def test_sheetflow():
flux = np.array(
[1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
1.00000000e+00, 1.00000000e+00, 1.00000000e+06,
1.87867966e+06, 2.72182541e+06, 3.54415588e+06,
4.35303038e+06, 5.15223689e+06, 5.94408409e+06,
6.73016503e+06, 6.51166745e+06, 1.00000000e+00,
1.00000000e+06, 2.12132034e+06, 3.24264069e+06,
4.35965005e+06, 5.47056275e+06, 6.57568517e+06,
7.67583899e+06, 8.77188984e+06, 8.86460677e+06,
1.00000000e+00, 1.00000000e+06, 2.00000000e+06,
3.03553391e+06, 4.08578644e+06, 5.14392129e+06,
6.20695360e+06, 7.27341175e+06, 8.34244102e+06,
8.41347211e+06, 1.00000000e+00, 1.00000000e+06,
2.00000000e+06, 3.00000000e+06, 4.01040764e+06,
5.03248558e+06, 6.06512434e+06, 7.10666517e+06,
8.15550411e+06, 8.21025766e+06, 1.00000000e+00,
1.00000000e+06, 2.00000000e+06, 3.00000000e+06,
4.01040764e+06, 5.03248558e+06, 6.06512434e+06,
7.10666517e+06, 8.15550411e+06, 8.21025766e+06,
1.00000000e+00, 1.00000000e+06, 2.00000000e+06,
3.03553391e+06, 4.08578644e+06, 5.14392129e+06,
6.20695360e+06, 7.27341175e+06, 8.34244102e+06,
8.41347211e+06, 1.00000000e+00, 1.00000000e+06,
2.12132034e+06, 3.24264069e+06, 4.35965005e+06,
5.47056275e+06, 6.57568517e+06, 7.67583899e+06,
8.77188984e+06, 8.86460677e+06, 1.00000000e+00,
1.00000000e+06, 1.87867966e+06, 2.72182541e+06,
3.54415588e+06, 4.35303038e+06, 5.15223689e+06,
5.94408409e+06, 6.73016503e+06, 6.51166745e+06,
1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
1.00000000e+00])
mg = RasterModelGrid((NROWS, NCOLS), (DX, DX))
z = (3000. - mg.node_x) * 0.5
mg.at_node['topographic__elevation'] = z
mg.set_closed_boundaries_at_grid_edges(False, True, True, True)
mg.add_ones('node', 'water__unit_flux_in')
pfr = PotentialityFlowRouter(mg)
pfr.run_one_step()
assert_allclose(mg.at_node['surface_water__discharge'], flux)
def test_sheetflow():
flux = np.array([
1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+06,
1.87867966e+06, 2.72182541e+06, 3.54415588e+06, 4.35303038e+06,
5.15223689e+06, 5.94408409e+06, 6.73016503e+06, 6.51166745e+06,
1.00000000e+00, 1.00000000e+06, 2.12132034e+06, 3.24264069e+06,
4.35965005e+06, 5.47056275e+06, 6.57568517e+06, 7.67583899e+06,
8.77188984e+06, 8.86460677e+06, 1.00000000e+00, 1.00000000e+06,
2.00000000e+06, 3.03553391e+06, 4.08578644e+06, 5.14392129e+06,
6.20695360e+06, 7.27341175e+06, 8.34244102e+06, 8.41347211e+06,
1.00000000e+00, 1.00000000e+06, 2.00000000e+06, 3.00000000e+06,
4.01040764e+06, 5.03248558e+06, 6.06512434e+06, 7.10666517e+06,
8.15550411e+06, 8.21025766e+06, 1.00000000e+00, 1.00000000e+06,
2.00000000e+06, 3.00000000e+06, 4.01040764e+06, 5.03248558e+06,
6.06512434e+06, 7.10666517e+06, 8.15550411e+06, 8.21025766e+06,
1.00000000e+00, 1.00000000e+06, 2.00000000e+06, 3.03553391e+06,
