import fiona
import xarray as xr
import pandas as pd
import geopandas as gpd
import numpy as np
import matplotlib.pyplot as plt
import hvplot.xarray
import earthaccess
pd.set_option('display.max_columns', None) #make sure all columns displayed for shapefiles
From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link.
SWOT Hydrology Science Application Tutorial on the Cloud
Retrieving SWOT attributes (WSE, width, slope) and plotting a longitudinal profile along a river or over a basin
Requirement
This tutorial can only be run in an AWS cloud instance running in us-west-2: NASA Earthdata Cloud data in S3 can be directly accessed via earthaccess python library; this access is limited to requests made within the US West (Oregon) (code: us-west-2) AWS region.
Earthdata Login
An Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up.
This code runs using SWOT Level 2 Data Products (Version C, aka 2.0).
Notebook Authors: Arnaud Cerbelaud, Jeffrey Wade, NASA Jet Propulsion Laboratory - California Institute of Technology (Mar 2024)
Learning Objectives
- Retrieve SWOT hydrological attributes on river reaches within the AWS cloud (Cal/Val data). Query reaches by:
- River name
- Spatial bounding box
- Downstream tracing from reach id (e.g. headwater to outlet) for river longitudinal profiles
- Upstream tracing from reach id (e.g. outlet to full river network) for watershed analysis
- Plot a time series of WSE, width, slope data on the filtered data
- Visualize an interactive map of WSE, width, slope data on the filtered data
Import Packages
Authenticate
Authenticate your Earthdata Login (EDL) information using the earthaccess python package as follows:
earthaccess.login() # Login with your EDL credentials if asked<earthaccess.auth.Auth at 0x7f2fd5bbf220>1. Retrieve SWOT hydrological attributes on river reaches within the AWS cloud (Cal/Val data)
What data should we download?
- Optional step: Get the .kmz file of SWOT passes/swaths for the and import it into Google Earth for visualization
- Determine which pass number corresponds to the river/basin you want to look at! #### Search for multiple days of data
# Enter pass number
pass_number = ["341", "576", "298"] #e.g. 341, 576, 298 for Connecticut in NA, "236", "514", "542", "085", "363", "057", "335", "029" for Rhine in EU
# Enter continent code
continent_code = "NA" # e.g. "AF", "NA", "EU", "SI", "AS", "AU", "SA", "AR", "GR"
# Retrieves granulev and links list from the passes we want, in this case by passing to `earthdata.search_data` function the data collection shortname and temporal bounds
links_list = []
for p in range(len(pass_number)):
river_results = earthaccess.search_data(short_name = 'SWOT_L2_HR_RIVERSP_2.0',
temporal = ('2024-01-25 00:00:00', '2024-03-29 23:59:59'),
granule_name = "*Reach*_" + pass_number[p] + "_" + continent_code + "*")
for r in range(len(river_results)):
river_link = earthaccess.results.DataGranule.data_links(river_results[r], access='direct')[0]
links_list.append(river_link)Granules found: 2
Granules found: 3
Granules found: 3# Create fiona session to read data from zip files without download and extraction
fs_s3 = earthaccess.get_s3fs_session(results=river_results)
fiona_session=fiona.session.AWSSession(
aws_access_key_id=fs_s3.storage_options["key"],
aws_secret_access_key=fs_s3.storage_options["secret"],
aws_session_token=fs_s3.storage_options["token"]
)links_list['s3://podaac-swot-ops-cumulus-protected/SWOT_L2_HR_RiverSP_2.0/SWOT_L2_HR_RiverSP_Reach_010_341_NA_20240206T041926_20240206T041927_PIC0_01.zip',
's3://podaac-swot-ops-cumulus-protected/SWOT_L2_HR_RiverSP_2.0/SWOT_L2_HR_RiverSP_Reach_011_341_NA_20240227T010431_20240227T010433_PIC0_01.zip',
's3://podaac-swot-ops-cumulus-protected/SWOT_L2_HR_RiverSP_2.0/SWOT_L2_HR_RiverSP_Reach_010_576_NA_20240214T133056_20240214T133057_PIC0_01.zip',
's3://podaac-swot-ops-cumulus-protected/SWOT_L2_HR_RiverSP_2.0/SWOT_L2_HR_RiverSP_Reach_011_576_NA_20240306T101600_20240306T101602_PIC0_01.zip',
's3://podaac-swot-ops-cumulus-protected/SWOT_L2_HR_RiverSP_2.0/SWOT_L2_HR_RiverSP_Reach_012_576_NA_20240327T070054_20240327T070055_PIC0_01.zip',
's3://podaac-swot-ops-cumulus-protected/SWOT_L2_HR_RiverSP_2.0/SWOT_L2_HR_RiverSP_Reach_010_298_NA_20240204T150846_20240204T150850_PIC0_01.zip',
's3://podaac-swot-ops-cumulus-protected/SWOT_L2_HR_RiverSP_2.0/SWOT_L2_HR_RiverSP_Reach_011_298_NA_20240225T115352_20240225T115356_PIC0_01.zip',
's3://podaac-swot-ops-cumulus-protected/SWOT_L2_HR_RiverSP_2.0/SWOT_L2_HR_RiverSP_Reach_012_298_NA_20240317T083854_20240317T083858_PIC0_01.zip']Unzip selected files in Fiona session
# Initialize list of shapefiles containing all dates
SWOT_HR_shps = []
# Loop through queried granules to stack all acquisition dates
for j in range(len(links_list)):
# We use the zip+ prefix so fiona knows that we are operating on a zip file
river_shp_url = f"zip+{links_list[j]}"
# Read shapefile
with fiona.Env(session=fiona_session):
SWOT_HR_shps.append(gpd.read_file(river_shp_url)) Aggregate unzipped files into dataframe
# Combine granules from all acquisition dates into one dataframe
SWOT_HR_df = gpd.GeoDataFrame(pd.concat(SWOT_HR_shps, ignore_index=True))
# Sort dataframe by reach_id and time
SWOT_HR_df = SWOT_HR_df.sort_values(['reach_id', 'time'])
SWOT_HR_df| reach_id | time | time_tai | time_str | p_lat | p_lon | river_name | wse | wse_u | wse_r_u | wse_c | wse_c_u | slope | slope_u | slope_r_u | slope2 | slope2_u | slope2_r_u | width | width_u | width_c | width_c_u | area_total | area_tot_u | area_detct | area_det_u | area_wse | d_x_area | d_x_area_u | layovr_val | node_dist | loc_offset | xtrk_dist | dschg_c | dschg_c_u | dschg_csf | dschg_c_q | dschg_gc | dschg_gc_u | dschg_gcsf | dschg_gc_q | dschg_m | dschg_m_u | dschg_msf | dschg_m_q | dschg_gm | dschg_gm_u | dschg_gmsf | dschg_gm_q | dschg_b | dschg_b_u | dschg_bsf | dschg_b_q | dschg_gb | dschg_gb_u | dschg_gbsf | dschg_gb_q | dschg_h | dschg_h_u | dschg_hsf | dschg_h_q | dschg_gh | dschg_gh_u | dschg_ghsf | dschg_gh_q | dschg_o | dschg_o_u | dschg_osf | dschg_o_q | dschg_go | dschg_go_u | dschg_gosf | dschg_go_q | dschg_s | dschg_s_u | dschg_ssf | dschg_s_q | dschg_gs | dschg_gs_u | dschg_gssf | dschg_gs_q | dschg_i | dschg_i_u | dschg_isf | dschg_i_q | dschg_gi | dschg_gi_u | dschg_gisf | dschg_gi_q | dschg_q_b | dschg_gq_b | reach_q | reach_q_b | dark_frac | ice_clim_f | ice_dyn_f | partial_f | n_good_nod | obs_frac_n | xovr_cal_q | geoid_hght | geoid_slop | solid_tide | load_tidef | load_tideg | pole_tide | dry_trop_c | wet_trop_c | iono_c | xovr_cal_c | n_reach_up | n_reach_dn | rch_id_up | rch_id_dn | p_wse | p_wse_var | p_width | p_wid_var | p_n_nodes | p_dist_out | p_length | p_maf | p_dam_id | p_n_ch_max | p_n_ch_mod | p_low_slp | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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| 4454 | 72120300121 | -1.000000e+12 | -1.000000e+12 | no_data | 50.355053 | -77.287576 | no_data | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | 12582912 | 12582912 | 3 | 469762048 | -1.000000e+12 | 2 | -999 | 1 | -999 | -1.000000e+12 | 2 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | 1 | 1 | 72120300206, no_data, no_data, no_data | 72120300113, no_data, no_data, no_data | 248.199997 | 0.031587 | 45.0 | 217.523 | 86 | 228174.350 | 17135.322465 | -1.000000e+12 | 0 | 1 | 1 | 0 | LINESTRING (-77.37319 50.31698, -77.37277 50.3... |
