1 Rationale/Background
The MARS layers of rivers and catchments are derived from ECRINS database, available at EEA: http://projects.eionet.europa.eu/ecrins/library/hydrography/current_version/wfeczip . It consists of two spatial dataset, CTrN.mdb and Czhyd.mdb. More about documentation on ECRINS database is available on http://projects.eionet.europa.eu/ecrins/library/hydrography/current_version/documentationzip or http://www.eea.europa.eu/publications/eea-catchments-and-rivers-network
ECRINS feature class “C_Zhyd” presents ECRINS FEC layer: FUNCTIONAL ELEMENTARY
CATCHMENT, derived from CCM2 dataset by EEA; number of polygons: 181000; 7466 km 2 the largest; 0.01 km 2 the smallest; the average size is 50 km2;
ECRINS feature class “C_tr” presents ECRINS RIVERS layer; polylines, topologically connected to FEC; 1100000 objects; 7466 km the largest; 0.01 km the smallest; the average size is 50 km).
ECRINS FECs and ECRINS RIVERS layers are shown in Figure 1.
1

Figure 1: ECRINS FEC-s (grey line) and ECRINS RIVERS layers (blue lines).
For MARS purposes we have first complemented and updated ECRINS datasets with attributes that help user to search for, visualize and filter rivers and their catchments.
The ECRINS database extents over the European continent (Figure 2). For the MARS project we may
exclude rivers (and their catchments) for which data on pressures (such are CORINE land cover, emissions…) and state (WISE datasets) are not available and do not belong to any of rivers flowing over EU and Turkey territory. To follow MSFD concept of Regional seas that may be of interest to EU policy we still may add the other rivers flowing into the Black sea and the White Sea.
2

Figure 2: The extent of ECRINS database.
2 Processing ECRINS data on rivers and catchments for MARS purposes
We have complemented and updated ECRINS datasets with the aim to build a uniform MARS classification system, a uniform and systematic approach and to allow searching and visualizing of European rivers and catchments by names, order and size. The work with data sets should be easier and faster.
Descriptive parameters to fulfill this are as follows:
Stream order: rivers segments grouped into 1 st , 2 nd and 3 rd Pfafstetter stream order
-
River names : rivers segments belonging to 1 st , 2 nd and 3 rd order assigned up to three names
Unique identification code for rivers (for all rivers in 1 st , 2 nd and 3 rd order)
Catchment area – a polygon designated to each river in 1 st , 2 nd and 3 rd order
Catchment name
Catchment size category
We have produced the following GIS layers:
ECRINS_Rivers_123order
ECRINS _Catch_Order1
ECRINS _Catch_Order2
ECRINS _Catch_Order3
Method of work:
From ECRINS feature class »C_Zhyd« we first selected all the FECs located along the sea. Selection was done using the ECRINS attribute »Code_Arbo«. All the FECs located along the sea consist of 10 characters. The attribute »Code_Arbo« is also the root for all the FECs upstream. The attribute
»Code_Arbo« of the first FEC (along the sea) has ten characters. The »Code_Arbo« of FECs in the same catchment have the same first ten characters. Each upstream FEC, adjacent to the downstream
3

FEC, get one additional character (1 or 2, depends on a river side). With the attribute “Code_Arbo”
we then “found” all hinterland FECs and can define all belonging catchment areas. Figure 3 shows
grouped FECs of catchments having river month in the sea. They are first order catchments. Figure 4 and Figure 5 show how these catchments have been further delineated into second and third order.
Figure 3: Pfafstetter first order catchments (dark green – catchments > 50000 km2; yellow – catchments between 10000 and 50000 km2; orange - catchments between 5000 and 10000 km2; light green – catchments between 1000 and 5000 km2; lemon green – catchments < 1000 km2 with river; red – catchments < 1000 km2 without river)
4

