Land subsidence modelling for decision making on groundwater abstraction under emergency situation

Aichi, Masaatsu

doi:10.5194/piahs-382-403-2020

Cited by 3 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, a vertically one-dimensional (1D) model was adopted in this study. Although vertically 1D models have limitations (Gambolati and Freeze 1973), they have been applied and show good performance in previous studies for similar situations in California (Helm 1975), Mexico City (Ortega-Guerrero et al 1999), Shanghai (Liu and Helm 2008b), Tokyo (Aichi 2020) and Kuki and Mobara (Joshi et al 2020) in Japan, and central Taiwan (Hung et al 2012).…”

Section: Summary Of Observed Information and Modeling Strategymentioning

confidence: 94%

Land subsidence caused by seasonal groundwater level fluctuations in Kawajima (Japan) and one-dimensional numerical modeling with an evolutionary algorithm

Akitaya

Aichi

2022

Hydrogeol J

Self Cite

View full text Add to dashboard Cite

The causes of land subsidence in Kawajima, Japan, have been investigated through data compilation and numerical modeling. Land subsidence has progressed despite a gradual increase in the hydraulic head in the long term. Taking into account the temporal changes and depth distribution of groundwater abstractions, the contraction of formations, and the complexity of the hydrogeological structures, it is proposed that agricultural groundwater use is one of the main triggers for land subsidence. A one-dimensional numerical simulator for coupled groundwater flow and soil deformation was developed with an evolutionary algorithm for model calibration. The calculated spatiotemporal changes in the past-maximum effective stress showed that plastic consolidation in the clayey layers progressed part by part every summer season resulting in long-term and gradual land subsidence under the same range of groundwater level fluctuations. The results also showed that the plastic deformation occurred in both the Holocene and Pleistocene sediments in the drought years, leading to significant subsidence. The model’s predictive performance showed good potential except for a structural prediction error after the Tohoku Earthquake in 2011. The scenario analysis indicated that management of the groundwater level in summer is one of the effective countermeasures in suppressing land subsidence caused by seasonal groundwater level fluctuations. These methodologies and findings can be used for groundwater management in similar cases around the world. Additional investigation is necessary on the influence of large earthquakes in deformation conditions in order to further improve the developed model.

show abstract

Section: Summary Of Observed Information and Modeling Strategymentioning

confidence: 94%

Land subsidence caused by seasonal groundwater level fluctuations in Kawajima (Japan) and one-dimensional numerical modeling with an evolutionary algorithm

Akitaya

Aichi

2022

Hydrogeol J

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this study, we used the existing land subsidence simulator developed by [25]. The simulator performs a coupled analysis of saturated groundwater flow and vertical uniaxial soil deformation.…”

Section: Vertically One-dimensional Land Subsidence Simulatormentioning

confidence: 99%

“…The elastic deformation is described by linear elasticity, and the plastic (inelastic) deformation is described by a modified Cam-clay model [26]. This simulator was previously used in Tokyo [25] and Kawajima (Japan) [21]. Here, only the essential points are concisely introduced (see [21] for details).…”

Section: Vertically One-dimensional Land Subsidence Simulatormentioning

confidence: 99%

Land Subsidence Model Inversion with the Estimation of Both Model Parameter Uncertainty and Predictive Uncertainty Using an Evolutionary-Based Data Assimilation (EDA) and Ensemble Model Output Statistics (EMOS)

Akitaya,

Aichi

2024

Water

View full text Add to dashboard Cite

The nonlinearity nature of land subsidence and limited observations cause premature convergence in typical data assimilation methods, leading to both underestimation and miscalculation of uncertainty in model parameters and prediction. This study focuses on a promising approach, the combination of evolutionary-based data assimilation (EDA) and ensemble model output statistics (EMOS), to investigate its performance in land subsidence modeling using EDA with a smoothing approach for parameter uncertainty quantification and EMOS for predictive uncertainty quantification. The methodology was tested on a one-dimensional subsidence model in Kawajima (Japan). The results confirmed the EDA’s robust capability: Model diversity was maintained even after 1000 assimilation cycles on the same dataset, and the obtained parameter distributions were consistent with the soil types. The ensemble predictions were converted to Gaussian predictions with EMOS using past observations statistically. The Gaussian predictions outperformed the ensemble predictions in predictive performance because EMOS compensated for the over/under-dispersive prediction spread and the short-term bias, a potential weakness for the smoothing approach. This case study demonstrates that combining EDA and EMOS contributes to groundwater management for land subsidence control, considering both the model parameter uncertainty and the predictive uncertainty.

show abstract

Development of a novel method for groundwater flow/land subsidence model calibration using regional monitoring data on land surface displacement including regional crustal deformation

Aichi

2022

Journal of Groundwater Hydrology

View full text Add to dashboard Cite

Land subsidence modelling for decision making on groundwater abstraction under emergency situation

Cited by 3 publications

References 6 publications

Land subsidence caused by seasonal groundwater level fluctuations in Kawajima (Japan) and one-dimensional numerical modeling with an evolutionary algorithm

Land subsidence caused by seasonal groundwater level fluctuations in Kawajima (Japan) and one-dimensional numerical modeling with an evolutionary algorithm

Land Subsidence Model Inversion with the Estimation of Both Model Parameter Uncertainty and Predictive Uncertainty Using an Evolutionary-Based Data Assimilation (EDA) and Ensemble Model Output Statistics (EMOS)

Development of a novel method for groundwater flow/land subsidence model calibration using regional monitoring data on land surface displacement including regional crustal deformation

Contact Info

Product

Resources

About