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Understanding the alterations in spatial–temporal water level dynamics caused by natural and anthropogenic changes is essential for water resources management in estuaries, as this can directly impact the estuarine morphology, sediment transport, salinity intrusion, navigation conditions, and other factors. Here, we propose a simple triple linear regression model linking the water level variation on a daily timescale to the hydrodynamics at both ends of an estuary. The model was applied to the upper Yangtze River estuary (YRE) to examine the influence of the world's largest dam, the Three Gorges Dam (TGD), on the spatial–temporal water level dynamics within the estuary. It is shown that the regression model can accurately reproduce the water level dynamics in the upper YRE, with a root mean squared error (RMSE) of 0.061–0.150 m seen at five gauging stations for both the pre- and post-TGD periods. This confirms the hypothesis that the response of water level dynamics to hydrodynamics at both ends is mostly linear in the upper YRE. The regression model calibrated during the pre-TGD period was used to reconstruct the water level dynamics that would have occurred in the absence of the TGD's freshwater regulation. Results show that the spatial–temporal alterations in water levels during the post-TGD period are mainly driven by the variation in freshwater discharge due to the regulation of the TGD, which results in increased discharge during the dry season (from December to March) and a dramatic reduction in discharge during the wet-to-dry transitional period. The presented method to quantify the separate contributions made by changes in boundary conditions and geometry to spatial–temporal water level dynamics is particularly useful for determining scientific strategies for sustainable water resources management in dam-controlled or climate-driven estuaries worldwide. [ABSTRACT FROM AUTHOR]

Reconstructing tidal signals is indispensable for verifying altimetry products, forecasting water levels, and evaluating long-term trends. Uncertainties in the estimated tidal parameters must be carefully assessed to adequately select the relevant tidal constituents and evaluate the accuracy of the reconstructed water levels. Customary harmonic analysis uses ordinary least squares (OLS) regressions for their simplicity. However, the OLS may lead to incorrect estimations of the regression coefficient uncertainty due to the neglect of the residual autocorrelation. This study introduces two residual resamplings (moving-block and semiparametric bootstraps) for estimating the variability of tidal regression parameters and shows that they are powerful methods to assess the effects of regression errors with nontrivial autocorrelation structures. A Monte Carlo experiment compares their performance to four analytical procedures selected from those provided by the RT_Tide, UTide, and NS_Tide packages and the robustfit.m MATLAB function. In the Monte Carlo experiment, an iteratively reweighted least squares (IRLS) regression is used to estimate the tidal parameters for hourly simulations of one-dimensional water levels. Generally, robustfit.m and the considered RT_Tide method overestimate the tidal amplitude variability, while the selected UTide and NS_Tide approaches underestimate it. After some substantial methodological corrections the selected NS_Tide method shows adequate performance. As a result, estimating the regression variance–covariance with the considered RT_Tide, UTide, and NS_Tide methods may lead to the erroneous selection of constituents and underestimation of water level uncertainty, compromising the validity of their results in some applications. Significance Statement: At many locations, the production of reliable water level predictions for marine navigation, emergency response, and adaptation to extreme weather relies on the precise modeling of tides. However, the complicated interaction between tides, weather, and other climatological processes may generate large uncertainties in tidal predictions. In this study, we investigate how different statistical methods may lead to different quantification of tidal model uncertainty when using data with completely known properties (e.g., knowing the tidal signal, as well as the amount and structure of noise). The main finding is that most commonly used statistical methods may estimate incorrectly the uncertainty in tidal parameters and predictions. This inconsistency is due to some specific simplifying assumptions underlying the analysis and may be reduced using statistical techniques based on data resampling. [ABSTRACT FROM AUTHOR]

