95:. Also the potential effects of climate warming for shallow lakes have been studied. On the other hand, PCLake is applied by lake water resource managers that consider the turbid state as undesirable. They can use the model to define the critical loadings for their specific lakes and evaluate the effectiveness of restoration measures. For this purpose also a meta-model has been developed. The meta-model can be used by water managers to derive an estimate of the critical loading values for a certain lake based on only a few important parameters, without the need of running the full dynamical model.
1131:, young and adult whitefish and piscivorous fish. The main abiotic factors are transparency and the nutrients phosphorus (P), nitrogen (N) and silica (Si). At the base of the model are the water and nutrient budgets (in- and outflow). The model describes a completely mixed water body and comprises both the water column and the upper sediment layer. The overall nutrient cycles for N, P and Si are described as completely closed (except for in- and outflow and
1189:
1203:
1175:
78:(also called blue-green algae). Recovery of the clear state however is difficult as the critical nutrient loading for the switch back is often found to be lower than the critical loading towards the turbid state. Lowering the nutrient input thus does not automatically lead to a switch back to the clear water phase. Hence, the system shows
1159:(RIVM), within the framework of a research and restoration project on Lake Loosdrecht. It has been extended and improved since then. Parallel to PCLake, PCDitch was created, which is an ecosystem model for ditches and other linear water bodies. The models were further developed by dr. Jan H. Janse and colleagues at the
90:
PCLake is designed to study the effects of eutrophication on shallow lakes and ponds. On one hand, the model is used by scientists to study the general behavior of these ecosystems. For example, PCLake is used to understand the phenomena of alternative stable states and hysteresis, and in that light,
1142:
PCLake is calibrated against nutrient, transparency, chlorophyll and vegetation data on more than 40 European (but mainly Dutch) lakes, and systematic sensitivity and uncertainty analysis have been performed. Although PCLake is primarily used for Dutch lakes, it is likely that the model is also
1139:-a, transparency, cyanobacteria, vegetation cover and fish biomass, as well as the concentrations and fluxes of nutrients N, P and Si, and oxygen. Optionally, a wetland zone with marsh vegetation and water exchange with the lake can be included.
1163:(PBL), formerly part of the RIVM. Since 2009, the model is jointly owned by PBL and the Netherlands Institute of Ecology, where further development and application of PCLake is taking place, related to aquatic-ecological research.
58:: a clear state with submerged macrophytes and piscivorous fish, or a turbid state dominated by phytoplankton and benthivorous fish. A switch from one state to the other is largely driven by the input of nutrients (
1156:
74:, many of the world’s shallow lakes and ponds are in a eutrophic state with turbid waters and poor ecological quality. In this turbid state, the lake also becomes subject to algal blooms of toxic
1620:
Janse JH and others, 2010. Estimating the critical phosphorus loading of shallow lakes with the ecosystem model PCLake: sensitivity, calibration and uncertainty. Ecological
Modelling 221: 654–665
1322:
Janse JH and others, 2008. Critical phosphorus loading of different types of shallow lakes and the consequences for management estimated with the ecosystem model PCLake. Limnologica 38: 2003–2019
1245:
Janssen, Annette B. G.; Teurlincx, Sven; Beusen, Arthur H. W.; Huijbregts, Mark A. J.; Rost, Jasmijn; Schipper, Aafke M.; Seelen, Laura M. S.; Mooij, Wolf M.; Janse, Jan H. (2019-03-24).
66:) to the ecosystem. If the nutrient loading exceeds a critical value, eutrophication causes a switch from the clear to the turbid state. As a result of urban water pollution and/or
1578:
Kong, Xiangzhen (2016). "Hydrological regulation drives regime shifts: evidence from paleolimnology and ecosystem modeling of a large shallow
Chinese lake".
