A layered approach to parallel computing for spatially distributed hydrological modeling

Environmental Modelling and Software

Volumn 51, Issue , 2014, Pages 221-227

  Liu, Junzhi ^a,b   Zhu, A Xing ^a,c   Liu, Yongbo ^d   Zhu, Tongxin ^e   Qin, Cheng Zhi ^a  

^a INSTITUTE OF GEOGRAPHIC SCIENCES AND NATURAL RESOURCES RESEARCH   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^c UNIVERSITY OF WISCONSIN MADISON   (United States)

^d UNIVERSITY OF GUELPH   (Canada)

^e UNIVERSITY OF MINNESOTA DULUTH   (United States)

Author keywords

Distributed hydrological model; Domain decomposition; OpenMP; Parallel computing; Simulation units Layering

Indexed keywords

APPLICATION PROGRAMMING INTERFACES (API); CLIMATE MODELS; DOMAIN DECOMPOSITION METHODS; HYDROLOGY; PARALLEL PROCESSING SYSTEMS; PROGRAM PROCESSORS;

DIFFERENT RESOLUTIONS; DISTRIBUTED HYDROLOGICAL MODEL; HYDROLOGICAL MODELING; HYDROLOGICAL SIMULATIONS; OPENMP; PARALLEL COM- PUTING; SIMULATION UNITS; SPATIALLY DISTRIBUTED HYDROLOGICAL MODEL;

LARGE DATASET;

CHANNEL FLOW; COMPUTER SIMULATION; DATA SET; DECOMPOSITION; HYDROLOGICAL MODELING; NUMERICAL MODEL; SPATIAL DISTRIBUTION; WATERSHED;

EID: 84887003026     PISSN: 13648152     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.envsoft.2013.10.005     Document Type: Article

References (30)

1. Apostolopoulos T.K., Georgakakos K.P. Parallel computation for streamflow prediction with distributed hydrologic models. J.Hydrol. 1997, 197(1-4):1-24.
2. Borah D.K., Bera M. Watershed-scale hydrologic and nonpoint-source pollution models: review of applications. Trans. ASAE 2004, 47(3):789-803.
3. Bryan B.A. High-performance computing tools for the integrated assessment and modelling of social-ecological systems. Environ. Model. Softw. 2013, 39:295-303.
4. Chow V., Maidment D., Mays L. Applied Hydrology 1988, McGraw-Hill, New York.
5. Ciarapica L., Todini E. TOPKAPI: a model for the representation of the rainfall-runoff process at different scales. Hydrol. Process. 2002, 16(2):207-229.
6. Grayson R.B., Moore I.D., Mcmahon T.A. Physically based hydrologic modeling .1. A terrain-based model for investigative purposes. Water Resour. Res. 1992, 28(10):2639-2658.
7. Hessell R. Effects of grid cell size and time step length on simulation results of the Limburg soil erosion model (LISEM). Hydrol. Process. 2005, 19(15):3037-3049.
8. Li R.M., Simons D.B., Stevens M.A. Nonlinear kinematic wave approximation for water routing. Water Resour. Res. 1975, 11(2):245-252.
9. Li T.J., Wang G.Q., Chen J., Wang H. Dynamic parallelization of hydrological model simulations. Environ. Model. Softw. 2011, 26(12):1736-1746.
10. Linsley R.K., Kohler M.A., Paulhus J.L.H. Hydrology for Engineers 1975, McGraw-HIll, New York.
11. Liu J.Z., Zhu A.X., Qin C.Z. Estimation of theoretical maximum speedup ratio for parallel computing of grid-based distributed hydrological models. Comput. Geosci. 2013, 60:58-62.
12. Liu J.Z., Zhu A.X., Qin C.Z., Chen L.J., Wu H., Jiang J.C. Review on parallel computing of distributed hydrological models. Prog. Geogr. 2013, 32(4):538-547. (in Chinese).
13. Liu Y.B., De Smedt F. WetSpa Extension, Documentation and User Manual 2004, Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel, Belgium.
14. Liu Y.B., De Smedt F. Flood modeling for complex terrain using GIS and remote sensed information. Water Resour. Manag. 2005, 19(5):605-624.
15. Mattson T., Sanders B., Massingill B. Patterns for Parallel Programming 2004, Addison-Wesley Professional, Boston.
16. O'Callaghan J.F., Mark D.M. The extraction of drainage networks from digital elevation data. Comput. Vis. Graphics, Image Process. 1984, 28(3):323-344.
17. Qin C.Z., Zhan L.J. Parallelizing flow-accumulation calculations on graphics processing units - from iterative DEM preprocessing algorithm to recursive multiple-flow-direction algorithm. Comput. Geosciences 2012, 43:7-16.
18. Rauber T., Rünger G. Parallel Programming: For Multi-core and Cluster Systems 2010, Springer-Verlag New York Inc, New York.
19. Rojas R., Velleux M., Julien P.Y., Johnson B.E. Grid scale effects on watershed soil erosion models. J.Hydrol. Eng. 2008, 13(9):793-802.
20. Van Der Knijff J.M., Younis J., De Roo A.P.J. LISFLOOD: a GIS-based distributed model for river basin scale water balance and flood simulation. Int. J. Geogr. Inf. Sci. 2010, 24(2):189-212.
21. Vertessy R.A., Hatton T.J., Oshaughnessy P.J., Jayasuriya M.D.A. Predicting water yield from a mountain ash forest catchment using a terrain analysis based catchment model. J.Hydrol. 1993, 150(2-4):665-700.
22. Vivoni E.R., Mascaro G., Mniszewski S., Fasel P., Springer E.P., Ivanov V.Y., Bras R.L. Real-world hydrologic assessment of a fully-distributed hydrological model in a parallel computing environment. J.Hydrol. 2011, 409(1-2):483-496.
23. Wang G.S., Xia J., Niu C.W. Flow routing method and its application in distributed hydrological modeling. Geogr. Res. 2004, 23(2):175-182. (in Chinese).
24. Wang H., Fu X., Wang G., Li T., Gao J. Acommon parallel computing framework for modeling hydrological processes of river basins. Parallel Comput. 2011, 37(6-7):302-315.
25. Wang H., Zhou Y., Fu X., Gao J., Wang G. Maximum speedup ratio curve (MSC) in parallel computing of the binary-tree-based drainage network. Comput. Geosci. 2012, 38(1):127-135.
26. Wang H., Fu X., Wang Y., Wang G. AHigh-performance temporal-spatial discretization method for the parallel computing of river basins. Comput. Geosci. 2013, 58:62-68.
27. Wu Y., Li T., Sun L., Chen J. Parallelization of a hydrological model using the message passing interface. Environ. Model. Softw. 2013, 43:124-132.
28. Yalew S., van Griensven A., Ray N., Kokoszkiewicz L., Betrie G.D. Distributed computation of large scale SWAT models on the Grid. Environ. Model. Softw. 2013, 41:223-230.
29. Zhao G., Bryan B.A., King D., Song X., Yu Q. Parallelization and optimization of spatial analysis for large scale environmental model data assembly. Comput. Elect. Agric. 2012, 89:94-99.
30. Zhao G., Bryan B.A., King D., Luo Z., Wang E., Bende-Michl E., Song X., Yu Q. Large-scale, high-resolution agricultural systems modeling using a hybrid approach combining grid computing and parallel processing. Environ. Model. Softw. 2013, 41:98-106.