Estimating aquifer properties using groundwater hydrograph modelling

Shapoori, V.; Peterson, T. J.; Western, AW; Costelloe, Justin F.

doi:10.1002/hyp.10583

Cited by 19 publications

(15 citation statements)

References 34 publications

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“…Following Shapoori et al (), Ferris‐Knowles was reformulated as an impulse response function for the drawdown per unit pumping volume:

θ_{F} (t) = \frac{α}{t} \exp (\frac{- r^{2} β}{t})

where r is the distance between the observation bore and the production bore and α and β are calibration parameters. Following Shapoori et al (), transmissivity ( T ) and storativity ( S ) were then estimated:

T = \frac{1}{4 π α}

S = 4 italicT β .

…”

Section: Methodsmentioning

confidence: 99%

“…Ideally, long‐term pumping tests are conducted and appropriate drawdown equations are fit to the observed drawdown using the known extraction rate(s). However, too often pumping test estimates are not available or are biased due to insufficient duration (Shapoori et al ). Time‐series analysis of groundwater hydrographs that account for time‐varying groundwater extraction has been shown to be a reliable alternative to pumping tests (Bakker et al ; Shapoori et al ).…”

Section: Introductionmentioning

confidence: 99%

“…However, too often pumping test estimates are not available or are biased due to insufficient duration (Shapoori et al ). Time‐series analysis of groundwater hydrographs that account for time‐varying groundwater extraction has been shown to be a reliable alternative to pumping tests (Bakker et al ; Shapoori et al ). It requires only existing data and is comparable to a very long‐term pumping test with multiple production bores, each having a differing and time‐varying extraction rate.…”

Section: Introductionmentioning

confidence: 99%

“…It requires only existing data and is comparable to a very long‐term pumping test with multiple production bores, each having a differing and time‐varying extraction rate. When the complete history of groundwater usage is frequently metered (i.e., monthly), time‐series analysis allows the efficient estimation of aquifer properties and, when undertaken at many observation bores, a data‐driven estimation of aquifer heterogeneity (Shapoori et al ) and the cumulative impact of usage (Shapoori et al ). However, few regions have a complete and frequently metered record of groundwater usage and this inhibits estimation of the aquifer hydraulic properties from the groundwater hydrograph.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, our approach merges usage estimate types three and four. The approach is an extension of Shapoori et al (, , ), which is based upon impulse response functions (von Asmuth et al , ) extended to account for nonlinear partitioning of precipitation (Peterson and Western ). This prior work undertook time‐series modeling of individual groundwater hydrographs using daily meteorological time‐series data and historic monthly groundwater usage data; with the former used to simulate the meteorological drivers of groundwater head fluctuations and the latter the drawdown and recovery from groundwater extraction.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Joint Estimation of Gross Recharge, Groundwater Usage, and Hydraulic Properties within HydroSight

Peterson

Fulton

2019

Groundwater

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Groundwater management decisions are often founded upon estimates of aquifer hydraulic properties, recharge and the rate of groundwater usage. Too often hydraulic properties are unavailable, recharge estimates are very uncertain, and usage is unmetered or infrequently metered over only recent years or estimated using numerical groundwater models decoupled from the drivers of drawdown. This paper extends the HydroSight groundwater time‐series package ( http://peterson-tim-j.github.io/HydroSight/) to allow the joint estimation of gross recharge, transmissivity, storativity, and daily usage at multiple production bores. A genetic evolutionary scheme was extended from estimating time‐series model parameters to also estimating time series of usage that honor metered volumes at each production bore and produces (1) the best fit with the observed hydrograph and (2) plausible estimates of actual evapotranspiration and hence recharge. The reliability of the approach was rigorously tested. Repeated calibration of models for four bores produced estimates of transmissivity, storativity, and mean recharge that varied by a factor of 0.22‐0.32, 0.13‐0.2, and 0.03‐0.48, respectively, when recharge boundary effects were low and the error in monthly, quarterly, and biannual metered usage was generally <10%. Application to the 30 observation bores within the Warrion groundwater management area (Australia), produced a coefficient of efficiency of ≥0.80 at 22 bores and ≥0.90 at 12 bores. The aquifer transmissivity and storativity were reasonably estimated, and were consistent with independent estimates, while mean gross recharge may be slightly overestimated. Overall, the approach allows greater insights from the available data and provides opportunity for the exploration of usage and climatic scenarios.

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“…Following Shapoori et al (), Ferris‐Knowles was reformulated as an impulse response function for the drawdown per unit pumping volume:

θ_{F} (t) = \frac{α}{t} \exp (\frac{- r^{2} β}{t})

T = \frac{1}{4 π α}

S = 4 italicT β .

…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Joint Estimation of Gross Recharge, Groundwater Usage, and Hydraulic Properties within HydroSight

Peterson

Fulton

2019

Groundwater

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A time-series analysis framework for the flood-wave method to estimate groundwater model parameters

2016

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The flood-wave method is implemented within the framework of time-series analysis to estimate aquifer parameters for use in a groundwater model. The resulting extended flood-wave method is applicable to situations where groundwater fluctuations are affected significantly by time-varying precipitation and evaporation. Response functions for timeseries analysis are generated with an analytic groundwater model describing stream-aquifer interaction. Analytical response functions play the same role as the well function in a pumping test, which is to translate observed head variations into groundwater model parameters by means of a parsimonious model equation. An important difference as compared to the traditional flood-wave method and pumping tests is that aquifer parameters are inferred from the combined effects of precipitation, evaporation, and stream stage fluctuations. Naturally occurring fluctuations are separated in contributions from different stresses. The proposed method is illustrated with data collected near a lowland river in the Netherlands. Special emphasis is put on the interpretation of the streambed resistance. The resistance of the streambed is the result of stream-line contraction instead of a semi-pervious streambed, which is concluded through comparison with the head loss calculated with an analytical two-dimensional cross-section model.

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The good, the bad and the outliers: automated detection of errors and outliers from groundwater hydrographs

Peterson

Western

Chen³

2017

Hydrogeol J

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Suspicious groundwater-level observations are common and can arise for many reasons ranging from an unforeseen biophysical process to bore failure and data management errors. Unforeseen observations may provide valuable insights that challenge existing expectations and can be deemed outliers, while monitoring and data handling failures can be deemed errors, and, if ignored, may compromise trend analysis and groundwater model calibration. Ideally, outliers and errors should be identified but to date this has been a subjective process that is not reproducible and is inefficient. This paper presents an approach to objectively and efficiently identify multiple types of errors and outliers.The approach requires only the observed groundwater hydrograph, requires no particular consideration of the hydrogeology, the drivers (e.g. pumping) or the monitoring frequency, and is freely available in the HydroSight toolbox. Herein, the algorithms and time-series model are detailed and applied to four observation bores with varying dynamics. The detection of outliers was most reliable when the observation data were acquired quarterly or more frequently. Outlier detection where the groundwater-level variance is non-stationary or the absolute trend increases rapidly was more challenging, with the former likely to result in an under-estimation of the number of outliers and the latter an over-estimation in the number of outliers.

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Estimating aquifer properties using groundwater hydrograph modelling

Cited by 19 publications

References 34 publications

Joint Estimation of Gross Recharge, Groundwater Usage, and Hydraulic Properties within HydroSight

Joint Estimation of Gross Recharge, Groundwater Usage, and Hydraulic Properties within HydroSight

A time-series analysis framework for the flood-wave method to estimate groundwater model parameters

The good, the bad and the outliers: automated detection of errors and outliers from groundwater hydrographs

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