def setup_cache(self): utils.mkdir(self.testdir, reset=True) self.cfg_init() hef_file = get_demo_file('Hintereisferner_RGI5.shp') entity = gpd.read_file(hef_file).iloc[0] gdir = oggm.GlacierDirectory(entity, base_dir=self.testdir) tasks.define_glacier_region(gdir, entity=entity) tasks.glacier_masks(gdir) tasks.compute_centerlines(gdir) tasks.initialize_flowlines(gdir) tasks.compute_downstream_line(gdir) tasks.compute_downstream_bedshape(gdir) tasks.catchment_area(gdir) tasks.catchment_intersections(gdir) tasks.catchment_width_geom(gdir) tasks.catchment_width_correction(gdir) tasks.process_custom_climate_data(gdir) tasks.mu_candidates(gdir) mbdf = gdir.get_ref_mb_data()['ANNUAL_BALANCE'] res = climate.t_star_from_refmb(gdir, mbdf) tasks.local_mustar(gdir, tstar=res['t_star'], bias=res['bias']) tasks.apparent_mb(gdir) tasks.prepare_for_inversion(gdir) tasks.mass_conservation_inversion(gdir) return gdir
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
tasks.define_glacier_region(gdir) tasks.glacier_masks(gdir) tasks.compute_centerlines(gdir) tasks.initialize_flowlines(gdir) tasks.compute_downstream_line(gdir) tasks.catchment_area(gdir) tasks.catchment_width_geom(gdir) tasks.catchment_width_correction(gdir) cfg.PARAMS['baseline_climate'] = 'HISTALP' cfg.PARAMS['baseline_y0'] = 1850 tasks.process_histalp_data(gdir) mu_yr_clim = tasks.glacier_mu_candidates(gdir) mbdf = gdir.get_ref_mb_data() res = t_star_from_refmb(gdir, mbdf=mbdf.ANNUAL_BALANCE) local_t_star(gdir, tstar=res['t_star'], bias=res['bias'], reset=True) mu_star_calibration(gdir, reset=True) # For flux plot tasks.prepare_for_inversion(gdir, add_debug_var=True) # For plots years, temp_yr, prcp_yr = mb_yearly_climate_on_glacier(gdir) # which years to look at selind = np.searchsorted(years, mbdf.index) temp_yr = np.mean(temp_yr[selind]) prcp_yr = np.mean(prcp_yr[selind]) # Average oberved mass-balance
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_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
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 # -------------------- # 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'] # compute local t* and the corresponding mu* climate.local_t_star(gdir, tstar=t_star, bias=bias) climate.mu_star_calibration(gdir) from oggm.core import massbalance # instance the mass balance models mb_mod = massbalance.PastMassBalance(gdir) # ----------- # INVERSION # ----------- from oggm.core import inversion inversion.prepare_for_inversion(gdir)
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
mkdir(base_dir, reset=reset) entity = gpd.read_file(get_demo_file('Hintereisferner_RGI5.shp')).iloc[0] gdir = oggm.GlacierDirectory(entity, base_dir=base_dir) tasks.define_glacier_region(gdir, entity=entity) tasks.glacier_masks(gdir) tasks.compute_centerlines(gdir) tasks.initialize_flowlines(gdir) tasks.catchment_area(gdir) tasks.catchment_intersections(gdir) tasks.catchment_width_geom(gdir) tasks.catchment_width_correction(gdir) tasks.process_cru_data(gdir) tasks.mu_candidates(gdir, reset=True) res = t_star_from_refmb(gdir, gdir.get_ref_mb_data()['ANNUAL_BALANCE']) local_mustar(gdir, tstar=res['t_star'][-1], bias=res['bias'][-1], prcp_fac=res['prcp_fac'], reset=True) apparent_mb(gdir, reset=True) tasks.prepare_for_inversion(gdir, reset=True) f, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4), sharey=True) facs = np.append((np.arange(9)+1)*0.1, (np.arange(19)+2) * 0.5) vols1 = facs * 0. vols2 = facs * 0. vols3 = facs * 0. for i, f in enumerate(facs):
tasks.glacier_masks(gdir) tasks.compute_centerlines(gdir) tasks.initialize_flowlines(gdir) tasks.compute_downstream_line(gdir) tasks.catchment_area(gdir) tasks.catchment_width_geom(gdir) tasks.catchment_width_correction(gdir) data_dir = get_demo_file('HISTALP_precipitation_all_abs_1801-2014.nc') cfg.PATHS['cru_dir'] = os.path.dirname(data_dir) cfg.PARAMS['baseline_climate'] = 'HISTALP' cfg.PARAMS['baseline_y0'] = 1850 tasks.process_histalp_data(gdir) tasks.glacier_mu_candidates(gdir) mbdf = gdir.get_ref_mb_data() res = t_star_from_refmb(gdir, mbdf=mbdf.ANNUAL_BALANCE) local_t_star(gdir, tstar=res['t_star'], bias=res['bias'], reset=True) mu_star_calibration(gdir, reset=True) # For flux plot tasks.prepare_for_inversion(gdir, add_debug_var=True) # For plots mu_yr_clim = gdir.read_pickle('climate_info')['mu_candidates_glacierwide'] years, temp_yr, prcp_yr = mb_yearly_climate_on_glacier(gdir) # which years to look at selind = np.searchsorted(years, mbdf.index) temp_yr = np.mean(temp_yr[selind]) prcp_yr = np.mean(prcp_yr[selind])