Integrated Bayesian Multi-model approach to quantify input, parameter and conceptual model structure uncertainty in groundwater modeling

We thank Prof. Jasper Vrugt from University of California, Irvine, USA for his advice on the implementation of BMA. A draft version of a conference abstract appears online at AgEng2018.com but has not been published. The data used in this study are summarized and presented in the figures, tables, references and supporting information and will be available from the authors upon request ([email protected]).Peer reviewedPostprin

Trajectory analysis of land use and land cover maps to improve spatial-temporal patterns, and impact assessment on groundwater recharge

© 2017 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This author accepted manuscript is made available following 24 month embargo from date of publication (Sept 2017) in accordance with the publisher’s archiving policyLand use/land cover (LULC) change is a consequence of human-induced global environmental change. It is also considered one of the major factors affecting groundwater recharge. Uncertainties and inconsistencies in LULC maps are one of the difficulties that LULC timeseries analysis face and which have a significant effect on hydrological impact analysis. Therefore, an accuracy assessment approach of LULC timeseries is needed for a more reliable hydrological analysis and prediction. The objective of this paper is to assess the impact of land use uncertainty and to improve the accuracy of a timeseries of CORINE (coordination of information on the environment) land cover maps by using a new approach of identifying spatial–temporal LULC change trajectories as a pre-processing tool. This ensures consistency of model input when dealing with land-use dynamics and as such improves the accuracy of land use maps and consequently groundwater recharge estimation. As a case study the impact of consistent land use changes from 1990 until 2013 on groundwater recharge for the Flanders-Brussels region is assessed. The change trajectory analysis successfully assigned a rational trajectory to 99% of all pixels. The methodology is shown to be powerful in correcting interpretation inconsistencies and overestimation errors in CORINE land cover maps. The overall kappa (cell-by-cell map comparison) improved from 0.6 to 0.8 and from 0.2 to 0.7 for forest and pasture land use classes respectively. The study shows that the inconsistencies in the land use maps introduce uncertainty in groundwater recharge estimation in a range of 10–30%. The analysis showed that during the period of 1990–2013 the LULC changes were mainly driven by urban expansion. The results show that the resolution at which the spatial analysis is performed is important; the recharge differences using original and corrected CORINE land cover maps increase considerably with increasing spatial resolution. This study indicates that improving consistency of land use map timeseries is of critical importance for assessing land use change and its environmental impact

Centimeter-scale secondary information on hydraulic conductivity using a hand-held air permeameter on borehole cores

Saturated hydraulic conductivity (Ks) is one of the most important parameters determining groundwater flow and contaminant transport in both unsaturated and saturated porous media. Determining the small-scale variability of this parameter is key to evaluate implications on effective parameters at the larger scale. Moreover, for stochastic simulations of groundwater flow and contaminant transport, accurate models on the spatial variability of Ks are very much needed. While several well-established laboratory methods exist for determining Ks, investigating the small-scale variability remains a challenge. If several tens to hundreds of metres of borehole core has to be hydraulically characterised at the centimetre to decimetre scale, several hundreds to thousands of Ks measurements are required, which makes it very costly and time-consuming should traditional methods be used. With reliable air permeameters becoming increasingly available from the late 80’s, a fast and effective indirect method exists to determine Ks. Therefore, the use of hand-held air permeameter measurements for determining very accurate small-scale heterogeneity about Ks is very appealing. Very little is known, however, on its applicability for borehole cores that typically carry a small sediment volume. Therefore, the method was tested on several borehole cores of different size, originating from the Campine basin, Northern Belgium. The studied sediments are of Miocene to Pleistocene age, with a marine to continental origin, and consist of sand to clayey sand with distinct clay lenses, resulting in a Ks range of 7 orders of magnitude. During previous studies, two samples were taken from borehole cores each two meters for performing constant head lab permeameter tests. This data is now used as a reference for the air permeameter measurements that are performed with a resolution of 5 centimetres. Preliminary results indicate a very good correlation between the previously gathered constant head Ks data and the air permeability measurements, but a systematic bias seems to exist. A geostatistical analysis with cross-validation is performed to assess the predictive uncertainty on Ks, using both types of data. We conclude that performing hand-held air permeameter measurements on undisturbed borehole cores provides a very cost-effective way to obtain very detailed information in the framework of stochastic simulation and conditioning of heterogeneous hydraulic conductivity fields

Estimation of hydraulic conductivity and its uncertainty from grain-size data using GLUE and artificial neural networks

peer reviewedaudience: researcher, professionalVarious approaches exist to relate saturated hydraulic conductivity (Ks) to grain-size data. Most methods use a single grain-size parameter and hence omit the information encompassed by the entire grain-size distribution. This study compares two data-driven modelling methods, i.e.multiple linear regression and artificial neural networks, that use the entire grain-size distribution data as input for Ks prediction. Besides the predictive capacity of the methods, the uncertainty associated with the model predictions is also evaluated, since such information is important for stochastic groundwater flow and contaminant transport modelling. Artificial neural networks (ANNs) are combined with a generalized likelihood uncertainty estimation (GLUE) approach to predict Ks from grain-size data. The resulting GLUE-ANN hydraulic conductivity predictions and associated uncertainty estimates are compared with those obtained from the multiple linear regression models by a leave-one-out cross-validation. The GLUE-ANN ensemble prediction proved to be slightly better than multiple linear regression. The prediction uncertainty, however, was reduced by half an order of magnitude on average, and decreased at most by an order of magnitude. This demonstrates that the proposed method outperforms classical data-driven modelling techniques. Moreover, a comparison with methods from literature demonstrates the importance of site specific calibration. The dataset used for this purpose originates mainly from unconsolidated sandy sediments of the Neogene aquifer, northern Belgium. The proposed predictive models are developed for 173 grain-size -Ks pairs. Finally, an application with the optimized models is presented for a borehole lacking Ks data