| 5170 | 72120300121 | -1.000000e+12 | -1.000000e+12 | no_data | 50.355053 | -77.287576 | no_data | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | 12582912 | 12582912 | 3 | 469762048 | -1.000000e+12 | 2 | -999 | 1 | -999 | -1.000000e+12 | 2 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | 1 | 1 | 72120300206, no_data, no_data, no_data | 72120300113, no_data, no_data, no_data | 248.199997 | 0.031587 | 45.0 | 217.523 | 86 | 228174.350 | 17135.322465 | -1.000000e+12 | 0 | 1 | 1 | 0 | LINESTRING (-77.37319 50.31698, -77.37277 50.3... |
| 3739 | 72120400051 | -1.000000e+12 | -1.000000e+12 | no_data | 49.864785 | -76.975406 | no_data | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | 12582912 | 12582912 | 3 | 469762048 | -1.000000e+12 | 2 | -999 | 1 | -999 | -1.000000e+12 | 2 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | 1 | 1 | 72120400063, no_data, no_data, no_data | 72120400043, no_data, no_data, no_data | 256.399994 | 2.569541 | 291.0 | 19982.483 | 91 | 308791.019 | 18231.842622 | -1.000000e+12 | 0 | 3 | 1 | 0 | LINESTRING (-77.06133 49.85771, -77.06092 49.8... |
| 4455 | 72120400051 | -1.000000e+12 | -1.000000e+12 | no_data | 49.864785 | -76.975406 | no_data | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | 12582912 | 12582912 | 3 | 469762048 | -1.000000e+12 | 2 | -999 | 1 | -999 | -1.000000e+12 | 2 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | 1 | 1 | 72120400063, no_data, no_data, no_data | 72120400043, no_data, no_data, no_data | 256.399994 | 2.569541 | 291.0 | 19982.483 | 91 | 308791.019 | 18231.842622 | -1.000000e+12 | 0 | 3 | 1 | 0 | LINESTRING (-77.06133 49.85771, -77.06092 49.8... |
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| 3128 | 76500000131 | 7.630360e+08 | 7.630361e+08 | 2024-03-06T10:26:58Z | 18.255350 | -66.012657 | no_data | 5.148830e+01 | 7.147400e-01 | 7.090500e-01 | -1.000000e+12 | -1.000000e+12 | -1.896964e-03 | 1.029344e-05 | 9.846570e-06 | -2.579608e-03 | -1.000000e+12 | 1.179402e-01 | 8.228150e+02 | 3.330908e+00 | -1.000000e+12 | -1.000000e+12 | 9.494502e+06 | 3.843551e+04 | 9.472037e+06 | 3.843550e+04 | 9.494502e+06 | -1.000000e+12 | -1.000000e+12 | 7.219900e+00 | 2.269424e+02 | 3.555010e+01 | -9.718222e+03 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | 25428408 | 25428408 | 2 | 542734 | 2.366000e-03 | 0 | -999 | 0 | 33 | 5.409836e-01 | 0 | -4.203993e+01 | -5.959560e-05 | -1.099997e-01 | -7.083979e-03 | -9.455572e-03 | 2.673383e-03 | -2.300245e+00 | -2.157397e-01 | -2.978376e-03 | 3.877690e-01 | 1 | 1 | 76500000141, no_data, no_data, no_data | 76500000123, no_data, no_data, no_data | 40.600002 | 43.251021 | 63.0 | 1149.474 | 61 | 43590.077 | 12103.909044 | -1.000000e+12 | 0 | 2 | 1 | 0 | LINESTRING (-65.99988 18.21743, -66.00022 18.2... |
| 3736 | 76500000131 | 7.648387e+08 | 7.648388e+08 | 2024-03-27T07:11:56Z | 18.255350 | -66.012657 | no_data | 6.214650e+01 | 2.573330e+00 | 2.571760e+00 | -1.000000e+12 | -1.000000e+12 | -3.331055e-03 | 3.849838e-05 | 3.838132e-05 | -3.053041e-03 | -1.000000e+12 | 2.653730e+00 | 1.336966e+03 | 4.963007e+00 | -1.000000e+12 | -1.000000e+12 | 1.465627e+07 | 5.440614e+04 | 1.461890e+07 | 5.440610e+04 | 1.465627e+07 | -1.000000e+12 | -1.000000e+12 | 6.300200e+00 | 2.618109e+02 | 1.134880e+02 | -9.406573e+03 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | 25428408 | 25428408 | 2 | 575498 | 2.549000e-03 | 0 | -999 | 1 | 25 | 4.098361e-01 | 2 | -4.208252e+01 | -6.235580e-05 | 7.611858e-02 | 1.698115e-03 | 3.022270e-03 | 1.955032e-03 | -2.290476e+00 | -1.451549e-01 | -6.110505e-03 | 0.000000e+00 | 1 | 1 | 76500000141, no_data, no_data, no_data | 76500000123, no_data, no_data, no_data | 40.600002 | 43.251021 | 63.0 | 1149.474 | 61 | 43590.077 | 12103.909044 | -1.000000e+12 | 0 | 2 | 1 | 0 | LINESTRING (-65.99988 18.21743, -66.00022 18.2... |
| 2523 | 76500000141 | 7.612333e+08 | 7.612334e+08 | 2024-02-14T13:41:54Z | 18.214094 | -65.999696 | no_data | 7.122600e+01 | 9.093000e-02 | 1.299000e-02 | -1.000000e+12 | -1.000000e+12 | -1.424304e-02 | 3.561229e-05 | 3.548571e-05 | -4.944832e-02 | -1.000000e+12 | 2.070532e-04 | 2.976488e+02 | 7.012993e+00 | -1.000000e+12 | -1.000000e+12 | 2.374517e+05 | 5.594670e+03 | 2.374517e+05 | 5.594700e+03 | 2.374517e+05 | -1.000000e+12 | -1.000000e+12 | 1.040700e+00 | 3.071884e+02 | 3.960115e+01 | -9.825143e+03 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | 25428408 | 25428408 | 2 | 542730 | 0.000000e+00 | 0 | -999 | 0 | 2 | 5.000000e-01 | 0 | -4.162250e+01 | -1.538206e-04 | -1.459826e-01 | -1.125507e-02 | -9.715600e-03 | 3.041269e-03 | -2.308736e+00 | -1.868137e-01 | -9.129797e-03 | 6.605130e-02 | 2 | 1 | 76500000161, 76500000151, no_data, no_data | 76500000131, no_data, no_data, no_data | 63.100002 | 20.898301 | 30.0 | 415.706 | 4 | 44387.835 | 797.757834 | -1.000000e+12 | 0 | 1 | 1 | 0 | LINESTRING (-66.00018 18.21038, -66.00018 18.2... |
| 3129 | 76500000141 | 7.630360e+08 | 7.630361e+08 | 2024-03-06T10:26:58Z | 18.214094 | -65.999696 | no_data | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | 3.717835e+02 | 7.669814e+00 | -1.000000e+12 | -1.000000e+12 | 2.965932e+05 | 6.118654e+03 | 2.965932e+05 | 6.118700e+03 | 2.965932e+05 | -1.000000e+12 | -1.000000e+12 | 0.000000e+00 | 2.817323e+02 | 3.577332e+01 | -1.006446e+04 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | 29360570 | 29360570 | 3 | 503351296 | 0.000000e+00 | 0 | -999 | 1 | 0 | 0.000000e+00 | 2 | -4.161941e+01 | -1.000000e+12 | -1.098480e-01 | -6.989030e-03 | -9.287705e-03 | 2.667307e-03 | -2.297567e+00 | -2.175007e-01 | -2.980941e-03 | 4.020248e-01 | 2 | 1 | 76500000161, 76500000151, no_data, no_data | 76500000131, no_data, no_data, no_data | 63.100002 | 20.898301 | 30.0 | 415.706 | 4 | 44387.835 | 797.757834 | -1.000000e+12 | 0 | 1 | 1 | 0 | LINESTRING (-66.00018 18.21038, -66.00018 18.2... |
| 3737 | 76500000141 | 7.648387e+08 | 7.648388e+08 | 2024-03-27T07:11:56Z | 18.214094 | -65.999696 | no_data | 8.508610e+01 | 9.025000e-02 | 6.690000e-03 | -1.000000e+12 | -1.000000e+12 | -9.286700e-03 | 4.201405e-05 | 4.190681e-05 | -1.123384e-02 | -1.000000e+12 | 1.623781e-04 | 8.786977e+02 | 1.250254e+01 | -1.000000e+12 | -1.000000e+12 | 7.009880e+05 | 9.974001e+03 | 7.009880e+05 | 9.974000e+03 | 7.009880e+05 | -1.000000e+12 | -1.000000e+12 | 1.628500e+00 | 1.992412e+02 | 1.928564e+01 | -9.878682e+03 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | 25428408 | 25428408 | 2 | 540682 | 0.000000e+00 | 0 | -999 | 0 | 3 | 7.500000e-01 | 2 | -4.163959e+01 | -6.160100e-05 | 7.617028e-02 | 1.573288e-03 | 2.859416e-03 | 1.950453e-03 | -2.290005e+00 | -1.457697e-01 | -6.110666e-03 | 0.000000e+00 | 2 | 1 | 76500000161, 76500000151, no_data, no_data | 76500000131, no_data, no_data, no_data | 63.100002 | 20.898301 | 30.0 | 415.706 | 4 | 44387.835 | 797.757834 | -1.000000e+12 | 0 | 1 | 1 | 0 | LINESTRING (-66.00018 18.21038, -66.00018 18.2... |
5886 rows × 127 columns
Exploring the dataset
What acquisition dates and rivers do our downloaded files cover?
