Morris Sensitivity Analysis for Hydrothermal Coupling Parameters of Embankment Dam: A Case Study

Ren, Jie; Zhang, Wenbing; Yang, Jie

doi:10.1155/2019/2196578

Cited by 19 publications

(10 citation statements)

References 30 publications

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“…A large number of applications in civil engineering relate to groundwater flow, such as slope stability [21][22][23]; surface/subsurface soil erosion and sediment transport [24][25][26]; dam safety, including piping under and through dams [27][28][29][30][31][32]; groundwater contamination [33][34][35][36]; stability of artificially freezing ground [37,38]; sustainable management of water resources [39][40][41][42]; interaction between groundwater and surface water [43,44]; and karst collapse pillars [45][46][47].…”

Section: Introductionmentioning

confidence: 99%

Seepage Velocity: Large Scale Mapping and the Evaluation of Two Different Aquifer Conditions (Silty Clayey and Sandy)

et al. 2020

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Seepage velocity is a very important criterion in infrastructure construction. The planning of numerous large infrastructure projects requires the mapping of seepage velocity at a large scale. To date, however, no reliable approach exists to determine seepage velocity at such a scale. This paper presents a tool within ArcMap/Geographic Information System (GIS) software that can be used to map the seepage velocity at a large scale. The resultant maps include both direction and magnitude mapping of the seepage velocity. To verify the GIS tool, this study considered two types of aquifer conditions in two regions in Iraq: silty clayey (Babylon province) and sandy (Dibdibba in Karbala province). The results indicate that, for Babylon province, the groundwater flows from the northwest to southeast with a seepage velocity no more than 0.19 m/d; for the Dibdibba region, the groundwater flows from the west to the east with a seepage velocity not exceeding 0.27 m/d. The effectiveness of the presented tool in depicting the seepage velocity was thus demonstrated. The accuracy of the resultant maps depends on the resolution of the four essential maps (groundwater elevation head, effective porosity, saturated thickness, and transmissivity) and locations of wells that are used to collect the data.

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Section: Introductionmentioning

confidence: 99%

Seepage Velocity: Large Scale Mapping and the Evaluation of Two Different Aquifer Conditions (Silty Clayey and Sandy)

et al. 2020

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“…According to the results in Table 4, the average RMSE, R 2 , and Re values for the Ren model, Lu model, and Côté and Konrad model were 0.86°C, 0.95, and 3.57%, respectively, and each evaluation index reflected a satisfactory result. e Lu model is a thermal conductivity model that has been widely used in recent years [43,44] and provides a good simulation effect [18]. Research by Ren et al [18] revealed that coupled hydrothermal modeling based on the Lu model yielded better results than the traditional modeling technique, suggesting that changes in the equivalent thermal conductivity model can promote the accuracy of coupled riparian zone modeling.…”

Section: Evaluation Of the Simulation Results Of Different Equivalent Ermal Conductivity Modelsmentioning

confidence: 99%

A Hydrothermal Coupling Model for Estimating Temperature Variations in the Riparian Zone

Zhang

Shen

Ren

et al. 2020

Mathematical Problems in Engineering

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In research using heat tracing technology to investigate the lateral hyporheic exchange in the shallow geological body of the riparian zone, the accurate estimation of temperature changes can provide a scientific basis for quantifying the process of lateral hyporheic exchange. To improve the accuracy of estimating temperature changes in the riparian zone, a hydrothermal coupling model considering parameter heterogeneity was established based on existing models of the relationship between thermal conductivity and saturation. The model was verified by temperature data from laboratory experiments, and the effect of the thermal conductivity prediction models was compared with that of the partial differential equation (PDE) modeling approach. The results show that the established hydrothermal coupling model can effectively characterize the temperature changes observed in a generalized laboratory model of the riparian zone, and the model simulation effects vary with the equivalent thermal conductivity models. In addition, several thermal conductivity empirical models are suggested for further application. The model parameter sensitivity analysis indicated that the hydraulic conductivity ks, VG model parameters (α and β) and heat capacity of soil Cs have a relatively large effect on the temperature output of the model. The results of this study will provide reference for the selection of equivalent thermal conductivity model for simulating temperature variations in the riparian zone.

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“…The Morris method [24] is a type of screening method, which is suitable for nonlinear models with a large number of factors, and the calculation speed is fast. At the same time, the improved Morris index can also be quantitatively analyzed.…”

Section: Morris Sensitivity Analysis Methodsmentioning

confidence: 99%

Uncertainty and Sensitivity Analysis of Input Conditions in a Large Shallow Lake Based on the Latin Hypercube Sampling and Morris Methods

Pang

et al. 2021

Water

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We selected Tai Lake in China as the research area, and based on the Eco-lab model, we parameterized seven main external input conditions: discharge, carbon, nitrogen, phosphorus, wind speed, elevation, and temperature. We combined the LHS uncertainty analysis method and the Morris sensitivity analysis method to study the relationship between water quality and input conditions. The results showed that (1) the external input conditions had an uncertain impact on water quality. Among them, the uncertainties in total nitrogen concentration (TN) and total phosphorus concentration (TP) were mainly reflected in the lake entrance area, and the uncertainties of chlorophyll-a (Chl-a) and dissolved oxygen (DO) were mainly reflected in the lake center area. (2) The external input conditions had different sensitivities to different water layers. The bottom layer was most clearly and stably affected by input conditions. The TN and TP of the three different water layers were closely related to the flux into the lake, with average sensitivities of 83% and 78%, respectively. DO was mainly related to temperature and water elevation, with the bottom layer affected by temperatures as high as 98%. Chl-a was affected by all input factors except nitrogen and was most affected by wind speed, with an average of about 34%. Therefore, the accuracy of external input conditions can be effectively improved according to specific goals, reducing the uncertainty impact of the external input conditions of the model, and the model can provide a scientific reference for the determination of the mid- to long-term governance plan for Tai Lake in the future.

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Morris Sensitivity Analysis for Hydrothermal Coupling Parameters of Embankment Dam: A Case Study

Cited by 19 publications

References 30 publications

Seepage Velocity: Large Scale Mapping and the Evaluation of Two Different Aquifer Conditions (Silty Clayey and Sandy)

Seepage Velocity: Large Scale Mapping and the Evaluation of Two Different Aquifer Conditions (Silty Clayey and Sandy)

A Hydrothermal Coupling Model for Estimating Temperature Variations in the Riparian Zone

Uncertainty and Sensitivity Analysis of Input Conditions in a Large Shallow Lake Based on the Latin Hypercube Sampling and Morris Methods

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