Forecasting water temperature in lakes and reservoirs using seasonal climate prediction
dc.contributor.author
dc.date.accessioned
2021-06-07T12:40:56Z
dc.date.available
2021-06-07T12:40:56Z
dc.date.issued
2021-08-01
dc.identifier.issn
0043-1354
dc.identifier.uri
dc.description.abstract
Seasonal climate forecasts produce probabilistic predictions of meteorological variables for subsequent months. This provides a potential resource to predict the influence of seasonal climate anomalies on surface water balance in catchments and hydro-thermodynamics in related water bodies (e.g., lakes or reservoirs). Obtaining seasonal forecasts for impact variables (e.g., discharge and water temperature) requires a link between seasonal climate forecasts and impact models simulating hydrology and lake hydrodynamics and thermal regimes. However, this link remains challenging for stakeholders and the water scientific community, mainly due to the probabilistic nature of these predictions. In this paper, we introduce a feasible, robust, and open-source workflow integrating seasonal climate forecasts with hydrologic and lake models to generate seasonal forecasts of discharge and water temperature profiles. The workflow has been designed to be applicable to any catchment and associated lake or reservoir, and is optimized in this study for four catchment-lake systems to help in their proactive management. We assessed the performance of the resulting seasonal forecasts of discharge and water temperature by comparing them with hydrologic and lake (pseudo)observations (reanalysis). Precisely, we analysed the historical performance using a data sample of past forecasts and reanalysis to obtain information about the skill (performance or quality) of the seasonal forecast system to predict particular events. We used the current seasonal climate forecast system (SEAS5) and reanalysis (ERA5) of the European Centre for Medium Range Weather Forecasts (ECMWF). We found that due to the limited predictability at seasonal time-scales over the locations of the four case studies (Europe and South of Australia), seasonal forecasts exhibited none to low performance (skill) for the atmospheric variables considered. Nevertheless, seasonal forecasts for discharge present some skill in all but one case study. Moreover, seasonal forecasts for water temperature had higher performance in natural lakes than in reservoirs, which means human water control is a relevant factor affecting predictability, and the performance increases with water depth in all four case studies. Further investigation into the skillful water temperature predictions should aim to identify the extent to which performance is a consequence of thermal inertia (i.e., lead-in conditions)
dc.description.sponsorship
This is a contribution of the WATExR project (watexr.eu/), which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by MINECO-AEI (ES), FORMAS (SE), BMBF (DE), EPA (IE), RCN (NO), and IFD (DK), with co-funding by 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), SA Water (https://www.sawater.com.au/), Ruhrverband (www.ruhrverband.de), NIVA (www.niva.no) and NVE (https://www.nve.no/english/). We acknowledge the contribution of the Copernicus Climate Change Service (C3S) in the production of SEAS5. C3S provided the computer time for the generation of the re-forecasts for SEAS5 and for the production of the ocean reanalysis (ORAS5), used as initial conditions for the SEAS5 re-forecasts
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
dc.relation.isformatof
Reproducció digital del document publicat a: https://doi.org/10.1016/j.watres.2021.117286
dc.relation.ispartof
Water Research, 2021, vol. 201, art. núm. 117286
dc.relation.ispartofseries
Articles publicats (ICRA)
dc.rights
Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri
dc.subject
dc.title
Forecasting water temperature in lakes and reservoirs using seasonal climate prediction
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.relation.projectID
info:eu-repo/grantAgreement/EC/H2020/690462/EU/European Research Area for Climate Services/ERA4CS
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.identifier.doi
dc.type.peerreviewed
peer-reviewed
dc.relation.FundingProgramme
dc.relation.ProjectAcronym