Understanding the alterations in spatial-temporal water level dynamics caused by natural and anthropogenic changes is essential for water resources management in estuaries, as this can directly impact the estuarine morphology, sediment transport, salinity intrusion, navigation conditions, and other factors. Here, we propose a simple triple linear regression model linking the water level variation 5 on a daily timescale to the hydrodynamics at both ends of an estuary. The model was applied to the Yangtze River estuary (YRE) for examining the influence of the world's largest dam, the Three Gorges Dam (TGD), on the spatial-temporal water level dynamics within the estuary. It is shown that the regression model can accurately reproduce the water level dynamics in the YRE, with a root mean squared error (RMSE) of 0.063-0.151 m seen at five gauging stations for both the pre- and post-TGD periods. This confirms the hypothesis that the response of water level dynamics to hydrodynamics at both ends is mostly linear in the YRE. The regression model calibrated during the pre-TGD period was used to reconstruct the water level dynamics that would have occurred in absence of the TGD's freshwater regulation. Results show that the spatial-temporal alterations in water levels during the post-TGD period are mainly driven by the variation in freshwater discharge due to the regulation of the TGD, which results in increased discharge during the dry season (from December to March) and a dramatic reduction in discharge during the wet-to-dry transitional period. The presented method to quantify the separate contributions made by changes in boundary conditions and geometry on spatial-temporal water level dynamics is particularly useful for determining scientific strategies for sustainable water resources management in dam-controlled estuaries worldwide. [ABSTRACT FROM AUTHOR]

A fast, physically based, fully nonlinear, and nonsteady reach-averaged river model is developed to provide instantaneous freshwater discharge rates in the tidally influenced stretch of the St. Lawrence River (Canada) from water level measurements alone at two tide gauge stations. The model does not require any a priori knowledge of the river geometry (width and depth) or hydraulic conditions (hydraulic slope and friction coefficient) as those parameters are inferred by inverse modeling. The model is fast in that several decades of hourly discharges can be reproduced in few seconds on any modern desktop computer. The method is accurate in that it reproduces to within 3% the observed tidal discharge variability at 8 different cross-sections distributed along the tidally influenced portion of the St. Lawrence River. Although much simpler, the new method performs as well as, or better than, much more sophisticated models for reproducing daily and monthly discharge averages. Being physically based, it is also shown that the model can perform well outside the limited range of measurements that are generally available for calibration. The method is simple and easy to manage in that the main function only contains a few lines of code such that it could be readily implemented as a tool for real-time discharge monitoring of the St. Lawrence River near Québec City. Being physically based, the model developed here could likely be applied more generally to highly unsteady tidal rivers with flow reversals. [ABSTRACT FROM AUTHOR]

Assessing the impacts of both natural (e.g. tidal forcing from the ocean) and human-induced changes (e.g. dredging for navigation and land reclamation) on estuarine morphology is particularly important for the protection and management of the estuarine environment. In this study, a novel analytical approach is proposed for the assessment of estuarine morphological evolution in terms of tidally averaged depth on the basis of the observed water levels along the estuary. The key lies in deriving a relationship between wave celerity and tidal damping or amplification. For given observed water levels at two gauging stations, it is possible to have a first estimation of both wave celerity (distance divided by tidal travelling time) and tidal damping or amplification rate (tidal range difference divided by distance), which can then be used to predict the morphological changes via an inverse analytical model for tidal hydrodynamics. The proposed method is applied to the Lingdingyang Bay of the Pearl River Estuary, located on the southern coast of China, to analyse the historical development of the tidal hydrodynamics and morphological evolution. The analytical results show surprisingly good correspondence with observed water depth and volume in this system. The merit of the proposed method is that it provides a simple approach for understanding the decadal evolution of the estuarine morphology through the use of observed water levels, which are usually available and can be easily measured. [ABSTRACT FROM AUTHOR]

