import os
import gc
import sys
import vtk
vtk.vtkObject.GlobalWarningDisplayOff()
import warnings
import petsc4py
import numpy as np
from scipy import spatial
from mpi4py import MPI
from time import process_time
from vtk.util import numpy_support # type: ignore
from gospl.tools.constants import (
BOUNDARY_FLOW_SENTINEL,
DEPOSIT_FLOOR,
MISSING_DATA_SENTINEL,
)
if "READTHEDOCS" not in os.environ:
from gospl._fortran import mfdrcvrs
from gospl._fortran import epsfill
MPIrank = petsc4py.PETSc.COMM_WORLD.Get_rank()
MPIcomm = petsc4py.PETSc.COMM_WORLD
MPIsize = petsc4py.PETSc.COMM_WORLD.Get_size()
[docs]
class SEAMesh(object):
"""
This class encapsulates all the functions related to sediment transport and deposition in the marine environment for **river delivered sediments**.
.. note::
For an overview of solution to nonlinear ODE and PDE problems, one might find the online book from `Langtangen (2016) <http://hplgit.github.io/num-methods-for-PDEs/doc/pub/nonlin/pdf/nonlin-4print-A4-2up.pdf>`_ relevant.
"""
def __init__(self, *args, **kwargs):
"""
Initialisation of the `SEAMesh` class.
"""
self.coastDist = None
self.zMat = self._matrix_build_diag(np.zeros(self.lpoints))
return
[docs]
def _globalCoastsTree(self, coastXYZ, k_neighbors=1):
"""
This function takes all local coastline points and computes locally the distance of all marine points to the coastline.
:arg coastXYZ: local coastline coordinates
:arg k_neighbors: number of nodes to use when querying the kd-tree
"""
label_offset = np.zeros(MPIsize + 1, dtype=int)
label_offset[MPIrank + 1] = len(coastXYZ)
MPI.COMM_WORLD.Allreduce(MPI.IN_PLACE, label_offset, op=MPI.MAX)
offset = np.cumsum(label_offset)[:-1]
totpts = np.sum(label_offset)
# Edge case: no rank has any coastline points (all-continental run or
# all marine), so the cKDTree below would raise on an empty input.
if totpts == 0:
return
coastpts = np.zeros(((totpts) * 3,), dtype=np.float64)
MPI.COMM_WORLD.Allgatherv(
[coastXYZ, MPI.DOUBLE],
[coastpts, label_offset[1:] * 3, offset * 3, MPI.DOUBLE],
)
# Get coastlines points globally
tree = spatial.cKDTree(coastpts.reshape((totpts, 3)), leafsize=10)
self.coastDist[self.seaID], _ = tree.query(
self.lcoords[self.seaID, :], k=k_neighbors
)
if self.memclear:
del tree, coastpts, offset, totpts, label_offset
gc.collect()
return
[docs]
def _distanceCoasts(self, data, k_neighbors=1):
"""
This function computes for every marine vertices the distance to the closest coastline. It calls the private function:
- _globalCoastsTree
.. important::
The calculation takes advantage of the `vtkContourFilter` function from VTK library which is performed on the **global** VTK mesh. Once the coastlines have been extracted, the distances are obtained by querying a kd-tree (initialised with the coastal nodes) for marine vertices contained within each partition.
:arg data: local elevation Numpy Array
:arg k_neighbors: number of nodes to use when querying the kd-tree
"""
t0 = process_time()
self.coastDist = np.zeros(self.lpoints)
pointData = self.vtkMesh.GetPointData()
array = numpy_support.numpy_to_vtk(data, deep=1)
array.SetName("elev")
pointData.AddArray(array)
self.vtkMesh.SetFieldData(pointData)
cf = vtk.vtkContourFilter()
cf.SetInputData(self.vtkMesh)
cf.SetValue(0, self.sealevel)
cf.SetInputArrayToProcess(0, 0, 0, 0, "elev")
cf.GenerateTrianglesOff()
cf.Update()
if cf.GetOutput().GetPoints() is not None:
coastXYZ = numpy_support.vtk_to_numpy(cf.GetOutput().GetPoints().GetData())
else:
coastXYZ = np.zeros((0, 3), dtype=np.float64)
# Get coastlines points globally
self._globalCoastsTree(coastXYZ, k_neighbors)
if self.memclear:
del array, pointData, cf, coastXYZ
gc.collect()
if MPIrank == 0 and self.verbose:
print(
"Construct Distance to Coast (%0.02f seconds)" % (process_time() - t0),
flush=True,
)
return
[docs]
def _matOcean(self):
"""
This function builds from neighbouring slopes the downstream directions in the marine environment. It calls a Fortran subroutine that locally computes for each vertice:
- the indices of receivers (downstream) nodes depending on the desired number of flow directions (SFD to MFD).
