Reading water connect observations

This notebook introduces how to use the hydropandas package to read, process and visualise data from the South Australia Water Connect database.

[1]:

import contextily as cx
import matplotlib.pyplot as plt
import numpy as np

import hydropandas as hpd
from hydropandas.io.water_connect import get_locations_gdf, get_locations_within_extent

# enabling logging so we can see what happens in the background
hpd.util.get_color_logger("INFO");

[2]:

# get water connect observations within an extent
extent = (
    140.86,
    140.9,
    -38.05,
    -38.00,
)  # (left, right, lower, upper) bounds in lat/lon
oc = hpd.read_waterconnect(extent=extent, tmin="2000-1-1")
oc

INFO:hydropandas.io.water_connect.get_locations_gdf:Initializing Water Connect, this may take several minutes. This only has to be done the first time you use Water Connect via hydropandas.
INFO:hydropandas.io.water_connect.get_obs_list_from_extent:downloading waterconnect data from 62 observation points

obs well: 100%|██████████| 62/62 [00:49<00:00,  1.25it/s]

[2]:
unit screen_top filename screen_bottom x source tube_top y ground_level location tube_nr obs
name
105146 m AHD NaN NaN 140.877410 DEWNR (water connect) NaN -38.017369 13.01 702101091 GroundwaterObs 105146 -----metadata------ crs ...
105147 m AHD NaN NaN 140.887695 DEWNR, DEW (water connect) NaN -38.015521 12.92 702101092 GroundwaterObs 105147 -----metadata------ crs ...
105306 m AHD NaN NaN 140.871336 DEWNR (water connect) NaN -38.018425 12.99 702101251 GroundwaterObs 105306 -----metadata------ crs ...
105319 m AHD NaN NaN 140.897937 DEWNR (water connect) NaN -38.018118 13.29 702101264 GroundwaterObs 105319 -----metadata------ crs ...
187285 m AHD NaN NaN 140.891788 DEWNR (water connect) NaN -38.010617 12.22 702103385 GroundwaterObs 187285 -----metadata------ crs ...
187286 m AHD NaN NaN 140.885841 DEWNR (water connect) NaN -38.010855 11.77 702103386 GroundwaterObs 187286 -----metadata------ crs ...
196601 m AHD NaN NaN 140.877825 DEWNR (water connect) NaN -38.008414 11.80 702103412 GroundwaterObs 196601 -----metadata------ crs ...
232794 m AHD NaN NaN 140.861240 DEWNR (water connect) NaN -38.032324 11.09 702103471 GroundwaterObs 232794 -----metadata------ crs ...
235416 m AHD NaN NaN 140.861229 DEWNR (water connect) NaN -38.032504 10.82 702103480 GroundwaterObs 235416 -----metadata------ crs ...
253247 m AHD NaN NaN 140.876240 DEWNR, DEW (water connect) NaN -38.032026 7.12 702103526 GroundwaterObs 253247 -----metadata------ crs ...
253806 m AHD NaN NaN 140.867643 DEWNR (water connect) NaN -38.045536 6.02 702103539 GroundwaterObs 253806 -----metadata------ crs ...
254397 m AHD NaN NaN 140.882558 DEWNR (water connect) NaN -38.004894 11.41 702103542 GroundwaterObs 254397 -----metadata------ crs ...
303972 m AHD NaN NaN 140.886134 DEWNR (water connect) NaN -38.005655 11.58 702103639 GroundwaterObs 303972 -----metadata------ crs ...
314551 m AHD NaN NaN 140.890507 DEWNR (water connect) NaN -38.028777 27.12 702103656 GroundwaterObs 314551 -----metadata------ crs ...
387793 m AHD NaN NaN 140.866117 DEWNR (water connect) NaN -38.013664 -9999.00 702103725 GroundwaterObs 387793 -----metadata------ crs ...
391456 m AHD NaN NaN 140.879714 DEWNR, DEW (water connect) NaN -38.032349 7.39 702103731 GroundwaterObs 391456 -----metadata------ crs ...
391457 m AHD NaN NaN 140.899277 DEW (water connect) NaN -38.004013 21.07 702103732 GroundwaterObs 391457 -----metadata------ crs ...
391458 m AHD NaN NaN 140.897543 DEW (water connect) NaN -38.014105 13.43 702103733 GroundwaterObs 391458 -----metadata------ crs ... 
[3]:

# plot locations on map
ax = oc.to_gdf().plot(
    figsize=(10, 10),
)
cx.add_basemap(ax=ax, crs=4326)

# add labels
for idx, row in oc.iterrows():
    ax.annotate(text=idx, xy=(row["x"], row["y"]), horizontalalignment="center")
../_images/examples_09_water_connect_3_0.png 
[4]:

# plot measurements from a single observation
o = oc.get_obs(105147)
o["rswl"].plot(marker=".", label=o.name, ylabel=o.unit, legend=True)

[4]:

<Axes: xlabel='obs_date', ylabel='m AHD'>
../_images/examples_09_water_connect_4_1.png 
[5]:

# get data from a certain measurement well based on the drillhole number (dh_no)
o1 = hpd.GroundwaterObs.from_waterconnect(95360)
o1

[5]:

hydropandas.GroundwaterObs

95360
crs EPSG:7844
unit m AHD
screen_top NaN
filename
screen_bottom NaN
x 140.411598
source DEWNR, DEW (water connect)
tube_top NaN
y -36.841521
ground_level 41.73
location 692400271
tube_nr

rswl DHNO network Unit_Number Aquifer Easting Northing Zone Unit_No Obs_No ... swl pressure temperature artesian_ind dry_ind anom_ind pump_ind measured_during data_source Comments
obs_date
1971-06-10 31.83 95360 LLC_NTH 692400271 Qpcb 447537.57 5922546.51 54 6924-271 WLM010 ... 9.91 NaN NaN N NaN N N M DEWNR NaN
1971-06-22 32.33 95360 LLC_NTH 692400271 Qpcb 447537.57 5922546.51 54 6924-271 WLM010 ... 9.41 NaN NaN N NaN N N U DEWNR NaN
1971-08-31 31.75 95360 LLC_NTH 692400271 Qpcb 447537.57 5922546.51 54 6924-271 WLM010 ... 9.99 NaN NaN N NaN N N M DEWNR NaN
1972-01-12 31.71 95360 LLC_NTH 692400271 Qpcb 447537.57 5922546.51 54 6924-271 WLM010 ... 10.03 NaN NaN N NaN N N M DEWNR NaN
1972-03-19 31.44 95360 LLC_NTH 692400271 Qpcb 447537.57 5922546.51 54 6924-271 WLM010 ... 10.30 NaN NaN N NaN N N M DEWNR NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2024-03-13 30.34 95360 LLC_NTH 692400271 Qpcb 447537.57 5922546.51 54 6924-271 WLM010 ... 11.40 NaN NaN NaN NaN N N M DEW NaN
2024-09-10 30.18 95360 LLC_NTH 692400271 Qpcb 447537.57 5922546.51 54 6924-271 WLM010 ... 11.56 NaN NaN NaN NaN N N M DEW NaN
2025-03-18 29.88 95360 LLC_NTH 692400271 Qpcb 447537.57 5922546.51 54 6924-271 WLM010 ... 11.86 NaN NaN NaN NaN N N M DEW NaN
2025-09-10 30.12 95360 LLC_NTH 692400271 Qpcb 447537.57 5922546.51 54 6924-271 WLM010 ... 11.62 NaN NaN NaN NaN N N M DEW NaN
2026-03-31 29.90 95360 LLC_NTH 692400271 Qpcb 447537.57 5922546.51 54 6924-271 WLM010 ... 11.84 NaN NaN NaN NaN N N M DEW NaN

212 rows × 21 columns

[6]:

# get water levels from another drillhole and plot both
o2 = hpd.GroundwaterObs.from_waterconnect(119988)

# plot data
f, ax = plt.subplots(figsize=(10, 4))
o1["rswl"].plot(ylabel=o1.unit, label=o1.name, marker=".", color="b", ax=ax)
o2["rswl"].plot(ylabel=o2.unit, label=o2.name, marker=".", ax=ax, color="g")
ax.axhline(o1.ground_level, ls=":", color="b", label=f"ground level {o1.name}")
ax.axhline(o2.ground_level, ls=":", color="g", label=f"ground level {o2.name}")
ax.legend()

[6]:

<matplotlib.legend.Legend at 0x760b915cb290>
../_images/examples_09_water_connect_6_1.png

Find selection criteria

Very often you don’t know exactly the drillhole numbers (dh_no) of the measurements you want to download and it may be unfeasable to download all the observations in the extent. To get the data that you want you can follow these steps:

  1. get a geodataframe with the metadata of all the locations in the extent

  2. query the geodataframe by any metadata property such as REF_ELEV, STATUS, MAX_DEPTH

  3. request the measurement for the locations you obtained at step 2

[7]:

# step 1
extent = (
    140.86,
    140.9,
    -38.05,
    -38.00,
)  # (left, right, lower, upper) bounds in lat/lon

# get all locations
gdf = get_locations_gdf()
gdf_extent = get_locations_within_extent(gdf, extent)
gdf_extent

[7]:
UNIT_NO NAME EASTING NORTHING ZONE LAT LON REF_ELEV GRND_ELEV HUND ... TITLE_PREF TITLE_VOLU TITLE_FOLI TITLE_ID GRND_ELEV_ HGUID FTYPE NGIS LATEST_REF geometry
DHNO
105107 702101052 NaN 487754.44 5793936.86 54 -38.002154 140.860521 17.108 17.10 None ... CT 5410 833 CT5410/833 21.912327 0 None None None POINT (140.86052 -38.00215)
105108 702101053 NaN 490937.36 5793850.92 54 -38.002966 140.896774 -9999.000 23.05 None ... CT 5930 28 CT5930/28 23.581266 0 None None None POINT (140.89677 -38.00297)
105146 702101091 NaN 489239.43 5792250.81 54 -38.017369 140.877410 -9999.000 13.01 None ... NaN NaN NaN NaN 15.000000 0 None None None POINT (140.87741 -38.01737)
105147 702101092 NaN 490142.00 5792457.00 54 -38.015521 140.887695 13.759 12.92 None ... CT 5844 352 CT5844/352 15.086025 0 None None None POINT (140.88769 -38.01552)
105148 702101093 NaN 490193.38 5792402.91 54 -38.016009 140.888280 13.460 13.19 None ... CT 5460 747 CT5460/747 18.047085 0 None None None POINT (140.88828 -38.01601)
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
314551 702103656 NaN 490390.58 5790986.50 54 -38.028777 140.890507 -9999.000 27.12 None ... CT 6012 332 CT6012/332 27.664576 0 None None None POINT (140.89051 -38.02878)
387793 702103725 NaN 488247.57 5792660.48 54 -38.013664 140.866117 -9999.000 -9999.00 None ... CT 5936 493 CT5936/493 13.339000 0 None None None POINT (140.86612 -38.01366)
391456 702103731 NaN 489443.83 5790588.91 54 -38.032349 140.879714 8.141 7.39 None ... CT 6012 333 CT6012/333 7.243739 0 None None None POINT (140.87971 -38.03235)
391457 702103732 NaN 491157.23 5793735.00 54 -38.004013 140.899277 21.717 21.07 None ... NaN NaN NaN NaN 25.439241 0 None None None POINT (140.89928 -38.00401)
391458 702103733 NaN 491006.30 5792614.99 54 -38.014105 140.897543 14.245 13.43 None ... CT 5460 748 CT5460/748 15.673721 0 None None None POINT (140.89754 -38.01411)

62 rows × 81 columns

[8]:

# 2 query the GeoDataFrame

# print statistics of the reference elevation
gdf_extent.loc[gdf_extent["REF_ELEV"] == -9999, "REF_ELEV"] = np.nan
print(f"statistics of the reference elevation:\n{gdf_extent['REF_ELEV'].describe()}\n")

