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1 Tributaries of the Ahr: drainage area's and outflows¶

Let's have a look at the properties of the drainage area's and their outflows, by using GIS software and latitude and longitude coordinates.
There had a lot of preparation work to be done before the data of interest could be merged. A big part is setting up the geographical framework for the AOI area of interest, which was handled with python modules and QGIS.

Author: Van Oproy Kurt

1.0.1 Distance to the measurestation of Altenahr¶

'Latitude' & 'Longitude': source area coords
'Lati' & 'Long': outlet point coords
"Streamcode" is not following all official Streamcode numbers

array([6.85, 6.83, 6.71, 6.75, 6.88, 6.91, 6.93, 6.97, 6.97, 7.11, 6.94,
       6.65])

numpy.ndarray

((12, 2), (12, 2), (12,), (12,), (12, 2))

[[-1.   -1.  ]
 [-0.82 -0.82]
 [-0.64 -0.64]
 ...
 [ 0.64  0.64]
 [ 0.82  0.82]
 [ 1.    1.  ]] [50.54 50.53 50.47 50.44 50.28 50.3  50.34 50.35 50.42 50.46 50.57 50.44]

Correcting some issues with some shapefiles.

c:\program files\python38\lib\site-packages\geopandas\_compat.py:111: UserWarning: The Shapely GEOS version (3.8.1-CAPI-1.13.3) is incompatible with the GEOS version PyGEOS was compiled with (3.10.0-CAPI-1.16.0). Conversions between both will be slow.
  warnings.warn(

c:\program files\python38\lib\site-packages\geopandas\_compat.py:111: UserWarning: The Shapely GEOS version (3.8.1-CAPI-1.13.3) is incompatible with the GEOS version PyGEOS was compiled with (3.10.0-CAPI-1.16.0). Conversions between both will be slow.
  warnings.warn(

1.0.2 Outflow points of the side streams¶

This map shows the direction from source to outlet points of the main tributaries.

array([[ 6.65, 50.44,  6.81, 50.38],
       [ 6.85, 50.54,  6.97, 50.51],
       [ 6.83, 50.53,  6.94, 50.46],
       [ 7.11, 50.46,  7.06, 50.48]])

('EPSG', '26915')

'3.1.0'

cc = CRS('esri:7019') cc CRSError: Invalid projection: esri:7019: (Internal Proj Error: proj_create: crs not found)

<Geographic 2D CRS: EPSG:4258>
Name: ETRS89
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: Europe - onshore and offshore: Albania; Andorra; Austria; Belgium; Bosnia and Herzegovina; Bulgaria; Croatia; Cyprus; Czechia; Denmark; Estonia; Faroe Islands; Finland; France; Germany; Gibraltar; Greece; Hungary; Ireland; Italy; Kosovo; Latvia; Liechtenstein; Lithuania; Luxembourg; Malta; Moldova; Monaco; Montenegro; Netherlands; North Macedonia; Norway including Svalbard and Jan Mayen; Poland; Portugal; Romania; San Marino; Serbia; Slovakia; Slovenia; Spain; Sweden; Switzerland; United Kingdom (UK) including Channel Islands and Isle of Man; Vatican City State.
- bounds: (-16.1, 32.88, 40.18, 84.17)
Datum: European Terrestrial Reference System 1989
- Ellipsoid: GRS 1980
- Prime Meridian: Greenwich

c:\program files\python38\lib\site-packages\geopandas\_compat.py:111: UserWarning: The Shapely GEOS version (3.8.1-CAPI-1.13.3) is incompatible with the GEOS version PyGEOS was compiled with (3.10.0-CAPI-1.16.0). Conversions between both will be slow.
  warnings.warn(

geopandas.geodataframe.GeoDataFrame

<Geographic 2D CRS: EPSG:4258>
Name: ETRS89
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: Europe - onshore and offshore: Albania; Andorra; Austria; Belgium; Bosnia and Herzegovina; Bulgaria; Croatia; Cyprus; Czechia; Denmark; Estonia; Faroe Islands; Finland; France; Germany; Gibraltar; Greece; Hungary; Ireland; Italy; Kosovo; Latvia; Liechtenstein; Lithuania; Luxembourg; Malta; Moldova; Monaco; Montenegro; Netherlands; North Macedonia; Norway including Svalbard and Jan Mayen; Poland; Portugal; Romania; San Marino; Serbia; Slovakia; Slovenia; Spain; Sweden; Switzerland; United Kingdom (UK) including Channel Islands and Isle of Man; Vatican City State.
- bounds: (-16.1, 32.88, 40.18, 84.17)
Datum: European Terrestrial Reference System 1989
- Ellipsoid: GRS 1980
- Prime Meridian: Greenwich

1.0.3 Writing to Shapefile¶

The trick is the use a GeoSeries, not a GeoDF.

Sahr_ws_delin.plot();

Name                                            Watershed-2-1
Area                                                 46040474
geometry    POLYGON ((350595.1477082021 5597153.058980878,...
Name: 4, dtype: object

geopandas.geodataframe.GeoDataFrame

<Projected CRS: EPSG:32632>
Name: WGS 84 / UTM zone 32N
Axis Info [cartesian]:
- E[east]: Easting (metre)
- N[north]: Northing (metre)
Area of Use:
- name: Between 6°E and 12°E, northern hemisphere between equator and 84°N, onshore and offshore. Algeria. Austria. Cameroon. Denmark. Equatorial Guinea. France. Gabon. Germany. Italy. Libya. Liechtenstein. Monaco. Netherlands. Niger. Nigeria. Norway. Sao Tome and Principe. Svalbard. Sweden. Switzerland. Tunisia. Vatican City State.
- bounds: (6.0, 0.0, 12.0, 84.0)
Coordinate Operation:
- name: UTM zone 32N
- method: Transverse Mercator
Datum: World Geodetic System 1984
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich

Finally the Sahrbach shapefile is OK again, and the shape of the Upper and Mid Ahr is corrected as well.

pandas.core.series.Series

Name                                       Watershed-Sahrbach
Area                                                 45600000
geometry    POLYGON ((6.9613972476 50.515177182, 6.9614691...
Name: 1, dtype: object

1.0.3.1 Pluviometer locations¶

{'geometry': [<shapely.geometry.point.Point at 0x29f97e00af0>,
  <shapely.geometry.point.Point at 0x29f957def10>,
  <shapely.geometry.point.Point at 0x29f95be3520>,
  <shapely.geometry.point.Point at 0x29f95ec5190>,
  <shapely.geometry.point.Point at 0x29f95ec51f0>,
  <shapely.geometry.point.Point at 0x29f95ec5220>]}

dict

0    POINT (6.98111 50.48833)
1    POINT (6.85500 50.53354)
Name: geometry, dtype: geometry

0    POINT (6.86763 50.35999)
1    POINT (6.79389 50.26361)
2    POINT (7.03000 50.56000)
3    POINT (6.73948 50.41555)
4    POINT (7.01427 50.40470)
5    POINT (7.11314 50.55130)
Name: geometry, dtype: geometry

geopandas.geoseries.GeoSeries

UpperAhr_delin    POLYGON ((6.84334 50.27222, 6.84306 50.27222, ...
dtype: geometry

1.0.4 Building a Pluviometer GeoDataFrame¶

Index(['Barweiler', 'Freilingen', 'Neuenahr', 'Dreis-Brück', 'Kaltenborn',
       'Esch', 'Ahrbrück', 'Scheuerheck'],
      dtype='object', name='Pluviometer')

0      Barweiler
1     Freilingen
2       Neuenahr
3    Dreis-Brück
4     Kaltenborn
5           Esch
6       Ahrbrück
7    Scheuerheck
Name: Pluviometer, dtype: object

pandas.core.series.Series

Pluviometer
Barweiler      None
Dreis-Brück    None
Esch           None
Freilingen     None
Kaltenborn     None
Neuenahr       None
Name: geometry, dtype: geometry

add index, rainfall and elevation info...

C:\Users\Kurt\AppData\Local\Temp/ipykernel_8916/2434726898.py:1: UserWarning: Column names longer than 10 characters will be truncated when saved to ESRI Shapefile.
  pluvio_geometriedfX.to_file(r"C:\Users\Kurt\Documents\Lekan\pluvio_geometrie.shp")

c:\program files\python38\lib\site-packages\geopandas\_compat.py:111: UserWarning: The Shapely GEOS version (3.10.1-CAPI-1.16.0) is incompatible with the GEOS version PyGEOS was compiled with (3.10.0-CAPI-1.16.0). Conversions between both will be slow.
  warnings.warn(

concatenate the original df with additions df

C:\Users\Kurt\AppData\Local\Temp/ipykernel_18920/3817314484.py:1: UserWarning: Column names longer than 10 characters will be truncated when saved to ESRI Shapefile.
  pluvio_geometriedf8.to_file(r"C:\Users\Kurt\Documents\Lekan\pluvio_geometrie8.shp")

1.0.4.1 Map of rainfall gauges with IDW interpolation.¶

1.0.5 Building an outlet points GeoDataFrame¶

[6.944930767512368, 50.459138381369975] [6.97, 50.51]

{'geometry': [<shapely.geometry.point.Point at 0x26b74bbdaf0>,
  <shapely.geometry.point.Point at 0x26b732e7070>,
  <shapely.geometry.point.Point at 0x26b74cf69a0>,
  <shapely.geometry.point.Point at 0x26b74bb1820>,
  <shapely.geometry.point.Point at 0x26b74820a00>,
  <shapely.geometry.point.Point at 0x26b73271b80>,
  <shapely.geometry.point.Point at 0x26b73271f70>,
  <shapely.geometry.point.Point at 0x26b74cdee50>,
  <shapely.geometry.point.Point at 0x26b74a4c670>,
  <shapely.geometry.point.Point at 0x26b74bbdd00>,
  <shapely.geometry.point.Point at 0x26b74bbdb50>,
  <shapely.geometry.point.Point at 0x26b74bbdc70>]}

geopandas.geodataframe.GeoDataFrame

geopandas.geodataframe.GeoDataFrame

1.0.5.1 fiona: supported_drivers¶

{'AeronavFAA': 'r',
 'ARCGEN': 'r',
 'BNA': 'rw',
 'DXF': 'rw',
 'CSV': 'raw',
 'OpenFileGDB': 'r',
 'ESRIJSON': 'r',
 'ESRI Shapefile': 'raw',
 'FlatGeobuf': 'rw',
 'GeoJSON': 'raw',
 'GeoJSONSeq': 'rw',
 'GPKG': 'raw',
 'GML': 'rw',
 'OGR_GMT': 'rw',
 'GPX': 'rw',
 'GPSTrackMaker': 'rw',
 'Idrisi': 'r',
 'MapInfo File': 'raw',
 'DGN': 'raw',
 'PCIDSK': 'rw',
 'OGR_PDS': 'r',
 'S57': 'r',
 'SEGY': 'r',
 'SUA': 'r',
 'TopoJSON': 'r'}

1.0.5.2 Variable $GMT_CACHEDIR¶

may point to a directory where the user places cached data files downloaded from the GMT data server.
When \C GMT_CACHEDIR is not defined, then the default value \C HOME/.gmt/cache will be assumed. The cache directory can be emptied by running gmt gmt clear cache.