Figure 4: Pfafstetter second order catchments (grey lines on green colored area) delineated for first order catchments
(with 1 st order rivers - light blue lines) larger than 10000 km2. 2 nd order rivers with catchments larger than 10000 km2 are dark blue colored.
Figure 5: Pfafstetter third order catchments (grey lines on light violet color). 1 st order rivers have light blue lines, 2 nd order rivers are dark blue colored.
Results:
The “ECRINS_Rivers_123order” layer contains (Figure 6):
- rivers flowing to sea with catchment larger than 1000 km 2 (1 st order rivers),
- tributaries to 1 st order rivers with catchment larger than 10000 km 2 (2 nd order rivers),
tributaries to 2 nd order rivers with catchment larger than 10000 km 2 (3 rd order rivers).
5

Figure 6: »ECRINS_Rivers_123order« layer (blue lines) and »ECRINS_Catch_order1«layers (polygons) as prepared in
February 2015.
The »ECRINS_Catch_Order1« layer contains all ECRINS catchments with rivers discharges to the sea
(Pfafstetter first order catchments, Figure 6). All the first order catchments larger than 10000 km2
are delineated into polygons of the Pfafstetter second order, named »ECRINS_Catch_Order2« layer
(Figure 7). All the second order catchments larger than 10000 km
2 are delineated into polygons of the Pfafstetter third order, named »ECRINS_Catch_Order3« layer. The final »ECRINS_Catch_Order2« layer also contains the first order catchments smaller than 10000 km 2 and »ECRINS_Catch_Order3« layer also contains the second order catchments smaller than 10000 km 2 and first order catchments smaller than 10000 km 2 .
Figure 7: »ECRINS_Catch_order2«layer (green color) overlayed over the area with ECRINS datasets (light orange color)
6

3 MARS catchments and rivers layers datasets
For the MARS research purposes relevant GIS data of rivers and their catchments are those that can be related to:
data on pressures such as CORINE land cover (Figure 8) and emissions data or data on state of rivers are available; should at least include catchments that extend:
over territories of EU MS countries, Switzerland, Norway, Iceland, West Balkan and catchments in Turkey being part of the Black Sea basin; and include rivers flowing:
into the Baltic Sea.
The extent of available CORINE data (CLC layer) is presented with a green line in Figure 8. The first
variant for the spatial extent of the MARS geodatabase is shown in Figure 9 (variant 1).
In the variant 2 we add rivers flowing to the Black sea and the Tigris-Euphrates river system (variant
2, Figure 10). As a third option the catchments belonging to the White Sea are added to variant 2
(variant 3, Figure 11).
Figure 8: The extent of CLC layer (green outline) over the extent of ECRINS FEC-s, aggregated by the Pfafstetter first order catchments.
Greece does not have CORINE data. Similarly the Corine data do not exist for non-EEA member state, for which ECRINS data exist. They are Moldova, Ukraine, Belarus, Russia, Georgia, Azerbaijan,
Armenia, Syria, Iraq, Iran, Kazakhstan (Figure 8).
7

Figure 9: The extent of MARS geodatabase in variant 1. The green line represents the extent of the Corine land cover data.
Figure 10: The extent of MARS geodatabase in variant 2 (»MARS_Catch_Order1_v2« layer). Here are added catchments of the Black sea and other catchments in Turkey (Tigris – Euphrates river system), that were not included into version 1.
The green line represents the extent of the Corine land cover data.
Figure 11: Extent of MARS geodatabase in variant 3 (»MARS_Catch_Order1_v3« layer - version 3). Here all catchments belonging to the Black sea and White sea are included. The green line represents extent of the Corine land cover data.
8

4 MARS 1st, 2 nd and 3 rd order rivers
The list of MARS 1 st and 2nd order rivers as extracted from »ECRINS_Rivers_123Order« dataset
for variant 1 are given in Table 1 and Table 2. Figure 12 shows MARS rivers in variant 1, Figure 13
in variant 2 and Figure 14 in variant 3.
Figure 12: MARS rivers in variant 1 (»MARS_Rivers_123order_v1«) as prepared in February 2015.
Figure 13: MARS rivers in variant 2 (»MARS_Rivers_123order_v2«) as prepared in February 2015.
9

