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

Estimation and Impact Assessment of Input and Parameter Uncertainty in Predicting Groundwater Flow With a Fully Distributed Model

©2018. American Geophysical Union. All Rights Reserved. We present a general and flexible Bayesian approach using uncertainty multipliers to simultaneously analyze the input and parameter uncertainty of a groundwater flow model with consideration of the heteroscedasticity of the groundwater level error. Groundwater recharge and groundwater abstraction multipliers are introduced to quantify the uncertainty of the spatially distributed input data of the groundwater model in addition to parameter uncertainty. The heteroscedasticity of the groundwater level error is also considered in our Bayesian approach by incorporating a new heteroscedastic error model. The proposed methodology is applied in an overexploited aquifer in Bangladesh where groundwater abstraction and recharge data are highly uncertain. The results of the study confirm that consideration of recharge and abstraction uncertainty through the use of recharge and abstraction multipliers is feasible even in a fully distributed physically based groundwater flow model. Heteroscedasticity is present in the groundwater level error and has an effect on the model predictions and parameter distributions. The input uncertainty affects the model predictions and parameter distributions and it is the dominant source of uncertainty in the groundwater flow prediction. Additionally, the approach described also provides a new way to optimize the spatially distributed recharge and abstraction data along with the parameter values under uncertain input conditions. We conclude that considering model input uncertainty along with parameter uncertainty and heteroscedasticity of the groundwater level error is important for obtaining realistic model predictions and a correct estimation of the uncertainty bounds.status: publishe

Characterization of spatially variable riverbed hydraulic conductivity using electrical resistivity tomography and induced polarization

The spatial distribution of hydraulic conductivity (K) in riverbeds is essential to understand and model river–groundwater interactions. However, K in riverbeds varies over several orders of magnitude and its spatial distribution is closely linked to complex geological and fluvial processes. Investigating the local distribution and spatial heterogeneity of K is therefore a challenging task. The use of direct current (DC) and time-domain induced polarization (IP) geoelectrical methods to map qualitatively the spatial distribution of K within riverbeds is described. The approach is demonstrated for a test site situated in a typical lowland river in Belgium. Inverted geophysical parameters (resistivity, chargeability and normalized chargeability) are compared with estimates of K obtained through slug tests. In general, high values of K are observed in the middle of the river and lower values towards the banks, while the opposite is true for chargeability and normalized chargeability. Therefore, there exists an inverse correlation between K and IP geophysical parameters. Furthermore, geostatistical analyses using variograms show that all parameters have ranges of similar magnitudes. The strong correlation between K and chargeability or normalized chargeability can be explained by the fact that all three parameters are mainly controlled by clay and organic matter content

Characterization of riverbed sediments hydraulic conductivity using slug tests and electrical resistivity/Induced polarization tomography

C

Making water models more inclusive and interdisciplinary to underpin sustainable development

Reliable predictions of water systems’ response to external pressures and ongoing changes arehighly important to ensure informed decision-making to support sustainable water resourcesmanagement for human use and the functioning of healthy ecosystems. Recent strongdevelopment of numerical models offers a potential to understand and forecast water systemsunder anthropogenic and climatic influences to provide information for decision-making, processunderstanding of the ‘unseen’ part of the water cycle and hazard risk analysis. However, thereliability of numerical model predictions is strongly influenced by various sources ofuncertainties, data qualities and assumptions, and often lacks stakeholders' point-of-view. A new,improved approach is needed and in this paper, we present six basic principles to improve thereliability and accuracy of numerical water model predictions considering explicitly stakeholders'needs and, thereby, better serving the society. Six highlighted principles are: (i) clearly defining theobjectives and the purpose of the model, sustaining them during the entire modelling process; (ii)incorporating expert and local community knowledge through stakeholders' feedback; (iii)implementing a multi-model approach in which a range of conceptualizations are explored ; (iv)considering and representing the uncertainties arising from model inputs, parameters, conceptualmodel structure and measurement/information error; (v) translating the results to concrete andunderstandable strategies that policymakers can use for their informed decision-making; and (vi)long term capacity building and monitoring data collection to reduce knowledge gaps, test andimprove predictions. We argue that implementing these six principles reduces uncertainties,improves the predictive capacity of the numerical water models, and ensures informed decision-making to support sustainable water resources management and thereby serve society better