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| 001 | 153955 | ||
| 005 | 20220930130029.0 | ||
| 024 | 7 | _ | |a 10.1002/2013WR013725 |2 doi |
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| 037 | _ | _ | |a FZJ-2014-03395 |
| 082 | _ | _ | |a 550 |
| 100 | 1 | _ | |a Maxwell, Reed M. |0 P:(DE-HGF)0 |b 0 |e Corresponding Author |
| 245 | _ | _ | |a Surface-subsurface model intercomparison: A first set of benchmark results to diagnose integrated hydrology and feedbacks |
| 260 | _ | _ | |a Washington, DC |c 2014 |b AGU |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 153955 |2 PUB:(DE-HGF) |
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| 520 | _ | _ | |a There are a growing number of large-scale, complex hydrologic models that are capable of simulating integrated surface and subsurface flow. Many are coupled to land-surface energy balance models, biogeochemical and ecological process models, and atmospheric models. Although they are being increasingly applied for hydrologic prediction and environmental understanding, very little formal verification and/or benchmarking of these models has been performed. Here we present the results of an intercomparison study of seven coupled surface-subsurface models based on a series of benchmark problems. All the models simultaneously solve adapted forms of the Richards and shallow water equations, based on fully 3-D or mixed (1-D vadose zone and 2-D groundwater) formulations for subsurface flow and 1-D (rill flow) or 2-D (sheet flow) conceptualizations for surface routing. A range of approaches is used for the solution of the coupled equations, including global implicit, sequential iterative, and asynchronous linking, and various strategies are used to enforce flux and pressure continuity at the surface-subsurface interface. The simulation results show good agreement for the simpler test cases, while the more complicated test cases bring out some of the differences in physical process representations and numerical solution approaches between the models. Benchmarks with more traditional runoff generating mechanisms, such as excess infiltration and saturation, demonstrate more agreement between models, while benchmarks with heterogeneity and complex water table dynamics highlight differences in model formulation. In general, all the models demonstrate the same qualitative behavior, thus building confidence in their use for hydrologic applications. |
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| 588 | _ | _ | |a Dataset connected to CrossRef, juser.fz-juelich.de |
| 700 | 1 | _ | |a Putti, Mario |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Meyerhoff, Steven |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Delfs, Jens-Olaf |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Ferguson, Ian M. |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Ivanov, Valeriy |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Kim, Jongho |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Kolditz, Olaf |0 P:(DE-HGF)0 |b 7 |
| 700 | 1 | _ | |a Kollet, Stefan |0 P:(DE-Juel1)151405 |b 8 |u fzj |
| 700 | 1 | _ | |a Kumar, Mukesh |0 P:(DE-HGF)0 |b 9 |
| 700 | 1 | _ | |a Lopez, Sonya |0 P:(DE-HGF)0 |b 10 |
| 700 | 1 | _ | |a Niu, Jie |0 P:(DE-HGF)0 |b 11 |
| 700 | 1 | _ | |a Paniconi, Claudio |0 P:(DE-HGF)0 |b 12 |
| 700 | 1 | _ | |a Park, Young-Jin |0 P:(DE-HGF)0 |b 13 |
| 700 | 1 | _ | |a Phanikumar, Mantha S. |0 P:(DE-HGF)0 |b 14 |
| 700 | 1 | _ | |a Shen, Chaopeng |0 P:(DE-HGF)0 |b 15 |
| 700 | 1 | _ | |a Sudicky, Edward A. |0 P:(DE-HGF)0 |b 16 |
| 700 | 1 | _ | |a Sulis, Mauro |0 P:(DE-HGF)0 |b 17 |
| 773 | _ | _ | |a 10.1002/2013WR013725 |g Vol. 50, no. 2, p. 1531 - 1549 |0 PERI:(DE-600)2029553-4 |n 2 |p 1531 - 1549 |t Water resources research |v 50 |y 2014 |x 0043-1397 |
| 856 | 4 | _ | |y Publishers version according to licensing conditions. |z Published final document. |
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