Figure 14: MARS rivers in variant 3 (»MARS_Rivers_123order_v3«) as prepared in February 2015.
For the MARS purposes we have updated attributive part of the ECRINS RIVERS layer
(»ECRINS_Rivers_123order«):
From ECRINS RIVER objects we have selected segments that lie or represents first, second and third order catchments; they were assigned the same order (attribute ‘M_order’ added);
All segments of rivers that are 1 st , 2 nd and 3 rd order were assigned unique river identifier
(‘M_Rriver_ID’ code), since in ECRINS some segments do not have any code; some missed segments were added (between two ends of the arm);
All segment of rivers with the unique code “C_River_ID” have been assigned river names; there are three possibilities for river name (two possible national and international);
All segments were assigned information on a distance of its source (length of river from its source; information on a distance from its mouth is already in the ECRINS database;
All rivers segments were assigned information of average altitude (derived from EEA DEM data);
All rivers segments were assigned information of upstream catchment area.
Table 1: The list of MARS geodatabase rivers 1st order with catchment areas larger than 10000 km2
1 to find
2
6
7
8
9
BasinName = RiverName
Danube
3 Neva
4 To find
5 Vistula
Rhine
Elbe
Oder
Loire
Code_Arbo_ Area_km2 M_River_ID
R021077308 1054566
F020014234 802032
H030089460 279586
L030001694 195720
A020002710 193894
D020205974 160221
A030000948 143656
A030001054 118938
D020212550 116981
10

10 Douro
11 Rhone
12 Neman
13 Ebro
14 Daugava/Western Dvina
15 to find
16 Seine
17 Dniester
18 Po
19 Tagus
20 to find
21 Guadiana
22 Narva
23 Guadalquivir
24 to find
25 Garonne
26 Maritsa/Evros
27 Kemijoki
28 Gota
29 Weser
30 Glomma
31 Torne
32 Yesil
33 Kymijoki
34 Meuse
35 Angerman
36 Kem
37 Carsamba
38 Dal
39 Buyuk Menderes
40 Kokemaen
41 Umea
42 LottaLotta
43 Indals
44 Lule
45 Vardar
46 Oulujoki
47 Dordogne
48 Kocacay
49 Arbogaan
50 Kovda
51 Jucar
52 Ljusnan
53 Escaut / Schelde
E020137248 97419
D030212438 96619
A030000262 95925
E030149522 85612
H030091014 84608
J021055021 80684
D030207766 75990
G020183651 72531
C030002068 71327
E030149628 71202
J021057223 70573
E030150462 67063
H020112041 58126
E030150645 57052
R021076318 57018
D020221430 55703
J031073267 53026
H020099488 52513
I021052129 51464
A020004921 45211
I021050598 41911
I031035317 40112
J021055117 36126
H020110476 35709
D030207205 32047
I021042739 31815
H020099036 30903
J031074231 29140
I021047457 28638
J021074798 27383
H020109549 27124
I021041604 26939
H030085090 26771
I021043546 25839
I021039134 24554
J021065931 24397
H020101035 24242
D020221285 23902
J031073393 23753
I031037717 23076
H030086241 22406
E030150064 21555
I021046235 20024
D030207260 18949
11

54 Aksu
55 Patsoyoki
56 Tiber
57 Lielupe
58 Kalix
59 Drammensvassdraget
60 Mino
61 Adour
62 Strymonas
63 Gediz
64 Tenojoki
65 Seyhan
66 Segura
67 Thames
68 Pregolya
69 Yenice
70 Neretva
71 Drin
72 Motala Storm
73 Ljungan
74 Adige
75 Ceyhan
76 Ems
77 Iijoki
78 Venta
79 Shannon
80 Skellefte
81 Severn
82 Goksu (southern branch)
83 Skiensvassdraget
84 Pite
85 Thessalia
86 Humber
87 Vilaine
88 Humber
J021074093 18175
H020090622 18045
C030004657 17861
H020117430 17814
I021039003 17696
I031037617 17063
E030147210 16985
D030212035 16861
J021064716 16827
J021071377 16737
H030084628 15868
J021070850 15479
E020148511 14985
B020006577 13514
A020002094 13419
J021055894 13358
C030003496 13122
J031073094 13067
I021051464 12934
I031036471 12605
C030001998 12417
J021070606 12233
A030001201 12185
H020100653 11698
H020117064 11692
B030000978 11619
I021040371 11613
B020006329 11382
J021073918 11270
I031037890 11171
I021039528 11152
J031074666 10701
B030000969 10611
D020211940 10490
B020004545 10393
12