- the distances to the receivers based on mesh resolution.
- the associated weights calculated based on the number of receivers and proportional to the slope.
From these downstream directions, a local ocean downstream matrix is computed.
"""
# Define multiple flow directions for filled + eps elevations
hl = self.hLocal.getArray().copy()
minh = self.hGlobal.min()[1] + 0.1
if not self.flatModel:
# Only consider filleps in the first kms offshore
minh = max(minh, self.oFill)
hsmth = self._hillSlope(smooth=2)
hsmth[self.coastDist > self.offshore] = BOUNDARY_FLOW_SENTINEL
else:
minh = max(minh, self.oFill)
hsmth = hl.copy()
hsmth[self.idBorders] = BOUNDARY_FLOW_SENTINEL
# The filled + eps is done on the global grid, serially on rank 0, so
# assemble it there from the owned nodes only (hsmth is ghost-synced
# via _hillSlope's globalToLocal, so owned values are exact).
fillz = self._gatherGlobalOnRoot(hsmth)
if MPIrank == 0:
fillz = epsfill(minh, fillz)
# Send elevation + eps to other processors
fillEPS = MPI.COMM_WORLD.bcast(fillz, root=0)
fillz = fillEPS[self.locIDs]
if not self.flatModel:
fillz[self.coastDist > self.offshore] = hl[self.coastDist > self.offshore]
# Pass `self.gid` for deterministic exact-tie-break on slope —
# see fortran/functions.F90:mfdrcvrs.
rcv, _, wght = mfdrcvrs(
12, self.flowExp, fillz, BOUNDARY_FLOW_SENTINEL, self.gid,
)
# Set borders nodes
if self.flatModel:
rcv[self.idBorders, :] = np.tile(self.idBorders, (12, 1)).T
wght[self.idBorders, :] = 0.0
# Detect terminal sinks: cells whose every flow direction points
# back to themselves. After the dMat1 transpose below the column
# at each such cell is identically zero, so dMat1.mult would
# annihilate any sediment that lands there. _distOcean uses this
# mask to force-deposit at sinks instead of routing through dMat1.
# On flat models the open-boundary cells were artificially marked
# self-draining just above; exclude them here so the existing
# vdep[idBorders]=0 rule keeps handling outflux at the edge.
nodes_idx = np.arange(self.lpoints)
self.is_sink_local = np.all(
rcv.astype(int) == nodes_idx[:, None], axis=1
)
if self.flatModel:
self.is_sink_local[self.idBorders] = False
# Define downstream matrix based on filled + dir elevations
self.dMat1 = self.zMat.copy()
if not self.flatModel and self.Gmar > 0.:
self.dMat2 = self.iMat.copy()
nodes = np.arange(self.lpoints, dtype=petsc4py.PETSc.IntType)
