Analysis of Groundwater and Total Water Storage Changes in Poland Using GRACE Observations, In-situ Data, and Various Assimilation and Climate Models

Abstract

The Gravity Recovery and Climate Experiment (GRACE) observations have provided global observations of total water storage (TWS) changes at monthly intervals for over 15 years, which can be useful for estimating changes in GWS after extracting other water storage components. In this study, we analyzed the TWS and groundwater storage (GWS) variations of the main Polish basins, the Vistula and the Odra, using GRACE observations, in-situ data, GLDAS (Global Land Data Assimilation System) hydrological models, and CMIP5 (the World Climate Research Programme’s Coupled Model Intercomparison Project Phase 5) climate data. The research was conducted for the period between September 2006 and October 2015. The TWS data were taken directly from GRACE measurements and also computed from four GLDAS (VIC, CLM, MOSAIC, and NOAH) and six CMIP5 (FGOALS-g2, GFDL-ESM2G, GISS-E2-H, inmcm4, MIROC5, and MPI-ESM-LR) models. The GWS data were obtained by subtracting the model TWS from the GRACE TWS. The resulting GWS values were compared with in-situ well measurements calibrated using porosity coefficients. For each time series, the trends, spectra, amplitudes, and seasonal components were computed and analyzed. The results suggest that in Poland there has been generally no major TWS or GWS depletion. Our results indicate that when comparing TWS values, better compliance with GRACE data was obtained for GLDAS than for CMIP5 models. However, the GWS analysis showed better consistency of climate models with the well results. The results can contribute toward selection of an appropriate model that, in combination with global GRACE observations, would provide information on groundwater changes in regions with limited or inaccurate ground measurements.

Groundwater Storage Changes Derived from GRACE and GLDAS on Smaller River Basins—A Case Study in Poland

“…For the area of Poland, the porosity coefficient was estimated, e.g., in [29]. It obtained similar values in the Vistula and Odra basins (around 0.4).…”

Evaluation of the temporal variations of groundwater storage and its interactions with climatic variables using GRACE data and hydrological models: A study from Turkey

“…In GLDAS data model, remotely‐sensed data and in‐situ observations are integrated using sophisticated land surface modelling tools and data assimilation techniques (Ramillien, Famiglietti, & Wahr, 2008; Rodell, Velicogna, & Famiglietti, 2009) culminating in a very useful dataset of land surface states and fluxes especially in hydrology and climatology (Śliwińska et al, 2019). GLDAS mission simulates the hydro‐climatic variables using a range of land surface models (LSMs) including the Community Land Model (CLM), Variable Infiltration Capacity (VIC) Model, Noah Model, Mosaic Model and CLSM (Rahaman et al, 2019) with various temporal resolutions ranging from an hour to a month and spatial resolutions from 0.1 to 1.25° (Hu et al, 2019).…”

“…Thus, it is essential to manage these resources effectively to guarantee sustainable water use. Terrestrial water storage (TWS), which expresses the sum of all available water stored on and below the surface of the Earth, is one of the components of the hydrological cycle with a pivotal role in global and regional climate systems (Śliwińska, Birylo, Rzepecka, & Nastula, 2019). Precise knowledge about the variations of TWS is very important for climatologists and hydrologists for assessing the availability of water and understanding the changing climate patterns over time (Tangdamrongsub et al, 2020).…”

Evaluation of the temporal variations of groundwater storage and its interactions with climatic variables using remotely sensed data

“…The Global Land Data Assimilation System (GLDAS) is a data assimilation mission of the National Oceanic and Atmospheric Administration [NASA] and the National Centers for Environmental Prediction to simulate different hydro-climatic variables at a higher resolution. Remotely sensed data and in-situ observations are integrated using sophisticated land surface modeling tools and data assimilation techniques (Ramillien, Famiglietti & Wahr, 2008;Rodell, Velicogna & Famiglietti, 2009) culminating in a very useful dataset of land surface states and fluxes especially in hydrology and climatology (Sliwinska, Birylo, Rzepecka, & Nastula, 2019…”