4.08578644e+06, 5.14392129e+06, 6.20695360e+06, 7.27341175e+06,
8.34244102e+06, 8.41347211e+06, 1.00000000e+00, 1.00000000e+06,
2.12132034e+06, 3.24264069e+06, 4.35965005e+06, 5.47056275e+06,
6.57568517e+06, 7.67583899e+06, 8.77188984e+06, 8.86460677e+06,
1.00000000e+00, 1.00000000e+06, 1.87867966e+06, 2.72182541e+06,
3.54415588e+06, 4.35303038e+06, 5.15223689e+06, 5.94408409e+06,
6.73016503e+06, 6.51166745e+06, 1.00000000e+00, 1.00000000e+00,
1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00
])
mg = RasterModelGrid((NROWS, NCOLS), (DX, DX))
z = (3000. - mg.node_x) * 0.5
mg.at_node['topographic__elevation'] = z
mg.set_closed_boundaries_at_grid_edges(False, True, True, True)
mg.add_ones('node', 'water__unit_flux_in')
pfr = PotentialityFlowRouter(mg)
pfr.run_one_step()
assert_allclose(mg.at_node['surface_water__discharge'], flux)
def test_in_network():
# a valley network produced by stream power:
z = np.array([
3.12900830e-04, 3.66462671e-04, 9.44438515e-04, 1.45006772e-04,
1.91406099e-04, 4.42053204e-04, 2.99818052e-04, 5.45267467e-04,
4.12129514e-04, 7.43816953e-04, 1.59251681e-04, 7.39577249e+01,
6.68718419e+01, 2.95535987e+01, 7.69715256e+01, 4.61083179e+01,
5.70611522e+01, 6.99664564e+01, 8.29355817e+01, 5.85563532e-04,
2.60300016e-04, 8.99636726e+01, 1.11946320e+02, 5.16553709e+01,
1.38432251e+02, 1.02903579e+02, 1.27601424e+02, 8.59469601e+01,
4.77495429e+01, 8.00781223e-04, 4.13095981e-04, 7.41309307e+01,
1.31747968e+02, 1.11724617e+02, 1.71687943e+02, 1.93482716e+02,
1.61044205e+02, 1.34775824e+02, 7.63077925e+01, 8.85837646e-04,
6.94907676e-04, 5.53654246e+01, 9.95405009e+01, 1.81345288e+02,
1.88975196e+02, 1.78104180e+02, 1.75231160e+02, 1.14932425e+02,
5.12018382e+01, 1.26501797e-04, 8.34527300e-04, 8.80562940e+01,
1.24009142e+02, 1.55635807e+02, 1.56683637e+02, 1.62398410e+02,
1.04070282e+02, 6.99665030e+01, 6.41256897e+01, 5.50951003e-04,
2.02545919e-04, 3.05799760e+01, 5.02285119e+01, 8.31442034e+01,
1.75898080e+02, 1.80095770e+02, 1.23416767e+02, 6.97604901e+01,
3.04864568e+01, 4.42047775e-04, 5.75778629e-04, 8.13990441e+01,
1.17362500e+02, 1.35317452e+02, 1.78842796e+02, 9.65391990e+01,
1.16520146e+02, 1.59289373e+02, 8.29784784e+01, 4.21911459e-04,
7.83145812e-04, 8.44510235e+01, 6.85512072e+01, 3.99258990e+01,
9.07740020e+01, 4.47116751e+01, 1.05333999e+02, 1.04965376e+02,
6.42615041e+01, 8.48184002e-04, 9.92952214e-04, 5.44805365e-04,
6.83657298e-04, 4.27967811e-04, 4.40101095e-04, 5.47248461e-04,
5.77178429e-06, 4.39642103e-04, 4.80194778e-04, 9.24014550e-04
])
flux = np.array([
0.71259633, 1.8368437, 7.98879652, 9.29556375, 10.55574455, 7.24717392,
7.35872882, 4.50198662, 1.97243346, 0.54930274, 1.71629518, 4.0949232,
4.50641349, 19.45300944, 5.73451891, 10.2200909, 8.84475793,
