نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه مهندسی آب، دانشکده علوم کشاورزی، دانشگاه گیلان، رشت، استان گیلان، ایران
2 عضو هیات علمی گروه مهندسی آب دانشگاه گیلان
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Land subsidence, as a consequence of the excessive groundwater extraction, poses a serious threat to infrastructure and environment. In the current research, the land subsidence caused by groundwater withdrawal was investigated and modeled using a probabilistic analysis approach. A computer program was developed in MATLAB based on the modified Budryk–Knothe influence function and several copula functions were employed to model the dependence among input parameters. Given the significance of incorporating uncertainty in subsidence modeling, two approaches were considered and compared including the application of temporal random variables and the random variables related to mechanical soil properties. A case study was conducted in the Foomanat region of Guilan Province, characterized by silty clay soil. The results demonstrated that when the temporal random variables were considered, the variations in the range of calculated subsidence gradually decreased over time, indicating a more stable subsidence model. In contrast, incorporating the uncertainties associated with mechanical soil properties led to an increased scattering in subsidence values with time. Accordingly, the findings suggest that for short-term subsidence assessments, greater emphasis should be placed on soil mechanical properties, whereas temporal uncertainties become more dominant in long-term analyses. Generally, for the silty clay soil of the study area, the Frank copula was identified as the most appropriate function for modeling the joint probability distributions when the temporal random variables and random soil properties are considered.
کلیدواژهها [English]
Land subsidence, as one of the most critical consequences of excessive groundwater extraction, poses significant risks to infrastructure, agriculture, and the environment. Given the growing demand for groundwater in recent years, studying and predicting this phenomenon—particularly in vulnerable regions—has become increasingly important. Time plays a pivotal role in subsidence processes and the long-term behavior of soil, and overlooking its impact can lead to less accurate predictions. A thorough analysis of this phenomenon requires accounting for the inherent uncertainties of the influential parameters. A deeper understanding of these uncertainties, along with the role of time, can improve predictive models and contribute to a more effective groundwater resource management in at-risk areas.
The objective of this research is to investigate and model the land subsidence resulting from the excessive groundwater extraction using a probabilistic analysis approach. To achieve this goal, a computer program was developed in the MATLAB environment based on a modified influence function. Copula functions were also employed to model the correlations among the input parameters. In the Fumanat region of Guilan province characterized by silty clay soil, given the significance of incorporating uncertainties in subsidence modeling, two distinct sets of variables were examined including temporal random variables and random mechanical soil properties. Additionally, a sensitivity analysis was conducted to identify the most influential soil parameters affecting the subsidence.
The results indicated that in the first approach, incorporating the temporal parameters, both the range of variability and the coefficient of variation of subsidence decreased over time. The peak of the probability density function was lowest during the early and mid-periods, which gradually increased in the long term. Analyzing the performance of copula functions revealed that the Gumbel copula exhibited the highest coefficient of variation during short and medium terms, whereas the Clayton and Frank copula provided a lower coefficient of variation. In the second approach, where the uncertainties of soil mechanical properties were investigated, the range of data variability increased over time, while the coefficient of variation remained relatively constant. Moreover, the maximum values of probability density functions showed a descending trend. Comparing the performances of copulas indicated that the Frank copula yielded the lowest coefficients of variation. These results underscore the importance of selecting an efficient copula function in the complex analyses related to land subsidence.
This study provides compelling evidence regarding the significance of considering uncertainties in subsidence models. Since the probabilistic analysis is applied in contexts with a high level of uncertainty, it is recommended that the short-term subsidence investigation is better to focus on mechanical properties of soil, while in the long-term analyses the temporal variables should be more considered. Furthermore, the findings indicate that the Frank copula provides the most appropriate joint probability distributions for both the temporal variables and soil properties.
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
All authors contributed equally to the conceptualization of the article and writing of the original and subsequent drafts.
Data will be available on request from the authors.
The authors would like to thank all the people assisted in the present study including experts from the Regional Water Company of Guilan province and deputy for research and technology of the University of Guilan.
The authors avoided data fabrication, falsification, and plagiarism, and any form of misconduct.
The authors declare no conflict of interest.
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