<pygmt.src.config.config at 0x1f00b5fc2b0>

Het systeem kan het opgegeven pad niet vinden.

gmt gmtget [core] 6.2.0 [64-bit] [MP] - Get individual GMT default settings or download data sets

usage: gmt gmtget [-D<download>] [-G<defaultsfile>] [-I<inc>] [-L] [-N] [-Q] [PARAMETER1 PARAMETER2 PARAMETER3 ...] [-V[<level>]]

	For available PARAMETERS, see gmt.conf man page
	OPTIONS:
	-D Download data from the selected GMT server [oceania]
	    Append one of the directories to download:
	      cache: The entire contents of the cache directory.
	      data: The entire contents of the data directory.
	        Append =<planet> to only download the data/<planet> directory.
	        Append =<dataset1,dataset2...> to only download the stated datasets.
	    -Dall downloads both cache and all datasets.
	    Run "gmt docs data" to learn about available data sets.
	-G Set name of specific gmt.conf file to process.
	   [Default looks for file in current directory.  If not found,
	   it looks in the home directory, if not found it uses the GMT defaults].
	-I Limit the download of data sets to grid spacings of <inc> or larger [0].
	-L Write one parameter value per line [Default writes all on one line].
	-N Do NOT convert grids downloaded with -D to netCDF but leave as JP2.
	-Q In conjunction with -D, will list but not download the selected data.
	-V Change the verbosity level (currently t).
	   Choose among 7 levels; each level adds more messages:
	     q - Quiet, not even fatal error messages.
	     e - Error messages only.
	     w - Warnings [Default].
	     t - Timings (time-intensive operations only).
	     i - Informational messages (or just -V).
	     c - Compatibility warnings.
	     d - Debugging messages.
	-^ (or -) Print short synopsis message.
	-+ (or +) Print longer synopsis message.
	-? (or no arguments) Print this usage message.
	--PAR=<value> Temporarily override GMT default setting(s) (repeatable).
	(See gmt.conf man page for GMT default parameters).

'gmt.conf' is not recognized as an internal or external command,
operable program or batch file.

Index(['Bach', 'Distance', 'Lati', 'Long', 'Latitude', 'Longitude'], dtype='object')

1.0.6 Land Use Proportional.xlsx¶

I'll merge this land use table with a table containing distances and geometrical info, so I can plot a map.

1.0.6.1 Adding the "min" ,"max","mean" of the slopes of the drainage area's.¶

C:\Program Files\Python38\lib\site-packages\geopandas\_compat.py:111: UserWarning: The Shapely GEOS version (3.10.1-CAPI-1.16.0) is incompatible with the GEOS version PyGEOS was compiled with (3.10.0-CAPI-1.16.0). Conversions between both will be slow.
  warnings.warn(

C:\Users\Kurt\AppData\Local\Temp/ipykernel_12488/1758689715.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  catch_slope["min" ]= catch_slope["min" ] /1000000   # convers. to %
C:\Users\Kurt\AppData\Local\Temp/ipykernel_12488/1758689715.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  catch_slope["max" ]= catch_slope["max"] /1000000
C:\Users\Kurt\AppData\Local\Temp/ipykernel_12488/1758689715.py:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  catch_slope["mean" ]= catch_slope["mean"] /1000000
C:\Users\Kurt\AppData\Local\Temp/ipykernel_12488/1758689715.py:5: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  catch_slope["zone"]= catch_slope["zone"].astype('int32')

<AxesSubplot:xlabel='Bach'>

We make also a merger with a surface area dataframe. Note that the "Max_Q" is no longer correct due to picking some other rainfall sets.

The outflow for only 1 day was not so important as a feature, as it turned out...

<class 'pandas.core.frame.DataFrame'>
Int64Index: 12 entries, 0 to 11
Data columns (total 20 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   agriculture  12 non-null     object 
 1   grassland    12 non-null     object 
 2   forest       12 non-null     object 
 3   build        12 non-null     object 
 4   bare_soils   12 non-null     object 
 5   slope        12 non-null     object 
 6   slope_2      12 non-null     object 
 7   Bach_x       12 non-null     object 
 8   Distance     12 non-null     float64
 9   Lati         12 non-null     float64
 10  Long         12 non-null     float64
 11  Latitude     12 non-null     float64
 12  Longitude    12 non-null     float64
 13  Streamcode   12 non-null     int32  
 14  Bach_y       12 non-null     object 
 15  Area         12 non-null     float64
 16  Max_Q        12 non-null     float64
 17  Bache        12 non-null     object 
 18  SUM1407      12 non-null     float64
 19  Q_area       12 non-null     float64
dtypes: float64(9), int32(1), object(10)
memory usage: 1.9+ KB

1.0.6.2 Regression plots of land area and maximum outflow.¶

Note that these plots are only illustrative. For a better correlation rainfall-runoff we should add at least the soil texture, and land cover type. Also rainfall intensity, the saturation of upper soil horizon and the depth of subsoil saturation play a role.

Strange this positive relation of forests to higher outflows, but could it be that most forest are standing on steeper grounds than pastures or arable land?

Strange this positive relation of forests to higher outflows, but could it be that most forest are standing on steeper grounds than pastures or arable land

Staffelbach rainfall dataset was that of Neuenahr, which had lower volumes. Conversely, the Freilingen dataset was chosen for Ans- and Trierbach.

sns.lmplot( x='agriculture',y='Q_area', data=df_cd2);

Here the slope was entered in the calculation.

Buildings tend to be build on smoother surface area's for better stability.

sns.lmplot( x='build',y="Q_area", data=df_cd2, palette='tab20', ); # hue='Log_Q_area' ,legend='Bache',

1.0.7 ESA worldcover 2020 data¶

1.0.8 Comparison of Land use proportions from ...¶

the Land use classification map Germany 2019 and the ESA WorldCover 10m 2020 map.

The Land use classification map Germany 2019 is based on a map and data collected spanning the whole year 2019. In addition, this new map has been updated recently.
The ESA WorldCover 10m 2020 map bears in its title "2020", however it hasn't had recent updates, and it also contains data from an older timerange: 2018 until 2019.

It turns out that tree cover is not the same as forested land. When you zoom in you can see that there are small spots of tree cover within builded areas. This is not real forest, it is tree cover of only one or a few trees.

So, over the course of 1-1.5 year, 11 % of the forest of the Adenauerbach region has disappeared.
This is a cut rate that is not sustainable in terms of longterm forest management with tree species like oak and beech.
There might have been done some forced or illegal tree fellings.
Infestations of trees with the European spruce beetle fall under forced fellings.
Keep in mind that any "beetle wood" that was not sold due to poor price offerings, will most of times not appear in the official wood cuttings or wood sales data!

The difference in land use surface area claimed by the "newer" 2020 map.

1.0.9 Terrascope website with Python support¶

!pip3 install --extra-index-url https://artifactory.vgt.vito.be/api/pypi/python-packages/simple terracatalogueclient

Customize and run the following script:

1.0.10 Leaf area index Terrascope¶

The leaf area index (LAI) is a response parameter for photosynthesis as it directly relates to the light interception and gas exchange through the stomata. It is a dimensionless biophysical variable, which is calculated as the ratio of leaf area to per unit ground surface area. Monitoring of the LAI provides an understanding of dynamic changes in productivity, climate impacts on terrestrial ecosystems, energy balance, canopy water interception, and gas exchange. Therefore, understanding of the LAI is a crucial parameter for physiological, ecological, and climatological studies.

The values of this "Terrascope" Leaf area index range from 0 to 8. Picea abies has an effective LAI range of 3 to 8, from 40 % to 0 % defoliation. Defoliation is related to stand age and density. Leaf area index of Norway spruce stands in relation to age and defoliation.

<xarray.DataArray (band: 1, y: 3241, x: 3949)>
[12798709 values with dtype=float32]
Coordinates:
  * band         (band) int32 1
  * x            (x) float64 3.274e+05 3.274e+05 ... 3.669e+05 3.669e+05
  * y            (y) float64 5.6e+06 5.6e+06 5.6e+06 ... 5.568e+06 5.568e+06
    spatial_ref  int32 0
Attributes:
    scale_factor:  1.0
    add_offset:    0.0

[12798709 values with dtype=float32]

array([1])

array([327425., 327435., 327445., ..., 366885., 366895., 366905.])

array([5600035., 5600025., 5600015., ..., 5567655., 5567645., 5567635.])

array(0)

(array([ 557123., 1242962., 1771036., 1579836., 1087121.,  226332.,
          28482.,    1860.,      63.,     104.]),
 array([  0.,  25.,  50.,  75., 100., 125., 150., 175., 200., 225., 250.],
       dtype=float32),
 <BarContainer object of 10 artists>)

LAI histogram of the source map:

(array([19962032., 22822615., 24834645., 24183484., 17146888.,  5203523.,
         1296826.,   134381.,    15914.,     4883.]),
 array([  0.,  25.,  50.,  75., 100., 125., 150., 175., 200., 225., 250.],
       dtype=float32),
 <BarContainer object of 10 artists>)

1.0.10.1 Masking a raster using a shapefile¶

Note: I rescaled the 0-255 range to a 0-8 range, but the technical document mentions the scale as 0.04, so the LAI values here should be corrected by *0.04/(8/255)

1.2750000000000001

<xarray.DataArray (band: 1, y: 6507, x: 5368)>
[34929576 values with dtype=float32]
Coordinates:
  * band         (band) int32 1
  * x            (x) float64 3.18e+05 3.18e+05 3.18e+05 ... 3.717e+05 3.717e+05
  * y            (y) float64 5.618e+06 5.618e+06 ... 5.553e+06 5.553e+06
    spatial_ref  int32 0
Attributes:
    scale_factor:  1.0
    add_offset:    0.0

[34929576 values with dtype=float32]

array([1])

array([318023.81367 , 318033.814409, 318043.815149, ..., 371677.781051,
       371687.781791, 371697.78253 ])

array([5618462.896112, 5618452.895537, 5618442.894962, ..., 5553419.153738,
       5553409.153163, 5553399.152588])

array(0)

(array([ 557048., 1242907., 1771006., 1579822., 1087113.,  226329.,
          28482.,    1860.,      63.,     104.]),
 array([0. , 0.8, 1.6, 2.4, 3.2, 4. , 4.8, 5.6, 6.4, 7.2, 8. ],
       dtype=float32),
 <BarContainer object of 10 artists>)

<xarray.DataArray ()>
array(2.24, dtype=float32)
Coordinates:
    spatial_ref  int32 0

array(2.24, dtype=float32)

array(0)

To get rid of the patches with clouds and cloud shadows, we'll filter out the values under LAI index value 1:

<xarray.DataArray ()>
array(2.43, dtype=float32)
Coordinates:
    spatial_ref  int32 0

array(2.43, dtype=float32)

array(0)

The Upper Ahr area has much more black patches than for ex. the Weidenbach or Dennbach area. I suspect that forced fellings of trees infested with Ips Typographus or the European spruce beetle is the reason. Spruce= genus Picea The dry years of 2017 and 2018 made it easier for the beetle to spread out. On the otherhand, the genus Pinus has had troubles with the damage caused by the Phenops cyanea.

