Uncertainty assessment of spatially distributed nitrate reduction potential in groundwater using multiple geological realizations

Hansen, Anne Lausten; Gunderman, D.; He, Xin; Refsgaard, Jens Christian

doi:10.1016/j.jhydrol.2014.07.013

Cited by 44 publications

(36 citation statements)

References 29 publications

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“…Thus, the model predictions of drain flows are more reliable at larger scales. This is in line with findings from other studies on the predictive scale of distributed models (Hansen, Gunderman, He, & Refsgaard, 2014;He et al, 2015;Refsgaard et al, 2016).…”

Section: Using Twi To Predict Drain Flow Dynamicssupporting

confidence: 93%

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Importance of geological information for assessing drain flow in a Danish till landscape

Hansen

Storgaard²,

et al. 2018

Hydrological Processes

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There has, in recent years, been an increasing interest in developing nutrient load mitigation measures focussing on tile drains. To plan the location of such tile drain measures, it is important to know where in the landscape drain flow is generated and to understand the key factors governing drain flow dynamics. In the present study, we test two approaches to assess spatial patterns in drain flow generation and thereby assess the importance of including geological information. The approaches are the widely used topographical wetness index (TWI), based solely on elevation data, and hydrological models that include the subsurface geology. We set‐up an ensemble of 20 hydrological models based on 20 stochastically generated geological models to predict drain flow dynamics in the clay till Norsminde catchment in Denmark and test the results against TWI. We find that the hydrological models predict observed daily drain flow reasonably well. High drain flow volumes were found in stream valleys and in wetlands and lower drain flow volumes in the more hilly parts of the catchment. In spite of the apparent connection to the landscape, there was no statistically significant correlation between TWI and drain flow at grid scale (100 × 100 m). TWI was therefore not found to be a sufficient index on its own to assess where drain flow is generated, especially in the highlands of the catchment. The geology below 3 m was found to have a large impact on the drain flow, and correlations between sand percentage in the subsurface geology and drain flow volume were found to be statistically significant. Geological uncertainty therefore give rise to uncertainty on simulated drain flow, and this uncertainty was found to be high at the model grid scale but decreasing with increasing scale.

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Section: Using Twi To Predict Drain Flow Dynamicssupporting

confidence: 93%

“…The stochastic geological models were generated for the structure “Glacial Sequence 1.” (c) Histogram used to convert resistivity data to probability of sand. Figure modified from Hansen et al ()…”

Section: Methodsmentioning

confidence: 99%

Importance of geological information for assessing drain flow in a Danish till landscape

Hansen

Storgaard²,

et al. 2018

Hydrological Processes

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“…On the other hand, Rojas et al (2010) and Refsgaard et al (2012) conclude that geological model uncertainty is likely to dominate over parameter uncertainty for predictions of variables that have not been included in calibration, i.e. groundwater recharge in our case and nitrate transport in the parallel study by Hansen et al (2014).…”

Section: Discussionmentioning

confidence: 79%

“…A parallel study has been carried out using the two calibrated hydrological ensembles (HydEn 3 and 4) for nitrate transport modelling (Hansen et al ., ) in the same catchment. Their results are found to be similar to the present study that the hydrological simulation uncertainty can potentially be lowered by using geophysical data in combination with borehole data because geophysical data provide additional information on the geological structure in between boreholes.…”

Section: Resultsmentioning

confidence: 99%

Assessing hydrological model predictive uncertainty using stochastically generated geological models

Højberg

Jørgensen

et al. 2015

Hydrol. Process.

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Abstract:In distributed and coupled surface water-groundwater modelling, the uncertainty from the geological structure is unaccounted for if only one deterministic geological model is used. In the present study, the geological structural uncertainty is represented by multiple, stochastically generated geological models, which are used to develop hydrological model ensembles for the Norsminde catchment in Denmark. The geological models have been constructed using two types of field data, airborne geophysical data and borehole well log data. The use of airborne geophysical data in constructing stochastic geological models and followed by the application of such models to assess hydrological simulation uncertainty for both surface water and groundwater have not been previously studied. The results show that the hydrological ensemble based on geophysical data has a lower level of simulation uncertainty, but the ensemble based on borehole data is able to encapsulate more observation points for stream discharge simulation. The groundwater simulations are in general more sensitive to the changes in the geological structure than the stream discharge simulations, and in the deeper groundwater layers, there are larger variations between simulations within an ensemble than in the upper layers. The relationship between hydrological prediction uncertainties measured as the spread within the hydrological ensembles and the spatial aggregation scale of simulation results has been analysed using a representative elementary scale concept. The results show a clear increase of prediction uncertainty as the spatial scale decreases.

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“…The values for 200 m have been calculated by first aggregating simulated groundwater recharge to 200‐m grids, and then calculating the average standard deviations as described above. Figure (right) shows the results of a similar analysis for nitrate reduction in the groundwater zone (Hansen, Gunderman, He, & Refsgaard, ). The results from both figures clearly show the decrease of uncertainty with increased aggregation of results, and they also document that the uncertainty reduces by including the SkyTEM data.…”

Section: Examplesmentioning

confidence: 92%

Where are the limits of model predictive capabilities?

Refsgaard

Højberg

et al. 2016

Hydrological Processes

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Distributed hydrological models can make predictions with much finer spatial resolution than the supporting field data. They will, however, usually not have a predictive capability at model grid scale due to limitations of data availability and uncertainty of model conceptualizations. In previous publications, we have introduced the Representative Elementary Scale (RES) concept as the theoretically minimum scale at which a model with a given conceptualization has a potential for obtaining a predictive accuracy corresponding to a given acceptable accuracy. The new RES concept has similarities to the 25‐year‐old Representative Elementary Area concept, but it differs in the sense that while Representative Elementary Area addresses similarity between subcatchments by sampling within the catchment, RES focuses on effects of data or conceptualization uncertainty by Monte Carlo simulations followed by a scale analysis. In the present paper, we extend and generalize the RES concept to a framework for assessing the minimum scale of potential predictability of a distributed model applicable also for analyses of different model structures and data availabilities. We present three examples with RES analyses and discuss our findings in relation to Beven's alternative blueprint and environmental modeling philosophy from 2002. While Beven here addresses model structural and parameter uncertainties, he does not provide a thorough methodology for assessing to which extent model predictions for variables that are not measured possess opportunities to have meaningful predictive accuracies, or whether this is impossible due to limitations in data and models. This shortcoming is addressed by the RES framework through its analysis of the relationship between aggregation scale of model results and prediction uncertainties and for considering how alternative model structures and alternative data availability affects the results. We suggest that RES analysis should be applied in all modeling studies that aim to use simulation results at spatial scales smaller than the support scale of the calibration data.

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Uncertainty assessment of spatially distributed nitrate reduction potential in groundwater using multiple geological realizations

Cited by 44 publications

References 29 publications

Importance of geological information for assessing drain flow in a Danish till landscape

Importance of geological information for assessing drain flow in a Danish till landscape

Assessing hydrological model predictive uncertainty using stochastically generated geological models

Where are the limits of model predictive capabilities?

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