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| Type: | Resource | |
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| Created: | Feb 08, 2023 at 3:56 p.m. | |
| Last updated: | Feb 08, 2023 at 3:57 p.m. | |
| Citation: | See how to cite this resource |
| Sharing Status: | Public |
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| Views: | 581 |
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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.
Subject Keywords
Coverage
Spatial
Content
Additional Metadata
| Name | Value |
|---|---|
| DOI | 10.1029/2017WR021857 |
| Depth | |
| Scale | 1 001 - 10 000 km² |
| Layers | 1 |
| Purpose | Scientific investigation (not related to applied problem) |
| GroMoPo_ID | 331 |
| IsVerified | True |
| Model Code | MODFLOW |
| Model Link | https://doi.org/10.1029/2017WR021857 |
| Model Time | 1990-2000 |
| Model Year | 2018 |
| Model Authors | Mustafa, SMT; Nossent, J; Ghysels, G; Huysmans, M |
| Model Country | Bangladesh |
| Data Available | Report/paper only |
| Developer Email | syed.mustafa@vub.be |
| Dominant Geology | Unconsolidated sediments |
| Developer Country | Belgium |
| Publication Title | Estimation and Impact Assessment of Input and Parameter Uncertainty in Predicting Groundwater Flow With a Fully Distributed Model |
| Original Developer | No |
| Additional Information | |
| Integration or Coupling | None of the above |
| Evaluation or Calibration | Dynamic water levels |
| Geologic Data Availability | No |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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