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HYSOGs250m,用于基于曲线数的径流建模的全球网格化水文土壤组。

HYSOGs250m, global gridded hydrologic soil groups for curve-number-based runoff modeling.

作者信息

Ross C Wade, Prihodko Lara, Anchang Julius, Kumar Sanath, Ji Wenjie, Hanan Niall P

机构信息

New Mexico State University, Department of Plant and Environmental Sciences, Las Cruces, New Mexico 88003, USA.

New Mexico State University, Department of Animal and Range Sciences, Las Cruces, New Mexico 88003, USA.

出版信息

Sci Data. 2018 May 15;5:180091. doi: 10.1038/sdata.2018.91.

Abstract

Hydrologic soil groups (HSGs) are a fundamental component of the USDA curve-number (CN) method for estimation of rainfall runoff; yet these data are not readily available in a format or spatial-resolution suitable for regional- and global-scale modeling applications. We developed a globally consistent, gridded dataset defining HSGs from soil texture, bedrock depth, and groundwater. The resulting data product-HYSOGs250m-represents runoff potential at 250 m spatial resolution. Our analysis indicates that the global distribution of soil is dominated by moderately high runoff potential, followed by moderately low, high, and low runoff potential. Low runoff potential, sandy soils are found primarily in parts of the Sahara and Arabian Deserts. High runoff potential soils occur predominantly within tropical and sub-tropical regions. No clear pattern could be discerned for moderately low runoff potential soils, as they occur in arid and humid environments and at both high and low elevations. Potential applications of this data include CN-based runoff modeling, flood risk assessment, and as a covariate for biogeographical analysis of vegetation distributions.

摘要

水文土壤组(HSGs)是美国农业部曲线数(CN)法估算降雨径流的基本组成部分;然而,这些数据并非以适合区域和全球尺度建模应用的格式或空间分辨率 readily available。我们开发了一个全球一致的网格化数据集,根据土壤质地、基岩深度和地下水来定义水文土壤组。由此产生的数据产品——HYSOGs250m——代表了250米空间分辨率下的径流潜力。我们的分析表明,全球土壤分布以径流潜力适中偏高为主,其次是径流潜力适中偏低、高和低。径流潜力低的沙质土壤主要分布在撒哈拉沙漠和阿拉伯沙漠的部分地区。径流潜力高的土壤主要分布在热带和亚热带地区。径流潜力适中偏低的土壤没有明显的分布模式,因为它们出现在干旱和潮湿环境以及高海拔和低海拔地区。这些数据的潜在应用包括基于CN的径流建模、洪水风险评估以及作为植被分布生物地理分析的协变量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cfa4/5952866/b93e36a851f4/sdata201891-f1.jpg

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