A regional model-based algorithm to simulate root zone soil moisture with multi-source datasets

IF 6.5 1区农林科学 Q1 AGRONOMY

Agricultural Water Management Pub Date : 2025-09-13 DOI:10.1016/j.agwat.2025.109803

Yue Liu , Zhenxin Bao , Jianyun Zhang , Guoqing Wang , Yanqing Yang , Xianhong Meng

{"title":"A regional model-based algorithm to simulate root zone soil moisture with multi-source datasets","authors":"Yue Liu , Zhenxin Bao , Jianyun Zhang , Guoqing Wang , Yanqing Yang , Xianhong Meng","doi":"10.1016/j.agwat.2025.109803","DOIUrl":null,"url":null,"abstract":"<div><div>Spatiotemporal characteristics of root zone soil moisture (RZSM) play a key role in agricultural water management. Reanalysis and satellite products, e.g., ERA5, GLDAS, GLEAM and SMAP provide global-scale RZSM information, but the accuracy of these datasets usually falls short of practical needs in regional agricultural studies. We established a regional model-based algorithm for estimating RZSM, incorporating soil moisture (SM) measurements, as well as climate, topography, soil, vegetation and land use to account for the heterogeneity of the area. Taking the Yellow River basin, which spans multiple wet-dry regions, as the research object to validate the regional model and estimate RZSM information. The results showed that the superposition of predictor variables could progressively improve the performance of the regional model by setting different model input cases. The regional simulations outperformed those by nearby-station model, which was based on the parameter transplantation method, with a 21.2 % increase in the Pearson correlation coefficient (CC) and a 27.6 % decrease in the mean absolute error (MAE). The assimilated RZSM obtained using the regional model, driven by surface SM from reanalysis and satellite products, was closer to the measurements compared to the RZSM datasets provided by ERA5, GLDAS, GLEAM and SMAP, with CC improved by 38.0 %, 8.9 %, 1.5 %, and 3.1 %, and MAE decreased by 19.7 %, 5.5 %, 15.1 %, and 13.2 %, respectively. The ERA5-assimilated dataset exhibited the most significant improvement in accuracy in the study area, particularly in the semi-humid region. Overall, such a simulation scheme is proven to be effective in obtaining high-precision RZSM information at the regional scale and holds great potential for applications in agricultural decision-making and management.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"319 ","pages":"Article 109803"},"PeriodicalIF":6.5000,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Agricultural Water Management","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378377425005177","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRONOMY","Score":null,"Total":0}

引用次数: 0

Abstract

Spatiotemporal characteristics of root zone soil moisture (RZSM) play a key role in agricultural water management. Reanalysis and satellite products, e.g., ERA5, GLDAS, GLEAM and SMAP provide global-scale RZSM information, but the accuracy of these datasets usually falls short of practical needs in regional agricultural studies. We established a regional model-based algorithm for estimating RZSM, incorporating soil moisture (SM) measurements, as well as climate, topography, soil, vegetation and land use to account for the heterogeneity of the area. Taking the Yellow River basin, which spans multiple wet-dry regions, as the research object to validate the regional model and estimate RZSM information. The results showed that the superposition of predictor variables could progressively improve the performance of the regional model by setting different model input cases. The regional simulations outperformed those by nearby-station model, which was based on the parameter transplantation method, with a 21.2 % increase in the Pearson correlation coefficient (CC) and a 27.6 % decrease in the mean absolute error (MAE). The assimilated RZSM obtained using the regional model, driven by surface SM from reanalysis and satellite products, was closer to the measurements compared to the RZSM datasets provided by ERA5, GLDAS, GLEAM and SMAP, with CC improved by 38.0 %, 8.9 %, 1.5 %, and 3.1 %, and MAE decreased by 19.7 %, 5.5 %, 15.1 %, and 13.2 %, respectively. The ERA5-assimilated dataset exhibited the most significant improvement in accuracy in the study area, particularly in the semi-humid region. Overall, such a simulation scheme is proven to be effective in obtaining high-precision RZSM information at the regional scale and holds great potential for applications in agricultural decision-making and management.

查看原文本刊更多论文

基于区域模型的多源根区土壤湿度模拟算法

根区土壤水分时空特征在农业水分管理中起着关键作用。再分析和卫星产品如ERA5、GLDAS、GLEAM和SMAP提供了全球尺度的RZSM信息，但这些数据集的精度往往不能满足区域农业研究的实际需要。我们建立了一种基于区域模型的RZSM估算算法，该算法结合了土壤湿度（SM）测量数据，以及气候、地形、土壤、植被和土地利用，以考虑该地区的异质性。以横跨多个干湿区域的黄河流域为研究对象，对区域模型进行验证，估算RZSM信息。结果表明，通过设置不同的模型输入情况，预测变量的叠加可以逐步提高区域模型的性能。区域模拟的Pearson相关系数（CC）提高了21.2% %，平均绝对误差（MAE）降低了27.6% %，优于基于参数移植方法的近站模型。与ERA5、GLDAS、GLEAM和SMAP提供的RZSM数据集相比，在再分析和卫星产品的地表SM驱动下，利用区域模型获得的同化RZSM数据集与测量值更接近，CC分别提高了38.0 %、8.9 %、1.5 %和3.1 %，MAE分别降低了19.7 %、5.5 %、15.1 %和13.2 %。在研究区，特别是在半湿润地区，era5同化数据集的精度提高最为显著。综上所述，该模拟方案能够在区域尺度上获得高精度的RZSM信息，在农业决策和管理中具有很大的应用潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Agricultural Water Management 农林科学-农艺学

CiteScore

12.10

自引率

14.90%

发文量

648

审稿时长

4.9 months

期刊介绍： Agricultural Water Management publishes papers of international significance relating to the science, economics, and policy of agricultural water management. In all cases, manuscripts must address implications and provide insight regarding agricultural water management.