Regulators require the gas industry to assess the risks of unintentional release of chemicals to the environment and implement measures to mitigate it. Industry standard models for contaminant transport in aquifers do not explicitly model processes in the unsaturated zone and groundwater models often require long run times to complete simulation of complex processes. We propose a stochastic numerical-analytical hybrid model to overcome these two shortcomings and demonstrate its application to assess the risks associated with onshore gas drilling in the Otway Basin, South Australia. The novel approach couples HYDRUS-1D to an analytical solution to model contaminant transport in the aquifer. Groundwater velocities and chemical trajectories were derived from a particle tracking analysis. The most influential parameters controlling solute delivery to the aquifer were the soil chemical degradation constant and the hydraulic conductivity of a throttle soil horizon. Only 18% of the flow paths intercepted environmental receptors within a 1-km radius from the source, 87% of which had concentrations of <1% of the source. The proposed methodology assesses the risk to environmental assets and informs regulators to implement measures that mitigate risk down to an acceptable level.
Introduction Groundwater in the Middle East and North Africa region is a critical component of the water supply budget due to a (semi-)arid climate and hence limited surface water resources. Despite the significance, factors affecting the groundwater balance and overall sustainability of the resource are often poorly understood. This often includes recharge and discharge characteristics, groundwater extraction and impacts of climate change. The present study investigates the groundwater balance in the Dead Sea Basin aquifer in Jordan using a groundwater flow model developed using the MODFLOW. Methods The study aimed to simulate groundwater balance components and their effect on estimation of the aquifer's safe yield, and to also undertake a preliminary analysis of the impact of climate change on groundwater levels in the aquifer. Model calibration and predictive analysis was undertaken using a probabilistic modeling workflow. Spatially heterogeneous groundwater recharge for the historical period was estimated as a function of rainfall by simultaneously calibrating the recharge and aquifer hydraulic property parameters. Results and discussion The model indicated that annual average recharge constituted 5.1% of the precipitation over a simulation period of 6 years. The effect of groundwater recharge and discharge components were evaluated in the context of estimation of safe yield of the aquifer. The average annual safe yield is estimated as ~8.0 mm corresponding to the 80% of the calibrated recharge value. Simulated groundwater levels matched well with the declining trends in observed water levels which are indicative of unsustainable use. Long-term simulation of groundwater levels indicated that current conditions would result in large drawdown in groundwater levels by the end of the century. Simulation of climate change scenarios using projected estimates of rainfall and evaporation indicates that climate change scenarios would further exacerbate groundwater levels by relatively small amounts. These findings highlight the need to simulate the groundwater balance to better understand the water availability and future sustainability.
Abstract The coarse spatial resolution of the Gravity Recovery and Climate Experiment (GRACE) dataset has limited its application in local water resource management and accounting. Despite efforts to improve GRACE spatial resolution, achieving high resolution downscaled grids that correspond to local hydrological behaviour and patterns is still limited. To overcome this issue, we propose a novel statistical downscaling approach to improve the spatial resolution of GRACE-terrestrial water storage changes (ΔTWS) using precipitation, evapotranspiration (ET), and runoff data from the Australian Water Outlook. These water budget components drive changes in the GRACE water column in much of the global land area. Here, the GRACE dataset is downscaled from the original resolution of 1.0° × 1.0° to 0.05° × 0.05° over a large hydro-geologic basin in northern Australia (the Cambrian Limestone Aquifer—CLA), capturing sub- grid heterogeneity in ΔTWS of the region. The downscaled results are validated using data from 12 in-situ groundwater monitoring stations and water budget estimates of the CLA’s land water storage changes from April 2002 to June 2017. The change in water storage over time (ds/dt) estimated from the water budget model was weakly correlated (r = 0.34) with the downscaled GRACE ΔTWS. The weak relationship was attributed to the possible uncertainties inherent in the ET datasets used in the water budget, particularly during the summer months. Our proposed methodology provides an opportunity to improve freshwater reporting using GRACE and enhances the feasibility of downscaling efforts for other hydrological data to strengthen local-scale applications.
Abstract The Gnangara groundwater system is a highly productive water resource in southwestern Australia. However, it is considered one of the most vulnerable groundwater systems to climate change, due to consistent declines in precipitation and recharge, and regional climate models project further declines into the future. This study introduces a new framework underpinned by machine learning techniques to provide reliable estimates of precipitation‐based recharge over the whole Perth Basin (including the Gnangara system). By combining estimates of baseflow, groundwater evaporation, and extraction, groundwater recharge was estimated over the Perth (testing site) and Gnangara (calibration site) systems using downscaled Groundwater Storage Anomalies (GWSA) from the Gravity Recovery and Climate Experiment (GRACE) mission. The random forest regression (RFR) model was used to downscale the spatial resolution of GRACE to 0.05° (approx. 5 km), providing estimable signals over the relatively small calibration site (∼2,200 km 2 ) in order to discern any meaningful signals from the original GRACE resolution. Our study reveals that downscaled signals from GRACE can be used to provide precipitation‐based recharge estimates for groundwater systems accurately. However, the growing impacts of climate change, which has led to sporadic precipitation patterns over Western Australia, can limit the efficiency of satellite remote sensing methods in estimating recharge, especially in deep and complex aquifers.
Parameterization strategy impacts models' outputs and the associated uncertainty. This is particularly true for transient regional groundwater models where parameters can only be weakly constrained by insufficient observations. However, this is rarely investigated under any particular model structure. This study bridges this gap using a regional groundwater model developed to understand the impact of coal seam gas extraction on groundwater systems in a probabilistic framework. Two different parameterization schemes were implemented for hydraulic conductivity and specific storage. The first method solely relies on the relationship between hydraulic properties and burial depths. The second more complex strategy allows more spatial variations of hydraulic parameters using pilot points. The study provides new insights and practical guidance on the application of groundwater modelling for environmental impact assessment. The results suggest that the choice of model parameterization has a significant influence on predictive uncertainty. The model using the simple parameterization provides predictions with a much wider range than the model with a more sophisticated parameterization. This is because that the lowly parameterized model tends to generate more extreme effective hydraulic parameter fields unless the parameterization simplification converts the inverse problem to a (close to) well-posed problem that rarely exists for applied regional groundwater modelling. The potential impact of model parameterization should be discussed explicitly in groundwater modelling applications to support decision making to avoid misinterpretation of the modelling results.
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