Abstract: Embodiments relate to estimating river discharge and depth. Initially, observed velocities are used to generate a maximum velocity streamline for a river section, which is then used with an observed shoreline to construct a streamline curvilinear grid. The grid is used to interpolate scattered velocity data points, which are used with a bottom friction of the river section to approximate mean total head slope values. A least squares minimization scheme is applied to a velocity-slope relationship to estimate a bottom friction and discharge coefficient by fitting a difference in the predicted mean total head elevation values between upstream and downstream ends of the river section to a respective ?-average of the measured total head values of the river section. Discharge of the river section is determined based on the coefficient and a velocity-depth relationship and then used to generate a river forecast.

Abstract: Embodiments relate to estimating river discharge and depth. Initially, observed velocities are used to generate a maximum velocity streamline for a river section, which is then used with an observed shoreline to construct a streamline curvilinear grid. The grid is used to interpolate scattered velocity data points, which are used with a bottom friction of the river section to approximate mean total head slope values. A least squares minimization scheme is applied to a velocity-slope relationship to estimate a bottom friction and discharge coefficient by fitting a difference in the predicted mean total head elevation values between upstream and downstream ends of the river section to a respective ?-average of the measured total head values of the river section. Discharge of the river section is determined based on the coefficient and a velocity-depth relationship and then used to generate a river forecast.

Abstract: A computer-implemented inversion method for determining characteristics of a bottom roughness field using a numerical wave model is provided. Measured wave heights over an area of interest are compared to predicted wave heights calculated by a wave model using an estimated bottom roughness parameter. If the error between the measured wave heights and the predicted wave heights is within a specified tolerance level, the analysis ends and the value of the bottom roughness parameter used in the wave model is retrieved. If the error is not within the specified tolerance level, an Influence Matrix IM is used to obtain a revised estimated bottom roughness parameter. The wave model is re-run using the revised roughness parameter and the resulting predicted wave heights are compared to the measured wave heights. The inversion continues until the wave height error is within the specified tolerance level. When the inversion ends, the bottom roughness field that produced those predicted wave heights is retrieved.

Abstract: A computer-implemented inversion method for determining characteristics of a bottom roughness field using a numerical wave model is provided. Measured wave heights over an area of interest are compared to predicted wave heights calculated by a wave model using an estimated bottom roughness parameter. If the error between the measured wave heights and the predicted wave heights is within a specified tolerance level, the analysis ends and the value of the bottom roughness parameter used in the wave model is retrieved. If the error is not within the specified tolerance level, an Influence Matrix IM is used to obtain a revised estimated bottom roughness parameter. The wave model is re-run using the revised roughness parameter and the resulting predicted wave heights are compared to the measured wave heights. The inversion continues until the wave height error is within the specified tolerance level. When the inversion ends, the bottom roughness field that produced those predicted wave heights is retrieved.