Daily surface temperatures for 185,549 lakes in the conterminous United States estimated using deep learning (1980–2020)
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Abstract
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 (n = 185,549), and also in situ temperature observations for a subset of lakes (n = 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).
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Daily surface temperatures for 185,549 lakes in the conterminous United States estimated using deep learning (1980–2020) |
| Series title | Limnology & Oceanography: Letters |
| DOI | 10.1002/lol2.10249 |
| Volume | 7 |
| Issue | 4 |
| Year Published | 2022 |
| Language | English |
| Publisher | Wiley |
| Contributing office(s) | WMA - Integrated Information Dissemination Division |
| Description | 15 p. |
| First page | 287 |
| Last page | 301 |
| Country | United States |
| Google Analytic Metrics | Metrics page |