1.0.10.2 The GeoTiff rescaled to a 0 - 8 LAI-index.¶

The LAI-index is well estimated up to values of 6, reads the technical documentation.

0.16666666666666666

<AxesSubplot:xlabel='LAI_class', ylabel='Pct'>

Theoretically as well as practically, strong relationship between LAI and DIFN values exists: $LAI=-2.639*ln(DIFN) + 0.0033$ (r² = 0.998, n=17)

Leaf area index 21 07 2021

(𝐿𝐴𝐼-0.0033)/−2.639 =𝑙𝑛(𝐷𝐼𝐹𝑁)

<xarray.DataArray ()>
array(0.4)
Coordinates:
    spatial_ref  int32 0

array(0.4)

array(0)

<xarray.DataArray ()>
array(0.39)
Coordinates:
    spatial_ref  int32 0

array(0.39)

array(0)

1.0.10.3 Masking a combined raster using a shape: Upper Ahr¶

We clip the raster of forest area 2019 data, overlayed with the LAI index, with the Upper Ahr shapefile. The forest area 2019 data were multiplied by 10 to separate them from non-forest values, which were not altered.

<AxesSubplot:>

c:\program files\python38\lib\site-packages\rasterio\mask.py:88: UserWarning: shapes are outside bounds of raster. Are they in different coordinate reference systems?
  warnings.warn('shapes are outside bounds of raster. '

<xarray.DataArray (band: 1, y: 6507, x: 5368)>
[34929576 values with dtype=float32]
Coordinates:
  * band         (band) int32 1
  * x            (x) float64 3.18e+05 3.18e+05 3.18e+05 ... 3.717e+05 3.717e+05
  * y            (y) float64 5.618e+06 5.618e+06 ... 5.553e+06 5.553e+06
    spatial_ref  int32 0
Attributes:
    scale_factor:  1.0
    add_offset:    0.0

[34929576 values with dtype=float32]

array([1])

array([318023.81367 , 318033.814409, 318043.815149, ..., 371677.781051,
       371687.781791, 371697.78253 ])

array([5618462.896112, 5618452.895537, 5618442.894962, ..., 5553419.153738,
       5553409.153163, 5553399.152588])

array(0)

We only pick the area's classified as forest in 2019 , with a correction on the initial rescaling.

The Weidenbach forest shows more reflectance in the index range 3-4. In short it has healthier vegetation.

0.48705000000000004 7.956 13.8975 10.200000000000001

1.0.11 NDVI index via PROBA-V¶

Normalized difference vegetation index data obtained via remote sensing visible RGB and near IR. These maps and metadata were downloaded via the Terrascope website of VITO.

The Normalized Difference Vegetation Index (NDVI) is an index of plant greenness or photosynthetic activity, and is one of the most commonly used vegetation indices. Vegetation indices are based on the observation that different surfaces reflect different types of light differently. Photosynthetically active vegetation, in particular, absorbs most of the red light that hits it while reflecting much of the near infrared light. Vegetation that is dead or stressed reflects more red light and less near infrared light. Likewise, non-vegetated surfaces have a much more even reflectance across the light spectrum.
By taking the ratio of red and near infrared bands from a remotely-sensed image, an index of vegetation “greenness” can be defined. The NDVI is probably the most common of these ratio indices for vegetation. NDVI is calculated on a per-pixel basis as the normalized difference between the red and near infrared bands from an image, where NIR is the near infrared band value for a cell and RED is the red band value for the cell. NDVI can be calculated for any image that has a red and a near infrared band. The biophysical interpretation of NDVI is the fraction of absorbed photosynthetically active radiation.

Red band mean: 88.31570434570312

c:\program files\python38\lib\site-packages\geopandas\_compat.py:111: UserWarning: The Shapely GEOS version (3.8.1-CAPI-1.13.3) is incompatible with the GEOS version PyGEOS was compiled with (3.10.0-CAPI-1.16.0). Conversions between both will be slow.
  warnings.warn(

(1, 10980, 10980)

CRS.from_epsg(32632)

The value 255 is the No data value, and might be indicating a cloud cover, which the preprocessing algo's assign with a 0.

The area of the Upper Ahr was covered with clouds, as well as some parts of the Ansbach and Armuthsbach. So we better leave them out of the vegetation density assessment here.

gdf_nvdi

1.0.11.1 Spectral NVDI histograms of the streams¶

<AxesSubplot:>

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 13 entries, 0 to 12
Columns: 255 entries, Name to geometry
dtypes: float64(253), geometry(1), object(1)
memory usage: 26.0+ KB

The conversion between the physical values and digital number, is done by a scale and offset value. $PV = \frac {DN- offset}{scale}$
There is also a no data or not valid column.

0          1.0
1       7968.0
2      75977.0
3          1.0
4          0.0
5          0.0
6        154.0
7          0.0
8       6774.0
9     491753.0
10         8.0
11         1.0
12         0.0
Name: NVDI_0, dtype: float64

The percentage of nodata values for each stream.

0     1.76e-04
1     5.73e+00
2     1.17e+01
3     5.24e-04
4     0.00e+00
5     0.00e+00
6     3.35e-02
7     0.00e+00
8     1.24e+00
9     2.42e+01
10    4.25e-03
11    2.69e-04
12    0.00e+00
dtype: float64

'%.5f'

<AxesSubplot:xlabel='Area', ylabel='sum'>

<AxesSubplot:xlabel='Area', ylabel='sum'>

The NDVI has values in the range -0,08 (=no data) and 0,92, but the raster contains values from 0 to 255. So, after rescaling the values of the NDVI-band:

I didn't merge the complete classification rasters of 10 m resolution, due to their size, but I did merge the value rasters.

5610940.0

<open DatasetReader name='P:/Tiles/terrascope_20211228_NVDI/Sentinel2/NDVI_V2/2021/09/09/S2A_20210909T104021_32ULA_NDVI_V200/S2A_20210909T104021_32ULAB_SCENECLASSIFICATION_20M_merged.tif' mode='r'>

<matplotlib.legend.Legend at 0x250465d6430>

Index(['Name', 'NVDI_0', 'NVDI_1', 'NVDI_2', 'NVDI_3', 'NVDI_4', 'NVDI_5',
       'NVDI_6', 'NVDI_7', 'NVDI_8',
       ...
       'NVDI_244', 'NVDI_245', 'NVDI_246', 'NVDI_247', 'NVDI_248', 'NVDI_249',
       'NVDI_250', 'top_nvdi', 'top_area', 'geometry'],
      dtype='object', length=255)

1.0.11.2 NVDI index at 90,88, 86 and 84%¶

<AxesSubplot:xlabel='Pct_sum', ylabel='Name'>

c:\program files\python38\lib\site-packages\geopandas\geodataframe.py:1351: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)

<AxesSubplot:xlabel='Pct_sum', ylabel='Name'>

1.0.11.3 Check increasing non-linearity of NVDI and LAI with combination raster¶

1.1 Correlation of land use versus maximum outflow and surface area.¶

In a second study phase I'll add the rainfall amounts.

1.1.0.1 Precipitation aggregated statistics¶

Most rainfall data is collected by "official" or public pluviometers. However, for the Ahrbrück data this was a privately owned device. They both share the constraint that the online availability of the hourly data is constrained in time, so after 1 or a few months only daily data can be consulted.

This was good data, but we need also hourly data for the days leading up to 14 July.

<AxesSubplot:xlabel='datetime'>

Dreis-Brück          102.70
Nürburg-Barweiler     80.00
Kaltenborn            79.50
Adenau                82.50
Mayschoß              86.10
Freilingen_NRW       115.45
dtype: float64

Reading sheet RFmeans

We can now link the pluvio and stream properties together:

Index(['Armuthsbach', 'Sahrbach', 'Trierbach', 'Adenauerbach', 'Staffelbach',
       'Nohnerbach', 'Liersbach', 'Dennbach', 'Wirftbach', 'Ansbach'],
      dtype='object', name='Bache')

1.1.0.2 The total rainfall on 14-07-2021¶

This is the situation before I found the Ahrbrück pluvio data.

updated pluvio data after applying thiessen polygons on the catchments again:

1.1.1 Heatmap: individual discharges of main Ahr tributaries¶

and also the calculated discharge at Altenahr.

Resampling 15m. -> 1 hour.

We pick highwater times, and normalize the flow rates

C:\Users\Kurt\AppData\Local\Temp/ipykernel_12016/4269201848.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  Upper_["Flow"]= Upper_["Flow rate (m³/s)"]/ Upper_["Flow rate (m³/s)"].max()

'0.12.1'

1.1.2 Corine land cover 2018 data¶

1.2 Long historical rainfall series: Trier¶

There is a cutoff point at midnight, so I'll take a roling sum of 3 days.

Date
1972-12-31    44.0
1973-12-31    32.7
1974-12-31    43.1
1975-12-31    53.4
1976-12-31    31.2
1977-12-31    37.0
1978-12-31    24.4
1979-12-31    31.6
1980-12-31    56.7
1981-12-31    41.6
1982-12-31    32.6
1983-12-31    31.1
1984-12-31    39.3
1985-12-31    37.6
1986-12-31    28.3
1987-12-31    25.4
1988-12-31    28.1
1989-12-31    43.5
1990-12-31    34.2
1991-12-31    20.5
1992-12-31    27.0
1993-12-31    45.5
1994-12-31    46.0
1995-12-31    51.3
1996-12-31    37.9
1997-12-31    29.0
1998-12-31    37.0
1999-12-31    25.7
2000-12-31    48.8
2001-12-31    34.0
2002-12-31    27.5
2003-12-31    33.1
2004-12-31    34.2
2005-12-31    44.5
2006-12-31    36.2
2007-12-31    23.6
2008-12-31    33.1
2009-12-31    41.2
2010-12-31    21.1
2011-12-31    33.3
2012-12-31    28.5
2013-12-31    38.6
2014-12-31    32.0
2015-12-31    26.5
2016-12-31    31.9
2017-12-31    29.3
2018-12-31    43.4
2019-12-31    55.4
2020-12-31    28.8
2021-12-31    63.9
Freq: A-DEC, Name: Niederschlag, dtype: float64

Date
2021-07-14    63.9
1980-08-16    56.7
2019-07-11    55.4
1975-08-21    53.4
1995-01-22    51.3
2000-05-11    48.8
2000-07-07    47.3
1994-07-18    46.0
1993-07-30    45.5
2005-07-29    44.5
1972-11-17    44.0
1989-08-07    43.5
2018-09-05    43.4
1974-07-13    43.1
1972-07-24    42.9
2018-05-31    42.6
1981-04-26    41.6
2009-06-15    41.2
1984-05-05    39.3
2013-05-19    38.6
Name: Niederschlag, dtype: float64

<AxesSubplot:xlabel='Niederschlag', ylabel='Count'>

Date
2021-07-14    93.1
2021-07-15    80.0
1980-08-16    74.9
1980-08-17    74.9
1995-01-22    73.3
1995-01-23    71.8
2018-06-01    70.2
2003-01-03    69.5
1981-04-28    69.3
2004-05-08    68.7
2003-01-02    67.5
2000-07-09    66.4
1981-04-27    66.3
1984-09-08    64.9
2021-07-16    63.9
1975-08-22    62.4
1975-08-23    62.3
1978-12-30    61.7
2016-05-30    61.7
1978-12-31    61.2
Name: N3, dtype: float64

