Multi-objective unstructured triangular mesh generation for use in hydrological and land surface models

Abstract Unstructured triangular meshes are an efficient and effective landscape representation that are suitable for use in distributed hydrological and land surface models. Their variable spatial resolution provides similar spatial performance to high-resolution structured grids while using only a fraction of the number of elements. Many existing triangulation methods either sacrifice triangle quality to introduce variable resolution or maintain well-formed uniform meshes at the expense of variable triangle resolution. They are also generally constructed to only fulfil topographic constraints. However, distributed hydrological and land surface models require triangles of varying resolution to provide landscape representations that accurately represent the spatial heterogeneity of driving meteorology, physical parameters and process operation in the simulation domain. As such, mesh generators need to constrain the unstructured mesh to not only topography but to other important surface and sub-surface features. This work presents novel multi-objective unstructured mesh generation software that allows mesh generation to be constrained to an arbitrary number of important features while maintaining a variable spatial resolution. Triangle quality is supported as well as a smooth gradation from small to large triangles. Including these additional constraints results in a better representation of spatial heterogeneity than from classic topography-only constraints.

[1]  Naser El-Sheimy,et al.  Digital terrain modeling - acquistion, manipulation, and applications , 2005 .

[2]  Rüdiger Verfürth,et al.  Robust A Posteriori Error Estimates for Stationary Convection-Diffusion Equations , 2005, SIAM J. Numer. Anal..

[3]  Christopher J. Duffy,et al.  International Journal of Geographical Information Science an Efficient Domain Decomposition Framework for Accurate Representation of Geodata in Distributed Hydrologic Models an Efficient Domain Decomposition Framework for Accurate Representation of Geodata in Distributed Hydrologic Models , 2022 .

[4]  Cajo J. F. ter Braak,et al.  Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation , 2008 .

[5]  Dara Entekhabi,et al.  Preserving high-resolution surface and rainfall data in operational-scale basin hydrology: a fully-distributed physically-based approach , 2004 .

[6]  J. Westerink,et al.  One‐dimensional finite element grids based on a localized truncation error analysis , 2000 .

[7]  Alain Pietroniro,et al.  Effects of Spatial Aggregation of Initial Conditions and Forcing Data on Modeling Snowmelt Using a Land Surface Scheme , 2008 .

[8]  Kang-Tsung Chang,et al.  Introduction to Geographic Information Systems , 2001 .

[9]  Raymond J. Spiteri,et al.  Implications of mountain shading on calculating energy for snowmelt using unstructured triangular meshes , 2012 .

[10]  Chun-Jen Chen,et al.  A component-labeling algorithm using contour tracing technique , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[11]  C. Duffy,et al.  A Second‐Order Accurate, Finite Volume–Based, Integrated Hydrologic Modeling (FIHM) Framework for Simulation of Surface and Subsurface Flow , 2009 .

[12]  John W. Pomeroy,et al.  Coupled Modelling of Forest Snow Interception and Sublimation , 1998 .

[13]  John W. Pomeroy,et al.  Influence of snowpack and melt energy heterogeneity on snow cover depletion and snowmelt runoff simulation in a cold mountain environment , 2017 .

[14]  John W. Pomeroy,et al.  Intra‐basin variability of snowmelt water balance calculations in a subarctic catchment , 2006 .

[15]  T. Foken The energy balance closure problem: an overview. , 2008, Ecological applications : a publication of the Ecological Society of America.

[16]  Grant David,et al.  Influence of mesh structure on surgical healing in abdominal wall hernia repair , 2016 .

[17]  Pierre Alliez,et al.  2D Centroidal Voronoi Tessellations with Constraints , 2010 .

[18]  Michael G. Andreu,et al.  Introduction to Geographic Information Systems 1 , 2012 .

[19]  C. Duffy,et al.  A semidiscrete finite volume formulation for multiprocess watershed simulation , 2007 .

[20]  Philip Marsh,et al.  A shrub bending model to calculate the albedo of shrub‐tundra , 2014 .

[21]  P. Colella,et al.  Local adaptive mesh refinement for shock hydrodynamics , 1989 .

