Digital elevation models (DEM) are widely used for hydrological analysis – the assessment of surface runoff properties. ‘Raw’ DEMs are rarely suitable for reliable hydrological analysis, which requires DEM to be in so-called ‘hydrologilcally correct’ form. Hydrological correctness requires that all closed local depressions (pits) should be removed from DEM. There are a lot of hydrological DEM pre-processing (pit treatment) algorithms, including filling, breaching, and combined approaches, but most of them cannot preserve surface features from initial DEM. This paper presents a new algorithm for filling closed local depressions which replaces every pit with naturally-shaped surface, allowing preservation of initial surface features. Modification procedure of the proposed algorithm does not exceed depression boundaries and cannot change potentially reliable DEM fragments. Comparison of results of hydrological correction obtained by several algorithms shows that proposed feature-preserving filling procedure produces more reliable surface for further hydrological analysis, and the results are comparable to the results of breaching procedure.

Koshel Sergey, Candidate of Sciences in Geography, leading researcher, Faculty of Geography, Lomonosov Moscow State University. 119991, Moscow, Leninskie Gory, 1, MSU, Faculty of Geography. E-mail: skoshel@mail.ru.

Entin Andrey, engineer, Faculty of Geography, Lomonosov Moscow State University. 119991, Moscow, Leninskie Gory, 1, MSU, Faculty of Geography. E-mail: aentin@geogr.msu.ru.

Samsonov Timofey, Candidate of Sciences in Geography, leading researcher, Faculty of Geography, Lomonosov Moscow State University. 119991, Moscow, Leninskie Gory, 1, MSU, Faculty of Geography. E-mail: tsamsonov@geogr.msu.ru.