Date
1995-01-25    103.8
2021-07-14     93.9
2021-07-15     93.1
2021-07-16     93.1
2016-06-02     92.8
1995-01-26     92.5
2003-01-04     90.1
2003-01-05     87.0
2016-06-01     84.9
1984-09-08     84.7
1995-01-22     84.6
2003-01-02     84.2
1995-01-24     81.8
1998-10-31     81.0
1998-10-28     80.7
2021-07-17     80.0
1978-12-31     79.2
1993-12-23     78.5
1980-08-16     78.1
2000-07-08     77.2
Name: N5, dtype: float64

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 20 entries, 1975-08-22 to 2021-07-16
Data columns (total 1 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   N3      20 non-null     float64
dtypes: float64(1)
memory usage: 876.0 bytes

Date
1972-12-31    44.0
1973-12-31    32.7
1974-12-31    43.1
1975-12-31    53.4
1976-12-31    31.2
1977-12-31    37.0
1978-12-31    24.4
1979-12-31    31.6
1980-12-31    56.7
1981-12-31    41.6
1982-12-31    32.6
1983-12-31    31.1
1984-12-31    39.3
1985-12-31    37.6
1986-12-31    28.3
1987-12-31    25.4
1988-12-31    28.1
1989-12-31    43.5
1990-12-31    34.2
1991-12-31    20.5
1992-12-31    27.0
1993-12-31    45.5
1994-12-31    46.0
1995-12-31    51.3
1996-12-31    37.9
1997-12-31    29.0
1998-12-31    37.0
1999-12-31    25.7
2000-12-31    48.8
2001-12-31    34.0
2002-12-31    27.5
2003-12-31    33.1
2004-12-31    34.2
2005-12-31    44.5
2006-12-31    36.2
2007-12-31    23.6
2008-12-31    33.1
2009-12-31    41.2
2010-12-31    21.1
2011-12-31    33.3
2012-12-31    28.5
2013-12-31    38.6
2014-12-31    32.0
2015-12-31    26.5
2016-12-31    31.9
2017-12-31    29.3
2018-12-31    43.4
2019-12-31    55.4
2020-12-31    28.8
2021-12-31    63.9
Freq: A-DEC, Name: Niederschlag, dtype: float64

The topic was not outflow, but rainfall in this case.

<AxesSubplot:xlabel='Return Period (n+1)/m', ylabel='PEAK_FLOW_VALUE_Q(cfs)'>

Reference: "Flood flow frequency", Bulletin 17B

1.2.1 Feature importances study¶

Index(['wkt_geom', 'Treecover', 'Grassland', 'Cropland', 'Built-up',
       'Bare_spars', 'Waterbody', 'LULC_TOTAL', 'Unnamed: 0', 'agriculture',
       'grassland', 'forest', 'build', 'bare_soils', 'slope', 'sqrt_s',
       'Distance', 'Lati', 'Long', 'Latitude', 'Longitude', 'Streamcode',
       'Bach_y', 'Area', 'Max_Q', 'Bache', 'SUM1407', 'Q_area', 'Log_Q_area'],
      dtype='object')

Another idea: Insert the average, min and maximum slope of the catchment:

1.2.1.1 Alternatively, load a better table:¶

array([0.     , 0.00004, 0.00257, 0.     , 0.00001, 0.     , 0.00003,
       0.00001, 0.     , 0.00029, 0.     , 0.03368, 0.00066])

Or take water data from ESA table:

Note: last run was using the ESA and updated CLC map combination.

What contributes tho most to a maximum outflow of a stream in the AHr valley?

array(['agriculture', 'grassland', 'forest', 'build', 'bare_soils',
       'Treecover', 'Grassland', 'Cropland', 'Built-up', 'Bare_spars',
       'Waterbody', 'slope', 'sqrt_s', 'Area', 'Q_area'], dtype=object)

Index(['Adenauerbach', 'Ansbach', 'Armuthsbach', 'Dennbach', 'Liersbach',
       'Nohnerbach', 'Sahrbach', 'Staffelbach', 'Trierbach', 'Upper Ahr',
       'Vischelbach', 'Weidenbach', 'Wirftbach'],
      dtype='object', name='Bache')

apply a scaler

array([[-0.97, -0.58,  0.67, ..., -0.26,  0.08, -0.22],
       [-0.43, -0.25,  0.38, ...,  0.65, -0.65,  1.78],
       [ 1.36,  0.65, -0.88, ..., -0.37,  0.31, -0.08],
       ...,
       [-0.49,  0.14, -0.05, ..., -0.44, -0.56, -0.06],
       [-1.33, -1.06,  1.24, ...,  0.57, -0.27,  0.22],
       [ 0.53,  0.53, -0.63, ...,  0.41, -0.48, -0.7 ]])

Or work via a pipeline

Pipeline(steps=[('standardscaler', StandardScaler()),
                ('linearregression', LinearRegression())])

0.4080211574906166

Index(['agriculture', 'grassland', 'forest', 'build', 'bare_soils', 'slope_x',
       'Area', 'max', 'mean', 'SUM1407'],
      dtype='object')

1.2.1.2 Variable Importance Plot¶

Note: sum of rainfall must be added

Pipeline(steps=[('standardscaler', StandardScaler())])

explained_variance:  0.2087
mean_squared_log_error:  0.4949
r2:  -0.2372
MAE:  84.8102
MSE:  19954.0113
RMSE:  141.2587

7 KFolds results:

5 KFolds results:

The surface area of the catchment, the percentage grassland, the slope of the stream, build-up area's and bare soils are the 5 most important features.

RandomForestRegressor(max_depth=13, max_features='sqrt', n_estimators=800)

Index(['agriculture', 'grassland', 'forest', 'build', 'bare_soils',
       'Treecover', 'Grassland', 'Cropland', 'Built-up', 'Bare_spars',
       'Waterbody', 'slope', 'sqrt_s', 'Area', 'Max_Q', 'Q_area'],
      dtype='object')

1.2.2 Permutation of feature importances¶

RandomForestRegressor(bootstrap=False, n_estimators=1900, n_jobs=3, verbose=1)

array([[-0.52, -0.77,  0.78, ...,  1.67, -0.06,  0.03],
       [ 1.61,  0.71, -1.04, ..., -0.3 , -0.61, -0.59],
       [ 0.53,  0.53, -0.63, ..., -0.7 ,  0.31,  0.41],
       ...,
       [-1.35, -2.28,  1.97, ...,  0.81,  2.56,  2.32],
       [ 1.31,  1.75, -1.6 , ...,  0.17, -1.21, -1.38],
       [-0.43, -0.25,  0.38, ...,  1.78,  0.56,  0.65]])

array([[-0.52, -0.77,  0.78, ...,  1.67, -0.06,  0.03],
       [ 1.61,  0.71, -1.04, ..., -0.3 , -0.61, -0.59],
       [ 0.53,  0.53, -0.63, ..., -0.7 ,  0.31,  0.41],
       ...,
       [-1.35, -2.28,  1.97, ...,  0.81,  2.56,  2.32],
       [ 1.31,  1.75, -1.6 , ...,  0.17, -1.21, -1.38],
       [-0.43, -0.25,  0.38, ...,  1.78,  0.56,  0.65]])

array([[ 1.36,  0.65, -0.88,  0.15,  0.31, -0.35, -0.08, -0.42, -0.37],
       [ 0.32,  0.03, -0.15,  0.37,  0.13, -0.34, -0.15, -0.69, -0.68]])

array([ 0.52, -0.09])

RandomForestRegressor(bootstrap=False, n_estimators=1900, n_jobs=3, verbose=1)

array([0.  , 0.32, 0.32, 0.01, 0.03, 0.32, 0.  , 0.  , 0.  ])

Mid Ahr 9.48e-02 0.30 0.56 2.59e-02 1.41e-02

rf.predict([[9.48e-02 ,0.30 ,0.56,2.59e-02,1.41e-02 ]]) # land use, area, mm RF, np.log(680/ 115)

220.28719533659807

Weidenbach 4.28e-03 0.18 0.80 8.79e-03 5.95e-03

290.7183749754727

Now I got a value to back-test the Lekan runoff model.

1.2.2.1 squared error¶

1.2.2.2 absolute error¶

1.2.2.3 SelectKBest¶

1.4 Import Raster to PostgreSQL/PostGIS and view in QGIS¶

The command parameters are:

<SRID>—Spatial reference identifier
<PATH/TO/RASTER FILE>—Full path to the raster file (such as C:\MyData\land\landuse.tif)
<SCHEMA>—Target schema where the new raster table will be created
<DBTABLE>—New database table to be created (usually the same name as the source raster file)
<DATABASE>—Target database where the table will be created

The following example uses raster2pgsql to create an input file and upload it into 100x100 tiles. The -I option will create a spatial GiST index on the raster column after the table is created. This is strongly recommended for improved performance. The -C option will apply the raster constraints (SRID, pixel size and so on) to ensure the new raster table is correctly registered in the raster_columns view.

raster2pgsql -s 4236 -I -C -M *.tif -F -t 100x100 public.demelevation | psql -d gisdb

http://xarray.pydata.org/en/stable/user-guide/data-structures.html#dataset

1.4.1 Make a Xarray¶

-99.83

<xarray.Dataset>
Dimensions:       (lon: 1201, lat: 1201, grid_mapping: 12)
Coordinates:
  * lon           (lon) float64 6.0 6.001 6.002 6.003 ... 6.998 6.998 6.999 7.0
  * lat           (lat) float64 50.0 50.0 50.0 50.0 50.0 ... 51.0 51.0 51.0 51.0
  * grid_mapping  (grid_mapping) |S1 b's' b'c' b'a' b'l' ... b'c' b't' b'o' b'r'
Data variables:
    z             (lat, lon) float32 ...
Attributes:
    Conventions:  CF-1.7
    title:        Produced by grdconvert
    history:      grdconvert C:/Users/Kurt/.gmt/server/earth/earth_relief/ear...
    GMT_version:  6.2.0 [64-bit] [MP]

array([6.      , 6.000833, 6.001667, ..., 6.998333, 6.999167, 7.      ])

array([50.      , 50.000833, 50.001667, ..., 50.998333, 50.999167, 51.      ])

array([b's', b'c', b'a', b'l', b'e', b'_', b'f', b'a', b'c', b't', b'o', b'r'],
      dtype='|S1')

[1442401 values with dtype=float32]

	geometry
0	POLYGON ((6.57550 50.42198, 6.57271 50.39538, ...
1	POLYGON ((6.92448 50.27242, 6.84534 50.27173, ...
2	POLYGON ((6.59683 50.43010, 6.61911 50.44731, ...

	Name	Area	id	geometry
0	Watershed-Nohner	4.89e+07	1.46e+10	MULTIPOLYGON (((6.82428 50.35194, 6.82428 50.3...
1	Watershed-Nohner	4.89e+07	NaN	POLYGON ((6.79358 50.36074, 6.79351 50.36074, ...
2	None	NaN	1.46e+10	POLYGON ((6.82404 50.35554, 6.82760 50.35932, ...

