Spaceborne River Discharge from a Nonparametric Stochastic Quantile Mapping Function

Abstract:

The number of active gauges with open-data policy for discharge along rivers decreases over the last decades. Therefore, spaceborne measurements are investigated as alternatives. Among different techniques for estimating river discharge from space, developing a rating curve between the ground-based discharge and spaceborne river water level or width is the most straightforward one. However, this does not always lead to successful results, since the river section morphology often cannot simply be modeled by a limited number of model parameters. Moreover, such methods do not deliver a proper estimation of the discharge’s uncertainty as a result of the mismodeling and also the coarse assumptions made for the uncertainty of inputs.

In this study, we propose an algorithm for developing a nonparametric model for estimating river discharge and its uncertainty using spaceborne river width measurements. The algorithm employs a stochastic quantile mapping function scheme by, iteratively: 1) generating realizations of river discharge and width time series using Monte Carlo simulation, 2) obtaining a collection of quantile mapping functions by matching all possible permutations of simulated river discharge and width quantile functions, 3) adjusting the measurement uncertainties according to the point cloud scatter.

The proposed method is validated over four different river reaches along the Niger, Congo and Po rivers. The results show that the proposed algorithm outperforms the conventional rating curve technique for estimating river discharge.