Jordan Read
Simon Nemer Topp
Gretchen J. A. Hansen
Vipin Kumar
Jared D. Willard
2022
<p><span>The dataset described here includes estimates of historical (1980–2020) daily surface water temperature, lake metadata, and daily weather conditions for lakes bigger than 4 ha in the conterminous United States (</span><i>n</i><span> = 185,549), and also in situ temperature observations for a subset of lakes (</span><i>n</i><span> = 12,227). Estimates were generated using a long short-term memory deep learning model and compared to existing process-based and linear regression models. Model training was optimized for prediction on unmonitored lakes through cross-validation that held out lakes to assess generalizability and estimate error. On the held-out lakes with in situ observations, median lake-specific error was 1.24°C, and the overall root mean squared error was 1.61°C. This dataset increases the number of lakes with daily temperature predictions when compared to existing datasets, as well as substantially improves predictive accuracy compared to a prior empirical model and a debiased process-based approach (2.01°C and 1.79°C median error, respectively).</span></p>
application/pdf
10.1002/lol2.10249
en
Wiley
Daily surface temperatures for 185,549 lakes in the conterminous United States estimated using deep learning (1980–2020)
article