Table 2: The list of MARS geodatabase rivers 2nd order with catchment areas larger than 10000 km2
BasinName from
ECRINS
BasinName in MARS
River_ID as in ECRINS
RiverName_as exported from ECRINS to txt file
RiverName in GIS layer
M_River_ID
(in MARS geodatabase)
Catchment size of the river (spatial operation from
MARS_Catch_Order2)
1
Danube Danube Z_C0000879 Tysa Tysa Z_C0000879 149567
2
Neva Neva #Joker# Svir Svir to asssign 105304
3
Danube Danube #Joker#
Sava Sava Z_C0000941 100102
4
Neva Neva #Joker# Volkhov Volkhov to asssign 96328
5
Vistula Vistula Z_C0000861 Narew Narew Z_C0000861 74259
6
Oder Oder Z_C0000975 Warta Warta Z_C0000975 55640
7
Danube Danube Z_C0000880
Siret Siret Z_C0000880 44760
8
Danube Danube #Joker#
Drau Drau Z_C0000933 39679
9
Danube
Rhone
Danube
Rhone
#Joker#
Z_C0000740
Velika Morava
Sa ├┤ ne
Velika
Morava
Saône
Z_C0000955
Z_C0000740
37702
29504
10
Danube Danube Z_C0000882 Prut Prut Z_C0000882 28501
11
Rhine Rhine Z_C0000899 Moselle Moselle Z_C0000899 28199
12
Elbe Elbe Z_C0000885
Vltava Vltava Z_C0000885 28187
13
Rhine Rhine Z_C0000998 Main Main Z_C0000998 27235
14
Danube Danube Z_C0000995 Morava Morava Z_C0000995 26628
15
Danube Danube #Joker# Inn Inn Z_C0000940 25999
16
Elbe Elbe Z_C0000997
Saale Saale Z_C0000997 24407
17
Danube Danube Z_C0000900 Olt Olt Z_C0000900 23841
18
Elbe Elbe Z_C0000863 Havel Havel Z_C0000863 23510
19
Ebro Ebro #Joker# Ebro Segre Z_C0000054 22720
20
Loire Loire Z_C0000059
Maine Maine Z_C0000059 22351
21
Loire Loire Z_C0000036 Vienne Vienne Z_C0000036 21162
22
Danube Danube Z_C0000875 Vah Vah Z_C0000875 19977
23
Neva Neva #Joker#
Vuoksi
Tributary 1
Vuoksi
Tributary 1 to asssign 19541
24
Danube Danube Z_C0000129
Mosoni-Duna
Mosoni-
Duna Z_C0000129 18061
25
Rhine Rhine Z_C0000936 Aare Aare Z_C0000936 17624
Glomma #Joker# Vorma Vorma Z_C0000731 17400
26
Seine Seine Z_C0000056 Oise Oise Z_C0000056 16845
27
28
Vistula
Danube
Vistula
Danube
Z_C0001012
Z_C0000944
San
Tami ┼ í
San
Tamiš
Z_C0001012
Z_C0000944
16755
16649
29
Douro Douro Z_C0000029 Esla Esla Z_C0000029 16078
30
Weser Z_C0000819 Aller Aller Z_C0000819 15966
31
Garonne Garonne Z_C0000016 Tarn Tarn Z_C0000016 15718
32
33
Douro
Danube
Douro
Danube
Z_C0000030
#Joker#
Pisuerga
Si ├ │
Pisuerga
Sió
Z_C0000030
Z_C0000928
15643
14900
34
Kemijoki Kemijoki Z_C0000195
Ounasjoki Ounasjoki Z_C0000195 14539
35
Loire Loire Z_C0000037
Allier Allier Z_C0000037 14361
13