# Assemble all 12 flow directions in a single CSR pass instead of 12
# build+axpy iterations. ADD_VALUES makes receivers shared by several
# directions sum exactly as the per-direction axpy loop did, so the
# assembled matrix is identical; this just replaces 12 matrix builds +
# 12 sparse axpys (the dominant cost of this step) with one build and
# one (or two) axpy.
rcvFlat = rcv.astype(petsc4py.PETSc.IntType)
data = wght.copy()
data[rcvFlat == nodes[:, None]] = 0.0
indptr = np.arange(0, self.lpoints * 12 + 1, 12, dtype=petsc4py.PETSc.IntType)
offMat = self._matrix_build(nnz=(12, 12))
offMat.assemblyBegin()
offMat.setValuesLocalCSR(
indptr,
rcvFlat.ravel(),
data.ravel(),
addv=petsc4py.PETSc.InsertMode.ADD_VALUES,
)
offMat.assemblyEnd()
self.dMat1.axpy(1.0, offMat)
if not self.flatModel and self.Gmar > 0.:
self.dMat2.axpy(-1.0, offMat)
offMat.destroy()
if self.memclear:
del data, indptr, nodes
del hl, fillz, fillEPS, rcv, wght
gc.collect()
# Store flow direction matrix
self.dMat1.transpose()
if not self.flatModel and self.Gmar > 0.:
self.dMat2.transpose()
return
[docs]
def _depMarineSystem(self, sedflux):
r"""
Setup matrix for the marine sediment deposition.
The upstream incoming sediment flux is obtained from the total river sediment flux :math:`\mathrm{Q_{t_i}}` where:
.. math::
\mathrm{Q_{t_i}^{t+\Delta t} - \sum_{ups} w_{i,j} Q_{t_u}^{t+\Delta t}}= \mathrm{(\eta_i^{t} - \eta_i^{t+\Delta t}) \frac{\Delta t}{\Omega_i}}
which gives:
.. math::
\mathrm{Q_{s_i}} = \mathrm{Q_{t_i}} - \mathrm{(\eta_i^{t} - \eta_i^{t+\Delta t}) \frac{\Delta t}{\Omega_i}}
And the evolution of marine elevation is based on incoming sediment flux:
.. math::
\mathrm{\frac{\eta_i^{t+\Delta t}-\eta_i^t}{\Delta t}} = \mathrm{G{_m} Q_{s_i} / \Omega_i}
This system of coupled equations is solved implicitly using PETSc by assembling the matrix and vectors using the nested submatrix and subvectors and by using the ``fieldsplit`` preconditioner combining two separate preconditioners for the collections of variables.
:arg sedflux: incoming marine sediment volumes
:return: volDep (the deposited volume of the distributed sediments)
"""
hl = self.hLocal.getArray()
fDepm = np.full(self.lpoints, self.Gmar)
fDepm[fDepm > 0.99] = 0.99
fDepm[hl > self.sealevel] = 0.
# Define submatrices
A00 = self._matrix_build_diag(-fDepm)
A00.axpy(1.0, self.iMat)
A01 = self._matrix_build_diag(-fDepm * self.dt / self.larea)
A10 = self._matrix_build_diag(self.larea / self.dt)
# Assemble the matrix for the coupled system
mats = [[A00, A01], [A10, self.dMat2]]
sysMat = petsc4py.PETSc.Mat().createNest(mats=mats, comm=MPIcomm)
sysMat.assemblyBegin()
sysMat.assemblyEnd()
# Clean up.
A00.destroy()
A01.destroy()
A10.destroy()
self.dMat2.destroy()
# Create nested vectors
self.tmpL.setArray(1. - fDepm)
self.dm.localToGlobal(self.tmpL, self.tmp)
self.tmp.pointwiseMult(self.tmp, self.hGlobal)
self.tmpL.setArray(sedflux / self.dt)
self.dm.localToGlobal(self.tmpL, self.tmp1)
self.h.pointwiseMult(self.hGlobal, self.areaGlobal)
self.h.scale(1. / self.dt)
self.tmp1.axpy(1., self.h)
rhs_vec = petsc4py.PETSc.Vec().createNest([self.tmp, self.tmp1], comm=MPIcomm)
rhs_vec.setUp()
hq_vec = rhs_vec.duplicate()
# Define solver and precondition conditions
ksp = petsc4py.PETSc.KSP().create(petsc4py.PETSc.COMM_WORLD)