	Name	Area	id	fid	area_	geometry
0	Watershed-Nohner	48889366.0	1.455515e+10	NaN	NaN	MULTIPOLYGON (((6.82428 50.35194, 6.82428 50.3...
1	Watershed-Nohner	48889366.0	NaN	NaN	NaN	POLYGON ((6.79358 50.36074, 6.79351 50.36074, ...
2	None	NaN	1.455515e+10	NaN	NaN	POLYGON ((6.82404 50.35554, 6.82760 50.35932, ...
3	None	NaN	NaN	1.0	6.537983e+07	POLYGON ((6.92729 50.44280, 6.93719 50.44457, ...

	Name	Area	geometry
0	Watershed-1 residual	28772994	POLYGON ((6.940 50.456, 6.945 50.458, 6.939 50...
1	Watershed-1-1	850805542	POLYGON ((6.930 50.453, 6.930 50.453, 6.940 50...
2	Watershed-1	103493493	POLYGON ((6.985 50.517, 6.986 50.517, 6.988 50...
3	Watershed-2 residual	702957502	POLYGON ((352601.717 5603769.296, 352626.249 5...
4	Watershed-2-1	46040474	POLYGON ((350595.148 5597153.059, 350595.148 5...
5	Watershed-2	748925746	POLYGON ((345806.323 5597979.719, 345830.855 5...
6	Watershed-MiddleAhrvalley	59021317	POLYGON ((343698.322 5585916.143, 345362.956 5...

	Name	Area	geometry
0	Watershed-1 residual	28772994	POLYGON ((6.940 50.456, 6.945 50.458, 6.939 50...
1	Watershed-1-1	850805542	POLYGON ((6.930 50.453, 6.930 50.453, 6.940 50...
2	Watershed-1	103493493	POLYGON ((6.985 50.517, 6.986 50.517, 6.988 50...
3	Watershed-2 residual	702957502	POLYGON ((352601.717 5603769.296, 352626.249 5...
4	Watershed-2-1	46040474	POLYGON ((350595.148 5597153.059, 350595.148 5...
5	Watershed-2	748925746	POLYGON ((345806.323 5597979.719, 345830.855 5...
6	Watershed-MiddleAhrvalley	59021317	POLYGON ((343698.322 5585916.143, 345362.956 5...

	Bach	Distance	Lati	Long	Latitude	Longitude	Streamcode
Stream
Sahr	Sahrbach	2.17	50.51	6.97	50.54	6.85	27188
Liers	Liersbach	10.00	50.46	6.94	50.53	6.83	271872
Armuths	Armuthsbach	18.80	50.45	6.87	50.47	6.71	271856
Ans	Ansbach	22.00	50.43	6.85	50.44	6.75	271854
Nohner	Nohnerbach	35.50	50.36	6.83	50.28	6.88	271812
Trier	Trierbach	34.00	50.36	6.83	50.30	6.91	27184
Wirft	Wirftbach	22.00	50.37	6.84	50.34	6.93	271899
Adenauer	Adenauerbach	11.20	50.44	6.93	50.35	6.97	27186
Denn	Dennbach	6.30	50.48	6.99	50.42	6.97	2718748
Staffel	Staffelbach	10.20	50.48	7.03	50.46	7.11	271874
Vischel	Vischelbach	1.00	50.51	6.98	50.57	6.94	271892
Weiden	Weidenbach	8.30	50.48	7.02	50.39	7.03	2718741

	Latitude	Longitude	Height
Pluviometer
Barweiler	50.36	6.87	488.17
Freilingen	50.42	6.74	530.00
Neuenahr	50.55	7.11	130.00
Dreis-Brück	50.26	6.79	525.00
Kaltenborn	50.40	7.01	541.00
Esch	50.56	7.03	274.00
Ahrbrück	50.49	6.98	192.00
Scheuerheck	50.53	6.86	435.00

	geometry
0	POINT (6.86763 50.35999)
1	POINT (6.79389 50.26361)
2	POINT (7.03000 50.56000)
3	POINT (6.73948 50.41555)
4	POINT (7.01427 50.40470)
5	POINT (7.11314 50.55130)

	geometry
Ahrbrück	POINT (6.98111 50.48833)
Scheuerheck	POINT (6.85500 50.53354)

	geometry
0	POINT (6.97000 50.51000)
1	POINT (6.94493 50.45914)
2	POINT (7.02543 50.48225)
3	POINT (6.86918 50.45092)
4	POINT (6.85482 50.43257)
5	POINT (6.82811 50.36239)
6	POINT (6.83068 50.36311)
7	POINT (6.84294 50.37133)
8	POINT (6.93399 50.44348)
9	POINT (6.98600 50.47694)
10	POINT (6.97600 50.51162)
11	POINT (6.81000 50.38000)

	Flow rate (m³/s)Ans
Time_lag
2021-06-10 00:46:00	0.0
2021-06-10 01:20:00	0.0
2021-06-10 01:35:00	0.0
2021-06-10 01:46:00	0.0
2021-06-10 01:50:00	0.0
...	...
2021-10-13 16:54:00	0.0
2021-10-13 17:09:00	0.0
2021-10-13 17:16:00	0.0
2021-10-13 17:24:00	0.0
2021-10-13 17:39:00	0.0

	Mid Ahr	UpperAhr	Armuthsbach	Sahrbach	Trierbach	Adenauerbach	Staffelbach	Weidenbach	Nohnerbach	Liersbach	Dennbach	Wirftbach	Ansbach	Vischelbach	Rivervalley	Unnamed: 16	Unnamed: 17
Land Use
agriculture	0.09	0.14	0.14	0.09	0.11	2.23e-02	3.05e-02	4.28e-03	0.15	4.49e-02	3.30e-03	0.10	4.97e-02	0.05	0.08	53565.0	0.08
grassland	0.30	0.40	0.32	0.26	0.36	2.15e-01	2.58e-01	1.76e-01	0.32	2.00e-01	7.66e-02	0.31	2.42e-01	0.27	0.24	162796.0	0.24
forest	0.56	0.41	0.51	0.61	0.47	7.24e-01	6.96e-01	8.05e-01	0.49	7.40e-01	9.06e-01	0.54	6.85e-01	0.62	0.62	416011.0	0.62
build	0.03	0.03	0.02	0.03	0.04	3.57e-02	1.09e-02	8.79e-03	0.02	8.65e-03	6.26e-03	0.04	1.57e-02	0.01	0.03	22432.0	0.03
bare_soils	0.01	0.02	0.01	0.01	0.01	4.95e-03	5.21e-03	5.95e-03	0.02	6.30e-03	8.11e-03	0.02	8.11e-03	0.01	0.01	7335.0	0.01
slope	0.40	0.52	1.08	1.06	0.84	1.70e+00	1.67e+00	2.33e+00	0.96	2.30e+00	1.68e+00	1.49	1.37e+00	1.45	0.40	6893.0	nodata
slope_2	0.53	1.08	1.71	1.50	1.03	1.79e+00	2.99e+00	2.43e+00	1.56	1.99e+00	4.10e+00	2.30	2.50e+00	1.66	0.44	NaN	NaN

Land Use	agriculture	grassland	forest	build	bare_soils	slope	slope_2
Mid Ahr	0.09	0.3	0.56	0.03	0.01	0.4	0.53
UpperAhr	0.14	0.4	0.41	0.03	0.02	0.52	1.08
Armuthsbach	0.14	0.32	0.51	0.02	0.01	1.08	1.71
Sahrbach	0.09	0.26	0.61	0.03	0.01	1.06	1.5
Trierbach	0.11	0.36	0.47	0.04	0.01	0.84	1.03
Adenauerbach	0.02	0.22	0.72	0.04	0.0	1.7	1.79
Staffelbach	0.03	0.26	0.7	0.01	0.01	1.67	2.99
Weidenbach	0.0	0.18	0.8	0.01	0.01	2.33	2.43
Nohnerbach	0.15	0.32	0.49	0.02	0.02	0.96	1.56
Liersbach	0.04	0.2	0.74	0.01	0.01	2.3	1.99
Dennbach	0.0	0.08	0.91	0.01	0.01	1.68	4.1
Wirftbach	0.1	0.31	0.54	0.04	0.02	1.49	2.3
Ansbach	0.05	0.24	0.68	0.02	0.01	1.37	2.5
Vischelbach	0.05	0.27	0.62	0.01	0.01	1.45	1.66
Rivervalley	0.08	0.24	0.62	0.03	0.01	0.4	0.44
Unnamed: 16	53565.0	162796.0	416011.0	22432.0	7335.0	6893.0	NaN
Unnamed: 17	0.08	0.24	0.62	0.03	0.01	nodata	NaN

	zone	m²	sum	count	min	max	mean	geometry
0	27181	2.52e+08	5.14e+11	474197	0.00	7.76e+06	1.08e+06	None
1	271899	2.48e+07	5.82e+10	46641	4814.83	7.69e+06	1.25e+06	None
2	2718741	3.71e+07	1.68e+11	69621	16775.95	9.49e+06	2.41e+06	None
3	271812	3.16e+07	5.27e+10	59435	3169.89	5.75e+06	8.86e+05	None
4	271892	1.89e+07	4.47e+10	35467	4745.09	7.21e+06	1.26e+06	None
5	2718748	1.92e+07	8.56e+10	36061	17462.82	9.95e+06	2.38e+06	None
6	271874	3.70e+07	1.36e+11	69550	4245.12	7.51e+06	1.95e+06	None
7	27184	5.55e+07	1.10e+11	104321	5898.14	6.73e+06	1.06e+06	None
8	271856	7.29e+07	1.87e+11	136895	1281.80	1.03e+07	1.36e+06	None
9	271854	1.46e+07	4.47e+10	27419	12871.94	9.03e+06	1.63e+06	None
10	27186	5.70e+07	2.25e+11	107100	4360.62	9.98e+06	2.10e+06	None
11	27188	4.60e+07	1.28e+11	86330	3245.12	9.56e+06	1.48e+06	None
12	271872	2.64e+07	1.07e+11	49517	8374.52	9.08e+06	2.15e+06	None

	zone	min	max	mean
0	27181	0.00e+00	7.76	1.08
1	271899	4.81e-03	7.69	1.25
2	2718741	1.68e-02	9.49	2.41
3	271812	3.17e-03	5.75	0.89
4	271892	4.75e-03	7.21	1.26
5	2718748	1.75e-02	9.95	2.38
6	271874	4.25e-03	7.51	1.95
7	27184	5.90e-03	6.73	1.06
8	271856	1.28e-03	10.33	1.36
9	271854	1.29e-02	9.03	1.63
10	27186	4.36e-03	9.98	2.10
11	27188	3.25e-03	9.56	1.48
12	271872	8.37e-03	9.08	2.15

