Simple Bayesian reconstruction and forecasting of stream water temperature for ecologists-A tool using air temperature, optionally flow, in a time series decomposition approach.

ABSTRACT: Mitigating the impacts of global warming on wildlife entails four practical steps. First, we need to study how processes of interest vary with temperature. Second, we need to build good temperature scenarios. Third, processes can be forecast accordingly. Only then can we perform the fourth step, testing mitigating measures. While having good temperature data is essential, this is not straightforward for stream ecologists and managers. Water temperature (WT) data are often short and incomplete and future projections are currently not routinely available. There is a need for generic models which address this data gap with good resolution and current models are partly lacking. Here, we expand a previously published hierarchical Bayesian model that was driven by air temperature (AT) and flow (

SUBMITTER: Bal G 

PROVIDER: S-EPMC10506714 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature