Estimation and impact assessment of input and parameter uncertainty in predicting groundwater flow with a fully distributed model
Mustafa, S.M.T; Nossent, J.; Ghysels, G.; Huysmans, M. (2018). Estimation and impact assessment of input and parameter uncertainty in predicting groundwater flow with a fully distributed model. Water Resour. Res. 54(9): 6585-6608. https://dx.doi.org/10.1029/2017wr021857
In: Water Resources Research: a Journal of the Sciences of Water. American Geophysical Union: Washington etc.. ISSN 0043-1397; e-ISSN 1944-7973
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| Available in | Authors |
Waterbouwkundig Laboratorium: Non-open access 320732 [ request ]
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| Author keywords |
Bayesian approach; Heteroscedasticity; Uncertainty quantification; Groundwater flow model; Input uncertainty; Fully distributed |
| Authors | | Top |
- Mustafa, S.M.T
- Nossent, J.
- Ghysels, G.
- Huysmans, M.
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| Abstract |
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. |
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