36
50
Rhine
37
49
Loire
38
48
Rhone
39
Seine Seine
40
Danube Danube
41
Umea
42
Rhone Rhone
43
Kemijoki Kemijoki
44
Tagus Tagus
45
Garonne Garonne
46
Danube
47 Maritsa/Ev ros
Danube
Maritsa/Evr os
Seine
Rhone
Seine
Danube
Loire
Danube
Vistula
Rhine
Vistula
51
Narva Narva
52
Ebro Ebro
53
Kem
Z_C0000896
Z_C0000063
Z_C0001120
Z_C0000168
Z_C0000937
Z_C0000159
Neckar
Cher
Neckar
Cher
Z_C0000896
Z_C0000063
Durance Durance Z_C0001120
Marne Marne Z_C0000168
Arge ┼ č Argeş Z_C0000937
Vindel ├ Ąlven Vindelälven Z_C0000159
Isere Isere Z_C0000750
Z_C0000750
Z_C0000196
#Joker#
Z_C0000015
Z_C0000939
Kitinen
Jarama
Lot
Ialomi ┼ úa
Kitinen
Tagus
Lot
Ialomiţa
Z_C0000196
Z_C0000207
Z_C0000015
Z_C0000939
Z_C0000971
Z_C0000058
Z_C0000942
Z_C0000107
#Joker#
Z_C0000073
#Joker#
Ergene
Yonne
Jiu
Wieprz
Emajogi
Jal ├ │n
Pista
Ergene
Yonne
Jiu
Wieprz
Emajogi
Jalón
Pista
Z_C0000971
Z_C0000058
Z_C0000942
Z_C0000107
Z_E2240068
Z_C0000073 to asssign
11029
10817
10333
10307
10217
10170
10043
13897
13725
13166
12735
12571
12534
11771
11653
11600
11582
11118
14

5 MARS FEC, CATCHMENT and RIVER layers
ECRINS FEC layer has 181000 polygons (7466 km 2 the largest; 0.01 km 2 the smallest; the average size is 50 km2);
ECRINS RIVERS layer has 1100000 polylines, topologically connected to FEC;
When the extent of spatial data relevant for MARS study is selected (we propose variant 1:
»MARS_geodb_ext_v1«), MARS FEC layer is prepared with only 80000 polygons;
»ECRINS Rivers_123order« layer has segments of rivers having larger catchment than 1000 km 2 for 1 st order and 10000 km 2 for 2 nd and 3 rd order. The layer has 41866 polylines;
When these river objects are dissolved (all segment of one river joined into one object), three layers are produced:
-
-
MARS_Rivers_order 1: 88 objects (unique name, ID, other attributes)
MARS_Rivers_order 2: 53 objects (unique name, ID, other attributes)
MARS_Rivers_order 3: 15 objects (unique name, ID, other attributes)
When FEC polygons in catchments of rivers 1, 2 and 3 rd order (MARS rivers) are grouped layers, polygons of river catchments are prepared :
ECRINS_Catch_order1_p
ECRINS _Catch_order2_p
ECRINS _Catch_order3_p
…….only catchments larger than 1000 km2 for 1 st order rivers and larger than 10000 km2 for 2 nd and 3 rd order
The idea is, that for all FECs in these river catchments “hinterland polygons” are produced.
What hinterland means, is explained in
15

6 DRAVA River geodatabase – a case
The Drava river catchment has 934 functional elementary catchments (FEC) as shown in Figure
15. Within EEA yearly reporting for WISE »state of environment reporting« on river quality (WISE
SoE ), data are collected for 107 monitoring stations (Figure 16, Table 3).
To relate pressure data with data of river quality at each WISE SoE station a “hinterland polygon” is defined by grouping all FECs belonging to its watershed area (upstream catchment). A hinterland polygon for the station “SI_RV_1082” on the Mura river (a tributary of the Drava river)
is shown on Figure 17. In Table 3 the sizes of hinterland areas for all WISE SoE river quality
stations are given. They were calculated from hinterland polygons.
Figure 15: All the FEC-s (grey outline) constituting the Drava catchment (red outline) with the Drava river and its main tributary the Mura river
Figure 16: Locations of WISE SoE stations with data for river quality.
16

