIN-SMAP）。将SM2RAIN-SMAP与全球降水任务综合多卫星反演（GPM-IMERG）和SM2RAIN-Advanced SCATterometer （SM2RAIN-ASCAT）的精度进行了比较。利用土壤物性参数估算的降雨量与点尺度观测值吻合较好，Spearman秩相关系数（Rs）为0.7。SM2RAIN-SMAP和SM2RAIN-ASCAT的相关性在春季最高（0.77），各季节的平均相关系数为0.65。对不同土地覆盖类型的降雨估计性能分析表明，SM2RAIN-SMAP在农田中表现较好，而SM2RAIN-ASCAT在森林中表现较好。这种性能差异是由于植被拦截效应和土壤水分产品的系统误差的影响，而土壤水分产品的系统误差因传感器而异。拟议的方法使用相对简单的计算来提高降雨监测的准确性，并且有可能为季风地区的水文气象研究和灾害管理提供多样化和可靠的降雨数据。这种基于物理的方法为SM2RAIN中使用的传统经验校准方法提供了一种替代方案。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Hydrology 地学-地球科学综合

CiteScore

11.00

自引率

12.50%

发文量

1309

审稿时长

7.5 months

期刊介绍： The Journal of Hydrology publishes original research papers and comprehensive reviews in all the subfields of the hydrological sciences including water based management and policy issues that impact on economics and society. These comprise, but are not limited to the physical, chemical, biogeochemical, stochastic and systems aspects of surface and groundwater hydrology, hydrometeorology and hydrogeology. Relevant topics incorporating the insights and methodologies of disciplines such as climatology, water resource systems, hydraulics, agrohydrology, geomorphology, soil science, instrumentation and remote sensing, civil and environmental engineering are included. Social science perspectives on hydrological problems such as resource and ecological economics, environmental sociology, psychology and behavioural science, management and policy analysis are also invited. Multi-and interdisciplinary analyses of hydrological problems are within scope. The science published in the Journal of Hydrology is relevant to catchment scales rather than exclusively to a local scale or site.