Large-scale seasonal forecasts of river discharge by coupling local and global datasets with a stacked neural network: Case for the Loire River system.

Accurate prediction of river discharge is critical for a wide range of sectors, from human activities to environmental hazard management, especially in the face of increasing demand for water resources and climate change. To address this need, a multivariate model that incorporates both local and global data sources, including river and piezometer gauges, sea level, and climate parameters. By employing phase shift analysis, the model optimizes correlations between the target discharge and 12 parameters related to hydrologic and climatic systems, all sampled daily. In addition, a stacked LSTM - a more complex neural network architecture - is used to improve information extraction ability. Exploring river dynamics in the Loire-Bretagne basin and its surroundings, the investigation delves into predictions in daily time steps for one, three, and six months ahead. The resulting forecast features high accuracy and efficiency in predicting river discharge fluctuations, showcasing superior performance in forecasting drought periods over flood peaks. A detailed examination on data used highlights the significance of both local and global datasets in predicting river discharge, where the former dictates short-term predictions, while the latter drives long-range forecasts. Seasonally extended forecasting confirms a strong connection between the forecast leading time and the shift in data correlation, with lower correlation at a lag of 3 months due to seasonal changes affecting forecast quality, compensated by a higher correlation at a longer lag of 6 months. Such mutual effect in this multi-time-step forecasting improves the predictive quality of a six-month horizon, thus encourages progress in long-term prediction to a seasonal scale. The research establishes a practical foundation for effectively utilizing big data to leverage long-term forecasting of environmental dynamics.

Isotopes and groundwater management strategies under semi-arid area: case of the Souss upstream basin (Morocco).

This study concerns the Souss upstream basin. The objective is to investigate the characteristics of surface water and groundwater, to assess the impact of artificial recharge as reinforcement of the natural replenishment and assess the renewal of groundwater under semi-arid area. Two major water types are observed: (i) surface waters and upstream springs (least mineralized) and (ii) all groundwater samples (prevailing calcium and magnesium bicarbonate water type). Water isotopes show a low evaporation of precipitations during infiltration. Impoverishment in heavy isotopes is the characteristic of mountain rainfalls, or of a climate colder and wetter than present. Carbon-14 activities (34-94 pmc) indicate a long residence time. The artificial recharge is low compared to the reservoir volume, due to which the renewal rate is also low.