	wkt_geom	Treecover	Grassland	Cropland	Built-up	Bare_spars	Waterbody	LULC_TOTAL
Name
Adenauerbach	MultiPolygon (((4103890.61303603276610374 3029...	0.84	0.14	4.89e-03	1.76e-02	1.54e-03	1.00e-06	1167920
Ansbach	MultiPolygon (((4090406.74759881710633636 3042...	0.74	0.24	1.36e-02	6.92e-03	8.61e-04	0.00e+00	301917
Armuthsbach	MultiPolygon (((4086674.71472339006140828 3044...	0.57	0.38	3.90e-02	9.93e-03	1.28e-03	0.00e+00	1493201
Dennbach	MultiPolygon (((4105954.62636257708072662 3038...	0.96	0.03	1.07e-04	2.75e-03	6.60e-04	0.00e+00	392272
Liersbach	MultiPolygon (((4098323.02225150354206562 3050...	0.80	0.19	5.11e-03	2.03e-03	2.54e-04	0.00e+00	539988
Nohnerbach	MultiPolygon (((4092905.83836966659873724 3032...	0.56	0.33	9.53e-02	8.65e-03	1.07e-03	9.00e-06	652011
Sahrbach	MultiPolygon (((4100654.05724611924961209 3048...	0.66	0.32	1.18e-02	1.22e-02	1.28e-03	2.00e-06	945593
Staffelbach	MultiPolygon (((4110741.36151165282353759 3042...	0.77	0.22	1.03e-02	4.03e-03	1.12e-03	1.00e-06	758791
Trierbach	MultiPolygon (((4095046.19109569722786546 3031...	0.56	0.32	9.13e-02	2.52e-02	2.14e-03	0.00e+00	1136419
Upper Ahr	MultiPolygon (((4096023.07086387882009149 3022...	0.48	0.39	1.07e-01	1.70e-02	4.19e-03	7.59e-04	5178833
Vischelbach	MultiPolygon (((4104826.14673754805698991 3050...	0.70	0.28	9.06e-03	8.62e-03	1.51e-03	0.00e+00	386803
Weidenbach	MultiPolygon (((4108346.57140650320798159 3034...	0.88	0.12	1.48e-03	3.20e-03	5.35e-04	0.00e+00	763915
Wirftbach	MultiPolygon (((4101582.24368390860036016 3029...	0.61	0.32	4.55e-02	2.42e-02	1.16e-03	1.37e-04	511305

	Forest	Pastures	Arable	Buildings	Bare_sparse
Adenauerbach	0.11	-7.72e-02	-1.74e-02	-1.81e-02	-3.41e-03
Ansbach	0.06	-4.63e-03	-3.60e-02	-8.75e-03	-7.25e-03
Armuthsbach	0.06	6.10e-02	-1.00e-01	-1.27e-02	-1.20e-02
Dennbach	0.06	-4.46e-02	-3.20e-03	-3.51e-03	-7.45e-03
Liersbach	0.06	-1.17e-02	-3.98e-02	-6.62e-03	-6.05e-03
Nohnerbach	0.07	1.40e-02	-5.63e-02	-9.69e-03	-2.16e-02
Sahrbach	0.05	5.20e-02	-7.50e-02	-1.30e-02	-1.10e-02
Staffelbach	0.07	-4.05e-02	-2.02e-02	-6.91e-03	-4.09e-03
Trierbach	0.09	-3.80e-02	-1.89e-02	-1.82e-02	-1.19e-02
Upper Ahr	0.07	-1.54e-02	-2.98e-02	-1.15e-02	-1.70e-02
Vischelbach	0.08	4.75e-03	-3.73e-02	-2.50e-03	-9.42e-03
Weidenbach	0.07	-6.03e-02	-2.80e-03	-5.59e-03	-5.42e-03
Wirftbach	0.07	1.20e-02	-5.21e-02	-1.23e-02	-1.56e-02

	Forest	Pastures	Arable	Buildings	Bare_sparse
Adenauerbach	0.16	-0.36	-0.78	-0.51	-0.69
Ansbach	0.08	-0.02	-0.73	-0.56	-0.89
Armuthsbach	0.12	0.19	-0.72	-0.56	-0.90
Dennbach	0.07	-0.58	-0.97	-0.56	-0.92
Liersbach	0.09	-0.06	-0.89	-0.77	-0.96
Nohnerbach	0.15	0.04	-0.37	-0.53	-0.95
Sahrbach	0.08	0.20	-0.86	-0.51	-0.90
Staffelbach	0.10	-0.16	-0.66	-0.63	-0.79
Trierbach	0.19	-0.11	-0.17	-0.42	-0.85
Upper Ahr	0.18	-0.04	-0.22	-0.40	-0.80
Vischelbach	0.12	0.02	-0.80	-0.22	-0.86
Weidenbach	0.09	-0.34	-0.65	-0.64	-0.91
Wirftbach	0.13	0.04	-0.53	-0.34	-0.93

	Forest	Pastures	Arable	Buildings	Bare_sparse
Adenauerbach	15.71	-35.87	-78.02	-50.70	-68.88
Ansbach	8.31	-1.92	-72.59	-55.84	-89.38
Armuthsbach	12.41	19.36	-71.93	-56.21	-90.39
Dennbach	6.52	-58.24	-96.76	-56.13	-91.87
Liersbach	8.63	-5.85	-88.63	-76.54	-95.97
Nohnerbach	15.06	4.37	-37.13	-52.83	-95.29
Sahrbach	7.66	19.64	-86.35	-51.49	-89.56
Staffelbach	10.28	-15.73	-66.10	-63.14	-78.51
Trierbach	18.88	-10.61	-17.17	-41.96	-84.74
Upper Ahr	17.90	-3.81	-21.85	-40.47	-80.19
Vischelbach	12.49	1.74	-80.46	-22.49	-86.16
Weidenbach	9.20	-34.22	-65.37	-63.56	-91.01
Wirftbach	12.60	3.92	-53.35	-33.73	-93.10

	Unnamed: 0	agriculture	grassland	forest	build	bare_soils	slope	sqrt_s	Distance	Lati	Long	Latitude	Longitude	Streamcode	Bach_y	Area	Max_Q	Bache	SUM1407	Q_area	Log_Q_area
Bach_x
Armuthsbach	0	1.39e-01	0.32	0.51	2.27e-02	1.33e-02	1.71	1.31	18.80	50.45	6.87	50.47	6.71	271856	Armuths	72.60	139.48	Armuthsbach	140.85	1.92	-4.37
Sahrbach	1	8.68e-02	0.26	0.61	2.52e-02	1.23e-02	1.50	1.22	2.17	50.51	6.97	50.54	6.85	27188	Sahr	45.60	86.19	Sahrbach	140.85	1.89	-4.37
Trierbach	2	1.10e-01	0.36	0.47	4.34e-02	1.40e-02	1.03	1.01	34.00	50.36	6.83	50.30	6.91	27184	Trier	55.06	84.78	Trierbach	80.00	1.54	-3.78
Adenauerbach	3	2.23e-02	0.22	0.72	3.57e-02	4.95e-03	1.79	1.34	11.20	50.44	6.93	50.35	6.97	27186	Adenauer	58.45	108.72	Adenauerbach	80.00	1.86	-4.29
Staffelbach	4	3.05e-02	0.26	0.70	1.09e-02	5.21e-03	2.99	1.73	10.20	50.48	7.03	50.46	7.11	271874	Staffel	38.00	38.12	Staffelbach	79.00	1.00	-4.88
Weidenbach	5	4.28e-03	0.18	0.80	8.79e-03	5.95e-03	2.43	1.56	8.30	50.48	7.02	50.39	7.03	2718741	Weiden	37.90	77.85	Weidenbach	79.23	2.05	-4.50
Nohnerbach	6	1.52e-01	0.32	0.49	1.83e-02	2.27e-02	1.56	1.25	35.50	50.36	6.83	50.28	6.88	271812	Nohner	31.26	57.05	Nohnerbach	102.60	1.82	-3.99
Liersbach	7	4.49e-02	0.20	0.74	8.65e-03	6.30e-03	2.00	1.41	10.00	50.46	6.94	50.53	6.83	271872	Liers	28.94	77.87	Liersbach	115.45	2.69	-4.59
Dennbach	8	3.30e-03	0.08	0.91	6.26e-03	8.11e-03	4.10	2.02	6.30	50.48	6.99	50.42	6.97	2718748	Denn	20.00	46.28	Dennbach	99.99	2.31	-4.28
Wirftbach	9	9.76e-02	0.31	0.54	3.65e-02	1.68e-02	2.30	1.52	22.00	50.37	6.84	50.34	6.93	271899	Wirft	25.20	41.57	Wirftbach	80.00	1.65	-4.28
Ansbach	10	4.97e-02	0.24	0.68	1.57e-02	8.11e-03	2.50	1.58	22.00	50.43	6.85	50.44	6.75	271854	Ans	15.00	41.13	Ansbach	115.45	2.74	-4.05
Vischelbach	11	4.64e-02	0.27	0.62	1.11e-02	1.09e-02	1.66	1.29	1.00	50.51	6.98	50.57	6.94	271892	Vischel	20.70	40.00	Vischelbach	131.00	1.93	-4.59
Upper Ahr	12	1.36e-01	0.40	0.41	2.85e-02	2.12e-02	1.08	1.04	35.00	50.37	6.80	50.43	6.66	27181	UpperAhr	252.34	382.54	Upper Ahr	108.00	2.03	-4.59

	Forest_km2	Pastures_km2	Arable_km2	Buildings_km2	Bare_sparse_km2
Adenauerbach	6.65	-4.51	-1.02	-1.06	-0.20
Ansbach	0.85	-0.07	-0.54	-0.13	-0.11
Armuthsbach	4.59	4.43	-7.26	-0.93	-0.87
Dennbach	1.18	-0.89	-0.06	-0.07	-0.15
Liersbach	1.85	-0.34	-1.15	-0.19	-0.18
Nohnerbach	2.29	0.44	-1.76	-0.30	-0.68
Sahrbach	2.13	2.37	-3.42	-0.59	-0.50
Staffelbach	2.72	-1.54	-0.77	-0.26	-0.16
Trierbach	4.91	-2.09	-1.04	-1.00	-0.65
Upper Ahr	18.46	-3.88	-7.52	-2.91	-4.28
Vischelbach	1.62	0.10	-0.77	-0.05	-0.19
Weidenbach	2.81	-2.29	-0.11	-0.21	-0.21
Wirftbach	1.73	0.30	-1.31	-0.31	-0.39

	LAI_class	Aantal pixels	Vlak (m²)
0	0.5	824088	8.24e+07
1	2.0	3946594	3.95e+08
2	4.0	1708485	1.71e+08
3	5.5	15149	1.52e+06
4	6.5	312	3.12e+04
5	7.5	106	1.06e+04