print('Available dates are:')
print(np.unique([i[:10] for i in SWOT_HR_df['time_str']]))
print('Available rivers are:')
print(np.unique([i for i in SWOT_HR_df['river_name']]))Available dates are:
['2024-02-04' '2024-02-06' '2024-02-14' '2024-02-25' '2024-02-27'
'2024-03-06' '2024-03-17' '2024-03-27' 'no_data']
Available rivers are:
['Androscoggin River' 'Batten Kill' 'Bras du Nord' 'Canal de fuite'
"Chenal de l'Est" 'Concord River' 'Concord River; Sudbury River'
'Connecticut River' 'Connecticut River; Westfield River'
'Connecticut River; White River' 'Deerfield River' 'Farmington River'
'Fleuve Saint-Laurent' 'Hoosic River' 'Housatonic River' 'Howells River'
'Hudson River' 'Hudson River; Indian River' 'Indian River'
'Komaktorvik River' 'La Grande River' 'Lac Saint-Louis' 'Lamoille River'
'Magalloway River' 'Merrimack River' 'Missisquoi River' 'Mohawk River'
'Ottawa River' 'Otter Creek' 'Passumsic River' 'Pemigewasset River'
'Quinebaug River' 'Racquette River' 'Sacandaga River' 'Saguenay River'
'Saint Lawrence River' 'Saint Regis River'
'Saint Regis River; West Branch of the Saint Regis R' 'Shetucket River'
'South Nation River' 'Sudbury River' 'Thames River'
'Vieux-Comptoir River' 'Wappinger Creek' 'West River' 'White River'
'Winooski River' 'Wood Creek' 'no_data']Filter dataframe by river name of interest and plot selected reaches:
Note: Some rivers have multiple names, hence using the contains function
# Enter river name
river = "Connecticut River" # e.g. "Rhine", "Connecticut River"
## Filter dataframe
SWOT_HR_df_river = SWOT_HR_df[(SWOT_HR_df.river_name.str.contains(river))]
# Plot geopandas dataframe with 'explore' by reach id
SWOT_HR_df_river[['reach_id','river_name','geometry']].explore('reach_id', style_kwds=dict(weight=6))Make this Notebook Trusted to load map: File -> Trust Notebook
Filter dataframe by latitude/longitude of interest:
lat_start = {"Connecticut River": 41,
"Rhine": 46.5
}
lat_end = {"Connecticut River": 45,
"Rhine": 52
}
lon_start = {"Connecticut River": -73,
"Rhine": 6
}
lon_end = {"Connecticut River": -71,
"Rhine": 10
}
## Filter dataframe
SWOT_HR_df_box = SWOT_HR_df[(SWOT_HR_df.p_lat > lat_start[river]) & (SWOT_HR_df.p_lat < lat_end[river]) & (SWOT_HR_df.p_lon > lon_start[river]) & (SWOT_HR_df.p_lon < lon_end[river])]
# Plot geopandas dataframe with 'explore' by river name
SWOT_HR_df_box[['reach_id','river_name','geometry']].explore('river_name', style_kwds=dict(weight=6))Make this Notebook Trusted to load map: File -> Trust Notebook
2. River longitudinal profile: trace reaches downstream of given starting reach using rch_id_dn field
WARNING: This works as long as the data is exhaustive (no missing SWORD reaches)
First, let’s set up a dictionary relating all reaches in the dataset to their downstream neighbor
Note: rch_dn_dict[rch_id] gives a list of all the reaches directly downstream from rch_id
# Format rch_id_dn for dictionary. Rch_id_dn allows for multiple downstream reaches to be stored
# Also removes spaces in attribute field
rch_id_dn = [[x.strip() for x in SWOT_HR_df.rch_id_dn[j].split(',')] for j in range(0,len(SWOT_HR_df.rch_id_dn))]
# Filter upstream reach ids to remove 'no_data'
rch_id_dn_filter = [[x for x in dn_id if x.isnumeric()] for dn_id in rch_id_dn]
# Create lookup dictionary for river network topology: Downstream
rch_dn_dict = {SWOT_HR_df.reach_id[i]: rch_id_dn_filter[i] for i in range(len(SWOT_HR_df))}Then, starting from a given reach, let’s trace all connected downstream reaches
# Enter reach_id from which we will trace downstream (e.g. headwaters of the Connecticut River)
rch_dn_st = {"Connecticut River": '73120000691',
"Rhine": '23267000651'
}
# Initialize list to store downstream reaches, including starting reach
rch_dn_list = [rch_dn_st[river]]
# Retrieve first downstream id of starting reach and add to list
rch_dn_next = rch_dn_dict[rch_dn_st[river]][0]
# Trace next downstream reach until we hit the outlet (or here the last reach on file)
while len(rch_dn_next) != 0:
# Add reach to list if value exists
if len(rch_dn_next) != 0:
rch_dn_list.append(rch_dn_next)
# Recursively retrieve first downstream id of next reach
# Catch error if reach isn't in downloaded data
try:
rch_dn_next = rch_dn_dict[rch_dn_next][0]
except:
breakFinally, we filtered our downloaded data by the traced reaches to create a plot
# Filter downloaded data by downstream traced reaches
SWOT_dn_trace = SWOT_HR_df[SWOT_HR_df.reach_id.isin(rch_dn_list)]
# Remove reaches from rch_dn_list that are not present in SWOT data
rch_dn_list = [rch for rch in rch_dn_list if rch in SWOT_HR_df.reach_id.values]
SWOT_dn_trace[['reach_id','river_name','geometry']].explore('river_name', style_kwds=dict(weight=6))Make this Notebook Trusted to load map: File -> Trust Notebook
It looks like the data was cut short!
Must be an error in the SWORD Database. Let’s check by singling out the reach it left off on.
It looks like reach ‘73120000531’ at the time of this run (4/1/24) has ‘no_data’ in the rch_id_up and rch_id_dn fields when there should be values. To help out the SWORD database creators, we can report this discrepancy by clicking the Report Reach button at https://www.swordexplorer.com/. SWORD should be updated quarterly.
