Programmatic access to geospatial layers from the deca-millennial flood project

The relative and absolute changes in 1,000 and 10,000 flood frequency values have been computed for over 500 watersheds using two climate projection large ensembles, CESM1 and CanESM2 (see project overview for more info). These results are available on the PAVICS GeoServer, and this notebook shows how to get access to those files from a programming environment.

%matplotlib inline

import json

import geopandas as gpd
from owslib.wfs import WebFeatureService

# Connect to Ouranos' GeoServer WFS service.
url = "https://pavics.ouranos.ca/geoserver/wfs"
wfs = WebFeatureService(url, version="2.0.0")

typename = "public:decamillenial_flood_CC"
layer = wfs.contents[typename]
print(layer.abstract)
Climate change factors for 1,000 and 10,000-year design floods estimated from the CESM1 and CanESM2 Large Ensembles and the GR4J-Cemaneige hydrological model over the period 2080-2100 compared to 1990-2010.

In the next cell, we’ll download the data in the GeoJSON format, load it into a dictionary and instantiate a GeoDataFrame.

typename = "public:decamillenial_flood_CC"
txt = wfs.getfeature(typename=typename, outputFormat="json").read()
data = json.loads(txt.decode())
gdf = gpd.GeoDataFrame.from_features(data)

Let’s take a look at the top of the table.

gdf.head()
geometry ID name watershed_area NSE CESM1_PWM_T100_MUL CESM1_PWM_T100_P75_MUL CESM1_PWM_T100_P90_MUL CESM1_PWM_T100_P95_MUL CESM1_PWM_T100_P99_MUL ... CanESM2_PWM_T1000_ADD CanESM2_PWM_T1000_P75_ADD CanESM2_PWM_T1000_P90_ADD CanESM2_PWM_T1000_P95_ADD CanESM2_PWM_T1000_P99_ADD CanESM2_PWM_T10000_ADD CanESM2_PWM_T10000_P75_ADD CanESM2_PWM_T10000_P90_ADD CanESM2_PWM_T10000_P95_ADD CanESM2_PWM_T10000_P99_ADD
0 POLYGON ((-111.39170 54.17100, -111.42500 54.1... 06CD002 CHURCHILL RIVER ABOVE OTTER RAPIDS 119000.0 0.715358 -57.343822 -54.547526 -51.714055 -49.651469 -46.376278 ... -0.274774 -0.220550 -0.162439 -0.128182 -0.068190 -0.138914 -0.022957 0.136653 0.224204 0.422348
1 POLYGON ((-134.94580 59.57930, -134.80000 59.6... 09AA014 FANTAIL RIVER AT OUTLET OF FANTAIL LAKE 717.0 0.758993 27.442331 31.577337 35.676450 38.155664 43.461187 ... 3.627438 5.299869 7.212052 8.076060 10.182767 1.583382 5.487793 9.107839 11.806705 15.833487
2 POLYGON ((-130.19170 59.40850, -130.17920 59.4... 10AC004 BLUE RIVER NEAR THE MOUTH 1700.0 0.818958 8.734342 13.139980 17.049680 19.928544 24.816201 ... 4.524804 5.183105 6.138951 6.809163 7.634194 9.541766 11.049454 13.842788 15.958528 18.720224
3 POLYGON ((-126.10000 58.79600, -126.11250 58.8... 10BE007 TROUT RIVER AT KILOMETRE 783.7 ALASKA HIGHWAY 1170.0 0.846326 1.782831 6.310975 10.423199 13.551472 17.247709 ... -0.826701 0.289358 1.253341 1.857471 2.806194 -2.461317 -0.095001 1.757461 3.090589 4.684330
4 POLYGON ((-130.50830 58.44600, -130.52500 58.4... 10AC003 DEASE RIVER AT OUTLET OF DEASE LAKE 1520.0 0.961293 12.827155 17.025618 20.555976 22.540481 26.720056 ... 2.612100 3.271549 3.910924 4.246732 4.884688 3.814419 5.089299 6.404652 7.192629 7.993150

5 rows × 65 columns

And let’s create a basic plot for the values of one of the columns, here the relative change factor for the 100-year event estimated from the CanESM2 Large Ensemble, using the Probability Weighted Moment method to estimate GEV parameters.

col = "CanESM2_PWM_T100_MUL"
gdf.plot(column=col)
<Axes: >
../../_images/a4d0abf38d39e5fe0f3528d5bbb548b34c36f6154b0b48313509d4754c886ddb.png