def test_run_random_climate(self): """ Test the run_random_climate task for a climate based on the equilibrium period centred around t*. Additionally a positive and a negative temperature bias are tested. Returns ------- """ # let's not use the mass balance bias since we want to reproduce # results from mass balance calibration cfg.PARAMS['use_bias_for_run'] = False # read the Hintereisferner DEM hef_file = get_demo_file('Hintereisferner_RGI5.shp') entity = gpd.read_file(hef_file).iloc[0] # initialize the GlacierDirectory gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir) # define the local grid and glacier mask gis.define_glacier_region(gdir, entity=entity) gis.glacier_masks(gdir) # process the given climate file climate.process_custom_climate_data(gdir) # compute mass balance parameters ref_df = cfg.PARAMS['vas_ref_tstars_rgi5_histalp'] vascaling.local_t_star(gdir, ref_df=ref_df) # define some parameters for the random climate model nyears = 300 seed = 1 temp_bias = 0.5 # read the equilibirum year used for the mass balance calibration t_star = gdir.read_json('vascaling_mustar')['t_star'] # run model with random climate _ = vascaling.run_random_climate(gdir, nyears=nyears, y0=t_star, seed=seed) # run model with positive temperature bias _ = vascaling.run_random_climate(gdir, nyears=nyears, y0=t_star, seed=seed, temperature_bias=temp_bias, output_filesuffix='_bias_p') # run model with negative temperature bias _ = vascaling.run_random_climate(gdir, nyears=nyears, y0=t_star, seed=seed, temperature_bias=-temp_bias, output_filesuffix='_bias_n') # compile run outputs ds = utils.compile_run_output([gdir], input_filesuffix='') ds_p = utils.compile_run_output([gdir], input_filesuffix='_bias_p') ds_n = utils.compile_run_output([gdir], input_filesuffix='_bias_n') # the glacier should not change much under a random climate # based on the equilibirum period centered around t* assert abs(1 - ds.volume.mean() / ds.volume[0]) < 0.015 # higher temperatures should result in a smaller glacier assert ds.volume.mean() > ds_p.volume.mean() # lower temperatures should result in a larger glacier assert ds.volume.mean() < ds_n.volume.mean()
def test_run_constant_climate(self): """ Test the run_constant_climate task for a climate based on the equilibrium period centred around t*. Additionally a positive and a negative temperature bias are tested. """ # let's not use the mass balance bias since we want to reproduce # results from mass balance calibration cfg.PARAMS['use_bias_for_run'] = False # read the Hintereisferner DEM hef_file = get_demo_file('Hintereisferner_RGI5.shp') entity = gpd.read_file(hef_file).iloc[0] # initialize the GlacierDirectory gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir) # define the local grid and glacier mask gis.define_glacier_region(gdir, entity=entity) gis.glacier_masks(gdir) # process the given climate file climate.process_custom_climate_data(gdir) # compute mass balance parameters ref_df = cfg.PARAMS['vas_ref_tstars_rgi5_histalp'] vascaling.local_t_star(gdir, ref_df=ref_df) # define some parameters for the constant climate model nyears = 500 temp_bias = 0.5 _ = vascaling.run_constant_climate(gdir, nyears=nyears, output_filesuffix='') _ = vascaling.run_constant_climate(gdir, nyears=nyears, temperature_bias=+temp_bias, output_filesuffix='_bias_p') _ = vascaling.run_constant_climate(gdir, nyears=nyears, temperature_bias=-temp_bias, output_filesuffix='_bias_n') # compile run outputs ds = utils.compile_run_output([gdir], input_filesuffix='') ds_p = utils.compile_run_output([gdir], input_filesuffix='_bias_p') ds_n = utils.compile_run_output([gdir], input_filesuffix='_bias_n') # the glacier should not change under a constant climate # based on the equilibirum period centered around t* assert abs(1 - ds.volume.mean() / ds.volume[0]) < 1e-7 # higher temperatures should result in a smaller glacier assert ds.volume.mean() > ds_p.volume.mean() # lower temperatures should result in a larger glacier assert ds.volume.mean() < ds_n.volume.mean() # compute volume change from one year to the next dV_p = (ds_p.volume[1:].values - ds_p.volume[:-1].values).flatten() dV_n = (ds_n.volume[1:].values - ds_n.volume[:-1].values).flatten() # compute relative volume change, with respect to the final volume rate_p = abs(dV_p / float(ds_p.volume.values[-1])) rate_n = abs(dV_n / float(ds_n.volume.values[-1])) # the glacier should be in a new equilibirum for last 300 years assert max(rate_p[-300:]) < 0.001 assert max(rate_n[-300:]) < 0.001
