Reading waterinfo observations

This notebook introduces how to use the hydropandas package to read, process and visualise data from the Waterinfo database. In this notebook the ddlpy package (https://github.com/Deltares/ddlpy) is used to access the waterinfo api. This package can be installed using pip install rws-ddlpy.

[1]:

import contextily as ctx
import pandas as pd

import hydropandas as hpd
from hydropandas.io.waterinfo import get_locations_gdf

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

[2]:

# settings
grootheid_code = None
locatie = "schoonhoven"
proces_type = "meting"
tmin = pd.Timestamp("2020-1-1")
tmax = pd.Timestamp("2020-1-3")
extent = (110000, 125000, 429550, 449900)  # Schoonhoven

[3]:

# get waterinfo observations within an extent
oc = hpd.read_waterinfo(
    extent=extent,
    grootheid_code=grootheid_code,
    proces_type=proces_type,
    locatie=locatie,
    tmin=tmin,
    tmax=tmax,
    keep_all_obs=False,
)
oc

INFO:ddlpy.ddlpy.retrieve_or_load_catalog:Retrieving Waterwebservices catalog, this can take 30 seconds
INFO:hydropandas.io.waterinfo.get_obs_list_from_extent:downloading waterinfo measurements from 13 observation points

100%|██████████| 1/1 [00:00<00:00,  2.02it/s]

INFO:hydropandas.io.waterinfo.get_measurements_ddlpy:No measurements for locatie='schoonhoven', grootheid_code='50%_L', groepering_code='LEVDHD5', parameter_code='NVT' and proces_type='meting' between 2020-01-01 00:00:00 and 2020-01-03 00:00:00


100%|██████████| 1/1 [00:00<00:00,  2.25it/s]

INFO:hydropandas.io.waterinfo.get_measurements_ddlpy:No measurements for locatie='schoonhoven', grootheid_code='70%_L', groepering_code='LEVDHD5', parameter_code='NVT' and proces_type='meting' between 2020-01-01 00:00:00 and 2020-01-03 00:00:00


100%|██████████| 1/1 [00:00<00:00,  2.07it/s]

INFO:hydropandas.io.waterinfo.get_measurements_ddlpy:No measurements for locatie='schoonhoven', grootheid_code='80%_L', groepering_code='LEVDHD5', parameter_code='NVT' and proces_type='meting' between 2020-01-01 00:00:00 and 2020-01-03 00:00:00


100%|██████████| 1/1 [00:00<00:00,  2.11it/s]

INFO:hydropandas.io.waterinfo.get_measurements_ddlpy:No measurements for locatie='schoonhoven', grootheid_code='90%_L', groepering_code='LEVDHD5', parameter_code='NVT' and proces_type='meting' between 2020-01-01 00:00:00 and 2020-01-03 00:00:00


100%|██████████| 1/1 [00:00<00:00,  2.22it/s]

INFO:hydropandas.io.waterinfo.get_measurements_ddlpy:No measurements for locatie='schoonhoven', grootheid_code='HOOGWTDG', groepering_code='', parameter_code='NVT' and proces_type='meting' between 2020-01-01 00:00:00 and 2020-01-03 00:00:00


100%|██████████| 1/1 [00:00<00:00,  2.14it/s]

INFO:hydropandas.io.waterinfo.get_measurements_ddlpy:No measurements for locatie='schoonhoven', grootheid_code='HOOGWTNT', groepering_code='', parameter_code='NVT' and proces_type='meting' between 2020-01-01 00:00:00 and 2020-01-03 00:00:00


100%|██████████| 1/1 [00:00<00:00,  2.02it/s]

INFO:hydropandas.io.waterinfo.get_measurements_ddlpy:No measurements for locatie='schoonhoven', grootheid_code='LAAGWTDG', groepering_code='', parameter_code='NVT' and proces_type='meting' between 2020-01-01 00:00:00 and 2020-01-03 00:00:00


100%|██████████| 1/1 [00:00<00:00,  2.14it/s]

INFO:hydropandas.io.waterinfo.get_measurements_ddlpy:No measurements for locatie='schoonhoven', grootheid_code='NVT', groepering_code='', parameter_code='NVT' and proces_type='meting' between 2020-01-01 00:00:00 and 2020-01-03 00:00:00