Figure 17: A »hinterland polygon« for station on the Mura river, a tributary of the Drava river, with the WISE SoE code
»SV_RV_1082«.
Table 3: The list of FEC-s with WISE SoE river quality stations in the Drava catchment
WISE SoE station FEC code with WISE SoE Size of WISE SOE stations
[WaterbaseID] station (“ZHYD”) catchments (hinterland size)
6
7
4
5
1
2
3
AT_RV_21500027
AT_RV_21500107
AT_RV_21500116
AT_RV_21500317
AT_RV_21500327
AT_RV_21510446
AT_RV_21511416
8
9
AT_RV_21520117
AT_RV_21520127
10 AT_RV_21521406
11 AT_RV_21530137
12 AT_RV_21530157
13 AT_RV_21530336
14 AT_RV_21531177
15 AT_RV_21540427
16 AT_RV_21550207
17 AT_RV_21550217
18 AT_RV_21550366
F030003998
F030004228
F030004040
F030004259
F030004121
F030003763
F030003654
F030003775
F030004002
F030003568
F030004249
F030004239
F030004161
F030004300
F030004205
F030003835
F030003989
F030003867
950
32
639
4789
52
816
1437
347
244
[ Area_km2]
2477
5289
4851
7892
10699
227
885
1454
61
17

F030002673
F030003119
F030002739
F030002218
F030002332
F030003544
F030003891
F030003186
F020017406
F020015958
F030003731
F030004087
F030003996
F030003961
F030003911
F030003710
F020017704
F030004014
F030003983
F030003684
F030003838
F030003688
F030004152
F030003336
F030003428
F030003133
F030003304
F030003095
F030003101
F030002720
F030003668
F030003947
F030003813
F030003791
F030004176
F030004237
F030004193
F020018608
F030004068
F030003442
F030003258
F020017381
F020017620
F030002596
F030003876
F030002641
19 AT_RV_21550386
20 AT_RV_21551257
21 AT_RV_21551346
22 AT_RV_21551356
23 AT_RV_21552396
24 AT_RV_21553436
25 AT_RV_21560277
26 AT_RV_21570456
27 AT_RV_55010037
28 AT_RV_55020247
29 AT_RV_55020267
30 AT_RV_55020277
31 AT_RV_61400067
32 AT_RV_61400087
33 AT_RV_61400097
34 AT_RV_61400107
35 AT_RV_61400157
36 AT_RV_61400167
37 AT_RV_61400197
38 AT_RV_61400207
39 AT_RV_61400257
40 AT_RV_61400277
41 AT_RV_61400476
42 AT_RV_61400486
43 AT_RV_61400496
44 AT_RV_61400506
45 AT_RV_71500017
46 AT_RV_71500507
47 AT_RV_71500607
48 AT_RV_71510307
49 AT_RV_71560407
50 AT_RV_71560907
51 AT_RV_71565407
52 AT_RV_71565807
53 AT_RV_FW21500097
54 AT_RV_FW21500306
55 AT_RV_FW21550377
56 AT_RV_FW21551267
57 AT_RV_FW21560297
58 AT_RV_FW55010057
59 AT_RV_FW61400127
60 AT_RV_FW61400137
61 AT_RV_FW61400147
62 AT_RV_FW61400217
63 AT_RV_FW61400287
64 AT_RV_FW61400597
46
84
94
219
185
639
671
37
6294
483
331
453
759
848
310
370
119
93
299
2402
3287
4410
355
395
460
147
457
102
383
93
743
969
996
7321
9625
10198
1505
1116
4710
541
1196
141
268
12077
7399
2236
18