def _set_up_VAS_model(self): """Avoiding a chunk of code duplicate. Set's up a running volume/area scaling model, including all needed prepo tasks. """ # read the Hintereisferner DEM hef_file = get_demo_file('Hintereisferner_RGI5.shp') entity = gpd.read_file(hef_file).iloc[0] # initialize the GlacierDirectory gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir) # define the local grid and glacier mask gis.define_glacier_region(gdir, entity=entity) gis.glacier_masks(gdir) # process the given climate file climate.process_custom_climate_data(gdir) # run center line preprocessing tasks centerlines.compute_centerlines(gdir) centerlines.initialize_flowlines(gdir) centerlines.catchment_area(gdir) centerlines.catchment_intersections(gdir) centerlines.catchment_width_geom(gdir) centerlines.catchment_width_correction(gdir) # read reference glacier mass balance data mbdf = gdir.get_ref_mb_data() # compute the reference t* for the glacier # given the reference of mass balance measurements res = climate.t_star_from_refmb(gdir, mbdf=mbdf['ANNUAL_BALANCE']) t_star, bias = res['t_star'], res['bias'] # -------------------- # MASS BALANCE TASKS # -------------------- # compute local t* and the corresponding mu* vascaling.local_t_star(gdir, tstar=t_star, bias=bias) # instance the mass balance models mbmod = vascaling.VAScalingMassBalance(gdir) # ---------------- # DYNAMICAL PART # ---------------- # get reference area a0 = gdir.rgi_area_m2 # get reference year y0 = gdir.read_pickle('climate_info')['baseline_hydro_yr_0'] # get min and max glacier surface elevation h0, h1 = vascaling.get_min_max_elevation(gdir) model = vascaling.VAScalingModel(year_0=y0, area_m2_0=a0, min_hgt=h0, max_hgt=h1, mb_model=mbmod) return gdir, model
def test_local_t_star(self): # set parameters for climate file and mass balance calibration cfg.PARAMS['baseline_climate'] = 'CUSTOM' cfg.PARAMS['baseline_y0'] = 1850 cfg.PATHS['climate_file'] = get_demo_file('histalp_merged_hef.nc') cfg.PARAMS['run_mb_calibration'] = False # read the Hintereisferner hef_file = get_demo_file('Hintereisferner_RGI5.shp') entity = gpd.read_file(hef_file).iloc[0] # initialize the GlacierDirectory gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir) # define the local grid and the glacier mask gis.define_glacier_region(gdir, entity=entity) gis.glacier_masks(gdir) # run centerline prepro tasks centerlines.compute_centerlines(gdir) centerlines.initialize_flowlines(gdir) centerlines.catchment_area(gdir) centerlines.catchment_intersections(gdir) centerlines.catchment_width_geom(gdir) centerlines.catchment_width_correction(gdir) # process the given climate file climate.process_custom_climate_data(gdir) # compute the reference t* for the glacier # given the reference of mass balance measurements res = vascaling.t_star_from_refmb(gdir) t_star, bias = res['t_star'], res['bias'] # compute local t* and the corresponding mu* vascaling.local_t_star(gdir, tstar=t_star, bias=bias) # read calibration results vas_mustar_refmb = gdir.read_json('vascaling_mustar') # get reference t* list ref_df = cfg.PARAMS['vas_ref_tstars_rgi5_histalp'] # compute local t* and the corresponding mu* vascaling.local_t_star(gdir, ref_df=ref_df) # read calibration results vas_mustar_refdf = gdir.read_json('vascaling_mustar') # compute local t* and the corresponding mu* vascaling.local_t_star(gdir) # read calibration results vas_mustar = gdir.read_json('vascaling_mustar') # compare with each other assert vas_mustar_refdf == vas_mustar # TODO: this test is failing currently # np.testing.assert_allclose(vas_mustar_refmb['bias'], # vas_mustar_refdf['bias'], atol=1) vas_mustar_refdf.pop('bias') vas_mustar_refmb.pop('bias') # end of workaround assert vas_mustar_refdf == vas_mustar_refmb