	LAI_class	Aantal pixels	Vlak (m²)	Pct
0	0.5	824088	8.24e+07	1.27e-01
1	2.0	3946594	3.95e+08	6.08e-01
2	4.0	1708485	1.71e+08	2.63e-01
3	5.5	15149	1.52e+06	2.33e-03
4	6.5	312	3.12e+04	4.80e-05
5	7.5	106	1.06e+04	1.63e-05

	value	count	m²	geometry
0	0.03	1	100.09	None
1	0.06	5	500.46	None
2	0.10	27	2702.51	None
3	0.13	31	3102.88	None
4	0.16	35	3503.25	None
...	...	...	...	...
346	15.41	1	100.09	None
347	15.44	2	200.19	None
348	15.54	1	100.09	None
349	15.98	1	100.09	None
350	16.62	1	100.09	None

	value	count	m²	LAImean_10_9	m2_sum	geometry
387	15.47	5	500.07	0.4	2.49e+08	None
388	15.50	8	800.11	0.4	2.49e+08	None
389	15.54	3	300.04	0.4	2.49e+08	None
390	15.57	6	600.08	0.4	2.49e+08	None
391	15.60	8	800.11	0.4	2.49e+08	None
392	15.63	4	400.05	0.4	2.49e+08	None
393	15.66	4	400.05	0.4	2.49e+08	None
394	15.70	2	200.03	0.4	2.49e+08	None
395	15.73	2	200.03	0.4	2.49e+08	None
396	15.76	1	100.01	0.4	2.49e+08	None
397	15.79	2	200.03	0.4	2.49e+08	None
398	15.82	1	100.01	0.4	2.49e+08	None
399	15.86	1	100.01	0.4	2.49e+08	None
400	15.89	1	100.01	0.4	2.49e+08	None
401	15.95	1	100.01	0.4	2.49e+08	None
402	15.98	1	100.01	0.4	2.49e+08	None
403	16.11	1	100.01	0.4	2.49e+08	None
404	16.14	3	300.04	0.4	2.49e+08	None
405	16.18	1	100.01	0.4	2.49e+08	None
406	16.24	1	100.01	0.4	2.49e+08	None

	value	count	m²	LAImean_10_9	m2_sum	geometry
0	1.00e-03	776	77610.20	0.4	2.49e+08	None
1	3.30e-02	1777	177723.37	0.4	2.49e+08	None
2	6.50e-02	2651	265134.86	0.4	2.49e+08	None
3	9.70e-02	3367	336744.27	0.4	2.49e+08	None
4	1.29e-01	3949	394951.93	0.4	2.49e+08	None
5	1.61e-01	4589	458960.34	0.4	2.49e+08	None
6	1.93e-01	5131	513167.47	0.4	2.49e+08	None
7	2.25e-01	5805	580576.33	0.4	2.49e+08	None
8	2.57e-01	6223	622381.83	0.4	2.49e+08	None
9	2.89e-01	7052	705292.73	0.4	2.49e+08	None
10	3.21e-01	8093	809406.42	0.4	2.49e+08	None
11	3.53e-01	9272	927321.92	0.4	2.49e+08	None

	value	count	m²	geometry
193	10.51	106	10609.84	None
194	10.54	135	13512.54	None
195	10.58	131	13112.16	None
196	10.61	149	14913.84	None
197	10.64	152	15214.11	None
...	...	...	...	...
346	15.41	1	100.09	None
347	15.44	2	200.19	None
348	15.54	1	100.09	None
349	15.98	1	100.09	None
350	16.62	1	100.09	None

	Name	NVDI_-0.08	NVDI_-0.076	NVDI_-0.072	NVDI_-0.068	NVDI_-0.064	NVDI_-0.06	NVDI_-0.056	NVDI_-0.052	NVDI_-0.048	NVDI_-0.044	NVDI_-0.04	NVDI_-0.036	NVDI_-0.032	NVDI_-0.028	NVDI_-0.024	NVDI_-0.02	NVDI_-0.016	NVDI_-0.012	...	NVDI_0.876	NVDI_0.88	NVDI_0.884	NVDI_0.888	NVDI_0.892	NVDI_0.896	NVDI_0.9	NVDI_0.904	NVDI_0.908	NVDI_0.912	NVDI_0.916	NVDI_0.92	Pct_090	Pct_088	Pct_086	Pct_084	geometry
0	Adenauerbach	1	0	0	0	0	0	0	0	0	1	3	1	1	4	5	5	4	6	...	17061	15630	13632	10994	7859	4581	2302	923	357	102	28	10	4.05e-03	2.75e-02	3.41e-02	0.03	MULTIPOLYGON (((354088.022 5578396.254, 353454...
1	Ansbach	7968	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	...	3794	3545	3053	2383	1715	1156	742	516	333	237	135	156	5.05e-03	2.41e-02	3.09e-02	0.03	MULTIPOLYGON (((340437.976 5590492.134, 340466...
2	Armuthsbach	75977	0	0	2	0	2	0	0	2	3	1	0	1	1	1	2	1	3	...	18977	18743	17697	16379	14777	12030	9438	6666	4099	2430	1078	669	1.30e-02	2.58e-02	2.56e-02	0.02	MULTIPOLYGON (((336666.718 5593327.899, 337209...
3	Dennbach	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	...	8024	7649	6852	5844	4679	3589	2657	1635	952	475	221	92	1.39e-02	4.00e-02	4.21e-02	0.03	MULTIPOLYGON (((356035.394 5586944.013, 355697...
4	Liersbach	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	...	9766	9857	9429	9010	8279	7431	5927	4263	2639	1455	601	320	2.25e-02	3.75e-02	3.35e-02	0.03	MULTIPOLYGON (((348243.733 5598632.615, 348420...
5	Nohnerbach	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	...	6287	5152	4342	3673	2878	2303	1746	1272	786	421	183	54	5.50e-03	1.62e-02	3.08e-02	0.03	MULTIPOLYGON (((343066.104 5581068.545, 343060...
6	Sahrbach	154	1	2	2	2	0	3	2	5	1	4	6	3	4	6	6	6	4	...	16551	15932	14971	13547	11966	10143	8281	6181	4325	2962	1848	2096	1.80e-02	3.46e-02	3.23e-02	0.02	MULTIPOLYGON (((350595.148 5597153.059, 350595...
7	Staffelbach	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	...	9547	8458	7269	5837	4471	3347	2528	1788	1398	992	564	467	6.84e-03	2.29e-02	2.85e-02	0.03	MULTIPOLYGON (((360764.389 5591179.783, 360762...
8	Trierbach	6774	2	1	4	2	6	2	5	7	9	10	15	10	9	13	7	10	15	...	8895	7781	6257	4740	3555	2608	1878	1095	609	325	177	53	3.39e-03	1.41e-02	2.44e-02	0.03	MULTIPOLYGON (((345215.410 5580426.185, 345481...
9	Upper Ahr	491753	33	31	40	34	38	47	48	51	48	52	73	73	57	75	84	101	103	...	14428	12526	10884	8991	7257	5617	3898	2498	1577	1032	625	893	1.55e-03	4.97e-03	8.55e-03	0.01	MULTIPOLYGON (((346319.429 5571123.271, 346299...
10	Vischelbach	8	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	...	8094	8312	8207	7830	7048	5888	5093	3600	2620	1736	1161	1406	2.70e-02	4.41e-02	3.33e-02	0.02	MULTIPOLYGON (((354745.883 5598732.589, 354726...
11	Weidenbach	1	0	0	0	0	0	0	0	1	0	1	0	0	0	1	0	0	0	...	11081	9641	7599	5694	3855	2463	1346	659	246	93	21	7	3.62e-03	2.59e-02	3.37e-02	0.03	MULTIPOLYGON (((358474.215 5583513.930, 358222...
12	Wirftbach	0	2	5	6	5	5	5	4	4	3	5	3	8	8	7	9	11	6	...	4454	3852	3024	2282	1746	1265	971	631	351	167	89	30	3.90e-03	1.55e-02	2.85e-02	0.03	MULTIPOLYGON (((351783.517 5578107.266, 351763...

	NVDI_0.68	NVDI_0.684	NVDI_0.688	NVDI_0.692	NVDI_0.696	NVDI_0.7	NVDI_0.704	NVDI_0.708	NVDI_0.712	NVDI_0.716	NVDI_0.72	NVDI_0.724	NVDI_0.728	NVDI_0.732	NVDI_0.736	NVDI_0.74	NVDI_0.744	NVDI_0.748	NVDI_0.752	...	NVDI_0.848	NVDI_0.852	NVDI_0.856	NVDI_0.86	NVDI_0.864	NVDI_0.868	NVDI_0.872	NVDI_0.876	NVDI_0.88	NVDI_0.884	NVDI_0.888	NVDI_0.892	NVDI_0.896	NVDI_0.9	NVDI_0.904	NVDI_0.908	NVDI_0.912	NVDI_0.916	sum
Name
Adenauerbach	1120	1176	1209	1246	1308	1303	1486	1488	1484	1579	1649	1835	2027	2080	2340	2536	2734	3070	3307	...	19606	19692	19702	19414	19387	18948	17948	17061	15630	13632	10994	7859	4581	2302	923	357	102	28	506958
Ansbach	343	332	329	334	403	377	388	420	444	488	506	596	608	663	701	867	903	1033	1145	...	4751	4716	4542	4537	4389	4255	4091	3794	3545	3053	2383	1715	1156	742	516	333	237	135	126851
Armuthsbach	1834	1871	1947	2068	2106	2061	2250	2279	2355	2404	2356	2462	2597	2812	2841	3009	3217	3266	3431	...	18163	18523	18517	18611	18856	19014	19000	18977	18743	17697	16379	14777	12030	9438	6666	4099	2430	1078	515103
Dennbach	410	396	400	434	456	489	508	532	549	538	605	632	694	705	716	758	827	863	990	...	6668	7101	7490	8038	8283	8381	8304	8024	7649	6852	5844	4679	3589	2657	1635	952	475	221	179046
Liersbach	699	664	688	775	790	823	828	874	989	938	987	1054	1042	1184	1203	1210	1296	1274	1383	...	7917	7855	8596	8812	9293	9530	9619	9766	9857	9429	9010	8279	7431	5927	4263	2639	1455	601	239103
Nohnerbach	943	972	974	1043	1133	1107	1197	1196	1277	1356	1411	1558	1514	1627	1711	1892	1917	2133	2206	...	10750	10310	10085	9771	9353	8349	7460	6287	5152	4342	3673	2878	2303	1746	1272	786	421	183	263726
Sahrbach	1616	1675	1759	1708	1769	1922	1821	1900	2034	2107	2149	2220	2312	2387	2439	2513	2694	2779	2842	...	12407	13134	13900	14852	15373	16061	16344	16551	15932	14971	13547	11966	10143	8281	6181	4325	2962	1848	394582
Staffelbach	1613	1682	1733	1848	1933	1918	1999	2117	2319	2346	2480	2634	2690	2855	3024	3214	3398	3637	3883	...	10007	10053	10505	10520	10613	10450	10004	9547	8458	7269	5837	4471	3347	2528	1788	1398	992	564	321139
Trierbach	1881	1998	1976	2141	2171	2255	2337	2595	2519	2635	2835	2931	2961	3233	3311	3585	3984	4160	4428	...	14957	14734	14201	13528	12762	11704	10521	8895	7781	6257	4740	3555	2608	1878	1095	609	325	177	425604
Upper Ahr	11708	11910	12175	12215	12376	12685	12822	12930	13253	13433	13811	14040	14459	14781	15188	15470	16246	16710	16950	...	25126	24416	23081	21574	19982	18192	16271	14428	12526	10884	8991	7257	5617	3898	2498	1577	1032	625	1010614
Vischelbach	454	480	484	504	506	534	599	594	605	605	621	630	676	678	658	738	755	751	827	...	5016	5289	5798	6280	6793	7187	7688	8094	8312	8207	7830	7048	5888	5093	3600	2620	1736	1161	164848
Weidenbach	1123	1098	1121	1154	1232	1305	1361	1323	1454	1551	1623	1838	1934	2050	2190	2426	2615	2798	2982	...	11142	11760	12238	12548	12732	12747	12022	11081	9641	7599	5694	3855	2463	1346	659	246	93	21	313837
Wirftbach	716	737	763	775	749	861	927	956	991	1048	1117	1154	1224	1227	1407	1447	1518	1622	1806	...	7676	7468	7291	7087	6497	5806	5311	4454	3852	3024	2282	1746	1265	971	631	351	167	89	204855

