Journal Article

A Multi‐Model Ensemble of Baseline and Process‐Based Models Improves the Predictive Skill of Near‐Term Lake Forecasts

Thomas, R. Quinn, Carey, Cayelan C., Breef‐Pilz, Adrienne, Olsson, Freya, Moore, Tadhg N.

2024

Overview

Water temperature forecasting in lakes and reservoirs is a valuable tool to manage crucial freshwater resources in a changing and more variable climate, but previous efforts have yet to identify an optimal modeling approach. Here, we demonstrate the first multi‐model ensemble (MME) reservoir water temperature forecast, a forecasting method that combines individual model strengths in a single forecasting framework. We developed two MMEs: a three‐model process‐based MME and a five‐model MME that includes process‐based and empirical models to forecast water temperature profiles at a temperate drinking water reservoir. We found that the five‐model MME improved forecast performance by 8%–30% relative to individual models and the process‐based MME, as quantified using an aggregated probabilistic skill score. This increase in performance was due to large improvements in forecast bias in the five‐model MME, despite increases in forecast uncertainty. High correlation among the process‐based models resulted in little improvement in forecast performance in the process‐based MME relative to the individual process‐based models. The utility of MMEs is highlighted by two results: (a) no individual model performed best at every depth and horizon (days in the future), and (b) MMEs avoided poor performances by rarely producing the worst forecast for any single forecasted period (<6% of the worst ranked forecasts over time). This work presents an example of how existing models can be combined to improve water temperature forecasting in lakes and reservoirs and discusses the value of utilizing MMEs, rather than individual models, in operational forecasts. Key Points Aggregated lake temperature forecast skill was higher for multi‐model ensemble (MME) forecasts than individual model forecasts Including baseline empirical models (day‐of‐year, persistence) with process models improved MME forecast performance MME forecasts improved forecast skill by “hedging,” as no individual model performed best at all horizons or depths

• Publisher: John Wiley & Sons, Inc,Wiley
• Subject: Automation / baseline models / climate / Climate science / Data assimilation / Dimictic lakes / Drinking water / Empirical models / Environmental conditions / Forecast improvement / Forecasting / Freshwater / Freshwater resources / Inland water environment / Lakes / Mathematical models / multi‐model ensemble / process‐based models / Reservoir water / Reservoirs / Temperature forecasting / Temperature profile / Temperature profiles / uncertainty / Water quality / Water reservoirs / Water temperature / Water temperature forecasting / Weather forecasting