def test_run_until_equilibrium(self): """""" # let's not use the mass balance bias since we want to reproduce # results from mass balance calibration cfg.PARAMS['use_bias_for_run'] = False # read the Hintereisferner DEM hef_file = get_demo_file('Hintereisferner_RGI5.shp') entity = gpd.read_file(hef_file).iloc[0] # initialize the GlacierDirectory gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir) # define the local grid and glacier mask gis.define_glacier_region(gdir, entity=entity) gis.glacier_masks(gdir) # process the given climate file climate.process_custom_climate_data(gdir) # compute mass balance parameters ref_df = cfg.PARAMS['vas_ref_tstars_rgi5_histalp'] vascaling.local_t_star(gdir, ref_df=ref_df) # instance a constant mass balance model, centred around t* mb_model = vascaling.ConstantVASMassBalance(gdir) # add a positive temperature bias mb_model.temp_bias = 0.5 # create a VAS model: start with year 0 since we are using a constant # massbalance model, other values are read from RGI min_hgt, max_hgt = vascaling.get_min_max_elevation(gdir) model = vascaling.VAScalingModel(year_0=0, area_m2_0=gdir.rgi_area_m2, min_hgt=min_hgt, max_hgt=max_hgt, mb_model=mb_model) # run glacier with new mass balance model model.run_until_equilibrium(rate=1e-4) # equilibrium should be reached after a couple of 100 years assert model.year <= 300 # new equilibrium glacier should be smaller (positive temperature bias) assert model.volume_m3 < model.volume_m3_0 # run glacier for another 100 years and check volume again v_eq = model.volume_m3 model.run_until(model.year + 100) assert abs(1 - (model.volume_m3 / v_eq)) < 0.01
def _setup_mb_test(self): """Avoiding a chunk of code duplicate. Performs needed prepo tasks and returns the oggm.GlacierDirectory. """ # read the Hintereisferner DEM hef_file = get_demo_file('Hintereisferner_RGI5.shp') entity = gpd.read_file(hef_file).iloc[0] # initialize the GlacierDirectory gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir) # define the local grid and the glacier mask gis.define_glacier_region(gdir, entity=entity) gis.glacier_masks(gdir) # process the given climate file climate.process_custom_climate_data(gdir) # run centerline prepro tasks centerlines.compute_centerlines(gdir) centerlines.initialize_flowlines(gdir) centerlines.catchment_area(gdir) centerlines.catchment_intersections(gdir) centerlines.catchment_width_geom(gdir) centerlines.catchment_width_correction(gdir) # read reference glacier mass balance data mbdf = gdir.get_ref_mb_data() # compute the reference t* for the glacier # given the reference of mass balance measurements res = vascaling.t_star_from_refmb(gdir, mbdf=mbdf['ANNUAL_BALANCE']) t_star, bias = res['t_star'], res['bias'] # compute local t* and the corresponding mu* vascaling.local_t_star(gdir, tstar=t_star, bias=bias) # run OGGM mu* calibration climate.local_t_star(gdir, tstar=t_star, bias=bias) climate.mu_star_calibration(gdir) # pass the GlacierDirectory return gdir
def init_hef(reset=False, border=40, logging_level='INFO'): from oggm.core import gis, inversion, climate, centerlines, flowline import geopandas as gpd # test directory testdir = os.path.join(get_test_dir(), 'tmp_border{}'.format(border)) if not os.path.exists(testdir): os.makedirs(testdir) reset = True # Init cfg.initialize(logging_level=logging_level) cfg.set_intersects_db(get_demo_file('rgi_intersect_oetztal.shp')) cfg.PATHS['dem_file'] = get_demo_file('hef_srtm.tif') cfg.PATHS['climate_file'] = get_demo_file('histalp_merged_hef.nc') cfg.PARAMS['baseline_climate'] = '' cfg.PATHS['working_dir'] = testdir