five

Data for "Models for Predicting Water Retention in Pyrogenic Carbon (Biochar) and Biochar-Amended Soil at Low Water Contents"

收藏
DataCite Commons2025-12-12 更新2026-04-25 收录
下载链接:
http://www.hydroshare.org/resource/b0900dfe949f4086a28e3c63f1112917
下载链接
链接失效反馈
官方服务:
资源简介:
Data for "Models for Predicting Water Retention in Pyrogenic Carbon (Biochar) and Biochar-Amended Soil at Low Water Contents", submitted to Water Resources Research (2020). The dry end of the soil water retention curve (WRC) plays an important role in various hydrologic, solute transport, plant, and microbial processes. Despite increasing application of biochar as a soil amendment, knowledge about water retention in biochars and biochar-amended soils under dry conditions is lacking. Mechanistic models are presented to predict the WRC for biochars and biochar-amended soils at matric potential (ψ) < ~-1 MPa. For biochars, the amount of water retained is linked to biochar surficial oxygen content and pore volume and surface area distributions. The WRC for soils at dry conditions is predicted using specific surface area. The WRC model for biochar-amended soils is the sum of the contributions of models for biochar and soil. The model’s utility was examined for three natural soils and a uniform sand, a wood-based biochar, and ten different combinations of these soils and biochar. The accuracy of the model for biochars was further tested for six other pyrogenic carbonaceous materials (PCMs). The models agreed well with experimental data: for the biochar and PCMs, soils, and biochar-amended soils the root mean square error normalized to the range of water content was almost always < 10%. The line of best fit for predicted versus measured gravimetric water content at permanent wilting point had slope of 0.935 ± 0.013 and a coefficient of determination of 0.997. The applicability of these models for different biochars, soils, and their mixtures is discussed.
提供机构:
Consortium of Universities for the Advancement of Hydrologic Science, Inc
创建时间:
2025-12-12
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作