5.62433494, 3.63816461, 3.2288009, 3.64720491, 4.08050742, 3.55208345,
12.80703452, 4.51455295, 7.22640241, 4.82518478, 6.0364361, 8.21399889,
4.89790743, 6.37085345, 7.20139071, 3.66063349, 6.65749537, 4.5201495,
3.16880878, 4.56500213, 4.47976874, 5.91287906, 7.65823897, 6.75481467,
10.33585975, 7.95868492, 3.44967745, 3.30272455, 3.79822256,
3.72583825, 5.35268783, 11.33346169, 7.61218948, 13.23332132,
5.52663873, 4.71401637, 5.25380598, 6.32221242, 5.21692832, 9.95021529,
12.12012981, 9.62887235, 12.51966215, 12.20119879, 28.07778172,
21.69415422, 13.44016311, 3.42523128, 3.16880878, 5.79132165,
11.8326263, 20.62850507, 10.58826741, 10.25347039, 3.76593706,
3.58815683, 4.10381596, 3.23987285, 7.32040184, 4.62625103, 3.16880878,
4.05801502, 8.29763076, 1.42053684, 3.54385495, 4.9815377, 7.32862308,
5.884002, 12.71539344, 4.14640372, 4.17838374, 4.74896355, 1.84587995,
0.57901706, 1.94727505, 4.37381493, 5.18582678, 7.70532408, 6.86738548,
6.04728603, 2.94714791, 2.00238741, 0.88296836
])
potnt = np.array([
7.12596329e+23, 1.83684370e+24, 7.98879652e+24, 9.29556375e+24,
1.05557446e+25, 7.24717392e+24, 7.35872882e+24, 4.50198662e+24,
1.97243346e+24, 5.49302741e+23, 1.71629518e+24, 3.11607783e+00,
4.55006629e+00, 4.21948454e+01, 4.51089464e+00, 1.77476721e+01,
1.18679201e+01, 5.93094410e+00, 2.26828834e+00, 3.22880090e+24,
3.64720491e+24, 3.44492074e+00, 2.99141371e+00, 8.61459830e+01,
3.22150654e+00, 1.30493764e+01, 4.51470413e+00, 1.01713137e+01,
1.40167801e+01, 4.89790743e+24, 6.37085345e+24, 8.30460195e+00,
2.72370114e+00, 1.97019871e+01, 4.81322624e+00, 2.37203116e+00,
4.76156755e+00, 3.84190281e+00, 5.53509619e+00, 7.65823897e+24,
6.75481467e+24, 1.63796499e+01, 1.83238435e+01, 2.62626616e+00,
3.21546392e+00, 5.36483147e+00, 3.18817285e+00, 5.90189403e+00,
1.86765515e+01, 7.61218948e+24, 1.32333213e+25, 4.00510836e+00,
3.72650975e+00, 5.71530086e+00, 2.42783091e+01, 1.07981219e+01,
2.92283301e+01, 3.24892464e+01, 9.86359779e+00, 1.25196622e+25,
1.22011988e+25, 5.98718495e+01, 1.54766737e+02, 7.40803538e+01,
3.21059221e+00, 2.38746227e+00, 7.36767180e+00, 4.52838697e+01,
4.40547155e+01, 1.05882674e+25, 1.02534704e+25, 3.05098779e+00,
2.45849055e+00, 3.12955438e+00, 1.87486164e+00, 2.51124463e+01,
6.21239889e+00, 1.64106832e+00, 3.28554137e+00, 8.29763076e+24,
1.42053684e+24, 2.36944471e+00, 5.71708702e+00, 1.36764251e+01,
4.71435055e+00, 2.24231064e+01, 3.41364652e+00, 3.49569086e+00,
4.14217801e+00, 1.84587995e+24, 5.79017064e+23, 1.94727505e+24,
4.37381493e+24, 5.18582678e+24, 7.70532408e+24, 6.86738548e+24,
6.04728603e+24, 2.94714791e+24, 2.00238741e+24, 8.82968356e+23
])
mg = RasterModelGrid((NROWS, NCOLS), (DX, DX))
mg.add_field('node', 'topographic__elevation', z)
Qin = np.ones_like(z) * 100. / (60. * 60. * 24. * 365.25)