cfg.PARAMS['border'] = border hef_file = get_demo_file('Hintereisferner_RGI5.shp') entity = gpd.read_file(hef_file).iloc[0] gdir = oggm.GlacierDirectory(entity, reset=reset) if not gdir.has_file('inversion_params'): reset = True gdir = oggm.GlacierDirectory(entity, reset=reset) if not reset: return gdir gis.define_glacier_region(gdir) execute_entity_task(gis.glacier_masks, [gdir]) execute_entity_task(centerlines.compute_centerlines, [gdir]) centerlines.initialize_flowlines(gdir) centerlines.compute_downstream_line(gdir) centerlines.compute_downstream_bedshape(gdir) centerlines.catchment_area(gdir) centerlines.catchment_intersections(gdir) centerlines.catchment_width_geom(gdir) centerlines.catchment_width_correction(gdir) climate.process_custom_climate_data(gdir) mbdf = gdir.get_ref_mb_data()['ANNUAL_BALANCE'] res = climate.t_star_from_refmb(gdir, mbdf=mbdf) climate.local_t_star(gdir, tstar=res['t_star'], bias=res['bias']) climate.mu_star_calibration(gdir) inversion.prepare_for_inversion(gdir, add_debug_var=True) ref_v = 0.573 * 1e9 glen_n = cfg.PARAMS['glen_n'] def to_optimize(x): # For backwards compat _fd = 1.9e-24 * x[0] glen_a = (glen_n + 2) * _fd / 2. fs = 5.7e-20 * x[1] v, _ = inversion.mass_conservation_inversion(gdir, fs=fs, glen_a=glen_a) return (v - ref_v)**2 out = optimization.minimize(to_optimize, [1, 1], bounds=((0.01, 10), (0.01, 10)), tol=1e-4)['x'] _fd = 1.9e-24 * out[0] glen_a = (glen_n + 2) * _fd / 2. fs = 5.7e-20 * out[1] v, _ = inversion.mass_conservation_inversion(gdir, fs=fs, glen_a=glen_a, write=True) d = dict(fs=fs, glen_a=glen_a) d['factor_glen_a'] = out[0] d['factor_fs'] = out[1] gdir.write_pickle(d, 'inversion_params') # filter inversion.filter_inversion_output(gdir) inversion.distribute_thickness_interp(gdir, varname_suffix='_interp') inversion.distribute_thickness_per_altitude(gdir, varname_suffix='_alt') flowline.init_present_time_glacier(gdir) return gdir
def test_terminus_temp(self): """Testing the subroutine which computes the terminus temperature from the given climate file and glacier DEM. Pretty straight forward and somewhat useless, but nice finger exercise. """ # read the Hintereisferner DEM hef_file = get_demo_file('Hintereisferner_RGI5.shp') entity = gpd.read_file(hef_file).iloc[0] # initialize the GlacierDirectory gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir) # define the local grid gis.define_glacier_region(gdir, entity=entity) # process the given climate file climate.process_custom_climate_data(gdir) # read the following variable from the center pixel (46.83N 10.75E) # of the Hintereisferner HistAlp climate file for the # entire time period from October 1801 until September 2003 # - surface height in m asl. # - total precipitation amount in kg/m2 # - 2m air temperature in °C with utils.ncDataset(get_demo_file('histalp_merged_hef.nc')) as nc_r: ref_h = nc_r.variables['hgt'][1, 1] ref_t = nc_r.variables['temp'][:, 1, 1] # define a temperature anomaly temp_anomaly = 0 # specify temperature gradient temp_grad = -0.0065 # the terminus temperature must equal the input temperature # if terminus elevation equals reference elevation temp_terminus =\ vascaling._compute_temp_terminus(ref_t, temp_grad, ref_hgt=ref_h, terminus_hgt=ref_h, temp_anomaly=temp_anomaly) np.testing.assert_allclose(temp_terminus, ref_t + temp_anomaly) # the terminus temperature must equal the input terperature # if the gradient is zero for term_h in np.array([-100, 0, 100]) + ref_h: temp_terminus =\ vascaling._compute_temp_terminus(ref_t, temp_grad=0, ref_hgt=ref_h, terminus_hgt=term_h, temp_anomaly=temp_anomaly) np.testing.assert_allclose(temp_terminus, ref_t + temp_anomaly) # now test the routine with actual elevation differences # and a non zero temperature gradient for h_diff in np.array([-100, 0, 100]): term_h = ref_h + h_diff temp_diff = temp_grad * h_diff temp_terminus =\ vascaling._compute_temp_terminus(ref_t, temp_grad, ref_hgt=ref_h, terminus_hgt=term_h, temp_anomaly=temp_anomaly) np.testing.assert_allclose(temp_terminus, ref_t + temp_anomaly + temp_diff)
def test_run_until_and_store(self): """Test the volume/area scaling model against the oggm.FluxBasedModel. Both models run the Hintereisferner over the entire HistAlp climate period, initialized with the 2003 RGI outline without spin up. The following two parameters for length, area and volume are tested: - correlation coefficient - relative RMSE, i.e. RMSE/mean(OGGM). Whereby the results from the VAS model are offset with the average differences to the OGGM results. """ # read the Hintereisferner DEM hef_file = get_demo_file('Hintereisferner_RGI5.shp') entity = gpd.read_file(hef_file).iloc[0] # initialize the GlacierDirectory gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir) # define the local grid and glacier mask gis.define_glacier_region(gdir, entity=entity) gis.glacier_masks(gdir) # process the given climate file climate.process_custom_climate_data(gdir) # run center line preprocessing tasks centerlines.compute_centerlines(gdir) centerlines.initialize_flowlines(gdir) centerlines.compute_downstream_line(gdir) centerlines.compute_downstream_bedshape(gdir) centerlines.catchment_area(gdir) centerlines.catchment_intersections(gdir) centerlines.catchment_width_geom(gdir) centerlines.catchment_width_correction(gdir) # read reference glacier mass balance data mbdf = gdir.get_ref_mb_data() # compute the reference t* for the glacier # given the reference of mass balance measurements res = climate.t_star_from_refmb(gdir, mbdf=mbdf['ANNUAL_BALANCE']) t_star, bias = res['t_star'], res['bias'] # -------------------- # SCALING MODEL # -------------------- # compute local t* and the corresponding mu* vascaling.local_t_star(gdir, tstar=t_star, bias=bias) # instance the mass balance models vas_mbmod = vascaling.VAScalingMassBalance(gdir) # get reference area a0 = gdir.rgi_area_m2 # get reference year y0 = gdir.read_json('climate_info')['baseline_hydro_yr_0'] # get min and max glacier surface elevation h0, h1 = vascaling.get_min_max_elevation(gdir) vas_model = vascaling.VAScalingModel(year_0=y0, area_m2_0=a0, min_hgt=h0, max_hgt=h1, mb_model=vas_mbmod) # let model run over entire HistAlp climate period vas_ds = vas_model.run_until_and_store(2003) # ------ # OGGM # ------ # compute local t* and the corresponding mu* climate.local_t_star(gdir, tstar=t_star, bias=bias) climate.mu_star_calibration(gdir) # instance the mass balance models mb_mod = massbalance.PastMassBalance(gdir) # perform ice thickness inversion inversion.prepare_for_inversion(gdir) inversion.mass_conservation_inversion(gdir) inversion.filter_inversion_output(gdir) # initialize present time glacier flowline.init_present_time_glacier(gdir) # instance flowline model fls = gdir.read_pickle('model_flowlines') y0 = gdir.read_json('climate_info')['baseline_hydro_yr_0'] fl_mod = flowline.FluxBasedModel(flowlines=fls, mb_model=mb_mod, y0=y0) # run model and store output as xarray data set _, oggm_ds = fl_mod.run_until_and_store(2003) # temporal indices must be equal assert (vas_ds.time == oggm_ds.time).all() # specify which parameters to compare and their respective correlation # coefficients and rmsd values params = ['length_m', 'area_m2', 'volume_m3'] corr_coeffs = np.array([0.96, 0.90, 0.93]) rmsds = np.array([0.43e3, 0.14e6, 0.03e9]) # compare given parameters for param, cc, rmsd in zip(params, corr_coeffs, rmsds): # correlation coefficient assert corrcoef(oggm_ds[param].values, vas_ds[param].values) >= cc # root mean squared deviation rmsd_an = rmsd_bc(oggm_ds[param].values, vas_ds[param].values) assert rmsd_an <= rmsd
