A scalable coupled surface–subsurface flow model

Maet, Thomas De; Cornaton, Fabien; Hanert, Emmanuel

doi:10.1016/j.compfluid.2015.03.028

Cited by 7 publications

(2 citation statements)

References 37 publications

(71 reference statements)

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“…Numerical challenges associated with hydrological simulations over very large domains, in addition to obvious computational efficiency issues, include subgrid variability (for topography as mentioned previously, but also for other processes and controls) and mesh skewness and aspect ratio (distortion and imbalance between vertical and horizontal discretizations). Computational efficiency can be addressed by code parallelization, recognized even in early hydrological modeling studies [e.g., Meyer et al ., ] and recently assessed for coupled surface/subsurface models [ Kollet et al ., ; Hwang et al ., ; De Maet et al ., ]. For a catchment size of O(10 3 ) km 2 at O (10 0 −10 1 ) m DEM resolution and O (10 −2 −10 −1 ) m vertical discretization, the numerical grid will have O (10 9 −10 10 ) cells or degrees of freedom and could only be simulated on massively parallel architectures with highly efficient scaling.…”

Section: Progress Over Five Decadesmentioning

confidence: 99%

Physically based modeling in catchment hydrology at 50: Survey and outlook

2015

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Integrated, process‐based numerical models in hydrology are rapidly evolving, spurred by novel theories in mathematical physics, advances in computational methods, insights from laboratory and field experiments, and the need to better understand and predict the potential impacts of population, land use, and climate change on our water resources. At the catchment scale, these simulation models are commonly based on conservation principles for surface and subsurface water flow and solute transport (e.g., the Richards, shallow water, and advection‐dispersion equations), and they require robust numerical techniques for their resolution. Traditional (and still open) challenges in developing reliable and efficient models are associated with heterogeneity and variability in parameters and state variables; nonlinearities and scale effects in process dynamics; and complex or poorly known boundary conditions and initial system states. As catchment modeling enters a highly interdisciplinary era, new challenges arise from the need to maintain physical and numerical consistency in the description of multiple processes that interact over a range of scales and across different compartments of an overall system. This paper first gives an historical overview (past 50 years) of some of the key developments in physically based hydrological modeling, emphasizing how the interplay between theory, experiments, and modeling has contributed to advancing the state of the art. The second part of the paper examines some outstanding problems in integrated catchment modeling from the perspective of recent developments in mathematical and computational science.

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Section: Progress Over Five Decadesmentioning

confidence: 99%

Physically based modeling in catchment hydrology at 50: Survey and outlook

2015

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“…Two-dimensional surface flow modules can be found in many integrated surfacesubsurface flow models, such as the SHE model [23,24], InHM [25], Morita's model [26,27], MODHMS [28], WASH123 [29], HydroGeoSphere [30], ParFLOW [31], PIHM [32], He's model [33], FIHM [34], OpenGeoSys [35], FLUSH [36], PAWS [37], DrainFlow [38], De Maet's model [39], and IMCR2D [40]. However, only PIHM and WASH123 use fully dynamic shallow water equations (SWEs) to characterize surface flow; the other models use diffusive wave (DW) approximation [41] or kinematic wave (KW) approximation [42].…”

Section: Introductionmentioning

confidence: 99%

Development and Application of a New Open-Source Integrated Surface–Subsurface Flow Model in Plain Farmland

Yang,

Zhou,

Jiang

et al. 2024

Water

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Accurately characterizing rainfall runoff processes in plain farmland, especially at the plot scale with significant micro-topographic features, has presented challenges. Integrated surface–subsurface flow models with high-precision surface flow modules are appropriate tools, yet open-source versions are rare. To address this gap, we proposed an open-source integrated surface–subsurface flow model called the FullSWOF-Plain model, in which the one-dimensional subsurface module Hydrus-1D was integrated with a modified two-dimensional surface water flow module (FullSWOF-2D) using the sequential head method. Various experimental scenarios were simulated to validate the model’s performance, including two outflow cases (i.e., 1D and 2D) without infiltration, a classical one-dimensional infiltration case, and two typical rainfall events at the experimental field. The results demonstrate the accuracy of this proposed model, with the Nash–Sutcliffe efficiency (NSE) of the simulated discharge exceeding 0.90 in the experimental field case and the root mean squared error (RMSE) values for soil moisture at five depths consistently below 0.03 cm3/cm3. However, we observed a lag in the simulated response time of soil moisture due to the neglect of preferential flow. The micro-topography significantly influenced ponding time and ponding areas. Lower local terrain normally experienced earlier surface ponding. Scattered surface ponding water first occurred in the ditch and followed in the relatively low areas in the main field. The concentration process of surface runoff exhibited hierarchical characteristics, with the drainage ditch contributing the most discharge initially, followed by the connection of scattered puddles in the main field, draining excess surface water to the ditch through rills. This quantitative study sheds light on the impact of micro-topography on surface runoff in plain farmland areas.

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