Sources of skill in lake temperature, discharge and ice-off seasonal forecasting tools

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dc.contributor.authorClayer, Francoisen
dc.contributor.authorJackson-Blake, Leahen
dc.contributor.authorMercado-Bettin, Danielen
dc.contributor.authorShikhani, Muhammeden
dc.contributor.authorFrench, Andrewen
dc.contributor.authorMoore, Tadhgen
dc.contributor.authorSample, Jamesen
dc.contributor.authorNorling, Magnusen
dc.contributor.authorFrias, Maria-Doloresen
dc.contributor.authorHerrera, Sixtoen
dc.contributor.authorde Eyto, Elviraen
dc.contributor.authorJennings, Eleanoren
dc.contributor.authorRinke, Karstenen
dc.contributor.authorvan der Linden, Leonen
dc.contributor.authorMarce, Rafaelen
dc.date.accessioned2023-09-26T13:44:53Zen
dc.date.available2023-09-26T13:44:53Zen
dc.date.issued2023-03en
dc.description.abstractDespite high potential benefits, the development of seasonal forecasting tools in the water sector has been slower than in other sectors. Here we assess the skill of seasonal forecasting tools for lakes and reservoirs set up at four sites in Australia and Europe. These tools consist of coupled hydrological catchment and lake models forced with seasonal meteorological forecast ensembles to provide probabilistic predictions of seasonal anomalies in water discharge, temperature and ice-off. Successful implementation requires a rigorous assessment of the tools' predictive skill and an apportionment of the predictability between legacy effects and input forcing data. To this end, models were forced with two meteorological datasets from the European Centre for Medium-Range Weather Forecasts (ECMWF), the seasonal forecasting system, SEAS5, with 3-month lead times and the ERA5 reanalysis. Historical skill was assessed by comparing both model outputs, i.e. seasonal lake hindcasts (forced with SEAS5), and pseudo-observations (forced with ERA5). The skill of the seasonal lake hindcasts was generally low although higher than the reference hindcasts, i.e. pseudo-observations, at some sites for certain combinations of season and variable. The SEAS5 meteorological predictions showed less skill than the lake hindcasts. In fact, skilful lake hindcasts identified for selected seasons and variables were not always synchronous with skilful SEAS5 meteorological hindcasts, raising questions on the source of the predictability. A set of sensitivity analyses showed that most of the forecasting skill originates from legacy effects, although during winter and spring in Norway some skill was coming from SEAS5 over the 3-month target season. When SEAS5 hindcasts were skilful, additional predictive skill originates from the interaction between legacy and SEAS5 skill. We conclude that lake forecasts forced with an ensemble of boundary conditions resampled from historical meteorology are currently likely to yield higher-quality forecasts in most cases.en
dc.description.notesThis study was largely funded by the WATExR project (https://nivanorge.github.io/seasonal_forecasting_watexr/, last access: 23 March 2023), which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and by MINECO-AEI (ES), FORMAS (SE), BMBF (DE), EPA (IE), RCN (NO) and IFD (DK), with co-funding from the European Union (grant 690462). MINECO-AEI funded this research through projects PCIN-2017-062 and PCIN-2017-092. We thank all water quality and quantity data providers: Ens d'Abastament d'Aigua Ter-Llobregat (ATL; https://www.atl.cat/es, last access: 23 March 2023), SA Water (https://www.sawater.com.au/, last access: 23 March 2023), Wupperverband (https://www.wupperverband.de, last access: 23 March 2023), NIVA (https://www.niva.no, last access: 23 March 2023) and NVE (https://www.nve.no/english/, last access: 23 March 2023). We acknowledge ECMWF for providing the SEAS5 and ERA5 data. We are grateful to Samuel Monhart and one anonymous reviewer, who contributed to significantly improvement of the manuscript.en
dc.description.sponsorshipWATExR project; MINECO-AEI; FORMAS; BMBF; EPA; RCN; IFD; European Union [690462]; MINECO-AEI [PCIN-2017-062, PCIN-2017-092]en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.5194/hess-27-1361-2023en
dc.identifier.eissn1607-7938en
dc.identifier.issn1027-5606en
dc.identifier.issue6en
dc.identifier.urihttp://hdl.handle.net/10919/116332en
dc.identifier.volume27en
dc.language.isoenen
dc.publisherCopernicusen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectsensitivity-analysisen
dc.subjectsurface-temperatureen
dc.subjectstream temperatureen
dc.subjectchanging climateen
dc.subjectair-temperatureen
dc.subjectpredictionen
dc.subjectmodelen
dc.subjectpredictabilityen
dc.subjectprecipitationen
dc.subjectuncertaintyen
dc.titleSources of skill in lake temperature, discharge and ice-off seasonal forecasting toolsen
dc.title.serialHydrology and Earth System Sciencesen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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