A Variant of the Planchon and Darboux Algorithm for Filling Depressions in Raster Digital Elevation Models

Depression (pit or sink) filling is a key preprocessing step for the automatic hydrologic analysis of surface topography. The Planchon and Darboux (P&D) algorithm is a widely used depression filling algorithm. In this study, we propose an improved variant over the fastest sequential variant of the P&D algorithm for depression filling. Our variant introduces two important improvements compared with the fastest variant of the P&D algorithm, and greatly reduces redundant computation, as well as requires less memory space. Our algorithm can be easily integrated into many of the existing hydrologic analysis software packages. Moreover, our algorithm shares the same versatility as the P&D algorithm. Depressions can be replaced with surfaces either strictly horizontal, or slightly sloping. In the latter case, it is easier to calculate the flow direction matrix.

[1]  Jianya Gong,et al.  Extraction of drainage networks from large terrain datasets using high throughput computing , 2009, Comput. Geosci..

[2]  Chengzhi Qin,et al.  Parallelizing flow-accumulation calculations on graphics processing units - From iterative DEM preprocessing algorithm to recursive multiple-flow-direction algorithm , 2012, Comput. Geosci..

[3]  John B. Lindsay,et al.  Efficient hybrid breaching‐filling sink removal methods for flow path enforcement in digital elevation models , 2016 .

[4]  Constantijn J. Berends,et al.  A computationally efficient depression-filling algorithm for digital elevation models, applied to proglacial lake drainage , 2016 .

[5]  Chenghu Zhou,et al.  An adaptive approach to selecting a flow‐partition exponent for a multiple‐flow‐direction algorithm , 2007, Int. J. Geogr. Inf. Sci..

[6]  Xingwei Liu,et al.  An Automated Processing Algorithm for Flat Areas Resulting from DEM Filling and Interpolation , 2017, ISPRS Int. J. Geo Inf..

[7]  C. Rennó,et al.  Height Above the Nearest Drainage – a hydrologically relevant new terrain model , 2011 .

[8]  Richard Barnes.  Parallel Priority-Flood depression filling for trillion cell digital elevation models on desktops or clusters , 2016, Comput. Geosci..

[9]  Frédéric Darboux,et al.  A fast, simple and versatile algorithm to fill the depressions of digital elevation models , 2002 .

[10]  Xiaoli Liu,et al.  Parallel identification and filling of depressions in raster digital elevation models , 2017, Int. J. Geogr. Inf. Sci..

[11]  P. Cui,et al.  A new treatment of depression for drainage network extraction based on DEM , 2009 .

[12]  Guiyun Zhou,et al.  An efficient variant of the Priority-Flood algorithm for filling depressions in raster digital elevation models , 2016, Comput. Geosci..

[13]  Qing Zhu,et al.  An efficient depression processing algorithm for hydrologic analysis , 2006, Comput. Geosci..

[14]  W. Rieger.  A phenomenon‐based approach to upslope contributing area and depressions in DEMs , 1998 .

[15]  Guiyun Zhou,et al.  Efficient Priority-Flood depression filling in raster digital elevation models , 2019, Int. J. Digit. Earth.

[16]  Richard Barnes,et al.  Priority-Flood: An Optimal Depression-Filling and Watershed-Labeling Algorithm for Digital Elevation Models , 2015, Comput. Geosci..

[17]  Richard Gloaguen,et al.  TecDEM: A MATLAB based toolbox for tectonic geomorphology, Part 1: Drainage network preprocessing and stream profile analysis , 2011, Comput. Geosci..

[18]  Liyang Xiong,et al.  Landform‐oriented flow‐routing algorithm for the dual‐structure loess terrain based on digital elevation models , 2014 .

[19]  L. Wang,et al.  An efficient method for identifying and filling surface depressions in digital elevation models for hydrologic analysis and modelling , 2006, Int. J. Geogr. Inf. Sci..

[20]  P. Soille.  Optimal removal of spurious pits in grid digital elevation models , 2004 .

[21]  S. K. Jenson,et al.  Extracting topographic structure from digital elevation data for geographic information-system analysis , 1988 .

[22]  Matthew Baker,et al.  Extraction of hydrological proximity measures from DEMs using parallel processing , 2011, Environ. Model. Softw..

[23]  David G. Tarboton,et al.  A virtual tile approach to raster-based calculations of large digital elevation models in a shared-memory system , 2015, Comput. Geosci..

[24]  Guangqian Wang,et al.  An efficient and comprehensive method for drainage network extraction from DEM with billions of pixels using a size-balanced binary search tree , 2015 .

[25]  Ian Cluckie,et al.  An effective depression filling algorithm for DEM-based 2-D surface flow modelling , 2013 .