SWOT_HR_df[SWOT_HR_df.reach_id == '73120000531']| reach_id | time | time_tai | time_str | p_lat | p_lon | river_name | wse | wse_u | wse_r_u | wse_c | wse_c_u | slope | slope_u | slope_r_u | slope2 | slope2_u | slope2_r_u | width | width_u | width_c | width_c_u | area_total | area_tot_u | area_detct | area_det_u | area_wse | d_x_area | d_x_area_u | layovr_val | node_dist | loc_offset | xtrk_dist | dschg_c | dschg_c_u | dschg_csf | dschg_c_q | dschg_gc | dschg_gc_u | dschg_gcsf | dschg_gc_q | dschg_m | dschg_m_u | dschg_msf | dschg_m_q | dschg_gm | dschg_gm_u | dschg_gmsf | dschg_gm_q | dschg_b | dschg_b_u | dschg_bsf | dschg_b_q | dschg_gb | dschg_gb_u | dschg_gbsf | dschg_gb_q | dschg_h | dschg_h_u | dschg_hsf | dschg_h_q | dschg_gh | dschg_gh_u | dschg_ghsf | dschg_gh_q | dschg_o | dschg_o_u | dschg_osf | dschg_o_q | dschg_go | dschg_go_u | dschg_gosf | dschg_go_q | dschg_s | dschg_s_u | dschg_ssf | dschg_s_q | dschg_gs | dschg_gs_u | dschg_gssf | dschg_gs_q | dschg_i | dschg_i_u | dschg_isf | dschg_i_q | dschg_gi | dschg_gi_u | dschg_gisf | dschg_gi_q | dschg_q_b | dschg_gq_b | reach_q | reach_q_b | dark_frac | ice_clim_f | ice_dyn_f | partial_f | n_good_nod | obs_frac_n | xovr_cal_q | geoid_hght | geoid_slop | solid_tide | load_tidef | load_tideg | pole_tide | dry_trop_c | wet_trop_c | iono_c | xovr_cal_c | n_reach_up | n_reach_dn | rch_id_up | rch_id_dn | p_wse | p_wse_var | p_width | p_wid_var | p_n_nodes | p_dist_out | p_length | p_maf | p_dam_id | p_n_ch_max | p_n_ch_mod | p_low_slp | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 866 | 73120000531 | 7.605090e+08 | 7.605090e+08 | 2024-02-06T04:29:59Z | 44.224658 | -72.054978 | Connecticut River | 154.0388 | 1.46533 | 1.46257 | -1.000000e+12 | -1.000000e+12 | -0.005891 | 0.000053 | 0.000053 | -0.006434 | -1.000000e+12 | 0.896640 | 1392.178327 | 35.649174 | -1.000000e+12 | -1.000000e+12 | 15211862.6 | 389526.4859 | 15095057.3 | 389526.5 | 15211862.6 | -1.000000e+12 | -1.000000e+12 | 7.8523 | 105.676909 | 10.84167 | 9765.19336 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | 25428408 | 25428408 | 2 | 540686 | 0.007679 | 2 | -999 | 0 | 33 | 0.589286 | 0 | -28.335190 | 9.845300e-07 | 0.172423 | 0.010426 | 0.010006 | 0.005676 | -2.282196 | -0.045801 | -0.003250 | 0.121413 | 0 | 0 | no_data, no_data, no_data, no_data | no_data, no_data, no_data, no_data | 126.700005 | 3.094431 | 127.0 | 3164.993 | 56 | 479601.479 | 11140.899379 | -1.000000e+12 | 0 | 2 | 1 | 0 | LINESTRING (-72.05657 44.17983, -72.05663 44.1... |
| 2477 | 73120000531 | 7.612328e+08 | 7.612329e+08 | 2024-02-14T13:34:08Z | 44.224658 | -72.054978 | Connecticut River | 128.0371 | 0.32207 | 0.30924 | -1.000000e+12 | -1.000000e+12 | 0.000855 | 0.000167 | 0.000167 | 0.000768 | -1.000000e+12 | 0.006689 | 236.948849 | 1.282518 | -1.000000e+12 | -1.000000e+12 | 2640563.8 | 14292.4126 | 2161135.3 | 14292.4 | 2640563.8 | -1.000000e+12 | -1.000000e+12 | 8.2052 | 54.052922 | 0.21296 | -21062.49316 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | 8389049 | 8389049 | 1 | 14 | 0.181563 | 2 | -999 | 0 | 44 | 0.785714 | 1 | -28.335581 | 2.610410e-06 | -0.117483 | -0.005037 | -0.004920 | 0.005339 | -2.264607 | -0.021861 | -0.006972 | 0.192579 | 0 | 0 | no_data, no_data, no_data, no_data | no_data, no_data, no_data, no_data | 126.700005 | 3.094431 | 127.0 | 3164.993 | 56 | 479601.479 | 11140.899379 | -1.000000e+12 | 0 | 2 | 1 | 0 | LINESTRING (-72.05657 44.17983, -72.05663 44.1... |
| 1825 | 73120000531 | 7.623117e+08 | 7.623117e+08 | 2024-02-27T01:15:04Z | 44.224658 | -72.054978 | Connecticut River | 128.0144 | 1.19649 | 1.19310 | -1.000000e+12 | -1.000000e+12 | 0.001347 | 0.000243 | 0.000243 | 0.001149 | -1.000000e+12 | 0.059092 | 140.618093 | 1.530273 | -1.000000e+12 | -1.000000e+12 | 1567051.5 | 17053.4007 | 929680.4 | 17053.4 | 1567051.5 | -1.000000e+12 | -1.000000e+12 | 9.2751 | 57.946006 | 0.51076 | 9967.06689 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | 8651192 | 8651192 | 2 | 542734 | 0.406733 | 2 | -999 | 0 | 28 | 0.500000 | 0 | -28.334133 | 7.037000e-07 | -0.090734 | -0.008116 | -0.007447 | 0.004975 | -2.278251 | -0.039789 | -0.004193 | -0.351682 | 0 | 0 | no_data, no_data, no_data, no_data | no_data, no_data, no_data, no_data | 126.700005 | 3.094431 | 127.0 | 3164.993 | 56 | 479601.479 | 11140.899379 | -1.000000e+12 | 0 | 2 | 1 | 0 | LINESTRING (-72.05657 44.17983, -72.05663 44.1... |
| 3083 | 73120000531 | 7.630356e+08 | 7.630356e+08 | 2024-03-06T10:19:13Z | 44.224658 | -72.054978 | Connecticut River | 132.4360 | 0.33872 | 0.32654 | -1.000000e+12 | -1.000000e+12 | 0.000345 | 0.000050 | 0.000050 | 0.000516 | -1.000000e+12 | 0.004668 | 148.281475 | 0.719702 | -1.000000e+12 | -1.000000e+12 | 1652452.4 | 8020.3815 | 704909.5 | 8020.4 | 1652452.4 | -1.000000e+12 | -1.000000e+12 | 15.2661 | 97.873676 | 5.65395 | -21404.77344 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | 8651192 | 8651192 | 2 | 524302 | 0.573416 | 1 | -999 | 0 | 39 | 0.696429 | 1 | -28.334730 | 1.409300e-06 | -0.122787 | -0.012621 | -0.013141 | 0.004544 | -2.275936 | -0.138596 | -0.002577 | 0.981060 | 0 | 0 | no_data, no_data, no_data, no_data | no_data, no_data, no_data, no_data | 126.700005 | 3.094431 | 127.0 | 3164.993 | 56 | 479601.479 | 11140.899379 | -1.000000e+12 | 0 | 2 | 1 | 0 | LINESTRING (-72.05657 44.17983, -72.05663 44.1... |
| 3691 | 73120000531 | 7.648383e+08 | 7.648383e+08 | 2024-03-27T07:04:16Z | 44.224658 | -72.054978 | Connecticut River | 132.4095 | 0.19755 | 0.17586 | -1.000000e+12 | -1.000000e+12 | 0.001078 | 0.000065 | 0.000065 | 0.000907 | -1.000000e+12 | 0.001526 | 167.129932 | 0.874109 | -1.000000e+12 | -1.000000e+12 | 1862500.1 | 9741.0965 | 987509.4 | 9741.1 | 1862500.1 | -1.000000e+12 | -1.000000e+12 | 8.0515 | 124.612257 | -0.06486 | -21380.69141 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | 8651192 | 8651192 | 2 | 524302 | 0.469794 | 1 | -999 | 0 | 44 | 0.785714 | 1 | -28.335330 | 1.471800e-06 | 0.020488 | 0.006227 | 0.005686 | 0.003217 | -2.283199 | -0.149064 | -0.003512 | 0.247772 | 0 | 0 | no_data, no_data, no_data, no_data | no_data, no_data, no_data, no_data | 126.700005 | 3.094431 | 127.0 | 3164.993 | 56 | 479601.479 | 11140.899379 | -1.000000e+12 | 0 | 2 | 1 | 0 | LINESTRING (-72.05657 44.17983, -72.05663 44.1... |
3. Watershed analysis: trace reaches upstream of starting reach using rch_id_up field
WARNING: This works as long as the data is exhaustive (no missing SWORD reaches)
First, let’s set up a dictionary relating all reaches in the dataset to their upstream neighbor
Note: rch_up_dict[rch_id] gives a list of all the reaches directly upstream from rch_id
# Format rch_id_up for dictionary. Rch_id_up allows for multiple upstream reaches to be stored
# Also removes spaces in attribute field
rch_id_up = [[x.strip() for x in SWOT_HR_df.rch_id_up[j].split(',')] for j in range(0,len(SWOT_HR_df.rch_id_up))]
# Filter upstream reach ids to remove 'no_data'
rch_id_up_fil = [[x for x in ups_id if x.isnumeric()] for ups_id in rch_id_up]
# Create lookup dictionary for river network topology: Upstream
rch_up_dict = {SWOT_HR_df.reach_id[i]: rch_id_up_fil[i] for i in range(len(SWOT_HR_df))}Then, starting from a given reach, let’s trace all connected upstream reaches
This adds a bit of complexity, as we need to keep track of multiple branches upstream of the starting reach.