# Options prefix so the coupled marine-deposition solve can be tuned from
# the command line without editing code, e.g.
# `-marine_dep_pc_fieldsplit_type additive`.
ksp.setOptionsPrefix("marine_dep_")
ksp.setType(petsc4py.PETSc.KSP.Type.TFQMR)
ksp.setOperators(sysMat)
ksp.setTolerances(rtol=self.rtol)
pc = ksp.getPC()
pc.setType("fieldsplit")
nested_IS = sysMat.getNestISs()
pc.setFieldSplitIS(('h', nested_IS[0][0]), ('q', nested_IS[0][1]))
# Schur-complement fieldsplit. This marine-deposition system has the same
# two-field (elevation h / sediment-flux q) coupled structure as the
# detachment-limited SPL solve (see eroder/SPL._coupledEDSystem): the h
# and q blocks are near-triangular M-matrices the per-block ILU solves
# almost exactly, so a Schur factorisation captures the full
# deposition<->routing coupling and converges in a handful of outer
# iterations -- versus ~100+ for the default additive (block-diagonal)
# split, which only sees part of the coupling. This changes only the
# preconditioner, so the solution (to `rtol`) is unchanged. Defaults are
# injected into the options DB guarded by `hasName`, so PETSC_OPTIONS /
# `-marine_dep_*` still override.
opts = petsc4py.PETSc.Options()
for key, val in (
("marine_dep_pc_fieldsplit_type", "schur"),
("marine_dep_pc_fieldsplit_schur_fact_type", "full"),
("marine_dep_pc_fieldsplit_schur_precondition", "selfp"),
):
if not opts.hasName(key):
opts.setValue(key, val)
subksps = pc.getFieldSplitSubKSP()
subksps[0].setType("preonly")
subksps[0].getPC().setType("gasm")
subksps[1].setType("preonly")
subksps[1].getPC().setType("gasm")
# Apply the Schur defaults (and any `-marine_dep_*` overrides).
ksp.setFromOptions()
ksp.solve(rhs_vec, hq_vec)
r = ksp.getConvergedReason()
# Note: on KSP divergence we log and continue. Marine deposition this
# step may be inaccurate; operators should monitor this warning.
if r < 0:
KSPReasons = self._make_reasons(petsc4py.PETSc.KSP.ConvergedReason())
if MPIrank == 0:
print(
"Linear solver for marine deposition failed to converge after iterations",
ksp.getIterationNumber(),
flush=True,
)
print("with reason: ", KSPReasons[r], flush=True)
else:
if MPIrank == 0 and self.verbose:
print(
"Linear solver for marine deposition converge after %d iterations"
% ksp.getIterationNumber(),
flush=True,
)
# Update the solution
self.newH = hq_vec.getSubVector(nested_IS[0][0])
# Clean up
subksps[0].destroy()
subksps[1].destroy()
nested_IS[0][0].destroy()
nested_IS[1][0].destroy()
nested_IS[0][1].destroy()
nested_IS[1][1].destroy()
pc.destroy()
ksp.destroy()
sysMat.destroy()
hq_vec.destroy()
rhs_vec.destroy()
# Get the marine deposition volume
self.tmp.waxpy(-1.0, self.hGlobal, self.newH)
self.dm.globalToLocal(self.tmp, self.tmpL)
volDep = self.tmpL.getArray().copy() * self.larea
volDep[volDep < 0] = 0.
petsc4py.PETSc.garbage_cleanup()
return volDep
[docs]
def _distOcean(self, sedflux):
"""
Based on the incoming marine volumes of sediment and maximum clinoforms slope we distribute locally sediments downslope.
:arg sedflux: incoming marine sediment volumes
:return: vdep (the deposited volume of the distributed sediments)
"""
marVol = self.maxDepQs.copy()
sinkVol = sedflux.copy()
vdep = np.zeros(self.lpoints, dtype=float)