65 AT_RV_FW71500967
66 HR_RV_21012
67 HR_RV_21084
68 HR_RV_29130
69 HR_RV_29160
70 HR_RV_29210
71 HU_RV_05FF08
72 HU_RV_05FF13
73 HU_RV_05FF27
74 HU_RV_06FF20
75 HU_RV_06FF37
76 HU_RV_HU3Rv4391
77 HU_RV_HU3Rv4781
78 HU_RV_HU3Rv6451
79 HU_RV_HU3Rv6461
80 HU_RV_HU3Rv6591
81 IT_RV_11404
82 IT_RV_11405
83 IT_RV_ITA06UD105
84 IT_RV_ITA06UD113
85 IT_RV_ITA06UD129
86 SI_RV_1082
87 SI_RV_1140
88 SI_RV_2010
89 SI_RV_2650
90 AT_RV_61400516
91 AT_RV_FW21531167
92 IT_RV_ITA06UD84
93 IT_RV_ITA06UD171
94 IT_RV_ITA06UD114
95 IT_RV_ITA06UD83
96 SI_RV_2005
97 SI_RV_1010
98 SI_RV_2200
99 SI_RV_2199
100 HU_RV_HU3Rv6612
101 HU_RV_HU3Rv6611
102 HU_RV_HU3Rv4382
103 HU_RV_HU3Rv4381
104 HR_RV_29120
105 HU_RV_HU3Rv8161
106 HR_RV_29111
107 AT_RV_FW61400267
F020016455
F030004096
F030004125
F030004386
F030004383
F030004390
F020018063
F020018554
F030004146
F030004412
F030003731
F030004300
F030004300
F030004383
F030004383
F030003992
F030004823
F020019726
F020019903
F020019448
F030004324
F020020897
F020020988
F020019741
F030004250
F030004092
F030004418
F020021151
F020017785
F020017067
F030004386
F030004176
F020017381
F020019448
F020019448
F020016455
F020016455
F030004802
F020020949
F020020928
F030004250
F030004802
F030003544
341
12674
918
219
244
244
71
71
215
143
72
92
71
19
10442
408
13327
619
31120
1202
306
116
2020
927
280
42
15369
138
1007
563
92
12077
9625
15369
15369
215
215
37500
34236
34236
13327
37500
848
19