100%|██████████| 1/1 [00:00<00:00,  2.10it/s]

INFO:hydropandas.io.waterinfo.get_measurements_ddlpy:No measurements for locatie='schoonhoven', grootheid_code='NVT', groepering_code='LEVDHD5', parameter_code='NVT' and proces_type='meting' between 2020-01-01 00:00:00 and 2020-01-03 00:00:00


100%|██████████| 1/1 [00:00<00:00,  2.17it/s]
100%|██████████| 1/1 [00:00<00:00,  2.09it/s]

INFO:hydropandas.io.waterinfo.get_measurements_ddlpy:No measurements for locatie='schoonhoven', grootheid_code='NVT', groepering_code='', parameter_code='NVT' and proces_type='meting' between 2020-01-01 00:00:00 and 2020-01-03 00:00:00


100%|██████████| 1/1 [00:00<00:00,  1.23it/s]
100%|██████████| 1/1 [00:00<00:00,  2.05it/s]

[3]:

	y	x	source	unit	location	obs
name
Schoonhoven NVT	439419.244242	118061.727209	waterinfo (ddlpy)	DIMSLS	Schoonhoven	WaterlvlObs Schoonhoven NVT -----metadata-----...
Schoonhoven Waterhoogte	439419.244242	118061.727209	waterinfo (ddlpy)	cm NAP	Schoonhoven	WaterlvlObs Schoonhoven Waterhoogte -----metad...
Schoonhoven Waterhoogte (1)	439419.244242	118061.727209	waterinfo (ddlpy)	cm NAP	Schoonhoven	WaterlvlObs Schoonhoven Waterhoogte (1) -----m...

[4]:

# show all measurement types within the extent
gdf_meas = get_locations_gdf()
gdf_meas.loc[
    locatie,
    [
        "Grootheid.Code",
        "Grootheid.Omschrijving",
        "Groepering.Code",
        "Groepering.Omschrijving",
        "Parameter.Code",
        "Parameter.Omschrijving",
        "ProcesType",
    ],
]

INFO:ddlpy.ddlpy.retrieve_or_load_catalog:Loading Waterwebservices catalog from cache

[4]:

	Grootheid.Code	Grootheid.Omschrijving	Groepering.Code	Groepering.Omschrijving	Parameter.Code	Parameter.Omschrijving	ProcesType
Code
schoonhoven	50%_L	50 percentiel van de levendigheid	LEVDHD5	Levendigheid	NVT	NVT	meting
schoonhoven	70%_L	70 percentiel van de levendigheid	LEVDHD5	Levendigheid	NVT	NVT	meting
schoonhoven	80%_L	80 percentiel van de levendigheid	LEVDHD5	Levendigheid	NVT	NVT	meting
schoonhoven	90%_L	90 percentiel van de levendigheid	LEVDHD5	Levendigheid	NVT	NVT	meting
schoonhoven	HOOGWTDG	Hoogwater dag			NVT	NVT	meting
schoonhoven	HOOGWTNT	Hoogwater nacht			NVT	NVT	meting
schoonhoven	LAAGWTDG	Laagwater dag			NVT	NVT	meting
schoonhoven	NVT	NVT			NVT	NVT	meting
schoonhoven	NVT	NVT	LEVDHD5	Levendigheid	NVT	NVT	meting
schoonhoven	NVT	NVT	GETETBRKD2	Getijextreem berekend	NVT	NVT	astronomisch
schoonhoven	NVT	NVT	GETETM2	Getijextremen	NVT	NVT	meting
schoonhoven	NVT	NVT			NVT	NVT	meting
schoonhoven	WATHTE	Waterhoogte			NVT	NVT	astronomisch
schoonhoven	WATHTE	Waterhoogte			NVT	NVT	verwachting
schoonhoven	WATHTE	Waterhoogte			NVT	NVT	meting
schoonhoven	WATHTE	Waterhoogte	GETETBRKD2	Getijextreem berekend	NVT	NVT	astronomisch
schoonhoven	WATHTE	Waterhoogte	GETETM2	Getijextremen	NVT	NVT	meting

[5]:

# get data from a certain location and grootheid
o1 = hpd.WaterlvlObs.from_waterinfo(
    locatie="schoonhoven",
    grootheid_code="WATHTE",
    groepering_code="",
    proces_type="meting",
    tmin=tmin,
    tmax=tmax,
    location_gdf=gdf_meas,  # specifying the location_gdf signficantly speeds up the process
)
o1

100%|██████████| 1/1 [00:00<00:00,  1.21it/s]

[5]:

hydropandas.WaterlvlObs

	Schoonhoven Waterhoogte
y	439419.244242
x	118061.727209
source	waterinfo (ddlpy)
filename
unit	cm NAP
crs	EPSG:28992
location	Schoonhoven

	value
time
2020-01-01 01:00:00	52.0
2020-01-01 01:10:00	49.0
2020-01-01 01:20:00	47.0
2020-01-01 01:30:00	44.0
2020-01-01 01:40:00	42.0
...	...
2020-01-03 00:20:00	90.0
2020-01-03 00:30:00	88.0
2020-01-03 00:40:00	85.0
2020-01-03 00:50:00	83.0
2020-01-03 01:00:00	81.0

289 rows × 1 columns

[6]:

# get data from a certain location and grootheid
o2 = hpd.WaterlvlObs.from_waterinfo(
    locatie="schoonhoven",
    grootheid_code="WATHTE",
    groepering_code="",
    proces_type="astronomisch",
    tmin=tmin,
    tmax=tmax,
    location_gdf=gdf_meas,  # specifying the location_gdf signficantly speeds up the process
)

# plot data
ax = o1["value"].plot(ylabel=o1.unit, label=o1.name, legend=True)
o2["value"].plot(ylabel=o2.unit, label=o2.name, marker="o", legend=True, ax=ax);

100%|██████████| 1/1 [00:00<00:00,  1.20it/s]

../_images/examples_08_waterinfo_6_1.png

[7]:

# get all measurement points within the Netherlands
gdf = hpd.io.waterinfo.get_locations_gdf()
gdf = hpd.io.waterinfo.get_locations_within_extent(
    gdf, extent=(482.06, 306602.42, 284182.97, 637049.52)
)
ax = gdf.plot(figsize=(10, 10))
ctx.add_basemap(ax=ax, crs=28992, alpha=0.5)

../_images/examples_08_waterinfo_7_0.png

Water quality data

The Waterinfo database also contains water quality data

[8]:

# get chloride concentration from the Oosterschelde (Bommenede 1 (b))
o_cl = hpd.WaterlvlObs.from_waterinfo(
    locatie="bommenede",
    parameter_code="Cl",
    tmin="2025-6-25",
    tmax="2025-6-30",
    location_gdf=gdf,
)

# plot data
ax = o_cl["value"].plot(ylabel=o_cl.unit, label=o_cl.name, legend=True)

INFO:hydropandas.io.waterinfo.get_measurements_ddlpy:Multiple observation points match critera, select first one with measurements

100%|██████████| 1/1 [00:01<00:00,  1.03s/it]

../_images/examples_08_waterinfo_9_2.png

Or download all chloride measurements within a certain extent

[9]:

oc = hpd.read_waterinfo(
    extent=(80000, 90000, 429550, 449900),
    parameter_code="Cl",
    tmin="2025-6-1",
    tmax="2025-6-10",
)
oc.plots.interactive_map(popup_width=350)

INFO:hydropandas.io.waterinfo.get_obs_list_from_extent:downloading waterinfo measurements from 8 observation points

100%|██████████| 1/1 [00:01<00:00,  1.86s/it]
100%|██████████| 1/1 [00:01<00:00,  1.99s/it]
100%|██████████| 1/1 [00:00<00:00,  1.97it/s]

INFO:hydropandas.io.waterinfo.get_measurements_ddlpy:No measurements for locatie='vlaardingen', grootheid_code='CONCTTE', groepering_code='', parameter_code='Cl' and proces_type='meting' between 2025-06-01 00:00:00 and 2025-06-10 00:00:00


100%|██████████| 1/1 [00:00<00:00,  2.25it/s]

