Spaceborne River Discharge From a Nonparametric Stochastic Quantile Mapping Function

The number of active gauges with open-data policy for discharge monitoring along rivers has decreased over the last decades. Therefore, spaceborne measurements are investigated as alternatives. Among different techniques estimating river from space, developing a rating curve between ground-based and water level or width is most straightforward one. However, this does not always lead to successful results, since section morphology often cannot simply be modeled by limited parameters. Moreover, such methods do deliver proper estimation discharge's uncertainty result mismodeling also coarse assumptions made inputs. Here, we propose nonparametric model its measurements. employs stochastic quantile mapping scheme by, iteratively: (a) generating realizations time series using Monte Carlo simulation, (b) obtaining collection functions matching all possible permutations simulated functions, (c) adjusting measurement uncertainties according point cloud scatter. We validate our method 14 reaches Niger, Congo, Po Rivers, several in Mississippi basin. Our results show that proposed algorithm can mitigate effect noise mismodeling. delivers meaningful estimated allows us calibrate error bars situ