Evaluating the Use of "Goodness-of-Fit" Metrics in GRACE Validation: GRACE Accuracy for Monitoring Groundwater Dynamics
Abstract* The researcher, Mohamed Akl, is funded by a full scholarship from the Ministry of Higher Education of the Arab Republic of Egypt. Abstract: The Gravity Recovery and Climate Experiment (GRACE) satellite has proven to be an excellent tool for monitoring changes in total water storage (TWS), which vertically integrate water storage changes from the land surface to the deepest aquifers. The objective of many GRACE studies is to isolate groundwater storage changes from changes in TWS using independent in-situ, remotely sensed, simulated, or assimilated data to remove other water budget components. Using auxiliary datasets to account for water budget components have revealed large biases and uncertainties, especially over high latitude regions, leading to accumulating errors in GRACE-GW estimates. Comparisons with in-situ groundwater observations permit assessments to evaluate how accurately we can isolate groundwater storage signals from TWSA. Goodness-of-fit (GOF) indices e.g., spearman correlation, mean square error (MSE), Nash-Sutcliffe Efficiency (NSE), and the Kling-Gupta Efficiency (KGE), are commonly applied hydrologic fit metrics that express similarity of time series. Such metrics are used here to compare GRACE-GW estimations and in-situ groundwater observations. The use of GOF indices is constrained by their substantial sampling uncertainty, and controversial interpretation, which may lead to wrong judgement on GRACE-GW estimations. Bias, nonlinearity, and non-normality introduce challenges in our use and interpretation of GOF applied to GRACE-GW time series. The goal of this work is to improve interpretation and use of GOF metrics to validate GRACE-GW estimates, highlighting the importance of assessing multiple GOF criteria beyond simply correlation often applied in GRACE studies. Our results document that poor performance of GOF metrics do not simply translate to inaccurate extraction of GRACE-GW time series but may be attributed to the GOF metric applied. We show that a rigorous assessment of GOF enhances our ability to interpret GRACE-GW change.