Terrestrial hydrologic cycle: many coupled processes

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Terrestrial hydrologic cycle: many coupled processes

Wylder, Greer, Founder and Writer has reference to this Academic Journal, PHwiki organized this Journal The ParFlow Hydrologic Model: HPC Highlights in addition to Lessons Learned This work was per as long as med under the auspices of the U.S. Department of Energy by University of Cali as long as nia, Lawrence Livermore National Laboratory under contract No. W-7405-Eng-48. UCRL-PRES-XXXXXX Reed Maxwell Department of Geology in addition to Geologic Engineering Colorado School of Mines Terrestrial hydrologic cycle: many coupled processes Water resources Weather generating processes Biogeochemical cycles (N, C) Yet it is usually simulated with disconnected models Atmospheric Model L in addition to Surface Model Groundwater/Vadose Model Surface Water Model

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Moisture/heat flux Evapotranspiration Infiltration/Seepage Precipitation/Advection Runoff/Routing These models explicitly incorporate fluxes at air/l in addition to -surface/subsurface interfaces ParFlow is a combination of: Physics Solvers Parallelism Ground Surface Water Table Infiltration Front Saturated Zone Vadose Zone

ParFlow Watershed Model Groundwater L in addition to Surface PF.CLM= Parflow (PF) + Common L in addition to Model (CLM) Kollet in addition to Maxwell (2008), Kollet in addition to Maxwell (2006), Maxwell in addition to Miller (2005), Dai et al. (2003), Jones in addition to Woodward (2001); Ashby in addition to Falgout (1996) Surface in addition to soil column/root zone hydrology calculated by PF (removed from CLM) Overl in addition to flow/runoff h in addition to led by fully-coupled overl in addition to flow BC in PF (Kollet in addition to Maxwell, AWR, 2006) CLM is incorporated into PF as a module- fully coupled, fully mass conservative, fully parallel Dynamically coupled, 2D/3D OF/LS/GW Model Root Zone Flow Divide Air Flow Lines Vadose Zone Water Table Routed Water Vegetation Atmospheric Forcing Overl in addition to Flow: The Conductance Concept Kinematic wave eq Richards’ eq e.g. V in addition to erKwaak in addition to Loague (2001); P in addition to ay in addition to Huyakorn (2004) Exchange Flux yp Dz Dz / 2 ys = yp surface water subsurface computational nodes Overl in addition to Flow: General Pressure Formulation The greater of in addition to 0 Kollet in addition to Maxwell, AWR (2006) ground surface Kinematic wave eq ys = yp = y qbc=qe Neumann type BC v qr(x) ys q

Simulation Example Kollet & Maxwell, AWR, 2006 Water table below ground surface not to scale 3m 400m Low-K slab Coupled Model Example: Subsurface Heterogeneity can influence the Hydrograph Small Monte Carlo Simulation Kgeo = qrain Kollet in addition to Maxwell, AWR (2006) R in addition to om (Gaussian) Heterogeneity Water table below ground surface not to scale 400m 3m L in addition to Surface Models Simulates water in addition to energy balance near the l in addition to surface Single column soil-snow-vegetation biogeochemical model Atmospheric as long as cing Can be coupled to atmospheric models Simplistic, shallow, subsurface component Baker, et al, 2003; Dia, Zeng in addition to Dickinson, 2001

Soil Saturation Run offline, WY 1999 used as as long as cing (NARR) Spinup: Run over successive years until beginning-ending water in addition to energy balances drop below threshold Kollet in addition to Maxwell (2007) ParFlow Synopsis – Physics Fully parallel, multigrid-preconditioned, finite difference/finite volume 3D flow Groundwater equation (steady-state, e.g. Ashby in addition to Falgout 1996) Richards’ equation (transient, 3D; e.g. Jones in addition to Woodward 2001) Fully-coupled overl in addition to flow (via Kollet in addition to Maxwell 2006, overl in addition to flow boundary condition approach) NCAR-L in addition to Surface Model CLM integrated into ParFlow as module, all biogeophyiscal, energy budget at l in addition to surface, snow/snowmelt/compaction, some dynamic plant interactions ParFlow Synopsis – Physics (cont) Coupled to U of Oklahoma mesoscale atmospheric code ARPS (e.g. Maxwell, Chow, Kollet 2007) Coupled to NCAR Weather Research in addition to Forecasting (WRF) Code (Maxwell et al 2009) Couples to (integrates with) Lagrangian contaminant transport code (SLIM)

ParFlow- per as long as mance Efficient implementation results from efficient linear preconditioning (HyPre) efficient nonlinear solver (Kinsol –SUNDIALS) efficient coupling in addition to code operation/architecture All implementations scale linearly with problem size All implementations demonstrate excellent parallel scaling to large (~1000) processors For 3D, Steady-state groundwater ~100 X faster than typical GW code For 2D, transient Richards’ variably saturated ~10X faster than typical var-sat codes in 2D, much greater speedup in 3D Per as long as mance: Making the problem “harder” Ashby in addition to Falgout (1996) Per as long as mance: Making the problem bigger Ashby in addition to Falgout (1996)

Parallelization P2 P1 P3 P4 Parallelization Falgout in addition to Jones (1999) Parallelization- Distributed Memory P1 P2 Ghost Nodes Falgout in addition to Jones (1999)

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Per as long as mance: Serial in addition to Parallel Per as long as mance in addition to parallel per as long as mance are intricately linked To get good parallel per as long as mance the numerical algorithm must scale linearly with problem size If we want to run large problems in addition to our solver does not scale parallel per as long as mance will not be sustained Scaled Parallel Efficiency- Scaled Speedup Scaled parallel efficiency, E, is defined as the ratio of time to run a problem of varying size as we keep the per-processor work constant T = run time n = problem size p = number of processors Parallel Per as long as mance: Scaled Speedup of the Linear Problem Ashby in addition to Falgout (1996)

Scaled Parallel Efficiency of Coupled Model Perfect efficiency: double problem size in addition to processor same run time => E = 1 Kollet in addition to Maxwell, AWR (2006) Parallel Per as long as mance: Correlated GRF Simulation ParFlow Synopsis- code operation ParFlow written in ANSI C with object-oriented structure Parallel from “bottom-up” with ability to h in addition to le many communication sublayers (serial, shared-memory in addition to distributed memory implementation from one common physics core) OctTree technique to allow any general domain shapes in addition to geometries (topography, large-intermediate-scale geology) TCL/TK scripting interface w/ object-oriented structure Parallel Gaussian in addition to Parallel Turning B in addition to s stochastic r in addition to om field generators with ability to follow any geometry (e.g. Maxwell et al 2009)

Comparison to outflow in addition to saturation observations Overall favorable comparisons Trends (particularly SM) match very well Difficulty comparing due to resolution in addition to scale of observations Intent not to calibrate/predict but to underst in addition to process Kollet in addition to Maxwell (2007) Influence of Groundwater Dynamics on Energy Fluxes Kollet in addition to Maxwell (2008) (yearly averaged)

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