Reducing the ambiguity of karst aquifer models by pattern matching of flow and transport on catchment scale

Oehlmann, Sandra; Geyer, Tobias; Licha, Tobias; Sauter, Martin

doi:10.5194/hess-19-893-2015

Cited by 21 publications

(19 citation statements)

References 30 publications

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“…The forecast-specific nature of observation worth has also been reported previously (e.g., Dausman et al, 2010;Fienen et al, 2010;White et al, 2016). The worth of MRT observations relative to various hydraulic potential and discharge observations across the different forecasts are, in general terms, similar to those reported by Hunt et al (2006), Masbruch et al (2014), Oehlmann et al (2015), and Zell et al (2018) (especially when considering the discussion point in the following paragraph).…”

Section: Discussionsupporting

confidence: 79%

“…Direct evaluation of the likelihood term of Bayes' theorem is predicated on a "perfect" simulator to appropriately condition uncertain model parameters through data assimilation. In real-world modeling contexts, however, the presence of model error can invalidate even the most rigorous data assimilation techniques (e.g., Doherty and Welter, 2010;White et al, 2014;Oliver and Alfonzo, 2018). Therefore, when an imperfect simulator is used in a data assimilation framework, extreme care must be taken to assure that the model imperfections do not corrupt (through biased first moments or underestimated second moments) the forecast posterior distributions.…”

Section: Introductionmentioning

confidence: 99%

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On the assimilation of environmental tracer observations for model-based decision support

Knowling

White

Moore

et al. 2020

Hydrol. Earth Syst. Sci.

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It has been advocated that history matching numerical models to a diverse range of observation data types, particularly including environmental tracer concentrations and their interpretations and derivatives (e.g., mean age), constitutes an effective and appropriate means to improve model forecast reliability. This study presents two regionalscale modeling case studies that directly and rigorously assess the value of discrete tritium concentration observations and tritium-derived mean residence time (MRT) estimates in two decision-support contexts; "value" is measured herein as both the improvement (or otherwise) in the reliability of forecasts through uncertainty variance reduction and bias minimization as a result of assimilating tritium or tritiumderived MRT observations. The first case study (Heretaunga Plains, New Zealand) utilizes a suite of steady-state and transient flow models and an advection-only particle-tracking model to evaluate the worth of tritium-derived MRT estimates relative to hydraulic potential, spring discharge and river-aquifer exchange flux observations. The worth of MRT observations is quantified in terms of the change in the uncertainty surrounding ecologically sensitive spring discharge forecasts via first-order second-moment (FOSM) analyses. The second case study (Hauraki Plains, New Zealand) employs paired simple-complex transient flow and transport models to evaluate the potential for assimilation-induced bias in simulated surface-water nitrate discharge to an ecologically sensitive estuary system; formal data assimilation of tritium observations is undertaken using an iterative ensemble smoother. The results of these case studies indicate that, for the decision-relevant forecasts considered, tritium observations are of variable benefit and may induce damaging bias in forecasts; these biases are a result of an imperfect model's inability to properly and directly assimilate the rich information content of the tritium observations. The findings of this study challenge the advocacy of the increasing use of tracers, and of diverse data types more generally, whenever environmental model data assimilation is undertaken with imperfect models. This study also highlights the need for improved imperfect-model data assimilation strategies. While these strategies will likely require increased model complexity (including advanced discretization, processes and parameterization) to allow for appropriate assimilation of rich and diverse data types that operate across a range of spatial and temporal scales commensurate with a forecast of management interest, it is critical that increased model complexity does not preclude the application of formal data assimilation and uncertainty quantification techniques due to model instability and excessive run times.

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Section: Discussionsupporting

confidence: 79%

Section: Introductionmentioning

confidence: 99%

On the assimilation of environmental tracer observations for model-based decision support

Knowling

White

Moore

et al. 2020

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…their parameters lose their identifiability (Wagener et al, 2002;Beven, 2006). For that reason, recent research took advantage of auxiliary data, such as water quality data or tracer experiments (Hartmann et al, 2013b;Oehlmann et al, 2015). These studies showed that adding such information allows the necessary model parameters to be identified, therefore enabling the model to reflect the relevant processes.…”

Section: S Brenner Et Al: Process-based Chalk Groundwater Modellingmentioning

confidence: 99%

“…Birk et al, 2006;Reimann et al, 2011) or well explored study sites (e.g. Hill et al, 2010;Jackson et al, 2011;Oehlmann et al, 2015). Lumped karst modelling approaches consider physical processes on the scale of the entire karst system.…”

Section: S Brenner Et Al: Process-based Chalk Groundwater Modellingmentioning

confidence: 99%

Process-based modelling to evaluate simulated groundwater levels and frequencies in a Chalk catchment in south-western England

Brenner

Coxon

Howden

et al. 2018

Nat. Hazards Earth Syst. Sci.

