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.

Long-run forecasting surface and groundwater dynamics from intermittent observation data: An evaluation for 50 years.

The accurate prediction of water dynamics is critical for operational water resource management. In this study, we propose a novel approach to perform long-term forecasts of daily water dynamics, including river levels, river discharges, and groundwater levels, with a lead time of 7-30 days. The approach is based on the state-of-the-art neural network, bidirectional long short-term memory (BiLSTM), to enhance the accuracy and consistency of dynamic predictions. The operation of this forecasting system relies on an in-situ database observed for over 50 years with records gauging in 19 rivers, the karst aquifer, the English Channel, and the meteorological network in Normandy, France. To address the problem of missing measurements and gauge installations over time, we developed an adaptive scheme in which the neural network is regularly adjusted and re-trained in response to changing inputs during a long operation. Advances in BiLSTM with extensive learning past-to-future and future-to-past further help to avoid time-lag calibration that simplifies data processing. The proposed approach provides high accuracy and consistent prediction for the three water dynamics within a similar accuracy range as an on-site observation, with approximately 3 % error in the measurement range for the 7 day-ahead predictions and 6 % error for the 30 d-ahead predictions. The system also effectively fills the gap in actual measurements and detects anomalies at gauges that can last for years. Working with multiple dynamics not only proves that the data-driven model is a unified approach but also reveals the impact of the physical background of the dynamics on the performance of their predictions. Groundwater undergoes a slow filtration process following a low-frequency fluctuation, favoring long-term prediction, which differs from other higher-frequency river dynamics. The physical nature drives the predictive performance even when using a data-driven model.

Application of Large-Scale Inversion Algorithms to Hydraulic Tomography in an Alluvial Aquifer.

Large-scale inversion methods have been recently developed and permitted now to considerably reduce the computation time and memory needed for inversions of models with a large amount of parameters and data. In this work, we have applied a deterministic geostatistical inversion algorithm to a hydraulic tomography investigation conducted in an experimental field site situated within an alluvial aquifer in Southern France. This application aims to achieve a 2-D large-scale modeling of the spatial transmissivity distribution of the site. The inversion algorithm uses a quasi-Newton iterative process based on a Bayesian approach. We compared the results obtained by using three different methodologies for sensitivity analysis: an adjoint-state method, a finite-difference method, and a principal component geostatistical approach (PCGA). The PCGA is a large-scale adapted method which was developed for inversions with a large number of parameters by using an approximation of the covariance matrix, and by avoiding the calculation of the full Jacobian sensitivity matrix. We reconstructed high-resolution transmissivity fields (composed of up to 25,600 cells) which generated good correlations between the measured and computed hydraulic heads. In particular, we show that, by combining the PCGA inversion method and the hydraulic tomography method, we are able to substantially reduce the computation time of the inversions, while still producing high-quality inversion results as those obtained from the other sensitivity analysis methodologies.

Overview of preparedness and response for Middle East respiratory syndrome coronavirus (MERS-CoV) in Oman.

Several countries in the Middle East and around 22 countries worldwide have reported cases of human infection with the Middle East respiratory syndrome coronavirus (MERS-CoV). The exceptionally high fatality rate resulting from MERS-CoV infection in conjunction with the paucity of knowledge about this emerging virus has led to major public and international concern. Within the framework of the national acute respiratory illness surveillance, the Ministry of Health in the Sultanate of Oman has announced two confirmed cases of MERS-CoV to date. The aim of this report is to describe the epidemiological aspects of these two cases and to highlight the importance of public health preparedness and response. The absence of secondary cases among contacts of the reported cases can be seen as evidence of the effectiveness of infection prevention and control precautions as an important pillar of the national preparedness and response plan applied in the health care institutions in Oman.

Characterization of focused seepage through an earthfill dam using geoelectrical methods.

Resistivity and self-potential tomography can be used to investigate anomalous seepage inside heterogeneous earthen dams. The self-potential (SP) signals provide a unique signature to groundwater flow because the source current density responsible for the SP signals is proportional to the Darcy velocity. The distribution of the SP signals is also influenced by the distribution of the resistivity; therefore, resistivity and SP need to be used in concert to elucidate groundwater flow pathways. In this study, a survey is conducted at a small earthen dam in Colorado where anomalous seepage is observed on the downstream face at the dam toe. The data reveal SP and direct current resistivity anomalies that are used to delineate three anomalous seepage zones within the dam and to estimate the source of the localized seepage discharge. The SP data are inverted in two dimensions using the resistivity distribution to determine the distribution of the Darcy velocity responsible for the observed seepage. The inverted Darcy velocity agrees with an estimation of the Darcy velocity from the hydraulic conductivity obtained from a slug test and the observed head gradient.

Ionic contribution to the self-potential signals associated with a redox front.

In contaminant plumes or in the case of ore bodies, a source current density is produced at depth in response to the presence of a gradient of the redox potential. Two charge carriers can exist in such a medium: electrons and ions. Two contributions to the source current density are associated with these charge carriers (i) the gradient of the chemical potential of the ionic species and (ii) the gradient of the chemical potential of the electrons (i.e., the gradient of the redox potential). We ran a set of experiments in which a geobattery is generated using electrolysis reactions of a pore water solution containing iron. A DC power supply is used to impose a difference of electrical potential of 3 V between a working platinum electrode (anode) and an auxiliary platinum electrode (cathode). Both electrodes inserted into a tank filled with a well-calibrated sand infiltrated by a (0.01 mol L(-1) KCl+0.0035 mol L(-)(1) FeSO(4)) solution. After the direct current is turned off, we follow the pH, the redox potential, and the self-potential at several time intervals. The self-potential anomalies amount to a few tens of millivolts after the current is turned off and decreases over time. After several days, all the redox-active compounds produced initially by the electrolysis reactions are consumed through chemical reactions and the self-potential anomalies fall to zero. The resulting self-potential anomalies are shown to be much weaker than the self-potential anomalies observed in the presence of an electronic conductor in the laboratory or in the field. In the presence of a biotic or an abiotic electronic conductor, the self-potential anomalies can amount to a few hundred millivolts. These observations point out indirectly the potential role of bacteria forming biofilms in the transfer of electrons through sharp redox potential gradient in contaminant plumes that are rich in organic matter.

Reconstruction of the water table from self-potential data: a bayesian approach.

Ground water flow associated with pumping and injection tests generates self-potential signals that can be measured at the ground surface and used to estimate the pattern of ground water flow at depth. We propose an inversion of the self-potential signals that accounts for the heterogeneous nature of the aquifer and a relationship between the electrical resistivity and the streaming current coupling coefficient. We recast the inversion of the self-potential data into a Bayesian framework. Synthetic tests are performed showing the advantage in using self-potential signals in addition to in situ measurements of the potentiometric levels to reconstruct the shape of the water table. This methodology is applied to a new data set from a series of coordinated hydraulic tomography, self-potential, and electrical resistivity tomography experiments performed at the Boise Hydrogeophysical Research Site, Idaho. In particular, we examine one of the dipole hydraulic tests and its reciprocal to show the sensitivity of the self-potential signals to variations of the potentiometric levels under steady-state conditions. However, because of the high pumping rate, the response was also influenced by the Reynolds number, especially near the pumping well for a given test. Ground water flow in the inertial laminar flow regime is responsible for nonlinearity that is not yet accounted for in self-potential tomography. Numerical modeling addresses the sensitivity of the self-potential response to this problem.