# ^remember, flux is /s, so this is a small number!
mg.add_field('node', 'water__unit_flux_in', Qin)
pfr = PotentialityFlowRouter(mg, flow_equation='Manning')
pfr.run_one_step()
assert_allclose(mg.at_node['surface_water__discharge'], flux)
assert_allclose(mg.at_node['flow__potential'][mg.core_nodes],
potnt[mg.core_nodes])
def test_in_network():
# a valley network produced by stream power:
z = np.array(
[
3.12900830e-04,
3.66462671e-04,
9.44438515e-04,
1.45006772e-04,
1.91406099e-04,
4.42053204e-04,
2.99818052e-04,
5.45267467e-04,
4.12129514e-04,
7.43816953e-04,
1.59251681e-04,
7.39577249e01,
6.68718419e01,
2.95535987e01,
7.69715256e01,
4.61083179e01,
5.70611522e01,
6.99664564e01,
8.29355817e01,
5.85563532e-04,
2.60300016e-04,
8.99636726e01,
1.11946320e02,
5.16553709e01,
1.38432251e02,
1.02903579e02,
1.27601424e02,
8.59469601e01,
4.77495429e01,
8.00781223e-04,
4.13095981e-04,
7.41309307e01,
1.31747968e02,
1.11724617e02,
1.71687943e02,
1.93482716e02,
1.61044205e02,
1.34775824e02,
7.63077925e01,
8.85837646e-04,
6.94907676e-04,
5.53654246e01,
9.95405009e01,
1.81345288e02,
1.88975196e02,
1.78104180e02,
1.75231160e02,
1.14932425e02,
5.12018382e01,
1.26501797e-04,
8.34527300e-04,
8.80562940e01,
1.24009142e02,
1.55635807e02,
1.56683637e02,
1.62398410e02,
1.04070282e02,
6.99665030e01,
6.41256897e01,
5.50951003e-04,
2.02545919e-04,
3.05799760e01,
5.02285119e01,
8.31442034e01,
1.75898080e02,
1.80095770e02,
1.23416767e02,
6.97604901e01,
3.04864568e01,
4.42047775e-04,
5.75778629e-04,
8.13990441e01,
1.17362500e02,
1.35317452e02,
1.78842796e02,
9.65391990e01,
1.16520146e02,
1.59289373e02,
8.29784784e01,
4.21911459e-04,
7.83145812e-04,
8.44510235e01,
6.85512072e01,
3.99258990e01,
9.07740020e01,
4.47116751e01,
1.05333999e02,
1.04965376e02,
6.42615041e01,
8.48184002e-04,
9.92952214e-04,
5.44805365e-04,
6.83657298e-04,
4.27967811e-04,
4.40101095e-04,
5.47248461e-04,
5.77178429e-06,
4.39642103e-04,
4.80194778e-04,
9.24014550e-04,
]
)
flux = np.array(
[
0.71259633,
1.8368437,
7.98879652,
9.29556375,
10.55574455,
7.24717392,
7.35872882,
4.50198662,
1.97243346,
0.54930274,
1.71629518,
4.0949232,
4.50641349,
19.45300944,
5.73451891,
10.2200909,
8.84475793,
5.62433494,
3.63816461,
3.2288009,
3.64720491,
4.08050742,
3.55208345,
12.80703452,
4.51455295,
7.22640241,
4.82518478,
6.0364361,
8.21399889,
4.89790743,
6.37085345,
7.20139071,
3.66063349,
6.65749537,
4.5201495,
3.16880878,
4.56500213,
4.47976874,
5.91287906,
7.65823897,
6.75481467,
10.33585975,
7.95868492,
3.44967745,
3.30272455,
3.79822256,
3.72583825,
5.35268783,
11.33346169,
7.61218948,
13.23332132,
5.52663873,
4.71401637,