1160:
1331:
Mooij WM and others, 2007. Predicting the effect of climate change on temperate shallow lakes with the ecosystem model PCLake. Hydrobiologia 584: 443–454
1471:"Critical phosphorus loading of different types of shallow lakes and the consequences for management estimated with the ecosystem model PCLake"
1247:"PCLake+: A process-based ecological model to assess the trophic state of stratified and non-stratified freshwater lakes worldwide"
1640:
1304:
Janse JH, 1997. A model of nutrient dynamics in shallow lakes in relation to multiple stable states. Hydrobiologia 342/343: 1–8
1635:
1135:). Inputs to the model are: lake hydrology, nutrient loading, dimensions and sediment characteristics. The model calculates
1112:
42:. PCLake is used both by scientist and water managers. PCLake is in 2019 extended to PCLake+, which can be applied to
1354:
Schep, S. (in Dutch) 2010. Neuraal netwerk PCLake ten behoeve van KRW-verkenner. Witteveen+Bos; rapportnr. UT565-2-1
1313:
Janse JH, 2005. Model studies on the eutrophication of shallow lakes and ditches. PhD thesis. Wageningen
University
1504:"Spatial identification of critical nutrient loads of large shallow lakes: Implications for Lake Taihu (China)"
55:
1221:
1295:
Scheffer M, 1993. Alternative equilibria in shallow lakes. Trends in
Ecology & Evolution 8: 275–-279
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lakes in other regions, if parameters are adjusted or some small changes to the model are made.
1107:(>300), the model may be characterized as relatively complex. The main biotic variables are
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The first version of PCLake (by then called PCLoos) was built in the early 1990s at the
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Li, Dianchi (2019). "Modeling nutrients in Lake
Dianchi (China) and its watershed".
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National Institute for Public Health and the Environment
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groups and their interrelations, within the general framework of
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the relative importance of lake features such as water depth or
31:
16:
Mathematical model of eutrophication effects in shallow lakes
1095:
Mathematically, PCLake is composed of a set of coupled
54:
Typically, shallow lakes are in one of two contrasting
34:
and ponds. PCLake models explicitly the most important
1342:"Metamodel PCLake - Planbureau voor de Leefomgeving"
1475:Imnologica-Ecology and Management of Inland Waters
8:
99:Lakes that have been simulated using PCLake
1161:Netherlands Environmental Assessment Agency
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1502:Janssen, Annette B. G. (2017).
1264:10.1016/j.ecolmodel.2019.01.006
1:
1545:Agricultural Water Management
1143:applicable to comparable non-
1521:10.1016/j.watres.2017.04.045
1113:submerged aquatic vegetation
1557:10.1016/j.agwat.2018.08.023
1488:10.1016/j.limno.2008.06.001
1657:
1099:. With a large number of
56:alternative stable states
848:Frisian Lakes (average)
1641:Environmental chemistry
1222:Water quality modelling
311:Nieuwkoop, Noordeinder
294:Reeuwijk, Nieuwenbroek
1097:differential equations
763:Langeraar, Noordeinde
328:Nieuwkoop, Zuideinder
1636:Mathematical modeling
1580:Global Change Biology
68:intensive agriculture
1251:Ecological Modelling
1018:Botshol Kleine Wije
587:Binnenbedijkte Maas
345:Reeuwijk, Elfhoeven
277:Naarden, Wijde Blik
260:Naarden, Grote Meer
226:Langeraar, Geerplas
192:Botshol, Grote Wije
1469:Janse, Jan (2008).
100:
30:effects in shallow
1195:Environment portal
1117:primary production
141:Bergse Achterplas
124:Hollands-Ankeveen
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24:mathematical model
1592:10.1111/gcb.13416
1151:Model development
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1119:. A simplified
1101:state variables
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382:Zuidlaardermeer
116:Lake Area (km)
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72:catchment areas
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40:nutrient cycles
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834:Netherlands
817:Netherlands
800:Netherlands
783:Netherlands
766:Netherlands
749:Netherlands
732:Netherlands
729:Drontermeer
715:Netherlands
698:Netherlands
679:Netherlands
660:Netherlands
643:Netherlands
626:Netherlands
609:Netherlands
605:Brielse Meer
590:Netherlands
573:Netherlands
468:Gara, South
403:Netherlands
386:Netherlands
367:Netherlands
348:Netherlands
331:Netherlands
314:Netherlands
297:Netherlands
280:Netherlands
263:Netherlands
246:Netherlands
229:Netherlands
212:Netherlands
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178:Netherlands
161:Netherlands
144:Netherlands
127:Netherlands
93:fetch length
89:
53:
19:
18:
1514:: 276–287.
