Abstract
Accurate and timely flood forecasts are essential for effective management of flood disasters, which has become increasingly frequent over the last decade. Obtaining such forecasts requires high resolution integrated weather and flood models with computational costs optimized to provide sufficient lead time. Existing overland flood modeling software packages do not readily scale to topography grids of large size and only permit coarse resolution modeling of large regions. In this paper, we present a highly scalable, integrated flood forecasting system called IFM that runs on both shared and distributed memory architectures, effectively allowing the computation of domains with billions of cells. In order to optimize IFM for large areas, we focus on the computationally expensive overland routing engine. We describe a parallelization scheme and novel strategies to partition irregular domains to minimize load imbalance in the presence of memory constraints that results in 40% reduction in time compared to best uniform partitioning. We demonstrate the scalability of the proposed approach for up to 8192 processors on large scale real-world domains. Our model can provide a 48-hour flood forecast on a watershed of 656 million cells in under 5 minutes.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
10 costliest floods worldwide ordered by overall losses, http://www.munichre.com/app_pages/www/res/pdf/NatCatService/significant_natural_catastrophes/2012/NatCatSERVICE_significant_floods_eco_en.pdf
Gill, M.A.: Flood routing by the Muskingum method. Journal of Hydrology 36(34), 353–363 (1978)
Li, J., Liao, W.K., Choudhary, A., Ross, R., Thakur, R., Gropp, W., Latham, R., Siegel, A., Gallagher, B., Zingale, M.: Parallel netCDF: A High-Performance Scientific I/O Interface. In: SC (2003)
Malakar, P., et al.: A divide and conquer strategy for scaling weather simulations with multiple regions of interest. In: SC 2012, pp. 37:1–37:11 (2012)
Michalakes, J., et al.: WRF Nature Run. In: SC (2007)
Moussa, R., Bocquillon, C.: Algorithms for solving the diffusive wave flood routing equation. Hydrological Processes 10(1), 105–123 (1996)
Neal, J., Fewtrell, T., Trigg, M.: Parallelisation of storage cell flood models using OpenMP. Environmental Modelling & Software 24(7), 872–877 (2009)
Neal, J.C., Fewtrell, T.J., Bates, P.D., Wright, N.G.: A comparison of three parallelisation methods for 2D flood inundation models. Environ. Model. Softw. 25(4), 398–411 (2010)
Priestnall, G., Jaafar, J., Duncan, A.: Extracting urban features from LiDAR digital surface models. Computers, Environment and Urban Systems 24(2) (2000)
Sanders, B.F., Schubert, J.E., Detwiler, R.L.: ParBreZo: A parallel, unstructured grid, Godunov-type, shallow-water code for high-resolution flood inundation modeling at the regional scale. Advances in Water Resources 33(12), 1456–1467 (2010)
Singhal, S., Villa Real, L., George, T., Aneja, S., Sabharwal, Y.: A hybrid parallelization approach for high resolution operational flood forecasting. In: HiPC 2013 (2013)
Skamarock, W.C., et al.: A description of the Advanced Research WRF version 3. Tech. Rep. TN-475, NCAR (2008)
Todini, E.: The ARNO rainfall runoff model. J. Hydrology 175(14), 339–382 (1996)
Vreugdenhil, C.: Numerical Methods for Shallow-Water Flow. NATO Asi Series. Series C, Mathematical and Physical Science. Springer (1994)
Yen, B.: Channel Flow Resistance: Centennial of Manning’s Formula. Water Resources Pub. (1992)
Yu, D.: Parallelization of a two-dimensional flood inundation model based on domain decomposition. Environmental Modelling & Software 25(8), 935–945 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Singhal, S., Aneja, S., Liu, F., Real, L.V., George, T. (2014). IFM: A Scalable High Resolution Flood Modeling Framework. In: Silva, F., Dutra, I., Santos Costa, V. (eds) Euro-Par 2014 Parallel Processing. Euro-Par 2014. Lecture Notes in Computer Science, vol 8632. Springer, Cham. https://doi.org/10.1007/978-3-319-09873-9_58
Download citation
DOI: https://doi.org/10.1007/978-3-319-09873-9_58
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09872-2
Online ISBN: 978-3-319-09873-9
eBook Packages: Computer ScienceComputer Science (R0)