Estimating long-term groundwater storage and its controlling factors in Alberta, Canada

Abstract

Groundwater is one of the most important natural resources for economic development and environmental sustainability. In this study, we estimated groundwater storage in 11 major river basins across Alberta, Canada, using a combination of remote sensing (Gravity Recovery and Climate Experiment, GRACE), in situ surface water data, and land surface modeling estimates (GWSA sat ). We applied separate calculations for unconfined and confined aquifers, for the first time, to represent their hydrogeological differences. Storage coefficients for the individual wells were incorporated to compute the monthly in situ groundwater storage (GWSA obs ). The GWSA sat values from the two satellitebased products were compared with GWSA obs estimates. The estimates of GWSA sat were in good agreement with the GWSA obs in terms of pattern and magnitude (e.g., RMSE ranged from 2 to 14 cm). While comparing GWSA sat with GWSA obs , most of the statistical analyses provide mixed responses; however the Hodrick-Prescott trend analysis clearly showed a better performance of the GRACE-mascon estimate. The results showed trends of GWSA obs depletion in 5 of the 11 basins. Our results indicate that precipitation played an important role in influencing the GWSA obs variation in 4 of the 11 basins studied. A combination of rainfall and snowmelt positively influences the GWSA obs in six basins. Water budget analysis showed an availability of comparatively lower terrestrial water in 9 of the 11 basins in the study period. Historical groundwater recharge estimates indicate a reduction of groundwater recharge in eight basins during . The output of this study could be used to develop sustainable water withdrawal strategies in Alberta, Canada.Published by Copernicus Publications on behalf of the European Geosciences Union.

Distinct stores and the routing of water in the deep critical zone of a snow-dominated volcanic catchment

“…Moreover, recent focus on characterizing the deep subsurface structure or architecture is beginning to elicit a deeper understanding of the role of weathering, lithology, and hydrology in the overall function of the critical zone (CZ) and its landscape evolution (Riebe et al, 2017). The CZ is the near-surface terrestrial layer of the Earth that spans from the tops of trees down to unweathered bedrock where water, rock, air, and life meet and interact (Brantley et al, 2006(Brantley et al, , 2007Anderson et al, 2007;Chorover et al, 2007;Küsel et al, 2016). Herein, we use the term subsurface structure or architecture to refer to physical properties of the subsurface such as its lithology, fracture density, and location and the extent of geologic heterogeneities that may impact the movement of water through the subsurface.…”

“…A current focus of hydrology is identifying and quantifying groundwater stores (Holbrook et al, 2014;McDonnell, 2017;Rempe and Dietrich, 2018;Dralle et al, 2018;Bhanja et al, 2018), and geophysics is an important tool for examining CZ architecture and its influence on water storage and movement. For example, McGuffy (2017) used seismic refraction surveys to estimate porosity and found that initial porosity plays a significant role in bedrock weathering in granitic and rhyolitic tuff CZs.…”

Importance of spatial and depth-dependent drivers in groundwater level modeling through machine learning

“…The IGBM alluvial aquifer system exhibits significant spatial variation in hydraulic properties, groundwater recharge, storage, and MacDonald et al, 2016;Bonsor et al, 2017;Bhanja et al, 2018). Therefore, it is crucial to analyze each observation wells to show the spatial variability in model performances.…”

Seasonal variations and long-term trends of groundwater over the Canadian landmass

“…The GRACE TWS data have been used to quantify variations and long-term trends of W ground at regional and global scales in various studies (e.g., Rodell et al 2007Rodell et al , 2009Swenson et al 2006;Famiglietti et al 2011;Richey 2014;Huang et al 2016;Bahanja et al 2018;Shamsudduha and Taylor 2020;Opie et al 2020). As GRACE cannot separate the different components of TWS, the W soil , W snow and W surf estimated from other independent approaches such as landsurface models (LSMs), were usually used to separate W ground from GRACE TWS.…”