	NVDI_0.76	NVDI_0.764	NVDI_0.768	NVDI_0.772	NVDI_0.776	NVDI_0.78	NVDI_0.784	NVDI_0.788	NVDI_0.792	NVDI_0.796	NVDI_0.8	NVDI_0.804	NVDI_0.808	NVDI_0.812	NVDI_0.816	NVDI_0.82	NVDI_0.824	NVDI_0.828	NVDI_0.832	...	NVDI_0.848	NVDI_0.852	NVDI_0.856	NVDI_0.86	NVDI_0.864	NVDI_0.868	NVDI_0.872	NVDI_0.876	NVDI_0.88	NVDI_0.884	NVDI_0.888	NVDI_0.892	NVDI_0.896	NVDI_0.9	NVDI_0.904	NVDI_0.908	NVDI_0.912	NVDI_0.916	sum
Name
Adenauerbach	4210	4730	5154	5771	6596	7279	7986	8881	9634	10471	11224	12402	13300	14199	15030	15715	16444	17039	17941	...	19606	19692	19702	19414	19387	18948	17948	17061	15630	13632	10994	7859	4581	2302	923	357	102	28	468263
Ansbach	1284	1470	1581	1669	1782	1879	2104	2263	2425	2644	2740	2980	3137	3436	3795	3936	4007	4363	4313	...	4751	4716	4542	4537	4389	4255	4091	3794	3545	3053	2383	1715	1156	742	516	333	237	135	114713
Armuthsbach	3749	3714	4034	4313	4601	4773	5316	5751	6414	7084	7921	8920	9947	10947	11994	13358	14517	15545	16208	...	18163	18523	18517	18611	18856	19014	19000	18977	18743	17697	16379	14777	12030	9438	6666	4099	2430	1078	464418
Dennbach	1142	1182	1282	1413	1499	1743	1797	2030	2146	2417	2806	3036	3229	3629	3908	4300	4441	4761	5182	...	6668	7101	7490	8038	8283	8381	8304	8024	7649	6852	5844	4679	3589	2657	1635	952	475	221	166475
Liersbach	1573	1726	1857	2024	2206	2440	2508	2909	3204	3444	3719	4095	4436	4674	4873	5072	5483	5783	6054	...	7917	7855	8596	8812	9293	9530	9619	9766	9857	9429	9010	8279	7431	5927	4263	2639	1455	601	218916
Nohnerbach	2481	2909	3060	3177	3447	3997	4183	4523	4999	5438	5892	6353	6861	7307	7819	8088	8718	9260	9504	...	10750	10310	10085	9771	9353	8349	7460	6287	5152	4342	3673	2878	2303	1746	1272	786	421	183	234216
Sahrbach	3169	3336	3437	3726	3980	4004	4433	4527	5011	5236	5708	6065	6372	6828	7338	8005	8603	9271	9786	...	12407	13134	13900	14852	15373	16061	16344	16551	15932	14971	13547	11966	10143	8281	6181	4325	2962	1848	350904
Staffelbach	4299	4461	4670	4965	5109	5311	5733	5819	6202	6425	6778	7055	7282	7758	7894	8011	8341	8518	8868	...	10007	10053	10505	10520	10613	10450	10004	9547	8458	7269	5837	4471	3347	2528	1788	1398	992	564	269706
Trierbach	5048	5569	5898	6333	6843	7167	7858	8478	9180	9637	10541	11345	12019	12742	13349	13848	14495	14807	15345	...	14957	14734	14201	13528	12762	11704	10521	8895	7781	6257	4740	3555	2608	1878	1095	609	325	177	366855
Upper Ahr	18304	18766	19514	19951	20463	20970	21832	22352	22999	23435	24094	24571	24752	25204	25240	25664	25591	25630	25801	...	25126	24416	23081	21574	19982	18192	16271	14428	12526	10884	8991	7257	5617	3898	2498	1577	1032	625	729664
Vischelbach	916	953	1016	987	1134	1205	1259	1359	1452	1496	1814	1871	2032	2275	2540	2820	3114	3454	3767	...	5016	5289	5798	6280	6793	7187	7688	8094	8312	8207	7830	7048	5888	5093	3600	2620	1736	1161	152299
Weidenbach	3653	3934	4098	4554	4854	5208	5656	6049	6232	6452	6771	6817	6913	7392	7296	7820	8095	8589	8995	...	11142	11760	12238	12548	12732	12747	12022	11081	9641	7599	5694	3855	2463	1346	659	246	93	21	277354
Wirftbach	2048	2274	2509	2679	3011	3338	3726	4048	4489	4691	5242	5598	6036	6340	6802	6977	7305	7524	7835	...	7676	7468	7291	7087	6497	5806	5311	4454	3852	3024	2282	1746	1265	971	631	351	167	89	181923

	agriculture	grass	forest	build	soils	Max_Q	Area
count	1.20e+01	12.00	12.00	1.20e+01	1.20e+01	12.00	12.00
mean	6.55e-02	0.25	0.65	2.03e-02	1.07e-02	69.92	37.39
std	5.05e-02	0.08	0.13	1.25e-02	5.39e-03	31.94	17.59
min	3.30e-03	0.08	0.47	6.26e-03	4.95e-03	38.12	15.00
25%	2.85e-02	0.21	0.54	1.04e-02	6.22e-03	41.46	24.07
50%	4.80e-02	0.26	0.65	1.70e-02	9.52e-03	67.45	34.58
75%	1.01e-01	0.31	0.73	2.78e-02	1.35e-02	85.14	47.97
max	1.52e-01	0.36	0.91	4.34e-02	2.27e-02	139.48	72.60

	Dreis-Brück	Nürburg-Barweiler	Kaltenborn	Adenau	Mayschoß	Freilingen_NRW
datetime
2021-07-14 00:00:00	0.0	0.0	0.0	0.0	0.0	0.00
2021-07-14 01:00:00	0.0	0.0	0.0	0.0	0.0	0.00
2021-07-14 02:00:00	0.0	0.0	0.0	0.0	0.0	0.00
2021-07-14 03:00:00	0.0	0.0	0.0	0.0	0.0	0.00
2021-07-14 04:00:00	0.0	0.0	0.0	0.0	0.0	0.00
2021-07-14 05:00:00	0.0	0.0	0.0	0.0	0.1	0.00
2021-07-14 06:00:00	0.0	0.0	0.0	0.0	1.2	0.14
2021-07-14 07:00:00	0.1	0.4	0.3	0.5	1.9	3.81
2021-07-14 08:00:00	1.0	2.3	1.6	0.1	1.0	4.92
2021-07-14 09:00:00	4.3	5.0	3.1	2.7	1.2	3.17
2021-07-14 10:00:00	2.9	2.4	5.8	2.0	6.1	4.58
2021-07-14 11:00:00	11.4	12.2	4.1	7.6	10.2	3.92
2021-07-14 12:00:00	19.3	18.9	14.6	19.6	4.9	3.37
2021-07-14 13:00:00	10.8	14.4	9.8	8.5	10.8	2.56
2021-07-14 14:00:00	8.8	10.3	8.6	10.3	9.5	8.01
2021-07-14 15:00:00	6.2	6.6	6.2	5.2	6.6	14.74
2021-07-14 16:00:00	6.5	7.5	7.5	9.3	9.1	12.78
2021-07-14 17:00:00	17.0	NaN	5.8	5.3	9.2	19.43
2021-07-14 18:00:00	6.0	NaN	4.3	3.9	5.2	18.69
2021-07-14 19:00:00	4.8	NaN	4.2	4.4	6.5	8.32
2021-07-14 20:00:00	3.5	NaN	3.6	3.1	2.6	6.06
2021-07-14 21:00:00	0.1	NaN	0.0	0.0	0.0	0.74
2021-07-14 22:00:00	0.0	NaN	0.0	0.0	0.0	0.10
2021-07-14 23:00:00	0.0	NaN	0.0	0.0	0.0	0.11

	Barweiler	Dreis-Brück	Kaltenborn	Freilingen	Esch	Scheuerheck&Wald	Ahrbrück	Neuenahr
PRECIP
Mean1407	4.71	4.27	3.30	4.81	3.71	6.40	5.46	2.80
Max1407	18.90	19.34	14.55	19.43	15.03	35.00	NaN	9.20
SUM1407	80.00	102.60	79.23	115.45	89.10	140.85	131.08	67.20
Mean1307	0.86	0.94	0.87	0.62	1.25	NaN	0.69	0.71
Max1307	6.00	7.07	10.91	3.95	26.20	NaN	NaN	14.50
SUM1307	20.60	22.49	20.76	14.77	30.00	NaN	16.60	17.80

	Flow rate (m³/s)	Latitude	Longitude
Time
2021-07-13 00:00:00	0.00e+00	50.38	6.81
2021-07-13 00:15:00	0.00e+00	50.38	6.81
2021-07-13 00:30:00	0.00e+00	50.38	6.81
2021-07-13 00:45:00	5.00e-05	50.38	6.81
2021-07-13 01:00:00	5.00e-05	50.38	6.81

	Flow rate (m³/s)	Latitude	Longitude
Time
2021-06-10 00:00:00	0.0	50.52	6.99
2021-06-10 01:00:00	0.0	50.52	6.99
2021-06-10 02:00:00	0.0	50.52	6.99
2021-06-10 03:00:00	0.0	50.52	6.99
2021-06-10 04:00:00	0.0	50.52	6.99

	Flow rate (m³/s)	Latitude	Longitude
Time
2021-07-07 00:00:00	0.0	50.51	6.97
2021-07-07 00:15:00	0.0	50.51	6.97
2021-07-07 00:30:00	0.0	50.51	6.97
2021-07-07 00:45:00	0.0	50.51	6.97
2021-07-07 01:00:00	0.0	50.51	6.97

	Flow rate (m³/s)	Latitude	Longitude
Time
2021-07-17 04:00:00	0.0	50.38	6.81
2021-07-17 05:00:00	0.0	50.38	6.81
2021-07-17 06:00:00	0.0	50.38	6.81
2021-07-17 07:00:00	0.0	50.38	6.81
2021-07-17 08:00:00	0.0	50.38	6.81

	Flow rate (m³/s)	Latitude	Longitude	Flow
Time
2021-07-15 06:00:00	0.33	50.44	6.93	2.84e-03
2021-07-15 07:00:00	0.12	50.44	6.93	1.00e-03
2021-07-15 08:00:00	0.04	50.44	6.93	3.40e-04

	Time	Flow rate (m³/s)	Latitude	Longitude	Flow
18	2021-07-15 04:00:00	0.99	50.36	6.83	1.17e-02
19	2021-07-15 05:00:00	0.46	50.36	6.83	5.45e-03
20	2021-07-15 06:00:00	0.21	50.36	6.83	2.46e-03
21	2021-07-15 07:00:00	0.09	50.36	6.83	1.09e-03
22	2021-07-15 08:00:00	0.04	50.36	6.83	4.69e-04