TY  - JOUR
A1  - Elmi, Omid
A1  - Tourian, Mohammad J.
A1  - Bárdossy, András
A1  - Sneeuw, Nico
T1  - Spaceborne River Discharge From a Nonparametric Stochastic Quantile Mapping Function
Y1  - 2021-12-21
VL  - 57
IS  - 12
JF  - Water Resources Research
DO  - 10.1029/2021WR030277
PB  - 
N2  - 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 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 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. Here, we propose a nonparametric model for estimating river discharge and its uncertainty from spaceborne river width measurements. The model employs a stochastic quantile mapping scheme by, iteratively: (a) generating realizations of river discharge and width time series using Monte Carlo simulation, (b) obtaining a collection of quantile mapping functions by matching all possible permutations of simulated river discharge and width quantile functions, and (c) adjusting the measurement uncertainties according to the point cloud scatter. We validate our method over 14 different river reaches along the Niger, Congo, Po Rivers, and several river reaches in the Mississippi river basin. Our results show that the proposed algorithm can mitigate the effect of measurement noise and also possible mismodeling. Moreover, the proposed algorithm delivers a meaningful uncertainty for the estimated discharge and allows us to calibrate the error bars of in situ discharge measurements.
N2  - Plain Language Summary: Water scarcity affects the life of more than 2 billion people around the world. The situation will likely worsen as populations and the demand for freshwater grows. Since the demand for freshwater is increasing year by year, the importance of detecting and monitoring the Earth's freshwater is addressed in actions about ensuring the availability and sustainable management of water. However, due to a decline in the number of active gauges, the dynamic of surface water storage has been hardly quantified. The recent breakthroughs in spaceborne geodetic techniques enable us to overcome the lack of comprehensive measurements of freshwater resources, which is a major impediment for a realistic understanding of the hydrological water cycle. Several approaches have been developed for estimating river discharge from space. Among them, developing an empirical rating curve function between ground‐based discharge observations and spaceborne measurements of a physical quantity of the corresponding river reach like water level or river width is the most straightforward one. To release from the bottleneck of characterizing the river section geometry through a limited number of model parameters, we have introduced a nonparametric approach to estimate river discharge from spaceborne river width measurements.
N2  - Key Points: The relation between river width and discharge is modeled by a stochastic quantile mapping function. Being nonparametric, our discharge estimation mapping function does not require the selection of a model for discharge estimation. The method allows to calibrate uncertainties of in situ discharge.
UR  - http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/9833
ER  -