1 Introduction This document provides an overview of the theoretical framework underlying the algorithm employed in computing long discharge time series data (Q) using different approaches. Section two of this document will focus on discussing the available ancillary data essential for implementing these two approaches. Following that, the third section will delve into defining calibration and validation periods for each station as defined in [RD-1] and updated when WSE time series have been computed [RD-2]. Subsequently, section four will elaborate on the methodology used to derive discharge from altimeters.
This section will incorporate the discussion of three different methods based on data availability. Additionally, it will explain the three approaches derived for these methods: the Bayesian approach when overlap time is present between discharge data and water surface elevation, the quantile approach when it is not and specific approach especially for arctic basins. The fifth section will describe the procedure to derive river discharge from river width.
Using the so-called Non-Parametric Quantile Mapping (NPQM) technique, a data-driven approach that statistically relates the distribution of observed discharge to satellite-based river width measurements, enabling discharge reconstruction even in the absence of simultaneous observations. The NPQM algorithm uses Monte Carlo simulations to generate multiple realizations of discharge and width time series, accounts for measurement uncertainties, and derives a non-parametric mapping function by pairing quantiles of discharge and width without assuming any specific functional form. The sixth section will describe the procedure to derive river discharge from multispectral images.
First, a description on the extraction of reflectance indices is provided along with a multi-mission approach to generate a single time series. Successively, the river discharge estimation is presented based on the similar approaches used for the altimeters (rating curves and quantile approach). Finally, in section seven, the multi-sensor river discharge approach is presented. Several merging procedures have been considered in previous phase of the project. The selected approach consist of using river discharge at Level-3, which requires the combination of multiple river discharge products independently obtained by the different sensors.
The details approach of this product will be provided in the next release of the document (version 2.1). 2 Available ancillary data An initial analysis of the available data was conducted in WP2 [RD-1] to select stations where the estimation of long-term flow would be particularly valuable and feasible. Since WP2 [RD-1], and specifically during the generation of water height time series using altimetry (WP3.1 [RD-2]), this list of stations has been revised for various reasons, as detailed in the ATBD of WP3.1 [RD-2]. For the remainder of the project, we will investigate a total of 54 stations distributed across 18 different basins (see here:
https://climate.esa.int/fr/projects/river-discharge/ and [RD3]). From this updated list, we procured combined time series of water heights dating back to at least 2002, as well as time series from associated missions. In addition to these water height data, observed flow data from various global databases were incorporated: D.3 River Discharge ATBD. Reference: RD_CCI-0006-ATBD - Issue 2.0 – 31/07/2025 Open/Public © 2019 CLS. All rights reserved. Proprietary and Confidential. 7/31