[22]  Philip Marsh,et al.  APPLICATION OF A DISTRIBUTED BLOWING SNOW MODEL TO THE ARCTIC , 1997 .

[23]  John W. Pomeroy,et al.  A distributed model of blowing snow over complex terrain , 1999 .

[24]  Jonathan Richard Shewchuk,et al.  Delaunay refinement algorithms for triangular mesh generation , 2002, Comput. Geom..

[25]  Ming-ko Woo,et al.  Spatial variability of hillslope water balance, wolf creek basin, subarctic yukon , 2001 .

[26]  M. F. Fuller,et al.  Practical Nonparametric Statistics; Nonparametric Statistical Inference , 1973 .

[27]  Michael Lehning,et al.  Spatial and temporal variability of snow depth and ablation rates in a small mountain catchment , 2010 .

[28]  Dara Entekhabi,et al.  Generation of triangulated irregular networks based on hydrological similarity , 2004 .

[29]  S. Hagen,et al.  Terrain-driven unstructured mesh development through semi-automatic vertical feature extraction , 2015 .

[30]  John W. Pomeroy,et al.  Implications of spatial distributions of snow mass and melt rate for snow-cover depletion: theoretical considerations , 2004, Annals of Glaciology.

[31]  John W. Pomeroy,et al.  Modeling Forest Cover Influences on Snow Accumulation, Sublimation, and Melt , 2004 .

[32]  Volker John,et al.  A numerical study of a posteriori error estimators for convection–diffusion equations , 2000 .

[33]  John F. O'Callaghan,et al.  The extraction of drainage networks from digital elevation data , 1984, Comput. Vis. Graph. Image Process..

[34]  Richard Barnes,et al.  Parallel non-divergent flow accumulation for trillion cell digital elevation models on desktops or clusters , 2016, Environ. Model. Softw..

[35]  Jonathan Richard Shewchuk,et al.  Adaptive Precision Floating-Point Arithmetic and Fast Robust Geometric Predicates , 1997, Discret. Comput. Geom..

[36]  Alain Pietroniro,et al.  Influence of landscape aggregation in modelling snow-cover ablation and snowmelt runoff in a sub-arctic mountainous environment , 2008 .

[37]  Timothy E. Link,et al.  Shrub tundra snowmelt , 2006 .

[38]  W. J. Conover,et al.  Practical Nonparametric Statistics , 1972 .

[39]  Jonathan Richard Shewchuk,et al.  Unstructured Mesh Generation , 2011 .

[40]  Javier Murillo,et al.  Influence of mesh structure on 2D full shallow water equations and SCS Curve Number simulation of rainfall/runoff events , 2012 .

[41]  S. Hagen,et al.  Topographic accuracy assessment of bare earth lidar-derived unstructured meshes , 2013 .

[42]  Michael A. Wulder Satellite land cover mapping of Canada's forests , 2006 .

[43]  Attila R. Imre,et al.  Fractals and the Korcak-law: a history and a correction , 2016 .

[44]  Michael Lehning,et al.  Simulation of seasonal snow-cover distribution for glacierized sites on Sonnblick, Austria, with the Alpine3D model , 2008, Annals of Glaciology.

[45]  Jay Lee,et al.  Comparison of existing methods for building triangular irregular network, models of terrain from grid digital elevation models , 1991, Int. J. Geogr. Inf. Sci..

[46]  Scott C. Hagen,et al.  An Unstructured Mesh Generation Algorithm for Shallow Water Modeling , 2002 .

[47]  John W. Pomeroy,et al.  Temporal Variation in Snowcover Area During Melt in Prairie and Alpine Environments , 1993 .

[48]  Joannes J. Westerink,et al.  Two-dimensional, unstructured mesh generation for tidal models , 2001 .

[49]  Scott C. Hagen,et al.  2D unstructured mesh generation for oceanic and coastal tidal models from a localized truncation error analysis with complex derivatives , 2007 .

[50]  Jim Ruppert,et al.  A Delaunay Refinement Algorithm for Quality 2-Dimensional Mesh Generation , 1995, J. Algorithms.