5.25380598,
6.32221242,
5.21692832,
9.95021529,
12.12012981,
9.62887235,
12.51966215,
12.20119879,
28.07778172,
21.69415422,
13.44016311,
3.42523128,
3.16880878,
5.79132165,
11.8326263,
20.62850507,
10.58826741,
10.25347039,
3.76593706,
3.58815683,
4.10381596,
3.23987285,
7.32040184,
4.62625103,
3.16880878,
4.05801502,
8.29763076,
1.42053684,
3.54385495,
4.9815377,
7.32862308,
5.884002,
12.71539344,
4.14640372,
4.17838374,
4.74896355,
1.84587995,
0.57901706,
1.94727505,
4.37381493,
5.18582678,
7.70532408,
6.86738548,
6.04728603,
2.94714791,
2.00238741,
0.88296836,
]
)
potnt = np.array(
[
7.12596329e23,
1.83684370e24,
7.98879652e24,
9.29556375e24,
1.05557446e25,
7.24717392e24,
7.35872882e24,
4.50198662e24,
1.97243346e24,
5.49302741e23,
1.71629518e24,
3.11607783e00,
4.55006629e00,
4.21948454e01,
4.51089464e00,
1.77476721e01,
1.18679201e01,
5.93094410e00,
2.26828834e00,
3.22880090e24,
3.64720491e24,
3.44492074e00,
2.99141371e00,
8.61459830e01,
3.22150654e00,
1.30493764e01,
4.51470413e00,
1.01713137e01,
1.40167801e01,
4.89790743e24,
6.37085345e24,
8.30460195e00,
2.72370114e00,
1.97019871e01,
4.81322624e00,
2.37203116e00,
4.76156755e00,
3.84190281e00,
5.53509619e00,
7.65823897e24,
6.75481467e24,
1.63796499e01,
1.83238435e01,
2.62626616e00,
3.21546392e00,
5.36483147e00,
3.18817285e00,
5.90189403e00,
1.86765515e01,
7.61218948e24,
1.32333213e25,
4.00510836e00,
3.72650975e00,
5.71530086e00,
2.42783091e01,
1.07981219e01,
2.92283301e01,
3.24892464e01,
9.86359779e00,
1.25196622e25,
1.22011988e25,
5.98718495e01,
1.54766737e02,
7.40803538e01,
3.21059221e00,
2.38746227e00,
7.36767180e00,
4.52838697e01,
4.40547155e01,
1.05882674e25,
1.02534704e25,
3.05098779e00,
2.45849055e00,
3.12955438e00,
1.87486164e00,
2.51124463e01,
6.21239889e00,
1.64106832e00,
3.28554137e00,
8.29763076e24,
1.42053684e24,
2.36944471e00,
5.71708702e00,
1.36764251e01,
4.71435055e00,
2.24231064e01,
3.41364652e00,
3.49569086e00,
4.14217801e00,
1.84587995e24,
5.79017064e23,
1.94727505e24,
4.37381493e24,
5.18582678e24,
7.70532408e24,
6.86738548e24,
6.04728603e24,
2.94714791e24,
2.00238741e24,
8.82968356e23,
]
)
mg = RasterModelGrid((NROWS, NCOLS), xy_spacing=(DX, DX))
mg.add_field("node", "topographic__elevation", z)
Qin = np.ones_like(z) * 100.0 / (60.0 * 60.0 * 24.0 * 365.25)
# ^remember, flux is /s, so this is a small number!
mg.add_field("node", "water__unit_flux_in", Qin)
pfr = PotentialityFlowRouter(mg, flow_equation="Manning")
pfr.run_one_step()
assert_allclose(mg.at_node["surface_water__discharge"], flux)
assert_allclose(mg.at_node["flow__potential"][mg.core_nodes], potnt[mg.core_nodes])
