Data assimilation experiments inform monitoring needs for near‐term ecological forecasts in a eutrophic reservoir

Abstract

Ecosystems around the globe are experiencing changes in both the magnitude and fluctuations of environmental conditions due to land use and climate change. In response, ecologists are increasingly using near‐term, iterative ecological forecasts to predict how ecosystems will change in the future. To date, many near‐term, iterative forecasting systems have been developed using high temporal frequency (minute to hourly resolution) data streams for assimilation. However, this approach may be cost‐prohibitive or impossible for forecasting ecological variables that lack high‐frequency sensors or have high data latency (i.e., a delay before data are available for modeling after collection). To explore the effects of data assimilation frequency on forecast skill, we developed water temperature forecasts for a eutrophic drinking water reservoir and conducted data assimilation experiments by selectively withholding observations to examine the effect of data availability on forecast accuracy. We used in situ sensors, manually collected data, and a calibrated water quality ecosystem model driven by forecasted weather data to generate future water temperature forecasts using Forecasting Lake and Reservoir Ecosystems (FLARE), an open source water quality forecasting system. We tested the effect of daily, weekly, fortnightly, and monthly data assimilation on the skill of 1‐ to 35‐day‐ahead water temperature forecasts. We found that forecast skill varied depending on the season, forecast horizon, depth, and data assimilation frequency, but overall forecast performance was high, with a mean 1‐day‐ahead forecast root mean square error (RMSE) of 0.81°C, mean 7‐day RMSE of 1.15°C, and mean 35‐day RMSE of 1.94°C. Aggregated across the year, daily data assimilation yielded the most skillful forecasts at 1‐ to 7‐day‐ahead horizons, but weekly data assimilation resulted in the most skillful forecasts at 8‐ to 35‐day‐ahead horizons. Within a year, forecasts with weekly data assimilation consistently outperformed forecasts with daily data assimilation after the 8‐day forecast horizon during mixed spring/autumn periods and 5‐ to 14‐day‐ahead horizons during the summer‐stratified period, depending on depth. Our results suggest that lower frequency data (i.e., weekly) may be adequate for developing accurate forecasts in some applications, further enabling the development of forecasts broadly across ecosystems and ecological variables without high‐frequency sensor data.

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

“…This finding is in agreement with a previous water temperature forecast study at the same reservoir, which found high forecast skill from a persistence model deeper in the lake and higher skill from a PM at the surface (Thomas et al, 2020). Individual PMs have been shown to be successful at forecasting water temperature dynamics at the lake surface at short horizons (Thomas et al, 2020;Wander et al, 2024). As weather forecast skill degrades further into the future, there is a subsequent reduction in water temperature forecasting skill at these shallower depths at longer horizons (Carey et al, 2022b;Thomas et al, 2020).…”

“…However, the skill of these models is often limited by the skill of other forecasts (e.g., weather and inflow discharge) needed as model driver data (Mercado-Bettín et al, 2021;Thomas et al, 2020). Moreover, PMs also often demonstrate substantial differences in skill among forecasted sites (Thomas et al, 2023) and depths (Thomas et al, 2020), as well as at different times of year (e.g., in thermally stratified vs. mixed conditions; Thomas et al, 2020;Wander et al, 2024).…”