# Drain initial sediment at terminal sinks BEFORE the routing loop.
# dMat1 has zero columns at sink cells (rcv==self for every flow
# direction, see _matOcean), so the very first dMat1.mult below
# would annihilate any sedflux that landed directly at a sink. On
# global sphere fixtures this is the dominant mass-loss path:
# vSed naturally accumulates at deep ocean basins (sinks are
# sediment attractors), so a large fraction of the marine supply
# arrives at sinks in the very first iteration. Without this
# pre-drain ~25% of total redistributed sediment vanishes per run
# (regression: test_mass_conservation; AGENTS.md Known bugs entry
# dated 2026-06). The in-loop force-deposit below additionally
# catches sediment that arrives at sinks from upstream during
# later iterations.
if self.is_sink_local.any():
sink_mass = sinkVol[self.is_sink_local]
if (sink_mass != 0).any():
vdep[self.is_sink_local] += sink_mass
sinkVol[self.is_sink_local] = 0.0
self.tmpL.setArray(sinkVol)
self.dm.localToGlobal(self.tmpL, self.tmp)
# Convergence threshold: relative to initial input volume (1e-6 of
# total) with a fixed floor of 1 m^3. Scales with input magnitude so
# continental-scale runs converge in far fewer iterations than the
# previous fixed `> 1.0 m^3` rule, while small grids still terminate
# cleanly. A max-iteration cap protects against pathological cases.
sumExcess = self.tmp.sum()
initial_total = sumExcess
threshold = max(1.0, 1.0e-6 * initial_total)
max_iters = 5000
step = 0
while sumExcess > threshold:
if step >= max_iters:
if MPIrank == 0:
print(
" --- Marine routing did not converge after %d "
"iterations (residual %.3e m^3); continuing."
% (max_iters, sumExcess),
flush=True,
)
break
# Move to downstream nodes
self.dMat1.mult(self.tmp, self.tmp1)
self.dm.globalToLocal(self.tmp1, self.tmpL)
# In case there is too much sediment coming in
sinkVol = self.tmpL.getArray().copy()
excess = sinkVol >= marVol
sinkVol[excess] -= marVol[excess]
vdep[excess] += marVol[excess]
marVol[excess] = 0.0
# In case there is some room to deposit sediments
noexcess = np.invert(excess)
marVol[noexcess] -= sinkVol[noexcess]
vdep[noexcess] += sinkVol[noexcess]
vdep[self.idBorders] = 0.0
sinkVol[noexcess] = 0.0
sinkVol[self.idBorders] = 0.0
# Force-deposit at terminal sinks. Their dMat1 column is zero
# (rcv==self for all directions, see _matOcean), so the next
# dMat1.mult would annihilate any remaining sinkVol at these
# cells once marVol is exhausted. Without this guard ~25% of
# marine sediment vanished at saturated basin floors on global
# sphere runs (regression: test_mass_conservation; see
# AGENTS.md Known bugs entry dated 2026-06). The overflow
# accumulates above clinoH at the sink; lateral spreading is
# subsequently handled by _diffuseOcean in seaChange.
sink_mask = self.is_sink_local
if sink_mask.any():
vdep[sink_mask] += sinkVol[sink_mask]
sinkVol[sink_mask] = 0.0
# Find where excess and sink
self.tmpL.setArray(sinkVol)
self.dm.localToGlobal(self.tmpL, self.tmp)
# One Allreduce per iteration that doubles as the loop condition
# for the next pass and the print value for this pass.
sumExcess = self.tmp.sum()
if MPIrank == 0 and self.verbose:
if step % 100 == 0:
print(
" --- Marine excess (sum in km3) %0.05f | iter %d"
% (sumExcess * 10.e-9, step),
flush=True
)
step += 1
# Drain any residual sediment still in self.tmp at loop exit
# (because the convergence threshold is non-zero, or because the
# max_iters cap was hit). The in-loop force-deposit at sinks
# should already drain almost everything; this is the safety net
# that converts the few remaining cubic metres to a local deposit
# rather than silently dropping them.
self.dm.globalToLocal(self.tmp, self.tmpL)
residual = self.tmpL.getArray().copy()
if residual.any():
vdep += residual
vdep[self.idBorders] = 0.0
if self.memclear:
del marVol, sinkVol
gc.collect()
return vdep
[docs]
def _marineFineFraction(self, hl, sedFlux, fineFlux):
"""
Dual-lithology (3c): set the per-node fine fraction of the marine
deposit so fine concentrates in deep / distal water and coarse stays
proximal (near the coast), conserving the marine fine volume.