# Enter reach_id from which we will trace upstream (e.g. outlet of the Connecticut River)
rch_up_st = {"Connecticut River": '73120000021',
"Rhine": '23265000051'
}
# Initialize list to store traced upstream reaches, including starting reach
rch_up_list = [rch_up_st[river]]
# Retrieve ids of reaches upstream of starting reach and add to list
rch_up_next = rch_up_dict[rch_up_st[river]]
# For upstream tracing, we need to set a list of next upstream ids to start while loop
rch_next_id = rch_up_next
# Loop until no more reaches to trace
while len(rch_next_id) != 0:
# Initialize list to store next upstream ids
rch_next_id = []
# Loop through next upstream ids for given reach
for rch_up_sel in rch_up_next:
# Get values of existing upstream ids of rch_up_next reaches
# If reach isn't in SWOT data (usually ghost reaches), continue to next reach
try:
rch_next_id = rch_next_id + rch_up_dict[rch_up_sel]
except:
continue
# Append id to list
rch_up_list.append(rch_up_sel)
# If reaches exist, add to list for next cycle of tracing
if len(rch_next_id) != 0:
rch_up_next = rch_next_idFinally, we filtered our downloaded data by the traced reaches to create a plot
# Filter downloaded data by upstream traced reaches
SWOT_up_trace = SWOT_HR_df[SWOT_HR_df.reach_id.isin(rch_up_list)]
# Remove reaches from rch_up_list that are not present in SWOT data
rch_up_list = [rch for rch in rch_up_list if rch in SWOT_HR_df.reach_id.values]
SWOT_up_trace[['reach_id','river_name','geometry']].explore('river_name', style_kwds=dict(weight=6))Make this Notebook Trusted to load map: File -> Trust Notebook
It looks like the data was cut short!
Must be an error in the SWORD Database. Let’s check by singling out the reach it left off on.
It looks like reach ‘73120000131’ at the time of this run (4/1/24) has ‘no_data’ in the rch_id_up field when there should be values. To help out the SWORD database creators, we can report this discrepancy by clicking the Report Reach button at https://www.swordexplorer.com/. SWORD should be updated quarterly.
SWOT_HR_df[SWOT_HR_df.reach_id == '73120000131']| reach_id | time | time_tai | time_str | p_lat | p_lon | river_name | wse | wse_u | wse_r_u | wse_c | wse_c_u | slope | slope_u | slope_r_u | slope2 | slope2_u | slope2_r_u | width | width_u | width_c | width_c_u | area_total | area_tot_u | area_detct | area_det_u | area_wse | d_x_area | d_x_area_u | layovr_val | node_dist | loc_offset | xtrk_dist | dschg_c | dschg_c_u | dschg_csf | dschg_c_q | dschg_gc | dschg_gc_u | dschg_gcsf | dschg_gc_q | dschg_m | dschg_m_u | dschg_msf | dschg_m_q | dschg_gm | dschg_gm_u | dschg_gmsf | dschg_gm_q | dschg_b | dschg_b_u | dschg_bsf | dschg_b_q | dschg_gb | dschg_gb_u | dschg_gbsf | dschg_gb_q | dschg_h | dschg_h_u | dschg_hsf | dschg_h_q | dschg_gh | dschg_gh_u | dschg_ghsf | dschg_gh_q | dschg_o | dschg_o_u | dschg_osf | dschg_o_q | dschg_go | dschg_go_u | dschg_gosf | dschg_go_q | dschg_s | dschg_s_u | dschg_ssf | dschg_s_q | dschg_gs | dschg_gs_u | dschg_gssf | dschg_gs_q | dschg_i | dschg_i_u | dschg_isf | dschg_i_q | dschg_gi | dschg_gi_u | dschg_gisf | dschg_gi_q | dschg_q_b | dschg_gq_b | reach_q | reach_q_b | dark_frac | ice_clim_f | ice_dyn_f | partial_f | n_good_nod | obs_frac_n | xovr_cal_q | geoid_hght | geoid_slop | solid_tide | load_tidef | load_tideg | pole_tide | dry_trop_c | wet_trop_c | iono_c | xovr_cal_c | n_reach_up | n_reach_dn | rch_id_up | rch_id_dn | p_wse | p_wse_var | p_width | p_wid_var | p_n_nodes | p_dist_out | p_length | p_maf | p_dam_id | p_n_ch_max | p_n_ch_mod | p_low_slp | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4289 | 73120000131 | 7.603747e+08 | 7.603748e+08 | 2024-02-04T15:12:22Z | 42.150023 | -72.611327 | Connecticut River; Westfield River | 17.8123 | 1.75473 | 1.75242 | -1.000000e+12 | -1.000000e+12 | 0.000037 | 0.000070 | 0.000069 | -0.001077 | -1.000000e+12 | 0.149820 | 269.926514 | 0.917276 | -1.000000e+12 | -1.000000e+12 | 4913349.6 | 16696.7556 | 4020726.7 | 16696.8 | 4913349.6 | -1.000000e+12 | -1.000000e+12 | 7.4382 | 77.889768 | 3.07849 | -17744.08203 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | 8651194 | 8651194 | 2 | 524298 | 0.181673 | 1 | -999 | 0 | 63 | 0.692308 | 1 | -29.751362 | 0.000009 | -0.086159 | -0.000891 | -0.001675 | 0.005661 | -2.308405 | -0.038409 | -0.007717 | -0.119022 | 0 | 1 | no_data, no_data, no_data, no_data | 73120000121, no_data, no_data, no_data | 11.400001 | 17.519935 | 283.0 | 3330.789 | 91 | 151626.309 | 18201.09844 | -1.000000e+12 | 0 | 2 | 1 | 0 | LINESTRING (-72.58355 42.08302, -72.58318 42.0... |
| 826 | 73120000131 | 7.605090e+08 | 7.605090e+08 | 2024-02-06T04:29:28Z | 42.150023 | -72.611327 | Connecticut River; Westfield River | 12.6637 | 0.19421 | 0.17210 | -1.000000e+12 | -1.000000e+12 | 0.000114 | 0.000017 | 0.000017 | 0.000086 | -1.000000e+12 | 0.001538 | 287.245279 | 0.797441 | -1.000000e+12 | -1.000000e+12 | 5228595.3 | 14515.4526 | 4606508.3 | 14515.5 | 5228595.3 | -1.000000e+12 | -1.000000e+12 | 9.1697 | 44.128698 | 1.25673 | 22611.02344 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | 8651192 | 8651192 | 2 | 524302 | 0.118978 | 1 | -999 | 0 | 79 | 0.868132 | 0 | -29.758129 | 0.000009 | 0.174483 | 0.010891 | 0.010403 | 0.005627 | -2.314298 | -0.038225 | -0.003412 | 0.275083 | 0 | 1 | no_data, no_data, no_data, no_data | 73120000121, no_data, no_data, no_data | 11.400001 | 17.519935 | 283.0 | 3330.789 | 91 | 151626.309 | 18201.09844 | -1.000000e+12 | 0 | 2 | 1 | 0 | LINESTRING (-72.58355 42.08302, -72.58318 42.0... |
| 5005 | 73120000131 | 7.621774e+08 | 7.621775e+08 | 2024-02-25T11:57:28Z | 42.150023 | -72.611327 | Connecticut River; Westfield River | 18.6698 | 0.71056 | 0.70483 | -1.000000e+12 | -1.000000e+12 | -0.000197 | 0.000013 | 0.000013 | -0.000449 | -1.000000e+12 | 0.016066 | 176.825294 | 0.749504 | -1.000000e+12 | -1.000000e+12 | 3180801.8 | 13482.3639 | 2471691.4 | 13482.4 | 3180801.8 | -1.000000e+12 | -1.000000e+12 | 14.1412 | 61.492919 | 25.47558 | -18295.04688 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | 25428408 | 25428408 | 2 | 524298 | 0.222934 | 1 | -999 | 0 | 57 | 0.626374 | 1 | -29.754559 | 0.000009 | -0.107900 | -0.010993 | -0.011123 | 0.004992 | -2.318859 | -0.020506 | -0.004491 | 0.533674 | 0 | 1 | no_data, no_data, no_data, no_data | 73120000121, no_data, no_data, no_data | 11.400001 | 17.519935 | 283.0 | 3330.789 | 91 | 151626.309 | 18201.09844 | -1.000000e+12 | 0 | 2 | 1 | 0 | LINESTRING (-72.58355 42.08302, -72.58318 42.0... |
| 1785 | 73120000131 | 7.623117e+08 | 7.623117e+08 | 2024-02-27T01:14:33Z | 42.150023 | -72.611327 | Connecticut River; Westfield River | 14.2290 | 56.55282 | 56.55275 | -1.000000e+12 | -1.000000e+12 | 0.000227 | 0.000755 | 0.000755 | 0.000221 | -1.000000e+12 | 105.629895 | 174.128982 | 0.481409 | -1.000000e+12 | -1.000000e+12 | 3020238.5 | 8349.9574 | 1643032.9 | 8350.0 | 3020238.5 | -1.000000e+12 | -1.000000e+12 | 3.7414 | 59.205995 | -254.38090 | 22761.77734 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | 8651194 | 8651194 | 2 | 559118 | 0.455992 | 1 | -999 | 1 | 40 | 0.439560 | 0 | -29.787819 | 0.000010 | -0.097246 | -0.010257 | -0.009368 | 0.004914 | -2.311219 | -0.055498 | -0.004463 | -0.923738 | 0 | 1 | no_data, no_data, no_data, no_data | 73120000121, no_data, no_data, no_data | 11.400001 | 17.519935 | 283.0 | 3330.789 | 91 | 151626.309 | 18201.09844 | -1.000000e+12 | 0 | 2 | 1 | 0 | LINESTRING (-72.58355 42.08302, -72.58318 42.0... |