def test_yearly_mb_temp_prcp(self): """Test the routine which returns the yearly mass balance relevant climate parameters, i.e. positive melting temperature and solid precipitation. The testing target is the output of the corresponding OGGM routine `get_yearly_mb_climate_on_glacier(gdir)`. """ # read the Hintereisferner DEM hef_file = get_demo_file('Hintereisferner_RGI5.shp') entity = gpd.read_file(hef_file).iloc[0] # initialize the GlacierDirectory gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir) # define the local grid and glacier mask gis.define_glacier_region(gdir, entity=entity) gis.glacier_masks(gdir) # run centerline prepro tasks centerlines.compute_centerlines(gdir) centerlines.initialize_flowlines(gdir) centerlines.catchment_area(gdir) centerlines.catchment_intersections(gdir) centerlines.catchment_width_geom(gdir) centerlines.catchment_width_correction(gdir) # process the given climate file climate.process_custom_climate_data(gdir) # get yearly sums of terminus temperature and solid precipitation years, temp, prcp = vascaling.get_yearly_mb_temp_prcp(gdir) # use the OGGM methode to get the mass balance # relevant climate parameters years_oggm, temp_oggm, prcp_oggm = \ climate.mb_yearly_climate_on_glacier(gdir) # the energy input at the glacier terminus must be greater than (or # equal to) the glacier wide average, since the air temperature drops # with elevation, i.e. the mean deviation must be positive, using the # OGGM data as reference assert md(temp_oggm, temp) >= 0 # consequentially, the average mass input must be less than (or equal # to) the mass input integrated over the whole glacier surface, i.e. # the mean deviation must be negative, using the OGGM data as reference # TODO: does it actually?! And if so, why?! @ASK assert md(prcp_oggm, prcp) <= 0 # correlation must be higher than set threshold assert corrcoef(temp, temp_oggm) >= 0.94 assert corrcoef(prcp, prcp_oggm) >= 0.98 # get terminus temperature using the OGGM routine fpath = gdir.get_filepath('gridded_data') with ncDataset(fpath) as nc: mask = nc.variables['glacier_mask'][:] topo = nc.variables['topo'][:] heights = np.array([np.min(topo[np.where(mask == 1)])]) years_height, temp_height, _ = \ climate.mb_yearly_climate_on_height(gdir, heights, flatten=False) temp_height = temp_height[0] # both time series must be equal np.testing.assert_array_equal(temp, temp_height) # get solid precipitation averaged over the glacier # (not weighted with widths) fls = gdir.read_pickle('inversion_flowlines') heights = np.array([]) for fl in fls: heights = np.append(heights, fl.surface_h) years_height, _, prcp_height = \ climate.mb_yearly_climate_on_height(gdir, heights, flatten=True) # correlation must be higher than set threshold assert corrcoef(prcp, prcp_height) >= 0.99 # TODO: assert absolute values (or differences) of precipitation @ASK # test exception handling of out of bounds time/year range with self.assertRaises(climate.MassBalanceCalibrationError): # start year out of bounds year_range = [1500, 1980] _, _, _ = vascaling.get_yearly_mb_temp_prcp(gdir, year_range=year_range) with self.assertRaises(climate.MassBalanceCalibrationError): # end year oud of bounds year_range = [1980, 3000] _, _, _ = vascaling.get_yearly_mb_temp_prcp(gdir, year_range=year_range) with self.assertRaises(ValueError): # get not N full years t0 = datetime.datetime(1980, 1, 1) t1 = datetime.datetime(1980, 3, 1) time_range = [t0, t1] _, _, _ = vascaling.get_yearly_mb_temp_prcp(gdir, time_range=time_range) # TODO: assert gradient in climate file?! pass
def test_solid_prcp(self): """Tests the subroutine which computes solid precipitation amount from given total precipitation and temperature. """ # read the Hintereisferner DEM hef_file = get_demo_file('Hintereisferner_RGI5.shp') entity = gpd.read_file(hef_file).iloc[0] # initialize the GlacierDirectory gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir) # define the local grid gis.define_glacier_region(gdir, entity=entity) # process the given climate file climate.process_custom_climate_data(gdir) # read the following variable from the center pixel (46.83N 10.75E) # of the Hintereisferner HistAlp climate file for the # entire time period from October 1801 until September 2003 # - surface height