1. Gruber S., Peckham S. Land-Surface Parameters and Objects in Hydrology // Geomorphometry: concepts, software, applications / T. Hengl, H.I. Reuter (Eds). Elsevier, 2009. V. 33. P. 171-194.
2. Koshel S.M., Entin A.L Contemporary methods of the overland flow distribution calculation from digital elevation models // Geomorphologists. Modern methods and techniques of digital elevation modeling in Earth sciences. Moscow: Media-Press, 2016. P. 24-34.
3. Jones R. Algorithms for using a DEM for mapping catchment areas of stream sediment samples // Computers and Geosciences. 2002. V. 28, Issue 9. P. 1051-1060.
4. O’Callaghan J.F., Mark D.M. The extraction of drainage networks from digital elevation data // Computer vision, graphics, and image processing. 1984. V. 28, Issue 3. P. 323-344.
5. Lindsay J.B. Efficient hybrid breaching-filling sink removal methods for flow path enforcement in digital elevation models // Hydrological Processes. 2016. V. 30, Issue 6. P. 846-857.
6. Lindsay J.B., Creed I.F. Distinguishing actual and artefact depressions in digital elevation data // Computers and Geosciences. 2006. V. 32, Issue 8. P. 1192-1204.
7. Temme A.J.A.M., Schoorl J.M., Veldkamp A. Algorithm for dealing with depressions in dynamic landscape evolution models // Computers and Geosciences. 2006. V. 32, Issue 4. P. 452-461.
8. Martz L.W., Garbrecht J. The treatment of flat areas and depressions in automated drainage analysis of raster digital elevation models // Hydrological Processes. 1998. V. 12, Issue 6. P. 843-855.
9. Jenson S.K., Domingue J.O. Extracting Topographic Structure from Digital Elevation Data for Geographic Information System Analysis // Photogrammetric Engineering and Remote Sensing. 1988. V. 54, Issue 11. P. 1593-1600.
10. Martz L.W., de Jong E. CATCH: A FORTRAN program for measuring catchment area from digital elevation models // Computers & Geosciences. 1988. V. 14, Issue 5. P. 627-640.
11. Barnes R., Lehman C., Mulla D. An efficient assignment of drainage direction over flat surfaces in raster digital elevation models // Computers and Geosciences. 2014. V. 62. P. 128-135.
12. Planchon O., Darboux F. A fast, simple and versatile algorithm to fill the depressions of digital elevation models // CATENA. 2002. V. 46, Issue 2-3. P. 159-176.
13. Wang L., Liu H. An efficient method for identifying and filling surface depressions in digital elevation models for hydrologic analysis and modelling // International Journal of Geographical Information Science. 2006. V. 20, Issue 2. P. 193-213.
14. Barnes R., Lehman C., Mulla D. Priority-flood: An optimal depression-filling and watershed-labeling algorithm for digital elevation models // Computers & Geosciences. 2014. V. 62. P. 117-127.
15. Barnes R. Parallel Priority-Flood depression filling for trillion cell digital elevation models on desktops or clusters // Computers & Geosciences. 2016. V. 96. P. 56-68.
16. Zhou G., Liu X, Fu S., Sun Z. Parallel identification and filling of depressions in raster digital elevation models // International Journal of Geographical Information Science. 2017. V. 31, Issue 6. P. 1061-1078.
17. Zhou G., Sun Z., Fu S. An efficient variant of the Priority-Flood algorithm for filling depressions in raster digital elevation models // Computers and Geosciences. 2016. V. 90. P. 87-96.
18. Rieger W. A phenomenon-based approach to upslope contributing area and depressions in DEMs // Hydrological Processes. 1998. V. 12, Issue 6. P. 857-872.
19. Martz L.W., Garbrecht J. An outlet breaching algorithm for the treatment of closed depressions in a raster DEM // Computers & Geosciences. 1999. V. 25, Issue 7. P. 835-844.
20. Soille P., Vogt J., Colombo R. Carving and adaptive drainage enforcement of grid digital elevation models // Water Resources Research. 2003. V. 39, Issue 12. P. 1058.
21. Soille P. Optimal removal of spurious pits in grid digital elevation models // Water Resources Research. 2004. V. 40, Issue 12. P. W12509.
22. Lindsay J.B., Creed I.F. Removal of artifact depressions from digital elevation models: Towards a minimum impact approach // Hydrological Processes. 2005. V. 19, Issue 16. P. 3113-3126.
23. Grimaldi S., Nardi F., di Benedetto F., Istanbulluoglu E., Bras R.L. A physically-based method for removing pits in digital elevation models // Advances in Water Resources. 2007. V. 30, Issue 10. P. 2151-2158.
24. Santini M., Grimaldi S., Nardi F., Petroselli A., Rulli M.C. Pre-processing algorithms and landslide modelling on remotely sensed DEMs // Geomorphology. 2009. V. 113, Issue 1-2. P. 110-125.
25. Hutchinson M.F. A new procedure for gridding elevation and stream line data with automatic removal of spurious pits // Journal of Hydrology. 1989. V. 106, Issue 3-4. P. 211-232.
26. Hutchinson M., Xu T., Stein J. Recent Progress in the ANUDEM Elevation Gridding Procedure // Geomorphometry. Redlands, 2011. P. 19-22.
27. Gallant J.C., Hutchinson M.F. A differential equation for specific catchment area // Water Resources Research. 2011. V. 47, Issue 5. W05535.
28. Qin C.-Z., Ai B.-B., Zhu A.-X., Liu J.-Z. An efficient method for applying a differential equation to deriving the spatial distribution of specific catchment area from gridded digital elevation models // Computers & Geosciences. 2017. V. 100, № 2. P. 94-102.
29. Koshel S.M., Entin A.L. Catchment area derivation from gridded digital elevation models using the flowline-tracing approach. Moscow University Bulletin. Series 5. Geography. 2017. No. 3. P. 42-50.
30. Pan F., Stieglitz M., McKane R.B. An algorithm for treating flat areas and depressions in digital elevation models using linear interpolation // Water Resources Research. 2012. V. 48, Issue 2. W00L10.
31. Mal’tsev K.A., Ermolaev O.P., Using DEMs for automatic plotting of catchments // Geomorpholgy. 2014. V. 1. P. 45-52.
32. Gartsman B.I., Bugayets A.N., Tegay N.D., Krasnopeev S.M. Analysis of the structure of river systems and the prospects for modeling hydrological processes // Geography and natural resources. 2008. V 2. P. 116-123.
33. Danielson J.J., Gesch D.B. Global multi-resolution terrain elevation data 2010 (GMTED2010): U.S. Geological Survey Open-File Report 2011-1073. 2011. 26 p.
34. Conrad O. et al. System for Automated Geoscientific Analyses (SAGA) v. 2.1.4 // Geoscientific Model Development. 2015. V. 8, Issue 7. P. 1991-2007.
35. Lindsay J.B. Whitebox GAT: A case study in geomorphometric analysis // Computers & Geosciences. 2016. V. 95. P. 75-84.
36. Digital geographic basemaps – VSEGEI [Electronic resource]. URL: http://www.vsegei.com/ru/info/topo/ (date of access: 27.10.2018).