# print unique names
print(f"unique value in status column:\n {gdf_extent['STAT_DESC'].unique()}\n")

# print statistics of the maximum depth
gdf_extent.loc[gdf_extent["MAX_DEPTH"] == -9999, "MAX_DEPTH"] = np.nan
print(f"statistics of the max depth:\n{gdf_extent['MAX_DEPTH'].describe()}\n")

# select all observation points with some conditions
gdf_selection = gdf_extent.loc[
    (gdf_extent["REF_ELEV"] > 12.5) & (gdf_extent["MAX_DEPTH"] > 15)
]
print("selected data:\n")
display(gdf_selection)

statistics of the reference elevation:
count    11.000000
mean     12.486364
std       4.364845
min       7.025000
25%       9.480500
50%      12.000000
75%      14.002000
max      21.717000
Name: REF_ELEV, dtype: float64

unique value in status column:
 <ArrowStringArray>
[          nan, 'Operational',   'Abandoned',  'Unequipped', 'Not Located',
     'Blocked',  'Backfilled']
Length: 7, dtype: str

statistics of the max depth:
count     61.000000
mean      68.838525
std      102.366295
min        0.100000
25%       15.500000
50%       24.000000
75%       56.000000
max      483.000000
Name: MAX_DEPTH, dtype: float64

selected data:
UNIT_NO NAME EASTING NORTHING ZONE LAT LON REF_ELEV GRND_ELEV HUND ... TITLE_PREF TITLE_VOLU TITLE_FOLI TITLE_ID GRND_ELEV_ HGUID FTYPE NGIS LATEST_REF geometry
DHNO
105107 702101052 NaN 487754.44 5793936.86 54 -38.002154 140.860521 17.108 17.10 None ... CT 5410 833 CT5410/833 21.912327 0 None None None POINT (140.86052 -38.00215)
105147 702101092 NaN 490142.00 5792457.00 54 -38.015521 140.887695 13.759 12.92 None ... CT 5844 352 CT5844/352 15.086025 0 None None None POINT (140.88769 -38.01552)
391457 702103732 NaN 491157.23 5793735.00 54 -38.004013 140.899277 21.717 21.07 None ... NaN NaN NaN NaN 25.439241 0 None None None POINT (140.89928 -38.00401)
391458 702103733 NaN 491006.30 5792614.99 54 -38.014105 140.897543 14.245 13.43 None ... CT 5460 748 CT5460/748 15.673721 0 None None None POINT (140.89754 -38.01411)

4 rows × 81 columns

[9]:

# 3 read data for selection criteria
oc = hpd.read_waterconnect(location_gdf=gdf_selection)
oc

INFO:hydropandas.io.water_connect.get_obs_list_from_extent:downloading waterconnect data from 4 observation points

obs well: 100%|██████████| 4/4 [00:03<00:00,  1.12it/s]

[9]:
unit screen_top filename screen_bottom x source tube_top y ground_level location tube_nr obs
name
105107 m AHD NaN NaN 140.860521 DEWNR (water connect) NaN -38.002154 17.10 702101052 GroundwaterObs 105107 -----metadata------ crs ...
105147 m AHD NaN NaN 140.887695 DEWNR, DEW (water connect) NaN -38.015521 12.92 702101092 GroundwaterObs 105147 -----metadata------ crs ...
391457 m AHD NaN NaN 140.899277 DEW (water connect) NaN -38.004013 21.07 702103732 GroundwaterObs 391457 -----metadata------ crs ...
391458 m AHD NaN NaN 140.897543 DEW (water connect) NaN -38.014105 13.43 702103733 GroundwaterObs 391458 -----metadata------ crs ... 
[10]:

# plot the data on an interactive map
oc["lat"] = oc["y"]
oc["lon"] = oc["x"]
oc.plots.interactive_map(popup_width=350)

ERROR:hydropandas.extensions.plots.interactive_plots:391457 has no data between None and None
INFO:hydropandas.extensions.plots.interactive_map:no iplot available for 391457

[10]:
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