INFO:hydropandas.io.waterinfo.get_measurements_ddlpy:No measurements for locatie='hoogvliet', grootheid_code='CONCTTE', groepering_code='', parameter_code='Cl' and proces_type='meting' between 2025-06-01 00:00:00 and 2025-06-10 00:00:00


100%|██████████| 1/1 [00:00<00:00,  2.21it/s]

INFO:hydropandas.io.waterinfo.get_measurements_ddlpy:No measurements for locatie='pernis', grootheid_code='CONCTTE', groepering_code='', parameter_code='Cl' and proces_type='meting' between 2025-06-01 00:00:00 and 2025-06-10 00:00:00


100%|██████████| 1/1 [00:00<00:00,  2.20it/s]

INFO:hydropandas.io.waterinfo.get_measurements_ddlpy:No measurements for locatie='europoort.hartelkering', grootheid_code='CONCTTE', groepering_code='', parameter_code='Cl' and proces_type='meting' between 2025-06-01 00:00:00 and 2025-06-10 00:00:00


100%|██████████| 1/1 [00:00<00:00,  2.23it/s]

INFO:hydropandas.io.waterinfo.get_measurements_ddlpy:No measurements for locatie='spijkenisse.oudemaas.brug', grootheid_code='CONCTTE', groepering_code='', parameter_code='Cl' and proces_type='meting' between 2025-06-01 00:00:00 and 2025-06-10 00:00:00


100%|██████████| 1/1 [00:00<00:00,  2.14it/s]

INFO:hydropandas.io.waterinfo.get_measurements_ddlpy:No measurements for locatie='rotterdam.lekhaven', grootheid_code='CONCTTE', groepering_code='', parameter_code='Cl' and proces_type='meting' between 2025-06-01 00:00:00 and 2025-06-10 00:00:00

[9]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Find selection criteria

Very often you don’t know exactly the names of the location, grootheid_code, groepering_code or parameter_code. To get the data that you want you can follow these steps:

get a geodataframe with all the locations in the extent
query the geodataframe to find either a location/grootheid_code/groepering_code or parameter_code
call read_waterinfo with your selection criteria and a tmin and tmax value

[10]:

# 1 download geodataframe with all measurement points in your extent
gdf = hpd.io.waterinfo.get_locations_gdf()
gdf_locatie = hpd.io.waterinfo.get_locations_within_extent(
    gdf, extent=(80000, 90000, 429550, 449900)
)

[11]:

# 2 query the GeoDataFrame

# print unique names
print(f"Unique values of Grootheid Code: \n{gdf_locatie['Grootheid.Code'].unique()}\n")
print(
    f"Unique values of Groepering Code: \n{gdf_locatie['Groepering.Code'].unique()}\n"
)
print(f"Unique values of Parameter Code: \n{gdf_locatie['Parameter.Code'].unique()}\n")
print(f"Unique values of Proces Type: \n{gdf_locatie['ProcesType'].unique()}\n")

# plot locations
ax = gdf_locatie.plot("Naam", figsize=(16, 6), legend=True)
ctx.add_basemap(ax=ax, crs=28992, alpha=0.5)

Unique values of Grootheid Code:
<ArrowStringArray>
[    '50%_L',     '70%_L',     '80%_L',     '90%_L',   'CONCTTE',    'GELDHD',
  'HOOGWTDG',  'HOOGWTNT',  'LAAGWTDG',   'MASSFTE',       'NVT',        'pH',
       'PMV',    'SALNTT', 'STROOMSHD',         'T',  'VERZDGGD',   'VOLMFTE',
    'WATHTE',    'WATOZT',   'WINDRTG',   'WINDSHD',     'ZICHT']
Length: 23, dtype: str

Unique values of Groepering Code:
<ArrowStringArray>
['LEVDHD5', '', 'GETETBRKD2', 'GETETM2', 'GETETOZBRKD2']
Length: 5, dtype: str

Unique values of Parameter Code:
<ArrowStringArray>
[     'NVT',       'GR',       'Cl',      'NKj',      'SO4',      'NH4',
      'NO2',  'sNO3NO2',    'BZV5a',        'F',       'O2',       'OS',
    'sMBAS',      'TOC',     'Ptot',     'sPO4',      'PO4',     'SiO2',
    'CHLFa',  'FolINDX', 'minrlole']
Length: 21, dtype: str

