GSTM2020-56
https://doi.org/10.5194/gstm2020-56
GRACE/GRACE-FO Science Team Meeting 2020
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Applications of Data‐Driven Techniques in Filling Temporal Gaps Within and Between GRACE and GRACE-FO Records

Mohamed Ahmed1, Bimal Gyawali1, and David Wiese2
Mohamed Ahmed et al.
  • 1Department of Physical and Environmental Sciences, Texas A&M University - Corpus Christi, Corpus Christi, TX, USA
  • 2Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

Terrestrial water storage (TWS) data derived from past Gravity Recovery and Climate Experiment (GRACE; April 2002–June 2017) and current GRACE-Follow On (GRACE-FO; June 2018–present) missions provide insights into mass transport within, and between, different Earth’s systems (e.g., atmosphere, oceans, groundwater, and ice sheets). However, there are currently temporal gaps within GRACE-derived TWS record (20 months) and between GRACE and GRACE-FO missions (11 months), within GRACE-FO-derived TWS record (2 months), and similar gaps could be experienced between GRACE-FO and GRACE-II missions. In this study, we compare the performance of different data-driven techniques in filling TWS gaps for 62 global watersheds. Additionally, these techniques are being applied to reconstruct TWS globally on a grid scale (1° × 1°). We used artificial neural networks (ANNs), support vector machines (SVMs), and multiple linear regression (MLR) models to predict TWS data (04/2002 – 03/2020) based on the knowledge of relevant climatic datasets such as rainfall, temperature, evapotranspiration, vegetation indices, climate indices. The performance of the developed models was evaluated using several standard measures such as the root mean square error (RMSE), correlation coefficient (R), and Nash-Sutcliff efficiency coefficient (NSE). Our preliminary results indicate: (1) ANN models show higher performance over the examined watersheds compared to the other models (RMSE: 5.20; R: 0.93; NSE: 0.88), (2) the performances of ANN, MLR, and SVM models depend mainly on the nature of factors that control TWS in each of the examined hydrologic systems, and (3) higher model performance is achieved when the model input data were further spectrally decomposed. Results of our research could be used to validate GRACE-FO datasets. Our research will promote additional and improved use of GRACE products by the scientific community, end-users, and decision makers by providing a continuous uninterrupted TWS record from GRACE and GRACE-FO missions.

How to cite: Ahmed, M., Gyawali, B., and Wiese, D.: Applications of Data‐Driven Techniques in Filling Temporal Gaps Within and Between GRACE and GRACE-FO Records, GRACE/GRACE-FO Science Team Meeting 2020, online, 27 October–29 Oct 2020, GSTM2020-56, https://doi.org/10.5194/gstm2020-56, 2020