Parallel Computation of River Basin Hydrologic Response Using DHM
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Transcript of Parallel Computation of River Basin Hydrologic Response Using DHM
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Parallel Computation of River Basin Parallel Computation of River Basin
Hydrologic Response Using DHMHydrologic Response Using DHM Reports Environmental Hydrology Team: NCSA
Alliance All-Hands meeting
May 23-25, 2001 Urbana, Illinois
Baxter E.Vieux
Daniel Weber
Fekadu G. Moreda
Henry Neeman
Zhengtao Cui
Contact: [email protected]
www.coe.ou.edu/emgis
University of Oklahoma, Norman, Oklahoma
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OverviewOverview
Objectives The Distributed Hydrologic Model Preparing the existing model for parallel
computing Parallelization Time of computation Coupling the model with ARPS
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ObjectivesObjectives
Near term– Couple atmospheric model and surface runoff
model for flood forecasting– Improve computational efficiency of surface
runoff model
Long term– Integrate model into EH system
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Rainfall
Infiltration
Runon Runon
Runoff
Stream
Overland
Direction
Flow Characteristics Channel Characteristics
- Cross-Section Geometry- Slope- Hydraulic Roughness
* Rainfall excess at each cell
- Soil infiltration rate - Rainfall rate - Runon from upslope
Grid Cell Resolution Finite ElementsConnectivity
Watershed Runoff Simulation
Runoff SimulationRunoff Simulation
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Digital WatershedDigital Watershed
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Arc.water.feaArc.water.feaImport DEM
Project Setup
Extraction
Simulation
Draw Hydrograph
Import DEM
Project Setup
Extraction
Simulation
Draw Hydrograph
Forecast Location
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Model componentsModel components
Time static (Preprocessing)– Importing DEM– Watershed delineation– Setup specific experiment
Time Dynamic– Extraction– Simulation– Routing
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Preparing The Model for Preparing The Model for Parallel ComputingParallel Computing
Optimizing the existing code (rewrite in C++)
Isolate the I/O operations
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ParallelizationParallelization
MPILoad balancing algorithm
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Load Balancing AlgorithmLoad Balancing Algorithm
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Processes (Descending loads)
Processor assigned in an alternate fashion
Basin
Proc
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Illinois River Basin, In Illinois River Basin, In Okllahoma and ArkansasOkllahoma and Arkansas
Illionois river at Tahek
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Computation based on Computation based on Subbasins Subbasins
Total # Subbasins =57
Max# Grid cells 12231
Min # Grid cells 54
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Load BalancingLoad Balancing
Processor # processes Processes Assigned
0 3 3 49 281 3 23 48 502 3 5 22 513 3 27 29 244 3 30 47 525 3 31 46 536 3 32 45 547 3 33 44 558 3 4 43 129 4 17 42 19 14
10 4 34 41 25 2011 4 35 8 6 1012 4 36 40 11 2113 4 37 9 18 1514 4 2 39 16 115 4 7 38 26 13
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Distribution of loadsDistribution of loads (16 Processors) (16 Processors)
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Distribution of loadDistribution of load (4 Processors) (4 Processors)
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Time of computationTime of computation
Computation Time vs. Number of ProcessorsMoniter Time = 10000 min. (7 days)Time Step = 2 secondsNumber of Cells = 172335
Number of Processors
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Co
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(H
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03:00:00
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09:00:00 9 h
5 h
3 h 1h:30min
01:00:00
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Prototype Operational DomainPrototype Operational Domain Illinois River Basin
Area: 2400km2
Resolution 30m x 30m
#Subbasins 370
7 days of monitoring
Timestep = 2sec
Prediction: better load balancing
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Coupling DHM with ARPSCoupling DHM with ARPS
Output of ARPS (Rainfall) -> Input to the surface runoff model
Flows at subbasin and river streams are predicted
Interface to run both models from webVisualization of results (VisAD)
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ARPS: Rainfall prediction:01hARPS: Rainfall prediction:01h
01z0 m m1- 5 mm5- 1 0 10-1 515-2 020-2 525-3 030-3 535-4 0No D ata
W atrsh edStre am s
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ARPS: Rainfall prediction:02hARPS: Rainfall prediction:02h
02z0 m m1- 5 mm5- 1 0 10-1 515-2 020-2 525-3 030-3 535-4 0No D ata
W atrsh edStre am s
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ARPS: Rainfall prediction:03hARPS: Rainfall prediction:03h
02z0 m m1- 5 mm5- 1 0 10-1 515-2 020-2 525-3 030-3 535-4 0No D ata
W atrsh edStre am s
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ARPS: Rainfall prediction:04hARPS: Rainfall prediction:04h
04z0 m m1- 5 mm5- 1 0 10-1 515-2 020-2 525-3 030-3 535-4 0No D ata
W atrsh edStre am s
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Flow PredictionFlow Prediction
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Time (min)
Flo
w m
3/s
Rainfallfor for hrs
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DiscussionDiscussion