| 5721 | 73120000131 | 7.639802e+08 | 7.639802e+08 | 2024-03-17T08:42:30Z | 42.150023 | -72.611327 | Connecticut River; Westfield River | 16.4579 | 1.45118 | 1.44838 | -1.000000e+12 | -1.000000e+12 | -0.000051 | 0.000020 | 0.000020 | -0.000040 | -1.000000e+12 | 0.101899 | 253.869874 | 0.921815 | -1.000000e+12 | -1.000000e+12 | 4365151.0 | 15850.0907 | 3943427.8 | 15850.1 | 4365151.0 | -1.000000e+12 | -1.000000e+12 | 8.2200 | 61.505402 | 34.55020 | -17854.23730 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | -1.000000e+12 | -1.000000e+12 | -1.000000e+12 | -999 | 25428408 | 25428408 | 2 | 524298 | 0.096611 | 1 | -999 | 0 | 56 | 0.615385 | 1 | -29.751930 | 0.000009 | -0.081659 | -0.008595 | -0.009871 | 0.003843 | -2.277789 | -0.089168 | -0.003424 | 0.688992 | 0 | 1 | no_data, no_data, no_data, no_data | 73120000121, no_data, no_data, no_data | 11.400001 | 17.519935 | 283.0 | 3330.789 | 91 | 151626.309 | 18201.09844 | -1.000000e+12 | 0 | 2 | 1 | 0 | LINESTRING (-72.58355 42.08302, -72.58318 42.0... |
4. Visualize and plot a time series of WSE/width/slope longitudinal profiles
Let’s create a time series dataframe from the downstream filtered database
# Retrieve all possible acquisition dates (keeping only YYYY-MM-DD)
dates = np.unique([i[:10] for i in [x for x in SWOT_HR_df['time_str'] if x!='no_data']])
# Create a new database for time series analysis with unique reach_ids
SWOT_dn_trace_time = SWOT_dn_trace.set_index('reach_id').groupby(level=0) \
.apply(lambda df: df.reset_index(drop=True)) \
.unstack().sort_index(axis=1, level=1)
SWOT_dn_trace_time.columns = ['{}_{}'.format(x[0],dates[x[1]]) for x in SWOT_dn_trace_time.columns]Plot a longitudinal profile for selected SWOT variable
# Explore variables you could choose to plot
for var in ["wse","slope","width","len"]:
print(SWOT_dn_trace.columns[SWOT_dn_trace.columns.str.contains(var)])Index(['wse', 'wse_u', 'wse_r_u', 'wse_c', 'wse_c_u', 'area_wse', 'p_wse',
'p_wse_var'],
dtype='object')
Index(['slope', 'slope_u', 'slope_r_u', 'slope2', 'slope2_u', 'slope2_r_u'], dtype='object')
Index(['width', 'width_u', 'width_c', 'width_c_u', 'p_width'], dtype='object')
Index(['p_length'], dtype='object')# Enter variable of interest for plotting
varstr = "wse"The error that outputs the following cell is because of the SWORD database discrepancy noted above. When this is corrected, only the graph should appear without the error.
# Find cumulative length on the longitudinal profile
length_list = np.nan_to_num([SWOT_dn_trace.p_length[SWOT_dn_trace.reach_id == rch].mean()/1000 for rch in rch_dn_list])
cumlength_list = np.cumsum(length_list)
## Plot a longitudinal profile from the downstream tracing database
## Plot a longitudinal profile from the downstream tracing database
plt.figure(figsize=(12,8))
for t in dates:
# Store the quantity of interest (wse, width etc.) at time t
value = SWOT_dn_trace_time.loc[rch_dn_list,varstr+'_'+t]
# Remove set negative values (bad observations) to NaN and forward fill NaNs
value[value < 0] = np.nan
value = value.ffill()
# Plot the data
plt.plot(cumlength_list, value, label = varstr+'_'+t)
plt.xlabel('Downstream Distance (km)')
plt.ylabel(varstr)
plt.legend()--------------------------------------------------------------------------- KeyError Traceback (most recent call last) File /srv/conda/envs/notebook/lib/python3.10/site-packages/pandas/core/indexes/base.py:3803, in Index.get_loc(self, key, method, tolerance) 3802 try: -> 3803 return self._engine.get_loc(casted_key) 3804 except KeyError as err: File /srv/conda/envs/notebook/lib/python3.10/site-packages/pandas/_libs/index.pyx:138, in pandas._libs.index.IndexEngine.get_loc() File /srv/conda/envs/notebook/lib/python3.10/site-packages/pandas/_libs/index.pyx:165, in pandas._libs.index.IndexEngine.get_loc() File pandas/_libs/hashtable_class_helper.pxi:5745, in pandas._libs.hashtable.PyObjectHashTable.get_item() File pandas/_libs/hashtable_class_helper.pxi:5753, in pandas._libs.hashtable.PyObjectHashTable.get_item() KeyError: 'wse_2024-03-06' The above exception was the direct cause of the following exception: KeyError Traceback (most recent call last) Cell In[24], line 12 8 plt.figure(figsize=(12,8)) 9 for t in dates: 10 11 # Store the quantity of interest (wse, width etc.) at time t ---> 12 value = SWOT_dn_trace_time.loc[rch_dn_list,varstr+'_'+t] 14 # Remove set negative values (bad observations) to NaN and forward fill NaNs 15 value[value < 0] = np.nan File /srv/conda/envs/notebook/lib/python3.10/site-packages/pandas/core/indexing.py:1067, in _LocationIndexer.__getitem__(self, key) 1065 if self._is_scalar_access(key): 1066 return self.obj._get_value(*key, takeable=self._takeable) -> 1067 return self._getitem_tuple(key) 1068 else: 1069 # we by definition only have the 0th axis 1070 axis = self.axis or 0 File /srv/conda/envs/notebook/lib/python3.10/site-packages/pandas/core/indexing.py:1247, in _LocIndexer._getitem_tuple(self, tup) 1245 with suppress(IndexingError): 1246 tup = self._expand_ellipsis(tup) -> 1247 return self._getitem_lowerdim(tup) 1249 # no multi-index, so validate all of the indexers 1250 tup = self._validate_tuple_indexer(tup) File /srv/conda/envs/notebook/lib/python3.10/site-packages/pandas/core/indexing.py:967, in _LocationIndexer._getitem_lowerdim(self, tup) 963 for i, key in enumerate(tup): 964 if is_label_like(key): 965 # We don't need to check for tuples here because those are 966 # caught by the _is_nested_tuple_indexer check above. --> 967 section = self._getitem_axis(key, axis=i) 969 # We should never have a scalar section here, because 970 # _getitem_lowerdim is only called after a check for 971 # is_scalar_access, which that would be. 972 if section.ndim == self.ndim: 973 # we're in the middle of slicing through a MultiIndex 974 # revise the key wrt to `section` by inserting an _NS File /srv/conda/envs/notebook/lib/python3.10/site-packages/pandas/core/indexing.py:1312, in _LocIndexer._getitem_axis(self, key, axis) 1310 # fall thru to straight lookup 1311 self._validate_key(key, axis) -> 1312 return self._get_label(key, axis=axis) File /srv/conda/envs/notebook/lib/python3.10/site-packages/pandas/core/indexing.py:1260, in _LocIndexer._get_label(self, label, axis) 1258 def _get_label(self, label, axis: int): 1259 # GH#5567 this will fail if the label is not present in the axis. -> 1260 return self.obj.xs(label, axis=axis) File /srv/conda/envs/notebook/lib/python3.10/site-packages/pandas/core/generic.py:4041, in NDFrame.xs(self, key, axis, level, drop_level) 4039 if axis == 1: 4040 if drop_level: -> 4041 return self[key] 4042 index = self.columns 4043 else: File /srv/conda/envs/notebook/lib/python3.10/site-packages/geopandas/geodataframe.py:1474, in GeoDataFrame.