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Data Assimilation & Metrics for Models in Decision
148 | P a g e Conclusion: The outputs of this project can inform groundwater resource
assessments and groundwater management decisions now and in the future for Victoria.
Data fusion – Merging model results with telemetry data to obtain design water levels, real-time monitoring and virtual sensors
Eduardo De Sousa 1 , Johanna Maria Zwinger 2 , Patrick Keilholz 2 , Michael Gabora 3 , Robin Marc Dufour 4
1. DHI, Brisbane, QLD, Australia 2. DHI, Munich, Germany 3. DHI, Denver, Colorado, USA 4. DHI, Lima, Peru
Groundwater levels and pore pressure distributions are important input data for many engineering calculations in civil engineering and mining. Direct measurements and numerical models are the main tools for obtaining this input, each having
different strengths and drawbacks. Models provide a continuous distribution in space and allow forecasting but are associated with a model-to-measurement misfit. Direct observations are usually very accurate and allow real-time monitoring but are
spatially sparsely distributed.
Consequently, optimal knowledge of hydraulic heads requires combining both
methods. The standard method is model calibration. This method can - if performed wisely – reduce the model predictive error associated to a minimum. At the end of the process however, the model’s output still contains a bias that propagates into further engineering decisions and can reduce their robustness. Also, assimilation of new measurement data into the model that is continuously collected in the field traditionally requires recalibration of the model. This process still requires high effort of labour and time, making it less suitable for real-time monitoring and fast
decisions.
To face these challenges, this presentation demonstrates how the results from a calibrated model are augmented using a bias correction method based on
geostatistical principles. Based on past performance of the model, the likely spatial and temporal distribution of the model error is estimated at unsampled or forecasted locations. By correcting for this bias, we can estimate the most likely “real” system state based on Bayesian principles including uncertainty margins.
The process is illustrated using case studies on generating design water levels and pore pressure distributions for civil engineering, pit dewatering and slope stability projects.
149 | P a g e The presentation shows why augmented estimates are generally more accurate than those of the calibrated model or interpolated data alone, especially in complex
hydrogeologic environments like mining areas. Additionally, we obtain confidence intervals for the estimates, which is useful for choosing appropriate safety factors and proposing new measurement locations.
On regional groundwater models as tools for informing management:
an example of effective and efficient decision-support modelling (Wairarapa Valley; NZ)
Brioch Hemmings 1 , Matthew J. Knowling 1
1. GNS Science, Lower Hutt, Greater Wellington, New Zealand
Publish consent withheld
Is Steady State Model Calibration sufficient?
Catherine Moore 1 , John Doherty 2 1. CSIRO, Brisbane, QLD, Australia
2. Watermark Numerical Computing, Brisbane, QLD, Australia
Parameter estimation and uncertainty analysis theory provide a series of guiding principles which can be used to assess the costs and benefits of model simplification in decision making. It can be shown that if a modelling prediction is data-informed, partially data-informed or not data-informed, then the approach to modelling can be very different.
We present an empirical demonstration of these guiding principles for the most challenging context of model simplification, e.g. partially data informed. Successful strategies for partially data-informed problems must avoid the perils of model complexity which include long run times and numerical instability. These strategies must also navigate the perils of model simplification, i.e. errors in uncertainty
estimates and predictive bias, so that such models retain the benefits of complexity, namely the ability to quantify uncertainty. This requires that prediction-specific complexity is retained, while those parts of a model that are of secondary importance to management-critical predictions can be simplified.
The empirical demonstration explores the costs and benefits of adopting the concept of “steady state” as our simplification strategy in the context of predicting the
increase in the duration of low stream flows in response to pumping of a nearby well.
The full Monty for a regional-scale CSG groundwater impact model
Daan Herckenrath 1 , Mark Gallagher 1 , Keith Phillipson 1 , Gerhard Schöning 1 , Sanjeev Pandey 1 , John Doherty 2
1. Office of Groundwater Impact Assessment, Brisbane, QLD, Australia 2. Watermark Numerical Computing, Brisbane, QLD, Australia
Since 2010 the Office of Groundwater Impact Assessment (OGIA) has been
responsible for assessing the impact of coal seam gas (CSG) extraction activities on groundwater resources in the Surat Cumulative Management Area (CMA) and preparing an Underground Water Impact Report (UWIR) every three years.
150 | P a g e As part of the 2019 UWIR, a regional groundwater flow model has been redeveloped to assess aquifer impacts from current and future CSG development. The
groundwater flow model is constructed using a modified version of MODFLOW-USG and simulates 34 layers of geological strata over a 450 km x 650 km area. It is a large, complex model, containing over 1.3 million active cells and accounting for dual phase flow, faults, aquifer reinjection and the partial completion of CSG wells into non-CSG reservoirs. The model was calibrated using a highly parameterized,
regularized inversion approach implemented using PEST and requiring adjustment of more than 18,000 parameters on the basis of nearly 65,000 observations. This dataset of observations included transient observations of head and head changes at 480 monitoring locations throughout the Surat CMA. Model calibration was followed by the generation of 450 alternative calibration-constrained parameter fields using the PEST-supported null space Monte Carlo method in order to explore post-
calibration predictive uncertainty.
Predictive uncertainty results included ranges of predicted short and long term CSG impacts on springs, water supply bores and overall aquifer water budgets. These results are used to support make good arrangements for water supply bores, inform spring impact management strategies and estimate aquifer recovery times.
This research demonstrates how the results of a predictive uncertainty analysis for a complex regional groundwater flow model can be used to support assessment of cumulative CSG groundwater impacts for a key groundwater system in Australia.