For each hinterland polygon various type of pressures are (or will be) quantified, qualitatively determined, described by classes or spatially related:
CORINE land cover (share/absolute value of land cover classes): agricultural, urban, forest
(Figure 19, Figure 20, Figure 21, Table 4),
population density and inhabitants count, number of point source emissions (industrial, communal) and degree of urbanization (data transferred from NUTS 2),
number of urban waste water treatment plants, population connected to waste water
treatment by level of treatment (Figure 21),
emission load of urban waste water (N, P)
agricultural production (crop products yield, poultry, milk and milk products, livestock and meat) and use of fertilizers (data transferred from NUTS 2),
industrial facilities and pollutant releases (E-PRTR database) - mineral, energy, paper and wood production and processing, intensive livestock production and aquaculture, beverage sector),
number of dams and other hydrotechnical structure and percentage of hydropower potential use for electricity production (potential calculated from average annual discharge or river and hydrographical head),
For each hinterland polygon also other pressures can be pre-prepared:
motorway and railway transport network (length/distance to river),
goods transport by inland waterways (data transferred from NUTS 2),
waste production and management (from E-PRTR, data transferred from NUTS 2).
For each hinterland polygon hydrological and other natural characteristics parameters can be defined:
average yearly rainfall and temperature, runoff coefficient, surface water resources as average yearly river discharge, length of river from source, hydrographical head, altitude of downstream section, the highest peak, average slope, average catchment altitude…,
share of natural areas or semi-natural areas along river(s),
soil properties, geology- hydrogeology, lithology.
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We have prepared an example hot to relate CORINE land cover data to the hinterlands of FECs, where a monitoring location with water quality data (WISE SoE quality stations) is situated.
In ArcGIS we first clipped the »Corine_clc_100_2006_polygon.shp« layer by the spatial extent of the
Drava catchment (»Catch_Drava.shp«. Then we intersected »CLC_100_2006_polygon_Drava« layer and »SoE_Drava_hinterlands.shp« layer. The result is »CLC_SoE_hinterladns_Drava_Diss.shp« layer, where each of 107 hinterland polygons is divided in polygons with the same class of CORINE land cover. For test example we used CORINE land cover class level 1.
Because the »SoE_Drava_hinterland.shp« polygons are overlapping, user has to select just one polygon from »SoE_Drava_hinterlands.shp«. This can be done with the command Definition Query in
Arc GIS by attribute »WaterbaseI«. Figure 18 shows an example for »WaterbaseI«=’SI_RV_1082’.
Figure 18: Writing a Definition Query in ArcGIS
Figure 19 shows us the selected hinterland polygon with CORINE land cover for FEC with SoE quality
station (WaterbaseID ‘SI_RV_1082’) and its attribute table. In the attribute table there is calculated the share of each land cover class for all the Drava catchment (attribute ‘SCS_pro_Dr’) and for selected hinterland polygon (attribute ‘CLCpro_SoE’).
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Figure 19: Selected hinterland polygon with CORINE land cover for FEC with SoE quality station (Waterbase ID
'SI_RV_1082')
On Figure 20 and in Table 4 there is an example for 5 selected hinterlands of the FECs with WISE SoE
quality stations (»WaterbaseI«: AT_RV_21500027, HR_RV_29130, HU_RV_05FF27,
IT_RV_ITA06UD171, SI_RV_1082) .
Figure 20: Hinterland polygons for selected FEC-s (WISE SoE stations) covered with Corine land cover layer
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Table 4: Share of land cover (Corine2000, label1) on the hinterland of the selected WISE SoE quality stations
Hinterland
WaterbaseID of
WISE SoE quality staion
ZHYD of FEC where WISE SoE quality station is located
Area [km2] clc2000_label1
Share of CLC on hinterland
[%]
AT_RV_21500027
HR_RV_29130
HU_RV_05FF27
IT_RV_ITA06UD171
SI_RV_1082
Drava catchment
F030003998
F020019903
F020019741
F030004383
F020018063
F030004892
2477
42
408
71
10442
39678
Agricultural areas
Artificial surfaces
Forest and semi natural areas
Wetlands
Agricultural areas
Artificial surfaces
Forest and semi natural areas
Water bodies
Agricultural areas
Artificial surfaces
Forest and semi natural areas
Water bodies
Wetlands
Agricultural areas
Artificial surfaces
Forest and semi natural areas
Water bodies
Agricultural areas
Artificial surfaces
Forest and semi natural areas
Water bodies
Wetlands
Agricultural areas
Artificial surfaces
Forest and semi natural areas
Water bodies
Wetlands 0,19
On Figure 21 there is a case of for the hinterland of the WISE SoE quality station »SI_RV_1082«. The
figure shows the locations of Urban Waste Water treatment discharge points as point pressure data and CORINE land cover as a dispersed pressure data (artificial surfaces, agricultural area).
1,21
96,78
0,89
23,94
4,98
70,81
0,20
0,07
36,31
3,80
58,84
0,85
5,90
63,09
3,72
32,27
0,54
0,38
1,11
8,38
1,55
90,06
0,01
61,01
5,09
28,01
23

Figure 21: Locations of Urban Waste Water treatment discharge point (black dots) and Corine land cover (artificial surfaces – red; agricultural areas – orange; forest and semi natural areas – green) for the WISE SoE quality station
»SI_RV_1082« hinterland on the Mura river
The described procedure on the extraction of CORINE land cover information for a hinterland of one
FEC can be used for any other pressure datasets that are spatial. The most prominent are spatial datasets of UWWTs (urban waste water treatment plants) and E-PRTR (pollutant release and transfer register), both presented with point objects.
There is a lot of statistical information available for NUT2 and NUT3 (aggregated information for statistical regions, yearly collected and disseminated). For that purpose, FECs are grouped. All FECs in a group take the appropriate information (weighted, normalized, equivalent etc) from a respective
NUT region. The method of transfer is specific to the type of information.
What are informative potentials of UWWT and E-PRTR datasets one can review downloading datasets at http://www3.fgg.uni-lj.si/~/mars/00_Pressure_Data . Here one can find also other pressures data relevant to MARS. The integration of these information into MARS geodatabase
Is a matter of development and decision by partners.
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7 MARS pilot catchments
A chapter on GIS data for pilot catchments : to be also developed after communication with partners.
The idea is to prepare generic GIS data and related pressure data for all pilot catchments in
MARS as presented for the Drava river catchment.
To import data that partners collect or model, the most important issue is to use same codification of objects (ECRINS updated for MARS for consistency), same spatial coordinate system (ETRS89) and same projection systems (LAE).
25