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研究生： 安莫山
Muhammad Zeeshan Ali
論文名稱： 多元感測資料時空整合於地層下陷與地下水推估
Multi-sensor data integration of spatio-temporal land subsidence & groundwater estimation
指導教授： 朱宏杰
Chu, Hone-Jay
學位類別： 博士
Doctor
系所名稱： 工學院 - 測量及空間資訊學系
Department of Geomatics
論文出版年： 2021
畢業學年度： 110
語文別： 英文
論文頁數： 110
中文關鍵詞： 数据整合 、空间回归 、地面沉降 、地下水 、时间序列分析
外文關鍵詞： Data integration, Spatial regression, Land subsidence, Groundwater, Time Series Analysis
相關次數： 點閱：110  下載：5 
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    • Abstract:
    Land subsidence is a severe problem worldwide. The land subsidence usually occurs in the urban and agriculture area where we have high groundwater extraction. Currently, due to water demand growth and climatic change, the groundwater storage or subsidence change is becoming more critical.
    This research considered most severe land subsidence area in Taiwan (Yunlin and Changhua county) and India for regional case studies. The subsidence data for different sensors (leveling, GPS and compaction wells) is available in the area, and groundwater storage information from GRACE data is also available globally. Due to the spatial heterogeneous relationship between the land subsidence and groundwater variations (spatially varying land subsidence due to groundwater extraction), spatial regression model has developed. Considering various data integration multiple sensors, spatial regression model offers a robust method for accurately estimating the spatial pattern of land subsidence from the groundwater drawdown. The dataset from remote sending is integrated with the ground observation data to develop the spatially varying skeletal storage coefficient which help to predict the groundwater variation by using only the remote sensing data. Hydraulic head change estimation model has been developed, where the GPS based subsidence is utilized for monthly hydraulic head change and further the hydraulic head estimations. Subsidence sensors at different locations show us the variation for space and time in the area. For the regional scale the GRACE based spatial mapping shown the high prone area, the ground observation wells data is integrated with the GRACE based GWS to develop the spatial maps of depth to groundwater level from whole India. This spatial regression model helps to integrate the remote sensing and ground observation data to develop the spatially varying groundwater level maps.
    The research findings show that the spatial models used in this study have high accuracy and reliability using the spatial dependency between groundwater and land subsidence without requiring the extensive calibrations or numerical models. The spatially distributed monitoring data of groundwater monitoring wells have estimated annual land subsidence for multiple years with the root mean square error (RMSE) of 0.6 cm using the spatial and temporal regression model. InSAR data is integrated with the ground observation data for annual groundwater drawdown with the estimation and predication accuracy of 0.93 and 0.7 r- square. The InSAR based land subsidence considering the spatial pattern using the spatial regression model for the estimation of groundwater drawdown, which cannot be considered using the traditional regression model. Data integration helped us to model the spatial pattern of hydraulic head change using GPS based subsidence. The spatial model is further used for estimation of monthly hydraulic head using GPS based land subsidence. Moreover, GRACE data are used to find the regional groundwater storage change with the integration of hydrological components. GWS based the spatial maps show the sufficient groundwater storage in the monsoon season in southern part of India. Finally, the regression model validation shows that the RMSE is 3 to 4 m in the regional area for the depth to groundwater level estimation.

    Abstract: i Acknowledgments iii Table of Figures ix Table of Abbreviations xii 1. Introduction 1 1.1. Background 1 1.2. Groundwater extraction and land subsidence relationship 2 1.3. Problem Statement 3 1.4. Research Objectives: 5 1.5. Thesis Structure 6 1.6. Data utilization with reference to Chapters 8 1.7. Thesis overall Chapter-wise Input/output & methodology 8 2. Land subsidence estimation using spatio-temporal regression models from monitoring wells data of groundwater-drawdown 12 2.1 Introduction 12 2.1.1 Description of the study area 13 2.1.2. Pertinent Datasets 14 2.2 Methodology 15 2.2.1 Data preparation 15 2.2.2 Drawdown-subsidence model 16 2.2.3 Model coefficient map and scenario for drawdown reduction 18 2.3 Results and Discussion 18 2.3.1 Spatio-temporal groundwater depletion 18 2.3.2 Land subsidence estimation using GWR and GTWR 20 2.3.3 Spatial pattern of model coefficients 24 2.3.4 Scenarios for reduced drawdown 27 2.4 Conclusion 28 3. Estimating annual groundwater drawdown from InSAR-derived land subsidence 30 3.1. Introduction 30 3.2. Material & Study area 32 3.2.1. SAR data 32 3.2.2. Ground observation data 33 3.2.3. Study Area 34 3.3. Methods 36 3.3.1. Subsidence monitoring from InSAR processing 36 3.3.2. Estimation and prediction of groundwater drawdown 37 3.4. Results & Discussion 39 3.4.1. D-InSAR land deformation 39 3.4.2. Groundwater level drawdown estimation 40 3.5. Conclusions 45 4. Spatio-temporal estimation of monthly groundwater levels from GPS-based land deformation 47 4.1. Introduction 47 4.1.1. Study area 49 4.2. Material and Methods 51 4.2.1. Datasets 51 4.2.2. Methods 51 4.2.2.1. Definition of head change and deformation (Stress/ Strain) 52 4.2.2.2. Spatio-temporal model for estimating deformation and hydraulic heads 53 4.3. Results and Discussion 54 4.3.1. GPS based deformation and compaction-well data Comparison 54 4.3.2. Time series analysis of deformation & head change 57 4.3.3. The relationship between land subsidence and head change 60 4.3.4. Spatial mapping of groundwater level changes from observed deformation 62 4.3.5. Model validation and spatiotemporal result 64 4.4. Conclusion 68 5. Spatio-Temporal Mapping of Groundwater Storage & depth Change with evaluation of land subsidence from GRACE, SAR and ground observation data 70 5.1 Introduction 70 5.2. Study area & Dataset 72 5.2.1. Study area 72 5.2.2. Datasets 74 5.2.2.1. GRACE dataset 74 5.2.2.2. GLDAS data 75 5.2.2.3. TRMM data 75 5.2.2.4. Ground monitoring wells data 75 5.3. Methods 76 5.3.1. GRACE 76 5.3.2. GRACE based GWS times-series & monthly spatial mapping 77 5.3.3. Trend analysis 78 5.3.4. Spatial estimation and prediction of depth to groundwater level 79 5.4. Result & Discussion 80 5.4.1. Monthly spatio-temporal mapping of GWS variation 80 5.4.2. GWS times series in hotspots of India 83 5.4.3. Validation results with ground observation monitoring stations 88 5.4.4. Depth to groundwater level estimation from GRACE based GWS 91 5.5. Conclusion 94 6. Conclusion and Future work 95 6.1. Evaluation of spatial regression models for land subsidence prediction from groundwater drawdown 95 6.2. InSAR & GPS based land subsidence for estimation & prediction of groundwater variation 96 6.3. Regional GWS for estimation of depth to GW level case 97 6.4. Future work 98 References 99

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