Composition only — the marine deposit geometry (already in ``self.tmp``
after ``_distOcean``/``_depMarineSystem``/``_diffuseOcean``) is NOT
modified, so elevations/flow are untouched. This is the subaqueous
analogue of ``sedplex._pitFineFraction``: the lower-Gmar "fines deposit
less readily / travel farther" behaviour is expressed as fine biased
toward the deeper deposits rather than re-routed.
Mechanism:
- Domain marine fine fraction ``ff_mar = Σ(fineFlux) / Σ(sedFlux)`` —
the fine that actually reached the sea after the continental
cascade (``fineFlux`` is the post-cascade fine input, ``sedFlux``
the total input, both zero outside the marine sinks). With the
fine-enriched overspill in the cascade, this is genuinely enriched
relative to the upstream-eroded composition.
- Water depth ``d = max(sealevel − hl, 0)`` is the distal/deep proxy.
- Fine fraction biased ∝ d, renormalised to the deposit-weighted mean
depth, so ``Σ(mdep·larea·ffrac) == ff_mar·Σ(mdep·larea)``.
:arg hl: pre-deposition local elevation
:arg sedFlux: marine input sediment volume per node (m^3)
:arg fineFlux: marine input fine sediment volume per node (m^3)
"""
self.dm.globalToLocal(self.tmp, self.tmpL)
mdep = self.tmpL.getArray().copy()
owned = self.inIDs == 1
# Fine fraction of sediment reaching the marine domain (post-cascade).
fineNum = float(np.sum(fineFlux[owned]))
den = float(np.sum(sedFlux[owned]))
fineNum = MPI.COMM_WORLD.allreduce(fineNum, op=MPI.SUM)
den = MPI.COMM_WORLD.allreduce(den, op=MPI.SUM)
ff_mar = fineNum / den if den > 0.0 else 0.0
# Deposit-weighted mean water depth.
depth = np.maximum(self.sealevel - hl, 0.0)
mvol = mdep * self.larea
dw = MPI.COMM_WORLD.allreduce(float(np.sum(mvol[owned])), op=MPI.SUM)
dwd = MPI.COMM_WORLD.allreduce(float(np.sum((mvol * depth)[owned])), op=MPI.SUM)
depthbar = dwd / dw if dw > 0.0 else 0.0
if depthbar > 0.0:
ffrac = np.clip(ff_mar * depth / max(depthbar, 1.0e-30), 0.0, 1.0)
marine = mdep > 0.0
self.depoFineFrac[marine] = ffrac[marine]
return
[docs]
def _marineProvFraction(self, sedFlux):
"""
In-model provenance (B2b, marine): set the marine deposit's source
composition to the **domain-wide composition of sediment reaching the
sea** — ``ff_mar[c] = Σ(provFrac[c]·sedFlux) / Σ sedFlux`` — applied
uniformly (no depth bias: provenance is a passive label, unlike the
grain-size sorting in ``_marineFineFraction``).
This is the marine analogue of the through-flux ``provFrac`` used on
land: it replaces the (often near-zero) through-flux composition at
marine-only nodes with the basin-delivered mix, so marine sink deposits
carry the right provenance. ``Σ_c ff_mar == 1`` (since ``Σ_c provFrac ==
1`` where there is flux), so ``stratP`` still partitions ``stratH``.
Like the dual marine fraction this is **domain-uniform** — it gets the
right total per-class fraction into the marine record, not a per-river-
mouth spatial split (the same standard as dual lithology).
:arg sedFlux: marine input sediment volume per node (m^3).