1274:2066/201655
1145:stratifying
1137:chlorophyll
1125:zooplankton
814:Kortenhoef
712:Nuldernauw
570:Waalboezem
519:Pollaphuca
243:Loosdrecht
86:Application
44:stratifying
1630:Categories
1233:References
1105:parameters
780:Mooie Nel
694:Wolderwijd
675:Veluwemeer
451:Visvijver
417:Blankaart
400:Nannewijd
113:Depth (m)
107:Lake name
80:hysteresis
60:phosphorus
50:Background
1565:158735560
1551:: 48–59.
1283:0304-3800
1257:: 23–32.
640:Zoommeer
623:Volkerak
553:Luknajno
434:Maten 13
1608:39503091
1600:27391103
1530:28477543
1167:See also
1121:food web
970:Denmark
953:Denmark
936:Denmark
919:Denmark
902:Denmark
885:Denmark
882:Nederso
868:Denmark
797:Het Hol
539:Ireland
522:Ireland
505:Ireland
502:Mullagh
488:Ireland
485:Maumwee
471:Ireland
454:Belgium
437:Belgium
420:Belgium
110:Country
64:nitrogen
1227:Ecopath
1129:benthos
1055:Dianchi
950:Lading
556:Poland
46:lakes.
1606:
1598:
1563:
1528:
1281:
1078:China
1074:Chaohu
1059:China
1040:China
1021:Spain
1004:Spain
1001:Nueva
987:Spain
984:Honda
933:Kvind
916:Hinge
704:18.00
685:32.40
649:15.80
632:45.70
536:Ramor
528:19.74
184:13.00
36:biotic
20:PCLake
1604:S2CID
1561:S2CID
1127:, zoo
1046:2250
1036:Taihu
1027:0.21
1010:0.27
993:0.09
976:0.46
959:0.45
942:0.15
925:0.91
908:0.72
899:Soby
891:0.14
874:0.11
865:Enso
857:5.18
840:1.00
823:1.93
806:0.30
789:1.16
772:0.75
755:4.52
738:5.40
721:8.70
666:0.10
615:4.91
596:1.58
579:0.78
562:6.30
545:7.41
511:0.35
494:0.27
477:2.03
460:0.01
443:0.03
426:0.30
409:1.00
392:6.00
373:8.52
354:1.09
337:1.00
320:1.50
303:1.04
286:0.38
269:1.70
252:9.40
235:0.28
218:3.20
201:0.53
167:0.60
150:0.41
133:0.85
32:lakes
1596:PMID
1526:PMID
1279:ISSN
1111:and
1084:760
1081:2.5
1065:298
1062:4.4
1043:1.9
1024:1.7
1007:1.5
990:1.0
973:2.0
956:1.0
939:1.9
922:1.2
905:3.0
888:1.6
871:1.9
854:1.4
837:1.0
820:1.2
803:1.0
786:4.5
769:1.9
752:3.9
735:1.3
718:2.1
701:1.9
682:1.6
663:1.3
646:6.0
629:5.0
612:5.5
593:4.0
576:3.8
559:1.8
542:3.0
525:6.8
508:2.3
491:2.0
474:1.0
457:0.8
440:0.8
423:1.0
406:1.0
389:1.0
370:2.8
351:2.0
334:3.0
317:3.0
300:1.8
283:1.0
266:1.3
249:2.2
232:1.9
215:2.4
198:2.0
181:1.8
164:2.0
147:2.0
130:1.3
62:and
1588:doi
1553:doi
1549:212
1516:doi
1512:119
1483:doi
1269:hdl
1259:doi
1255:396
1070:56
1051:55
1032:54
1015:53
998:52
981:51
964:50
947:49
930:48
913:47
896:46
879:45
862:44
845:43
828:42
811:41
794:40
777:39
760:38
743:37
726:36
709:35
690:34
671:33
654:32
637:31
620:30
601:29
584:28
567:27
550:26
533:25
516:24
499:23
482:22
465:21
448:20
431:19
414:18
397:17
378:16
359:15
342:14
325:13
308:12
291:11
274:10
104:nr
70:in
1632::
1602:.
1594:.
1584:23
1582:.
1559:.
1547:.
1524:.
1510:.
1506:.
1479:38
1477:.
1473:.
1359:^
1277:.
1267:.
1253:.
1249:.
257:9
240:8
223:7
206:6
189:5
172:4
155:3
138:2
121:1
82:.
1610:.
1590::
1567:.
1555::
1532:.
1518::
1491:.
1485::
1344:.
1285:.
1271::
1261::
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