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ÖgeModel-based prediction of water levels for the Great Lakes: a comparative analysis(Springer, 2024)This comprehensive study addresses the correlation between water levels and meteorological features, including air temperature, evaporation, and precipitation, to accurately predict water levels in lakes within the Great Lakes basin. Various models, namely multiple linear regression (MLR), nonlinear autoregressive network with exogenous inputs (NARX), Facebook Prophet (FB-Prophet), and long short-term memory (LSTM), are employed to enhance predictions of lake water levels. Results indicate that all models, except for FB-Prophet, perform well, particularly for Lakes Erie, Huron-Michigan, and Superior. However, MLR and LSTM show reduced performance for Lakes Ontario and St. Clair. NARX emerges as the top performer across all lakes, with Lakes Erie and Superior exhibiting the lowest error metrics—root mean square error (RMSE: 0.048 and 0.034), mean absolute error (MAE: 0.036 and 0.026), mean absolute percent error (MAPE: 0.021% and 0.014%), and alongside the highest R-squared value (R2: 0.977 and 0.968), respectively. Similarly, for Lake Huron-Michigan, NARX demonstrates exceptional predictive precision with an RMSE (0.029), MAE (0.022), MAPE (0.013%), and an outstanding R2 value of 0.995. Despite slightly higher error metrics, NARX consistently performs well for Lake Ontario. However, Lake St. Clair presents challenges for predictive performance across all models, with NARX maintaining relatively strong metrics with an RMSE (0.076), MAE (0.050), MAPE (0.029%), and R2 (0.953), reaffirming its position as the leading model for water level prediction in the Great Lakes basin. The findings of this study suggest that the NARX model accurately predicts water levels, providing insights for managing water resources in the Great Lakes region.
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ÖgeImpact of atmospheric rivers on the winter snowpack in the headwaters of Euphrates-Tigris basin(Springer, 2024)Understanding the hydrometeorological impacts of atmospheric rivers (ARs) on mountain snowpack is crucial for water resources management in the snow-fed river basins such as the Euphrates-Tigris (ET). In this study, we investigate the contribution of wintertime (December-January–February) ARs to precipitation and snowpack in the headwater regions of the ET Basin for the period of 1979–2019 using a state-of-the-art AR catalog and ERA5 reanalysis data. The results show that AR days in the headwaters region could be warmer by up to 3 °C and wetter by over 5 mm day−1 compared to non-AR days. The contribution of ARs to the total winter precipitation varies from year to year, with a maximum contribution of over 80% in 2010 and an average contribution of 60% over the 40-year period. While snow accumulation on AR days shows spatial variability, the average snow contribution is 27% of the seasonal average, ranging from 12 to 57% for different years. The south-facing parts of the mountain range experience significant snowmelt, with contributions ranging from 15 to 80% for different years. The high total precipitation (60%) and low snowpack (27%) contribution can be attributed to the semi-arid characteristics of the region and the occurrence of rain-on-snow events, where rain falling on existing snow rapidly melts the snowpack. The findings have implications for water resource management and call for continued research to improve our knowledge of ARs and their interactions with the complex terrain of the ET Basin.