How uncertain is the hydrological contribution to global sea level based on hydrological modelling compared to observational data?

Quantifying individual contributors to global and regional mean sea level along with corresponding uncertainties is crucial for future projections. However, the contribution of terrestrial hydrology seems to be the least known, but is particularly important, since in addition to the climate-driven changes human activities (such as groundwater pumping, irrigation, deforestation) have a large impact on global sea level changes. Under the common assumption that atmospheric water storage change is negligible, (total) terrestrial water storage anomalies (TWSA) represents a proxy for the hydrologic contribution. Generally, TWSA can be derived using models, observations or a combination of both. Each of the methods has its pros and cons.

In this study, we estimate the contribution of terrestrial hydrological cycle changes to global mean sea level along with corresponding uncertainties for 2003 - 2016 based on land TWSA time series derived (i) from WaterGAP Global Hydrological Model WGHM that also simulates anthropogenic effects and provides a partitioning of TWSA into global river discharge and evapotranspiration minus precipitation, (ii) satellite gravimetry data from GRACE, and (iii) from a joint inversion using GRACE and altimetry data. To realistically describe uncertainties in forcing data, model parameters, initial water states, and errors in the model structure, an ensemble of 30 runs is generated and analyzed. Because of well-known large inter-annual and decadal hydrological variations, we estimate time-varying trends using a Kalman filter framework in addition to the usually estimated linear trends. This approach provides more reliable trend and corresponding uncertainty estimates. Moreover, it naturally enables detecting any changes in rates, which is acceleration.