Assessing the contribution of groundwater to catchment travel time distributions through integrating conceptual flux tracking with explicit Lagrangian particle tracking

doi:10.1016/j.advwatres.2021.103849

	Assessing the contribution of groundwater to catchment travel time distributions through integrating conceptual flux tracking with explicit Lagrangian particle tracking
	Jing, Miao 1,4,5,6; Kumar, Rohini 4; Attinger, Sabine 3,4; Li, Qi 2,5; Lu, Chunhui 1,6; Hesse, Falk 3,4
刊名	ADVANCES IN WATER RESOURCES
	2021-03-01
卷号	149 页码:18
关键词	Travel time distribution Flux tracking Particle tracking Coupled model Predictive uncertainty
ISSN号	0309-1708
DOI	10.1016/j.advwatres.2021.103849
英文摘要	Travel time distributions (TTDs) provide an effective way to describe the transport and mixing processes of water parcels in a subsurface hydrological system. A major challenge in characterizing catchment TTD is quantifying the travel times in deep groundwater and its contribution to the streamflow TTD. Here, we develop and test a novel modeling framework for an integrated assessment of catchment scale TTDs through explicit representation of 3D-groundwater dynamics. The proposed framework is based on the linkage between a flux tracking scheme with the surface hydrologic model (mHM) for the soil-water compartment and a particle tracking scheme with the 3D-groundwater model OpenGeoSys (OGS) for the groundwater compartment. This linkage provides us with the ability to simulate the spatial and temporal dynamics of TTDs in these different hydrological compartments from grid scale to regional scale. We apply this framework in the Nagelstedt catchment in central Germany. Simulation results reveal that both shape and scale of grid-scale groundwater TTDs are spatially heterogeneous, which are strongly dependent on the topography and aquifer structure. The component-wise analysis of catchment TTD shows a time-dependent sensitivity of transport processes in soil zone and groundwater to driving meteorological forcing. Catchment TTD exhibits a power-law shape and fractal behavior. The predictive uncertainty in catchment mean travel time is dominated by the uncertainty in the deep groundwater rather than that in the soil zone. Catchment mean travel time is severely biased by a marginal error in groundwater characterization. Accordingly, we recommend to use multiple summary statistics to minimize the predictive uncertainty introduced by the tailing behavior of catchment TTD.
资助项目	Deutsche Forschungsgemeinschaft via Sonderforschungsbereich[CRC 1076 AquaDiva] ; Deutsche Forschungsgemeinschaft[HE-7028-1/2]
WOS研究方向	Water Resources
语种	英语
出版者	ELSEVIER SCI LTD
WOS记录号	WOS:000621604100007
内容类型	期刊论文
源URL	[http://119.78.100.198/handle/2S6PX9GI/25891]
专题	中科院武汉岩土力学所
通讯作者	Jing, Miao
作者单位	1.Hohai Univ, Yangtze Inst Conservat & Dev, Nanjing 210098, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Univ Potsdam, Inst Earth & Environm Sci, Karl Liebknecht Str 24-25, D-14476 Potsdam, Germany 4.UFZ Helmholtz Ctr Environm Res, Dept Computat Hydrosyst, Permoserstr 15, D-04318 Leipzig, Germany 5.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China 6.Hohai Univ, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing 210098, Peoples R China
推荐引用方式 GB/T 7714	Jing, Miao,Kumar, Rohini,Attinger, Sabine,et al. Assessing the contribution of groundwater to catchment travel time distributions through integrating conceptual flux tracking with explicit Lagrangian particle tracking[J]. ADVANCES IN WATER RESOURCES,2021,149:18.
APA	Jing, Miao,Kumar, Rohini,Attinger, Sabine,Li, Qi,Lu, Chunhui,&Hesse, Falk.(2021).Assessing the contribution of groundwater to catchment travel time distributions through integrating conceptual flux tracking with explicit Lagrangian particle tracking.ADVANCES IN WATER RESOURCES,149,18.
MLA	Jing, Miao,et al."Assessing the contribution of groundwater to catchment travel time distributions through integrating conceptual flux tracking with explicit Lagrangian particle tracking".ADVANCES IN WATER RESOURCES 149(2021):18.