in m asl. # - total precipitation amount in kg/m2 # - 2m air temperature in °C with utils.ncDataset(get_demo_file('histalp_merged_hef.nc')) as nc_r: ref_h = nc_r.variables['hgt'][1, 1] ref_p = nc_r.variables['prcp'][:, 1, 1] ref_t = nc_r.variables['temp'][:, 1, 1] # define needed parameters prcp_factor = 1 temp_all_solid = 0 temp_grad = -0.0065 # define elevation levels ref_hgt = ref_h min_hgt = ref_h - 100 max_hgt = ref_h + 100 # if the terminus temperature is below the threshold for # solid precipitation all fallen precipitation must be solid temp_terminus = ref_t * 0 + temp_all_solid solid_prcp = vascaling._compute_solid_prcp(ref_p, prcp_factor, ref_hgt, min_hgt, max_hgt, temp_terminus, temp_all_solid, temp_grad, prcp_grad=0, prcp_anomaly=0) np.testing.assert_allclose(solid_prcp, ref_p) # if the temperature at the maximal elevation is above the threshold # for solid precipitation all fallen precipitation must be liquid temp_terminus = ref_t + 100 solid_prcp = vascaling._compute_solid_prcp(ref_p, prcp_factor, ref_hgt, min_hgt, max_hgt, temp_terminus, temp_all_solid, temp_grad, prcp_grad=0, prcp_anomaly=0) np.testing.assert_allclose(solid_prcp, 0) # test extreme case if max_hgt equals min_hgt test_p = ref_p * (ref_t <= temp_all_solid).astype(int) solid_prcp = vascaling._compute_solid_prcp(ref_p, prcp_factor, ref_hgt, ref_hgt, ref_hgt, ref_t, temp_all_solid, temp_grad, prcp_grad=0, prcp_anomaly=0) np.testing.assert_allclose(solid_prcp, test_p)
cfg.PARAMS['use_multiprocessing'] = True # read the Hintereisferner DEM hef_file = get_demo_file('Hintereisferner_RGI5.shp') entity = gpd.read_file(hef_file).iloc[0] from oggm.core import gis # initialize the GlacierDirectory gdir = oggm.GlacierDirectory(entity, base_dir=testdir) # define the local grid and glacier mask gis.define_glacier_region(gdir, entity=entity) gis.glacier_masks(gdir) from oggm.core import climate # process the given climate file climate.process_custom_climate_data(gdir) from oggm.core import centerlines # run center line preprocessing tasks centerlines.compute_centerlines(gdir) centerlines.initialize_flowlines(gdir) centerlines.compute_downstream_line(gdir) centerlines.compute_downstream_bedshape(gdir) centerlines.catchment_area(gdir) centerlines.catchment_intersections(gdir) centerlines.catchment_width_geom(gdir) centerlines.catchment_width_correction(gdir) # -------------------- # MASS BALANCE TASKS # --------------------
def init_hef(reset=False, border=40, invert_with_sliding=True, invert_with_rectangular=True): # test directory testdir = os.path.join(get_test_dir(), 'tmp_border{}'.format(border)) if not invert_with_sliding: testdir += '_withoutslide' if not invert_with_rectangular: testdir += '_withoutrectangular' if not os.path.exists(testdir): os.makedirs(testdir) reset = True # Init cfg.initialize() cfg.set_intersects_db(get_demo_file('rgi_intersect_oetztal.shp')) cfg.PATHS['dem_file'] = get_demo_file('hef_srtm.tif') cfg.PATHS['climate_file'] = get_demo_file('histalp_merged_hef.nc') cfg.PARAMS['border'] = border cfg.PARAMS['use_optimized_inversion_params'] = True hef_file = get_demo_file('Hintereisferner_RGI5.shp') entity = gpd.read_file(hef_file).iloc[0] gdir = oggm.GlacierDirectory(entity, base_dir=testdir, reset=reset) if not gdir.has_file('inversion_params'): reset = True gdir = oggm.GlacierDirectory(entity, base_dir=testdir, reset=reset) if not reset: return gdir gis.define_glacier_region(gdir, entity=entity) execute_entity_task(gis.glacier_masks, [gdir]) execute_entity_task(centerlines.compute_centerlines, [gdir]) centerlines.initialize_flowlines(gdir) centerlines.compute_downstream_line(gdir) centerlines.compute_downstream_bedshape(gdir) centerlines.catchment_area(gdir) centerlines.catchment_intersections(gdir) centerlines.catchment_width_geom(gdir) centerlines.catchment_width_correction(gdir) climate.process_custom_climate_data(gdir) climate.mu_candidates(gdir) mbdf = gdir.get_ref_mb_data()['ANNUAL_BALANCE'] res = climate.t_star_from_refmb(gdir, mbdf) climate.local_mustar(gdir, tstar=res['t_star'][-1], bias=res['bias'][-1], prcp_fac=res['prcp_fac']) climate.apparent_mb(gdir) inversion.prepare_for_inversion( gdir, add_debug_var=True, invert_with_rectangular=invert_with_rectangular) ref_v = 0.573 * 1e9 if invert_with_sliding: def to_optimize(x): # For backwards compat _fd = 1.9e-24 * x[0] glen_a = (cfg.N + 2) * _fd / 2. fs = 5.7e-20 * x[1] v, _ = inversion.mass_conservation_inversion(gdir, fs=fs, glen_a=glen_a) return (v - ref_v)**2 out = optimization.minimize(to_optimize, [1, 1], bounds=((0.01, 10), (0.01, 10)), tol=1e-4)['x'] _fd = 1.9e-24 * out[0] glen_a = (cfg.N + 2) * _fd / 2. fs = 5.7e-20 * out[1] v, _ = inversion.mass_conservation_inversion(gdir, fs=fs, glen_a=glen_a, write=True) else: def to_optimize(x): glen_a = cfg.A * x[0] v, _ = inversion.mass_conservation_inversion(gdir, fs=0., glen_a=glen_a) return (v - ref_v)**2 out = optimization.minimize(to_optimize, [1], bounds=((0.01, 10), ), tol=1e-4)['x'] glen_a = cfg.A * out[0] fs = 0. v, _ = inversion.mass_conservation_inversion(gdir, fs=fs, glen_a=glen_a, write=True) d = dict(fs=fs, glen_a=glen_a) d['factor_glen_a'] = out[0] try: d['factor_fs'] = out[1] except IndexError: d['factor_fs'] = 0. gdir.write_pickle(d, 'inversion_params') # filter inversion.filter_inversion_output(gdir) inversion.distribute_thickness_interp(gdir, varname_suffix='_interp') inversion.distribute_thickness_per_altitude(gdir, varname_suffix='_alt') flowline.init_present_time_glacier(gdir) return gdir
def init_hef(reset=False, border=40): # test directory testdir = os.path.join(get_test_dir(), 'tmp_border{}'.format(border)) if not os.path.exists(testdir): os.makedirs(testdir) reset = True # Init cfg.initialize() cfg.set_intersects_db(get_demo_file('rgi_intersect_oetztal.shp')) cfg.PATHS['dem_file'] = get_demo_file('hef_srtm.tif') cfg.PATHS['climate_file'] = get_demo_file('histalp_merged_hef.nc') cfg.PARAMS['baseline_climate'] = '' cfg.PATHS['working_dir'] = testdir cfg.PARAMS['border'] = border hef_file = get_demo_file('Hintereisferner_RGI5.shp') entity = gpd.read_file(hef_file).iloc[0] gdir = oggm.GlacierDirectory(entity, reset=reset) if not gdir.has_file('inversion_params'): reset = True gdir = oggm.GlacierDirectory(entity, reset=reset) if not reset: return gdir gis.define_glacier_region(gdir, entity=entity) execute_entity_task(gis.glacier_masks, [gdir]) execute_entity_task(centerlines.compute_centerlines, [gdir]) centerlines.initialize_flowlines(gdir) centerlines.compute_downstream_line(gdir) centerlines.compute_downstream_bedshape(gdir) centerlines.catchment_area(gdir) centerlines.catchment_intersections(gdir) centerlines.catchment_width_geom(gdir) centerlines.catchment_width_correction(gdir) climate.process_custom_climate_data(gdir) mbdf = gdir.get_ref_mb_data()['ANNUAL_BALANCE'] res = climate.t_star_from_refmb(gdir, mbdf=mbdf) climate.local_t_star(gdir, tstar=res['t_star'], bias=res['bias']) climate.mu_star_calibration(gdir) inversion.prepare_for_inversion(gdir, add_debug_var=True) ref_v = 0.573 * 1e9 glen_n = cfg.PARAMS['glen_n'] def to_optimize(x): # For backwards compat _fd = 1.9e-24 * x[0] glen_a = (glen_n+2) * _fd / 2. fs = 5.7e-20 * x[1] v, _ = inversion.mass_conservation_inversion(gdir, fs=fs, glen_a=glen_a) return (v - ref_v)**2 out = optimization.minimize(to_optimize, [1, 1], bounds=((0.01, 10), (0.01, 10)), tol=1e-4)['x'] _fd = 1.9e-24 * out[0] glen_a = (glen_n+2) * _fd / 2. fs = 5.7e-20 * out[1] v, _ = inversion.mass_conservation_inversion(gdir, fs=fs, glen_a=glen_a, write=True) d = dict(fs=fs, glen_a=glen_a) d['factor_glen_a'] = out[0] d['factor_fs'] = out[1] gdir.write_pickle(d, 'inversion_params') # filter inversion.filter_inversion_output(gdir) inversion.distribute_thickness_interp(gdir, varname_suffix='_interp') inversion.distribute_thickness_per_altitude(gdir, varname_suffix='_alt') flowline.init_present_time_glacier(gdir) return gdir