Assessing hydrological model predictive uncertainty using stochastically generated geological models. Issue 19 (3rd May 2015)

• Record Type: Journal Article
• Authors: He, Xin; Højberg, Anker Lajer; Jørgensen, Flemming; Refsgaard, Jens Christian
• Is Part Of: Hydrological processes. Volume 29:Issue 19(2015:Sep.)
• Page Start: 4293
• Page End: 4311
• Publication Date: 2015-05-03
• Subjects: Hydrology -- Periodicals; Hydrology -- Research -- Periodicals; Hydrologic models -- Periodicals; Hydrological forecasting -- Periodicals; 631.432
• DOI: 10.1002/hyp.10488
• Languages: English
• ISSNs: 0885-6087

Abstract

<abstract abstract-type="main"> <title>Abstract</title> <p>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. Copyright © 2015 John Wiley & Sons, Ltd.</p> </abstract>