__getitem__(self, key) 1468 def __getitem__(self, key): 1469 """ 1470 If the result is a column containing only 'geometry', return a 1471 GeoSeries. If it's a DataFrame with any columns of GeometryDtype, 1472 return a GeoDataFrame. 1473 """ -> 1474 result = super().__getitem__(key) 1475 # Custom logic to avoid waiting for pandas GH51895 1476 # result is not geometry dtype for multi-indexes 1477 if ( 1478 pd.api.types.is_scalar(key) 1479 and key == "" (...) 1482 and not is_geometry_type(result) 1483 ): File /srv/conda/envs/notebook/lib/python3.10/site-packages/pandas/core/frame.py:3804, in DataFrame.__getitem__(self, key) 3802 if self.columns.nlevels > 1: 3803 return self._getitem_multilevel(key) -> 3804 indexer = self.columns.get_loc(key) 3805 if is_integer(indexer): 3806 indexer = [indexer] File /srv/conda/envs/notebook/lib/python3.10/site-packages/pandas/core/indexes/base.py:3805, in Index.get_loc(self, key, method, tolerance) 3803 return self._engine.get_loc(casted_key) 3804 except KeyError as err: -> 3805 raise KeyError(key) from err 3806 except TypeError: 3807 # If we have a listlike key, _check_indexing_error will raise 3808 # InvalidIndexError. Otherwise we fall through and re-raise 3809 # the TypeError. 3810 self._check_indexing_error(key) KeyError: 'wse_2024-03-06'
Map the longitudinal profile of selected SWOT variable
# Choose a date
date = dates[1]#Set one column as the active geometry in the new database
SWOT_dn_trace_time = SWOT_dn_trace_time.set_geometry("geometry_"+date)
#Set cleaner colorbar bounds for better visualization
vmin = np.percentile([i for i in SWOT_dn_trace_time[varstr+'_'+date] if i>0],5)
vmax = np.percentile([i for i in SWOT_dn_trace_time[varstr+'_'+date] if i>0],95)
# Interactive map
SWOT_dn_trace_time.explore(varstr+'_'+date,
vmin = vmin,
vmax = vmax,
cmap = "Blues", #"Blues",
control_scale = True,
tooltip = varstr+'_'+dates[0], # show "varstr+'_'+dates[0]" value in tooltip (on hover)
popup = True, # show all values in popup (on click)
#tiles = "CartoDB positron", # use "CartoDB positron" tiles
style_kwds=dict(weight=10)
)Make this Notebook Trusted to load map: File -> Trust Notebook
Supplemental
We can also map the upstream traced database
SWOT_up_trace_time = SWOT_up_trace.set_index('reach_id').groupby(level=0) \
.apply(lambda df: df.reset_index(drop=True)) \
.unstack().sort_index(axis=1, level=1)
SWOT_up_trace_time.columns = ['{}_{}'.format(x[0],dates[x[1]]) for x in SWOT_up_trace_time.columns]
SWOT_up_trace_time = SWOT_up_trace_time.set_geometry("geometry_"+date)
#Set cleaner colorbar bounds for better visualization
vmin = np.percentile([i for i in SWOT_up_trace_time[varstr+'_'+date] if i>0],5)
vmax = np.percentile([i for i in SWOT_up_trace_time[varstr+'_'+date] if i>0],95)
# Interactive map
SWOT_up_trace_time.explore(varstr+'_'+date,
vmin = vmin,
vmax = vmax,
cmap = "Blues", #"Blues",
control_scale = True,
tooltip = varstr+'_'+dates[0], # show "varstr+'_'+dates[0]" value in tooltip (on hover)
popup = True, # show all values in popup (on click)
tiles = "CartoDB positron", # use "CartoDB positron" tiles
style_kwds=dict(weight=5)
)Make this Notebook Trusted to load map: File -> Trust Notebook
How to filter out raster data to reveal water bodies?
We can also search by spatial bounding box. Raster tiles make it easy to do this since their footprints in the metadata are smaller. If you want the tile number, you can find it in the kmz file.
# Retrieve granules from all days to find the cycle corresponding to the desired date
raster_results = earthaccess.search_data(short_name = 'SWOT_L2_HR_Raster_100m_2.0',
temporal = ('2024-01-25 00:00:00', '2024-03-29 23:59:59'),
bounding_box = (-72.44, 42.27, -72.15, 42.54)) # Quabbin Reservoir in MassachusettsGranules found: 11Display details of accessed files
print([i for i in raster_results])[Collection: {'Version': '2.0', 'ShortName': 'SWOT_L2_HR_Raster_100m_2.0'}
Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Latitude': 41.952112658860564, 'Longitude': -72.42544746840993}, {'Latitude': 41.952112658860564, 'Longitude': -70.53315398295408}, {'Latitude': 43.348708275316724, 'Longitude': -70.53315398295408}, {'Latitude': 43.348708275316724, 'Longitude': -72.42544746840993}, {'Latitude': 41.952112658860564, 'Longitude': -72.42544746840993}]}}], 'BoundingRectangles': [{'WestBoundingCoordinate': -72.42544746840993, 'SouthBoundingCoordinate': 41.952112658860564, 'EastBoundingCoordinate': -70.53315398295408, 'NorthBoundingCoordinate': 43.348708275316724}]}, 'Track': {'Cycle': 10, 'Passes': [{'Pass': 35, 'Tiles': ['228L', '229L', '230L', '231L', '228R', '229R', '230R', '231R']}]}}}
Temporal coverage: {'RangeDateTime': {'EndingDateTime': '2024-01-26T06:07:09.985Z', 'BeginningDateTime': '2024-01-26T06:06:48.882Z'}}
Size(MB): 92.35498809814453
Data: ['https://archive.swot.podaac.earthdata.nasa.gov/podaac-swot-ops-cumulus-protected/SWOT_L2_HR_Raster_2.0/SWOT_L2_HR_Raster_100m_UTM19T_N_x_x_x_010_035_115F_20240126T060648_20240126T060709_PIC0_01.nc'], Collection: {'Version': '2.0', 'ShortName': 'SWOT_L2_HR_Raster_100m_2.0'}
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Temporal coverage: {'RangeDateTime': {'EndingDateTime': '2024-02-04T15:12:41.048Z', 'BeginningDateTime': '2024-02-04T15:12:19.946Z'}}
Size(MB): 71.12618827819824
Data: ['https://archive.swot.podaac.earthdata.nasa.gov/podaac-swot-ops-cumulus-protected/SWOT_L2_HR_Raster_2.0/SWOT_L2_HR_Raster_100m_UTM18T_N_x_x_x_010_298_040F_20240204T151219_20240204T151241_PIC0_01.nc'], Collection: {'Version': '2.0', 'ShortName': 'SWOT_L2_HR_Raster_100m_2.0'}
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Temporal coverage: {'RangeDateTime': {'EndingDateTime': '2024-02-06T04:29:57.503Z', 'BeginningDateTime': '2024-02-06T04:29:36.398Z'}}
Size(MB): 71.9098253250122
Data: ['https://archive.swot.podaac.earthdata.nasa.gov/podaac-swot-ops-cumulus-protected/SWOT_L2_HR_Raster_2.0/SWOT_L2_HR_Raster_100m_UTM18T_N_x_x_x_010_341_115F_20240206T042936_20240206T042957_PIC0_01.nc'], Collection: {'Version': '2.0', 'ShortName': 'SWOT_L2_HR_Raster_100m_2.0'}
Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Latitude': 41.95211265886055, 'Longitude': -72.7368779330462}, {'Latitude': 41.95211265886055, 'Longitude': -70.84458444759079}, {'Latitude': 43.34870827531672, 'Longitude': -70.84458444759079}, {'Latitude': 43.34870827531672, 'Longitude': -72.7368779330462}, {'Latitude': 41.95211265886055, 'Longitude': -72.7368779330462}]}}], 'BoundingRectangles': [{'WestBoundingCoordinate': -72.7368779330462, 'SouthBoundingCoordinate': 41.95211265886055, 'EastBoundingCoordinate': -70.84458444759079, 'NorthBoundingCoordinate': 43.34870827531672}]}, 'Track': {'Cycle': 10, 'Passes': [{'Pass': 576, 'Tiles': ['078L', '079L', '080L', '081L', '078R', '079R', '080R', '081R']}]}}}
Temporal coverage: {'RangeDateTime': {'EndingDateTime': '2024-02-14T13:34:58.030Z', 'BeginningDateTime': '2024-02-14T13:34:36.929Z'}}
