five

Data_Sheet_1_Explicit simulation of environmental gas tracers with integrated surface and subsurface hydrological models.docx

收藏
NIAID Data Ecosystem2026-03-14 收录
下载链接:
https://figshare.com/articles/dataset/Data_Sheet_1_Explicit_simulation_of_environmental_gas_tracers_with_integrated_surface_and_subsurface_hydrological_models_docx/21222362
下载链接
链接失效反馈
官方服务:
资源简介:
Environmental gas tracers allow inferring groundwater travel times and mixing ratios. Their concentrations are commonly interpreted with simplified and indirect approaches that are conceptually at odds with the high degree of complexity found in natural systems. However, the information content of the tracers can potentially be fully explored through the explicit simulation of an advection-dispersion transport equation, for example using integrated surface-subsurface hydrological models (ISSHMs). These integrated models can be used to explicitly simulate environmental tracers in complex environments. ISSHMs are usually variably saturated flow models. However, these models do not explicitly simulate gas partitioning with the aqueous phase, restricting explicit simulation of gas tracers to fully saturated conditions or to tracers with very low solubilities. We propose a mathematical formulation for the production of environmental gas tracers that are emanated in the subsurface. The production is scaled according to gas/water partitioning and water saturation, which is already computed by the model. Therefore, ISSHMs can now be used to their full potential to explicitly simulate tracer concentrations under variably saturated and dynamic conditions. The new formulation has been successfully verified against reference simulations provided with a multi-phase flow and transport model. In addition, explicit simulation of 222Rn and 37Ar groundwater concentrations in a synthetic alluvial river-groundwater system was demonstrated, for the first time, with an ISSHM.
创建时间:
2022-09-28
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