Day 4

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Paper title Spaceborne river discharge from a nonparametric stochastic quantile mapping function
Authors
  1. Omid Elmi Institute of Geodesy, University of Stuttgart Speaker
  2. Mohammad J. Tourian University of Stuttgart
  3. András Bárdossy Institute for Modelling Hydraulic and Environmental Systems, University of Stuttgart
  4. Nico Sneeuw University of Stuttgart, Institute of Geodesy
Form of presentation Poster
Topics
  • A7. Hydrology and Water Cycle
    • A7.01 Inland Water Storage and Runoff: Modeling, In Situ Data and Remote Sensing
Abstract text The number of active gauges with open-data policy for discharge monitoring along rivers has decreased over the last decades. Therefore, we cannot properly answer crucial questions about the amount of freshwater available on a certain river basin, the spatial and temporal dynamics of freshwater resources, or the distribution of the world’s freshwater resources in the future. The recent breakthroughs in spaceborne geodetic techniques enable us to overcome the lack of comprehensive measurements of freshwater resources and allow us to understand the hydrological water cycle more realistically. 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 promising results, since the power law rating curves describe a river section with a regular geometry. Such an assumption may cause a large modeling error. Moreover, rating curves do not deliver a proper estimation of discharge uncertainty as a result of the mismodelling and 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 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 algorithm’s estimates are improved in each iteration by updating the measurement uncertainties according to the difference between the measured and estimated values.

We validate the proposed algorithm over 14 river reaches along the Niger, Congo, Po and Mississippi rivers. Our results show that the proposed algorithm can mitigate the effect of measurement noise and also possible mismodelling. Moreover, the proposed algorithm delivers a meaningful discharge uncertainty. Evaluating the discharge estimates via the stochastic nonparametric quantile mapping function and the rating curve technique shows that the performance of the proposed algorithm is superior to the rating curve technique especially in challenging cases.