Unique values of Proces Type:
<ArrowStringArray>
['meting', 'astronomisch', 'verwachting']
Length: 3, dtype: str

../_images/examples_08_waterinfo_15_1.png

[12]:

# 3 read data for selection criteria
oc = hpd.read_waterinfo(
    parameter_code="Cl", tmin="2025-6-1", tmax="2025-6-10", location_gdf=gdf_locatie
)
oc

INFO:hydropandas.io.waterinfo.get_obs_list_from_extent:downloading waterinfo measurements from 8 observation points

100%|██████████| 1/1 [00:01<00:00,  1.95s/it]
100%|██████████| 1/1 [00:02<00:00,  2.07s/it]
100%|██████████| 1/1 [00:00<00:00,  2.19it/s]

INFO:hydropandas.io.waterinfo.get_measurements_ddlpy:No measurements for locatie='vlaardingen', grootheid_code='CONCTTE', groepering_code='', parameter_code='Cl' and proces_type='meting' between 2025-06-01 00:00:00 and 2025-06-10 00:00:00


100%|██████████| 1/1 [00:00<00:00,  2.15it/s]

INFO:hydropandas.io.waterinfo.get_measurements_ddlpy:No measurements for locatie='hoogvliet', grootheid_code='CONCTTE', groepering_code='', parameter_code='Cl' and proces_type='meting' between 2025-06-01 00:00:00 and 2025-06-10 00:00:00


100%|██████████| 1/1 [00:00<00:00,  2.20it/s]

INFO:hydropandas.io.waterinfo.get_measurements_ddlpy:No measurements for locatie='pernis', grootheid_code='CONCTTE', groepering_code='', parameter_code='Cl' and proces_type='meting' between 2025-06-01 00:00:00 and 2025-06-10 00:00:00


100%|██████████| 1/1 [00:00<00:00,  2.00it/s]

INFO:hydropandas.io.waterinfo.get_measurements_ddlpy:No measurements for locatie='europoort.hartelkering', grootheid_code='CONCTTE', groepering_code='', parameter_code='Cl' and proces_type='meting' between 2025-06-01 00:00:00 and 2025-06-10 00:00:00


100%|██████████| 1/1 [00:00<00:00,  2.08it/s]

INFO:hydropandas.io.waterinfo.get_measurements_ddlpy:No measurements for locatie='spijkenisse.oudemaas.brug', grootheid_code='CONCTTE', groepering_code='', parameter_code='Cl' and proces_type='meting' between 2025-06-01 00:00:00 and 2025-06-10 00:00:00


100%|██████████| 1/1 [00:00<00:00,  2.10it/s]

INFO:hydropandas.io.waterinfo.get_measurements_ddlpy:No measurements for locatie='rotterdam.lekhaven', grootheid_code='CONCTTE', groepering_code='', parameter_code='Cl' and proces_type='meting' between 2025-06-01 00:00:00 and 2025-06-10 00:00:00

[12]:

	y	x	source	unit	location	obs
name
Spijkenisse, Oude Maas, Brug (massa)Concentratie	430636.140864	82853.508175	waterinfo (ddlpy)	mg/l Cl	Spijkenisse, Oude Maas, Brug	WaterlvlObs Spijkenisse, Oude Maas, Brug (mass...
Rotterdam, Lekhaven (massa)Concentratie	435500.053455	89100.000484	waterinfo (ddlpy)	mg/l Cl	Rotterdam, Lekhaven	WaterlvlObs Rotterdam, Lekhaven (massa)Concent...

Retrieving data from WaterWebservices

In the future, the WaterWebservices will become available. Currently (2024-03-20), they are not yet working. When retrieving measurements, it is always indicated that the maximum number of measurements is exceeded. Even when retrieving measurements for only one day.

