The Gravity Recovery and Climate Experiment (GRACE) mission has monitored total water storage anomalies (TWSA) globally with unprecedented resolution and accuracy since 2002. However, many applications require a data-based, multi-decadal extended record of TWSA prior to the GRACE period and for bridging the eleven-months gap between GRACE and its successor GRACE Follow-On (GRACE-FO), that does not depend on hydrological modelling. Statistical and machine-learning ‘reconstruction’ approaches have been developed to this end, mostly via identifying relations of GRACE-derived TWSA to climate variables, and some regional or global land data sets are now publicly available. In this contribution, we compare the two global reconstructions by Humphrey and Gudmundsson (2019) and Li et al.(2021) mutually and against output from the water Global Analysis and Prognosis (WaterGAP) hydrological model from 1979 onwards, against large-scale mass-change derived from geodetic satellite laser ranging (SLR) from 1992 onwards, and finally against differing GRACE/-FO solutions from 2002 onwards. We find that the reconstructions agree surprisingly well in many regions at seasonal and sub-seasonal timescales, even in the pre-GRACE era. We find larger differences at inter-annual timescales which we speculate are in part due to the way reconstructions are trained, and in part on which specific GRACE solution they are trained as well as the climatological characteristics of the region. Our comparison against independent SLR data reveals that reconstructions (only) partially succeed in representing anomalous TWSA for regions that are influenced by large climate modes such as El Ni$\tilde{\text{n}}$o-Southern Oscillation (ENSO).

A document by Anne Springer. Click on the document to view its contents.

Although reflectometry had not been considered as a primary application of GPS and similar Global Navigation Satellite Systems (GNSS), fast-growing GNSS tracking networks has led to the emergence of GNSS interferometric reflectometry technique for monitoring surface changes such as water level. However, geodetic GNSS instruments are expensive, which is a limiting factor for their prompt and more widespread deployment as a dedicated environmental sensing technique. We present a prototype called Raspberry Pi Reflector (RPR) that includes a low-cost and low-maintenance single-frequency GPS module and a navigation antenna connected to an inexpensive Raspberry Pi microcomputer. A unit has been successfully operating for almost two years since March 2020 in Wesel (Germany) next to the Rhine river. Sub-daily and daily water levels are retrieved using spectral analysis of reflection data. The river level measurements from RPR are compared with a co-located river gauge. We find an RMSE of 7.6 cm in sub-daily estimates and 6 cm in daily means of river level. In August 2021, we changed the antenna orientation from upright to sideways facing the river. The RMSE dropped to 3 cm (sub-daily) and 1.5 cm (daily) with the new orientation. While satellite radar altimetry techniques have been utilized to monitor water levels with global coverage, their measurements are associated with moderate uncertainties and temporal resolution. Therefore, such low-cost and high-precision instruments can be paired with satellite data for calibrating, validating and modeling purposes. These instruments are financially (< US$ 150) and technically accessible worldwide.