"""
owned = self.inIDs == 1
den = MPI.COMM_WORLD.allreduce(float(np.sum(sedFlux[owned])), op=MPI.SUM)
if den <= 0.0:
return
ffmar = np.zeros(self.provNb)
for c in range(self.provNb):
num = MPI.COMM_WORLD.allreduce(
float(np.sum((self.provFrac[:, c] * sedFlux)[owned])), op=MPI.SUM
)
ffmar[c] = num / den
self.dm.globalToLocal(self.tmp, self.tmpL)
marine = self.tmpL.getArray() > 0.0
self.depoProvFrac[marine, :] = ffmar
return
[docs]
def seaChange(self):
"""
This function is the main entry point to perform marine river-induced deposition. It calls the private functions:
- _distanceCoasts
- _matOcean
- _diffuseOcean
"""
t0 = process_time()
# Set all nodes below sea-level as sinks
self.sinkIDs = self.lFill <= self.sealevel
# Define coastal distance for marine points
hl = self.hLocal.getArray().copy()
if self.clinSlp > 0.0:
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
self._distanceCoasts(hl)
# From the distance to coastline define the upper limit of the shelf to ensure a maximum slope angle
self.clinoH = self.sealevel - 1.0e-3 - self.coastDist * self.clinSlp # TODO-REFACTOR: value matches DEPOSIT_FLOOR but distinct role (clinoH 1mm offset below sealevel); do not replace
else:
self.clinoH = np.full(self.lpoints, self.sealevel - 1.0e-3, dtype=np.float64) # TODO-REFACTOR: value matches DEPOSIT_FLOOR but distinct role (clinoH 1mm offset); do not replace
self.clinoH[hl >= self.sealevel] = hl[hl >= self.sealevel]
self.clinoH[self.clinoH < hl] = hl[self.clinoH < hl]
self.maxDepQs = (self.clinoH - hl) * self.larea
# Get the volumetric marine sediment (m3) to distribute during the time step.
self.vSedLocal.copy(result=self.QsL)
sedFlux = self.QsL.getArray().copy() * self.dt
sedFlux[np.invert(self.sinkIDs)] = 0.0
sedFlux[sedFlux < 0] = 0.0
# Downstream direction matrix for ocean distribution
self._matOcean()
marDep = self._distOcean(sedFlux)
if not self.flatModel and self.Gmar > 0.:
vdep = self._depMarineSystem(marDep)
marDep = self._distOcean(vdep)
self.dMat1.destroy()
# Diffuse downstream
dh = np.divide(marDep, self.larea, out=np.zeros_like(self.larea), where=self.larea != 0)
# Drop sub-millimeter deposits: numerical cleanup to avoid accumulating
# round-off thicknesses across many timesteps.
dh[dh < DEPOSIT_FLOOR] = 0.
self.tmpL.setArray(dh)
self.dm.localToGlobal(self.tmpL, self.tmp)
self.dm.globalToLocal(self.tmp, self.tmpL)
dh = self.tmpL.getArray().copy()
self._diffuseOcean(dh)
# Update cumulative erosion and deposition as well as elevation
self.cumED.axpy(1.0, self.tmp)
self.dm.globalToLocal(self.cumED, self.cumEDLocal)
self.hGlobal.axpy(1.0, self.tmp)
self.dm.globalToLocal(self.hGlobal, self.hLocal)
# Update soil thickness
if self.cptSoil:
self.updateSoilThickness()
# Update erosion/deposition rates
self.dm.globalToLocal(self.tmp, self.tmpL)
add_rate = self.tmpL.getArray() / self.dt
self.tmpL.setArray(add_rate)
self.EbLocal.axpy(1.0, self.tmpL)
# Update stratigraphic layer parameters
if self.stratNb > 0:
if self.stratLith:
# Post-cascade fine reaching the sea (same sink mask as
# sedFlux), carried by the continental fine-enriched overspill.
fineFlux = self.vSedFLocal.getArray().copy() * self.dt
fineFlux[np.invert(self.sinkIDs)] = 0.0
fineFlux[fineFlux < 0] = 0.0
self._marineFineFraction(hl, sedFlux, fineFlux)
if getattr(self, "provOn", False):
self._marineProvFraction(sedFlux)
self.deposeStrat()
if MPIrank == 0 and self.verbose:
print(
"Distribute River Sediments in the Ocean (%0.02f seconds)"
% (process_time() - t0),
flush=True,
)
if self.memclear:
del marDep, dh, add_rate, hl, sedFlux
gc.collect()
return