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Abstract. Chalk aquifers are an important source of drinking water in the UK. Due to their properties, they are particularly vulnerable to groundwater-related hazards like floods and droughts. Understanding and predicting groundwater levels is therefore important for effective and safe water management. Chalk is known for its high porosity and, due to its dissolvability, exposed to karstification and strong subsurface heterogeneity. To cope with the karstic heterogeneity and limited data availability, specialised modelling approaches are required that balance model complexity and data availability. In this study, we present a novel approach to evaluate simulated groundwater level frequencies derived from a semi-distributed karst model that represents subsurface heterogeneity by distribution functions. Simulated groundwater storages are transferred into groundwater levels using evidence from different observations wells. Using a percentile approach we can assess the number of days exceeding or falling below selected groundwater level percentiles. Firstly, we evaluate the performance of the model when simulating groundwater level time series using a spilt sample test and parameter identifiability analysis. Secondly, we apply a split sample test to the simulated groundwater level percentiles to explore the performance in predicting groundwater level exceedances. We show that the model provides robust simulations of discharge and groundwater levels at three observation wells at a test site in a chalk-dominated catchment in south-western England. The second split sample test also indicates that the percentile approach is able to reliably predict groundwater level exceedances across all considered timescales up to their 75th percentile. However, when looking at the 90th percentile, it only provides acceptable predictions for long time periods and it fails when the 95th percentile of groundwater exceedance levels is considered. By modifying the historic forcings of our model according to expected future climate changes, we create simple climate scenarios and we show that the projected climate changes may lead to generally lower groundwater levels and a reduction of exceedances of high groundwater level percentiles.

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“…Due to the complexity of karst processes, karst models usually require more than six model parameters to reflect the most important hydrological processes. Some studies tried to compensate for this apparent lack of information by using auxiliary data such as gravimetric information (Mazzilli et al, 2012), artificial tracer experiments (Hartmann et al, 2012;Oehlmann et al, 2015), or hydrochemical information (Charlier et al, 2012;Hartmann et al, 2013bHartmann et al, , 2016. However, to our knowledge the problem of disinformative observations, either discharge observations or auxiliary information, has not been addressed explicitly in karst modelling studies.…”

mentioning

confidence: 99%

On the value of water quality data and informative flow states in karst modelling

Hartmann

Barberá

Andreo

2017

Hydrol. Earth Syst. Sci.

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Abstract. If properly applied, karst hydrological models are a valuable tool for karst water resource management. If they are able to reproduce the relevant flow and storage processes of a karst system, they can be used for prediction of water resource availability when climate or land use are expected to change. A common challenge to apply karst simulation models is the limited availability of observations to identify their model parameters. In this study, we quantify the value of information when water quality data (NO − 3 and SO 2− 4 ) is used in addition to discharge observations to estimate the parameters of a process-based karst simulation model at a test site in southern Spain. We use a three-step procedure to (1) confine an initial sample of 500 000 model parameter sets by discharge and water quality observations, (2) identify alterations of model parameter distributions through the confinement, and (3) quantify the strength of the confinement for the model parameters. We repeat this procedure for flow states, for which the system discharge is controlled by the unsaturated zone, the saturated zone, and the entire time period including times when the spring is influenced by a nearby river. Our results indicate that NO − 3 provides the most information to identify the model parameters controlling soil and epikarst dynamics during the unsaturated flow state. During the saturated flow state, SO 2− 4 and discharge observations provide the best information to identify the model parameters related to groundwater processes. We found reduced parameter identifiability when the entire time period is used as the river influence disturbs parameter estimation. We finally show that most reliable simulations are obtained when a combination of discharge and water quality date is used for the combined unsaturated and saturated flow states.

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Reducing the ambiguity of karst aquifer models by pattern matching of flow and transport on catchment scale

Cited by 21 publications

References 30 publications

On the assimilation of environmental tracer observations for model-based decision support

On the assimilation of environmental tracer observations for model-based decision support

Process-based modelling to evaluate simulated groundwater levels and frequencies in a Chalk catchment in south-western England

On the value of water quality data and informative flow states in karst modelling

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