Size(MB): 83.17313957214355
Data: ['https://archive.swot.podaac.earthdata.nasa.gov/podaac-swot-ops-cumulus-protected/SWOT_L2_HR_Raster_2.0/SWOT_L2_HR_Raster_100m_UTM19T_N_x_x_x_010_576_040F_20240214T133436_20240214T133458_PIC0_01.nc'], Collection: {'Version': '2.0', 'ShortName': 'SWOT_L2_HR_Raster_100m_2.0'}
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Temporal coverage: {'RangeDateTime': {'EndingDateTime': '2024-02-16T02:52:14.795Z', 'BeginningDateTime': '2024-02-16T02:51:53.691Z'}}
Size(MB): 88.53574657440186
Data: ['https://archive.swot.podaac.earthdata.nasa.gov/podaac-swot-ops-cumulus-protected/SWOT_L2_HR_Raster_2.0/SWOT_L2_HR_Raster_100m_UTM19T_N_x_x_x_011_035_115F_20240216T025153_20240216T025214_PIC0_01.nc'], Collection: {'Version': '2.0', 'ShortName': 'SWOT_L2_HR_Raster_100m_2.0'}
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Temporal coverage: {'RangeDateTime': {'EndingDateTime': '2024-02-25T11:57:46.348Z', 'BeginningDateTime': '2024-02-25T11:57:25.248Z'}}
Size(MB): 65.02922439575195
Data: ['https://archive.swot.podaac.earthdata.nasa.gov/podaac-swot-ops-cumulus-protected/SWOT_L2_HR_Raster_2.0/SWOT_L2_HR_Raster_100m_UTM18T_N_x_x_x_011_298_040F_20240225T115725_20240225T115746_PIC0_01.nc'], Collection: {'Version': '2.0', 'ShortName': 'SWOT_L2_HR_Raster_100m_2.0'}
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Temporal coverage: {'RangeDateTime': {'EndingDateTime': '2024-02-27T01:15:02.918Z', 'BeginningDateTime': '2024-02-27T01:14:41.812Z'}}
Size(MB): 60.877685546875
Data: ['https://archive.swot.podaac.earthdata.nasa.gov/podaac-swot-ops-cumulus-protected/SWOT_L2_HR_Raster_2.0/SWOT_L2_HR_Raster_100m_UTM18T_N_x_x_x_011_341_115F_20240227T011441_20240227T011502_PIC0_01.nc'], Collection: {'Version': '2.0', 'ShortName': 'SWOT_L2_HR_Raster_100m_2.0'}
Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Latitude': 41.95211265886055, 'Longitude': -72.7368779330462}, {'Latitude': 41.95211265886055, 'Longitude': -70.84458444759079}, {'Latitude': 43.34870827531672, 'Longitude': -70.84458444759079}, {'Latitude': 43.34870827531672, 'Longitude': -72.7368779330462}, {'Latitude': 41.95211265886055, 'Longitude': -72.7368779330462}]}}], 'BoundingRectangles': [{'WestBoundingCoordinate': -72.7368779330462, 'SouthBoundingCoordinate': 41.95211265886055, 'EastBoundingCoordinate': -70.84458444759079, 'NorthBoundingCoordinate': 43.34870827531672}]}, 'Track': {'Cycle': 11, 'Passes': [{'Pass': 576, 'Tiles': ['078L', '079L', '080L', '081L', '078R', '079R', '080R', '081R']}]}}}
Temporal coverage: {'RangeDateTime': {'EndingDateTime': '2024-03-06T10:20:02.252Z', 'BeginningDateTime': '2024-03-06T10:19:41.156Z'}}
Size(MB): 89.60176181793213
Data: ['https://archive.swot.podaac.earthdata.nasa.gov/podaac-swot-ops-cumulus-protected/SWOT_L2_HR_Raster_2.0/SWOT_L2_HR_Raster_100m_UTM19T_N_x_x_x_011_576_040F_20240306T101941_20240306T102002_PIC0_01.nc'], Collection: {'Version': '2.0', 'ShortName': 'SWOT_L2_HR_Raster_100m_2.0'}
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Temporal coverage: {'RangeDateTime': {'EndingDateTime': '2024-03-07T23:37:18.702Z', 'BeginningDateTime': '2024-03-07T23:36:57.601Z'}}
Size(MB): 99.20879173278809
Data: ['https://archive.swot.podaac.earthdata.nasa.gov/podaac-swot-ops-cumulus-protected/SWOT_L2_HR_Raster_2.0/SWOT_L2_HR_Raster_100m_UTM19T_N_x_x_x_012_035_115F_20240307T233657_20240307T233718_PIC0_01.nc'], Collection: {'Version': '2.0', 'ShortName': 'SWOT_L2_HR_Raster_100m_2.0'}
Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Latitude': 41.95211265886055, 'Longitude': -73.96975463204575}, {'Latitude': 41.95211265886055, 'Longitude': -72.07746114659037}, {'Latitude': 43.348708275316724, 'Longitude': -72.07746114659037}, {'Latitude': 43.348708275316724, 'Longitude': -73.96975463204575}, {'Latitude': 41.95211265886055, 'Longitude': -73.96975463204575}]}}], 'BoundingRectangles': [{'WestBoundingCoordinate': -73.96975463204575, 'SouthBoundingCoordinate': 41.95211265886055, 'EastBoundingCoordinate': -72.07746114659037, 'NorthBoundingCoordinate': 43.348708275316724}]}, 'Track': {'Cycle': 12, 'Passes': [{'Pass': 298, 'Tiles': ['078L', '079L', '080L', '081L', '078R', '079R', '080R', '081R']}]}}}
Temporal coverage: {'RangeDateTime': {'EndingDateTime': '2024-03-17T08:42:48.423Z', 'BeginningDateTime': '2024-03-17T08:42:27.323Z'}}
Size(MB): 72.32065773010254
Data: ['https://archive.swot.podaac.earthdata.nasa.gov/podaac-swot-ops-cumulus-protected/SWOT_L2_HR_Raster_2.0/SWOT_L2_HR_Raster_100m_UTM18T_N_x_x_x_012_298_040F_20240317T084227_20240317T084248_PIC0_01.nc'], Collection: {'Version': '2.0', 'ShortName': 'SWOT_L2_HR_Raster_100m_2.0'}
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Temporal coverage: {'RangeDateTime': {'EndingDateTime': '2024-03-27T07:05:05.812Z', 'BeginningDateTime': '2024-03-27T07:04:44.714Z'}}
Size(MB): 89.06233215332031
Data: ['https://archive.swot.podaac.earthdata.nasa.gov/podaac-swot-ops-cumulus-protected/SWOT_L2_HR_Raster_2.0/SWOT_L2_HR_Raster_100m_UTM19T_N_x_x_x_012_576_040F_20240327T070444_20240327T070505_PIC0_01.nc']]Select a single file for visualization
Open file using xarray
ds_raster = xr.open_mfdataset(earthaccess.open([raster_results[1]]), engine='h5netcdf')
ds_rasterOpening 1 granules, approx size: 0.07 GB
using endpoint: https://archive.swot.podaac.earthdata.nasa.gov/s3credentials<xarray.Dataset>
Dimensions: (x: 1574, y: 1574)
Coordinates:
* x (x) float64 5.839e+05 5.84e+05 ... 7.412e+05
* y (y) float64 4.645e+06 4.646e+06 ... 4.803e+06
Data variables: (12/39)
crs object ...
longitude (y, x) float64 dask.array<chunksize=(525, 525), meta=np.ndarray>
latitude (y, x) float64 dask.array<chunksize=(525, 525), meta=np.ndarray>
wse (y, x) float32 dask.array<chunksize=(787, 787), meta=np.ndarray>
wse_qual (y, x) float32 dask.array<chunksize=(1574, 1574), meta=np.ndarray>
wse_qual_bitwise (y, x) float64 dask.array<chunksize=(787, 787), meta=np.ndarray>
... ...
load_tide_fes (y, x) float32 dask.array<chunksize=(787, 787), meta=np.ndarray>
load_tide_got (y, x) float32 dask.array<chunksize=(787, 787), meta=np.ndarray>
pole_tide (y, x) float32 dask.array<chunksize=(787, 787), meta=np.ndarray>
model_dry_tropo_cor (y, x) float32 dask.array<chunksize=(787, 787), meta=np.ndarray>
model_wet_tropo_cor (y, x) float32 dask.array<chunksize=(787, 787), meta=np.ndarray>
iono_cor_gim_ka (y, x) float32 dask.array<chunksize=(787, 787), meta=np.ndarray>
Attributes: (12/49)
Conventions: CF-1.7
title: Level 2 KaRIn High Rate Raster Data Product
source: Ka-band radar interferometer
history: 2024-02-08T18:43:05Z : Creation
platform: SWOT
references: V1.2.1
... ...
x_min: 583900.0
x_max: 741200.0
y_min: 4645400.0
y_max: 4802700.0
institution: CNES
product_version: 01Plot raster data using custom filters to improve image quality
# Plot the data with a filter on water_frac > x, or sig0>50
ds_raster.wse.where(ds_raster.water_frac > 0.5).hvplot.image(y='y', x='x').opts(cmap='Blues', clim=(vmin,vmax), width=1000, height=750)