WHAT CAN WE LEARN FROM 100,000 FORECASTS? A SYNTHESIS OF SUBMISSIONS TO THE AQUATICS THEME OF THE NEON FORECASTING CHALLENGE

The NEON Forecasting Challenge (hereafter Challenge), hosted by the Ecological Forecasting Initiative Research Coordination Network, provides an open platform for participants to engage in ecological forecasting. The goal of the Challenge is to create a community of practice that builds capacity for ecological forecasting of NEON data products and improves our understanding of predictability across ecological variables. Here, we present results of a synthesis of a year of submissions to the aquatics theme of the Challenge, in which participants generated 1-30-day ahead forecasts of surface water temperature (SWT), dissolved oxygen, and chlorophyll-a at 34 stream and lake NEON sites across the USA. Aggregated across the year, 37 models were used to submit >120000 forecasts across all sites and variables. Forecasts were generated using process, machine learning, and statistical models that used a variety of weather covariates and represented uncertainty using different approaches. In general, SWT forecasts that included air temperature as a covariate and were process-based produced the most skillful predictions. Submitted SWT forecasts exhibited greater skill in lakes with more variable seasonal dynamics. Process models were able to capture the important coupling between air temperature and SWT, although empirical and machine-learning models offered valuable alternative methods. Altogether, our synthesis of thousands of forecasts highlights the value of using process-based models for skillful forecasting of lake water temperature.

Primary Presenter: Freya Olsson, Virginia Tech (freyao@vt.edu)

Authors:

Freya Olsson, Virginia Tech  (freyao@vt.edu)

Cayelan Carey, Virginia Tech  (cayelan@vt.edu)

Carl Boettiger, UC-Berkeley  (cboettig@berkeley.edu)

Gregory Harrison, Virginia Tech  (grepath@gmail.com)

Robert Ladwig, Aarhus University  (ladwigjena@gmail.com)

Marcus Lapeyrolerie, UC-Berkeley  (mlapeyro@berkeley.edu)

Abigail Lewis, Virginia Tech  (aslewis@vt.edu)

Mary Lofton, Virginia Tech  (melofton@vt.edu)

Felipe Montealegre-Mora, UC-Berkeley  (felimomo@berkeley.edu)

Joseph Rabaey, University of Minnesota  (rabae005@umn.edu)

Caleb Robbins, University of Alaska - Fairbanks  (cjrobbins@alaska.edu)

Xiao Yang, Southern Methodist University  (xnayang@mail.smu.edu)

R Quinn Thomas, Virginia Tech  (rqthomas@vt.edu)