Predicting stream water temperature with artificial neural networks based on open-access data

Predictions of stream water temperature are an important tool for assessing potential impacts of climate warming on aquatic ecosystems and for prioritizing targeted adaptation and mitigation measures. Since predictions require reliable baseline data, we assessed whether open-access data can serve as a suitable resource for accurate and reliable water temperature prediction using artificial neural networks (ANNs). For this purpose, we trained and tested ANNs in 16 small (<= 1m(s)(3/)) headwater streams of major types located in Bavaria, Germany. Between four and eight different combinations of input parameters were trained and tested for each stream ANN, based on data availability. These were air temperature (mean, minimum and maximum), day of the year, discharge, water level and sunshine duration per day. We found that the input combination with the highest accuracy (lowest RMSE) was stream-specific, suggesting that the optimal input combination cannot be generalized across streams. Using a reasonable, but random, input combination resulted in an increase in error (RMSE) of up to >100% compared to the stream-specific optimal combination. Hence, we conclude that the accuracy of water temperature prediction strongly depends on the availability of open-access input data. We also found that environmental parameters such as hydrological characteristics and the proportion of land use in the 5 m riparian strip and the entire catchment are important drivers, affecting the accuracy and reliability of ANNs. ANNs' prediction accuracy was strongly negatively related to river length, total catchment area and water level. High proportions of semi-natural and forested land cover correlated with a higher accuracy, while open-canopy land use types such as grassland were negatively associated with ANN accuracy. In conclusion, open-access data were found to be suitable for accurate and reliable predictions of water temperature using ANNs. However, we recommend incorporating stream-specific environmental information and tailor the combination of input parameters to individual streams in order to obtain optimal results.