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Cross-sectional gradients in water levels and velocities play a determining role in the circulation dynamics of tidal rivers and estuaries. Documenting and analyzing their variability throughout a tidal cycle require observations with high spatial and temporal resolution. A survey strategy and a data analysis procedure have been designed to obtain continuous and synoptic water level and velocity fields over a tidal cycle, along 13 cross-sections of the St. Lawrence fluvial estuary dominated by large tidal ranges. The method combines both RTK GPS and ADCP technologies for the simultaneous measurement of water levels and velocities along repeated boat transects, allowing fast data acquisition over wide river sections and under rapidly changing conditions. The reconstruction of continuous and synoptic fields is made by interpolation. Simplifying assumptions about data stationarity and/or homogeneity are avoided by adapting the interpolation procedures to the shape and distribution of the underlying data in a space-time reference frame, thus minimizing distortion in the reconstructed quantities. The capabilities and limitations of the method are assessed through error computations and comparisons with alternate data analysis methods and complementary data sets from tide gauges. With these latest observations, new insights into the tidal hydrodynamics of the St. Lawrence fluvial estuary are gained in regions of contrasting tidal and fluvial properties, specifically related to the effects of channel curvature and bathymetry on tidal propagation and cross-channel flow properties at the intratidal scale. [ABSTRACT FROM AUTHOR]

Measuring temporal and spatial variations in water level with high resolution and accuracy can provide fundamental insights into the hydrodynamics of marine and riverine systems. Real-time kinematic global positioning systems (RTK GPS), and by extension postprocessed kinematic (PPK) positioning, have provided the opportunity to achieve this goal, by allowing fast and straightforward measurements with subdecimeter accuracy. However, boat-mounted GPS are subject to movements of the water surface (e.g., waves, long-period heaves) as well as to the effects of dynamic draft. The latter contaminate the records and need to be separated and removed from the data. A method is proposed to postcorrect the elevation data using tilt information measured by an attitude sensor-in this case, an acoustic Doppler current profiler (ADCP) equipped with internal pitch and roll sensors. The technique uses iteratively reweighted least squares (IRLS) regressions to determine the position of the center of rotation (COR) of the boat that leads to optimal corrections. The COR is also allowed to change in time by performing the IRLS analyses on data subsamples, thus accounting for changes in weight distribution, for example, due to personnel movements. An example of application is presented using data collected in the Saint Lawrence fluvial estuary. The corrections exhibit significant reductions associated with the boat motion while keeping subtle variations in water levels likely related to local hydrodynamics. [ABSTRACT FROM AUTHOR]

[Display omitted] • Accounting forcing data uncertainty is key for reliable water level forecasts. • Ensemble forecasts improve accuracy and reliability of the prediction system. • Contribution of hydraulic model parametric uncertainty to ensemble spread is minor. Reliability of a water level ensemble prediction system (WL-EPS) is directly related to the quality of the available data and the processes integrated into each predictive layer. In this project, we evaluate a hydrometeorological ensemble prediction system (H-EPS) coupled to a hydraulic module (HM) developed on the Chaudière River watershed in Quebec, Canada. Ensemble streamflow forecasts account for the three main sources of uncertainty: meteorology (ensemble forecasts), initial conditions (through data assimilation), and model structure (multimodel). The HM consists of a 1D river model that is informed at the boundary conditions by the H-EPS outputs. In order to evaluate the forecasting system in an operational context, a sampling procedure is implemented on the hydrometeorological ensemble forecasts in order to limit the simulation time of the hydraulic component without altering the description of the uncertainty provided beforehand. Forecasts reliability and accuracy are evaluated, for events of various magnitude covering a wide range of flows, based on performance metrics such as the reliability diagram and the continuous rank probability score (CRPS). Overall, results show that the performances of the ensemble flow and water level forecasts provided by the proposed system are quite reliable over the range of forecast horizons evaluated. Water level forecasts are dependent on the quality of the streamflow forecast, which is coherent with the assumption that streamflow is the main source of uncertainty of the HM. However, at control point where flow and water level forecasts are available, a slight deterioration in the forecast reliability is noted from flow to water level; nonetheless, the reliability of the forecasts remains comparable, thus confirming the strong link between the H-EPS and HM uncertainties. Consideration of the uncertainty related to the roughness coefficient uncertainty turned out to have a negligible effect on the spread of the water level forecasts, confirming once more the domination of the hydrologic uncertainty. [ABSTRACT FROM AUTHOR]