Content Search

SEPA works with the Met Office to forecast flooding in Scotland, providing accurate and timely information to help emergency responders, local authorities, the public and other organisations prepare for flooding. Scottish Flood Forecasting Service This strategic partnership between the Scottish Environment Protection Agency (SEPA) and the Met Office combines hydrological and meteorological information for the first time, so that both organisations can share their expertise to improve the accuracy of flood forecasts for the whole of Scotland. We collect data from our network of over 250 rainfall, river and

This content has moved to our Beta website We are transitioning to a new website, and the content from this section has moved to our BETA site.  Please go to our Reservoirs topics page to find the most up to date information on: Controlled Reservoir Register (CRR) regulatory information, forms, and guidance civil sanction details and more If the information you are looking for is not p

This content has moved to our Beta website We are transitioning to a new website, and the content from this section has moved to our BETA site.  Please go to our Planning pages to find the most up-to-date information. We will continue to host the information here for a short period to support the transition.   What we do SEPA have various statutory responsibilities for land use plan

Further technical guidance This page brings together a number of ancillary guidance documents and previous email bulletin information relating to modelling of marine fish-farms for CAR licence applications. Auxiliary biomass sampling transect In areas where the tidal residual current is weak, the auxiliary transect (site-specific sampling 2) may be aligned along a bearing reciprocal to the primary

Marine aquaculture modelling Computer modelling is used as a guide to determining licensed discharge quantities of anti-parasitic chemicals and organic waste arising from marine fish-farm operations. The modelling tools promoted by SEPA provide robust predictions in most cases, requiring relatively little site-specific information. Nevertheless, the quality of the model outputs is dependent upon g