Useful information:

[13]:

# import json

# import requests
# from shapely.geometry import Point

# import nlmod

[14]:

# # request catalogus REST API
# url = "https://waterwebservices.beta.rijkswaterstaat.nl/test/METADATASERVICES/OphalenCatalogus"
# body = {"CatalogusFilter": {"Compartimenten": True, "Grootheden": True}}
# headers = {"content-type": "application/json"}
# r = requests.post(url, data=json.dumps(body), headers=headers)

# out = r.json()

[15]:

# # plot locations from REST API
# geometries = [Point(loc["Lon"], loc["Lat"]) for loc in out["LocatieLijst"]]
# gdf = gpd.GeoDataFrame(out["LocatieLijst"], geometry=geometries, crs=4258)
# gdf.to_crs(28992, inplace=True)
# # extent_nl_poly = nlmod.util.extent_to_polygon(
#     [482.06, 306602.42, 284182.97, 637049.52]
# )
# gdf = gdf.loc[gdf.within(extent_nl_poly)]
# ax = gdf.plot(figsize=(10, 10))
# nlmod.plot.add_background_map(ax=ax)

[16]:

# read REST API using an extent (Schoonhoven zuid-west)
# extent_schoon = nlmod.util.polygon_from_extent((117850, 118180, 439550, 439900))
# gdf_schoon = gdf.loc[gdf.within(extent_schoon.buffer(10000))]
# ax = gdf_schoon.plot("Code", figsize=(10, 10), legend=True)
# nlmod.plot.add_background_map(ax=ax, alpha=0.5)

[17]:

# # kies code en laat zien welke parameters erbij horen
# code = "schoonhoven"

# df_meta_locatie = pd.DataFrame(out["AquoMetadataLocatieLijst"]).set_index(
#     "Locatie_MessageID"
# )
# df_meta = pd.DataFrame(out["AquoMetadataLijst"])

# locatie_message_id = gdf.loc[gdf["Code"] == code, "Locatie_MessageID"].iloc[0]

# AquoMetaData_MessageIDs = df_meta_locatie.loc[
#     locatie_message_id, "AquoMetaData_MessageID"
# ]
# if isinstance(AquoMetaData_MessageIDs, int):
#     AquoMetaData_MessageIDs = [AquoMetaData_MessageIDs]
# df_meta.loc[AquoMetaData_MessageIDs]

[18]:

# # request om metingen op te halen
# code = "ameland.nes"
# aquometadata_message_id = 6

# url = "https://waterwebservices.beta.rijkswaterstaat.nl/test/ONLINEWAARNEMINGENSERVICES/OphalenWaarnemingen"
# body = {
#     "Locatie": {"Code": code},
#     "AquoPlusWaarnemingMetadata": {
#         "AquoMetadata": {
#             "Compartiment": {
#                 "Code": df_meta.loc[aquometadata_message_id, "Compartiment"]["Code"]
#             },
#             "Grootheid": {
#                 "Code": df_meta.loc[aquometadata_message_id, "Grootheid"]["Code"]
#             },
#         }
#     },
#     "Periode": {
#         "Begindatumtijd": "2024-06-01T00:00:00.000+01:00",
#         "Einddatumtijd": "2025-01-01T00:00:00.000+01:00",
#     },
# }
# headers = {"content-type": "application/json"}
# r = requests.post(url, data=json.dumps(body), headers=headers)
# if r.status_code == 204:
#     print("No data available")
# else:
#     meas = r.json()
#     print(meas)

[19]:

# # andere poging
# code = "ameland.nes"
# aquometadata_message_id = 6

# url = "https://waterwebservices.beta.rijkswaterstaat.nl/test/ONLINEWAARNEMINGENSERVICES/OphalenWaarnemingen"
# body = {
#     "Locatie": {"Code": code},
#     "AquoPlusWaarnemingMetadata": {
#         "AquoMetadata": {
#             "ProcesType": "verwachting",
#             "Grootheid": {
#                 "Code": df_meta.loc[aquometadata_message_id, "Grootheid"]["Code"]
#             },
#         }
#     },
#     "Periode": {
#         "Begindatumtijd": "2024-01-01T00:00:00.000+01:00",
#         "Einddatumtijd": "2024-01-02T00:00:00.000+01:00",
#     },
# }
# headers = {"content-type": "application/json"}
# r = requests.post(url, data=json.dumps(body), headers=headers)
# if r.status_code == 204:
#     print("No data available")
# else:
#     meas = r.json()
#     print(meas)

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