Although important predictions are obtained to support CSG impact assessment, it is becoming increasingly apparent that regional modelling should be supplemented by predictive uncertainty modelling at smaller scales to better represent local aquifer interconnectivity features and capture local groundwater system dynamics.
PEST ++ IES and cloud computing: case study of a numerically challenging mine-closure model with rigorous uncertainty analysis, within the confines of a realistic consulting timeframe and budget
Kevin Hayley 1
1. Groundwater Solutions, Kensington, VIC, Australia
Numerical groundwater modelling to support mining decisions is often challenging and time consuming. Simulation of open pit mining for model calibration or
prediction requires models that include, unsaturated flow, large magnitude hydraulic gradients, and often require transient simulations with time varying material
properties and boundary conditions. This combination of factors typically results in models with long simulation times and/or some level of numerical instability. In modelling practice, this fact can result in reduced effort for predictive uncertainty analysis, and ultimately decrease the value of the modelling to support
decisions. This study presents an early application of the iterative ensemble smother (IES) method of calibration constrained uncertainty analysis to address the
challenges of mining models and uncertainty quantification. The IES method was applied with PEST++ IES software and facilitated by highly parallelized computing using the Amazon EC2 cloud computing service.
An operating open pit mine in South Australia required estimation of long-term recovery pit water levels and inflow rates to support decisions regarding the long- term environmental impact of the project, and the feasibility of a proposed pumped hydro energy storage system. A groundwater observation dataset was available consisting of static water level measurements taken prior to the most recent mining
151 | P a g e activity from both project specific observation bores and public databases at 98 locations. Transient observations of groundwater level changes over 7 years of mine development were available at 16 locations. Initial model simulations indicated that the application of traditional finite difference-based methods of calibration and uncertainty analysis would be complicated by low magnitude numerical instabilities and require excessive computational effort due to multi-point derivatives or highly refined model grids and long simulation times.
The IES calibration successfully produced 150 model parameter realizations that acceptably reproduced groundwater observations. The flexibility of the IES method allowed for the inclusion of 1,493 adjustable parameters and geostatistical
realizations of hydraulic conductivity fields to be included in the analysis. The IES method out-performed finite difference methods when model simulations contained small magnitude numerical instabilities.
Stochastic knowledge integration for groundwater exploration in data scarce areas
Luk Peeters 1 , Dirk Mallants 1 , Tao Cui 2 , Andrew Taylor 1 , Trevor Pickett 2 , Mat Gilfedder 2 , Timothy Munday 3
1. CSIRO Land and Water, Urrbrae, SA, Australia 2. CSIRO Land and Water, Brisbane, QLD, Australia 3. CSIRO Mineral Resources, Perth, WA, Australia
We have developed a systematic probabilistic framework to spatially assess the potential for sustainable groundwater development. The workflow starts by explicitly defining sustainable groundwater extraction, in our case study, a groundwater abstraction that can provide 1ML/d for 10 years with a salinity of less than 2500 mg/L without causing a drawdown of more than 5% of the saturated thickness at 1m from the borehole.
The methodology is applied to groundwater exploration in the Anangu Pitjantjatjara Yankunytjatjara (APY) Lands in central Australia. In this arid region, a crystalline basement is covered with regolith and a vast system of palaeovalleys that are filled with sediments. Both the regolith and the palaeovalley systems are known to host aquifer systems. An ensemble of interfaces that define the boundaries between the basement and the overlying weathered rocks and palaeovalley sediments is
generated with a Bayesian Data Fusion methodology to ensure they are consistent with the available borehole, airborne electromagnetic and digital terrain information.
The surfaces defined by these interfaces are combined with probability distributions of hydraulic conductivity and storage to create ensembles of equivalent
transmissivity and storage. A similar procedure is used to generate ensembles of salinity that are consistent with the available knowledge of salinity distribution across the region.
Gridded water balance equations, in combination with the Theis equation, allow the rapid generation of ensembles of sustainable pumping volumes from these stochastic grids of hydraulic properties and salinity. The ensembles provide the probability of locating areas where the requirements of sustainable groundwater extraction are met.
The integration framework not only allows to rapidly identify prospective zones for sustainable groundwater extraction, it is transparent, can be iteratively and locally updated when new information becomes available.
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Spatial data mapping for reduction of uncertainty in groundwater modelling
Xuyan Wang 1
1. Jacobs, South Brisbane, QLD, Australia
Unbalanced monitoring distribution is always a big challenge in groundwater modelling for assessing the impact of regional groundwater systems on the local human activities. Pilot points as a tool are often used in such groundwater modelling for enhancing information from limit observations but still restricted to the
monitoring data distribution. We propose a novel spatial mapping strategy which combines fuzzy set theory and pilot point approach to form a logic potential from relevant spatial hydrogeological information for reduction of data uncertainty in the model calibration. An effective fuzzy logic approach, which incorporates random probability and fuzzy set theory, is introduced to capture the dispersion of the reliable monitoring information from the relevant regional hydrogeological and monitoring data to produce a map of “intensity” scores which allows the modeller to place “smart pilot points” at locations defined by their probability for producing informative constraints on the statistical distributions of the target variables. The method is demonstrated to be successfully applied for groundwater modelling with limit site monitoring data in a remote mine development project. In summary, the proposed approach intends to reduce the data uncertainty in the groundwater model calibration with the information gleaned from borrowing relevant spatial and
historical data associated with hydrogeological mapping while reducing the site monitoring costs.