HydroSat: a repository of global water cycle products from spaceborne geodetic sensors

Abstract

Abstract. Against the backdrop of global change, both in terms of climate and demography, there is a pressing need for monitoring the global water cycle. The publicly available global database is very limited in its spatial and temporal coverage worldwide. Moreover, the acquisition of in situ data and their delivery to the database are in decline since the late 1970s, be it for economical or political reasons. Given the insufficient monitoring from in situ gauge networks, and with no outlook for improvement, spaceborne approaches have been under investigation for some years now. Satellite-based Earth observation with its global coverage and homogeneous accuracy has been demonstrated to be a potential alternative to in situ measurements. This paper presents HydroSat as a repository of global water cycle products from spaceborne geodetic sensors. HydroSat provides time series and their uncertainty of: water level from satellite altimetry, surface water extent from satellite imagery, terrestrial water storage anomaly from satellite gravimetry, lake and reservoir water storage anomaly from a combination of satellite altimetry and imagery, and river discharge from either satellite altimetry or imagery. These products can contribute to understanding the global water cycle within the Earth system in several ways. They can act as inputs to hydrological models, they can play a complementary role to current and future spaceborne observations, and they can define indicators of the past and future state of the global freshwater system. The repository is publicly available through http://hydrosat.gis.uni-stuttgart.de.

HydroSat: geometric quantities of the global water cycle from geodetic satellites

“…Figure 7 presents the discharge estimated from river width, using the stochastic quantile mapping function algorithm over four different river reaches along the Niger, Congo and Po rivers (Elmi et al, 2021). High Nash-Sutcliffe model efficiency coefficient (NSE) values for the Niger River reaches (Fig.…”

“…The method is further improved by Elmi et al (2021) to infer a non-parametric model for estimating the river discharge and its uncertainty. The algorithm employs a stochastic quantile mapping function scheme by iteratively (1) generating realizations of river discharge and height (width) time series using a Monte Carlo simulation, (2) obtaining a collection of quantile mapping functions by matching all possible permutations of simulated river discharge and height (width) quantile functions, and (3) adjusting the measurement uncertainties according to the point cloud scatter.…”

“…However, registration is required to download the data. A snapshot of all data (taken in April 2021) with a total of 10 810 time series (34 time series on surface water extent, 1323 time series on water level, 36 time series on river discharge and 463 time series on water storage anomaly) is available in GFZ Data Services, which is accessible at https: //doi.org/10.5880/fidgeo.2021.017 (Tourian et al, 2021).…”