# A Machine Learning approach for Total Water storage anomaly eXtension back to 1980 (ML-TWiX)

## TL;DR

This paper introduces ML-TWiX, a dataset extending global water storage data back to 1980 using machine learning, enhancing climate and hydrology studies.

## Contribution

The novel contribution is a machine learning-based global TWSA dataset extending back to 1980, filling the pre-GRACE era gap.

## Key findings

- ML-TWiX uses Random Forest, XGBoost, and Gaussian Process Regression to reconstruct TWSA from 1980 to 2012.
- The dataset was validated against satellite laser ranging and sea level budget estimates.
- Ensemble averaging provides a unified TWSA product with spatial uncertainty estimates.

## Abstract

We present ML-TWiX, a global dataset of monthly total water storage anomalies (TWSA) reconstructed from 1980 to 2012, provided on a 0.5° × 0.5° global grid. While the GRACE and GRACE Follow-On satellite missions have provided valuable observations of global TWSA, their combined record spans just over two decades, limiting their utility for long-term climate and hydrological studies. ML-TWiX extends the GRACE-era record into the pre-GRACE period by learning from global hydrological and land surface model simulations using an ensemble of three machine learning models: Random Forest, XGBoost, and Gaussian Process Regression. The three machine learning models were independently used to reconstruct TWSA, and their outputs were subsequently combined through ensemble averaging to produce a unified product with spatially explicit uncertainty estimates. We validated ML-TWiX against multiple independent datasets, including satellite laser ranging, storage deduced from the water mass balance closure, and global mean sea level budget estimates. It provides a continuous reconstruction of global TWSA, enabling a wide range of applications in hydrology, climate science, and water resource assessment.