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GROUNDWATER RESOURCE MANAGEMENT UNDER UNCERTAINTY

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GROUNDWATER RESOURCE MANAGEMENT UNDER UNCERTAINTY

Groundwater as a vital resource is the main source of water for agricultural activities in arid and semi-arid regions, and should therefore be properly managed. Optimization techniques could be effective tools for designing optimal strategies. However, groundwater management is mostly encountered by many sources of uncertainties. The first part of this thesis presents a method for reducing uncertainty associated with groundwater recharge estimation. Regarding this goal, a semi-distributed hydrologic model is developed and applied in a complex alluvial-karst system in Firouzabad catchment (Iran) to estimate groundwater recharge from precipitation, subsurface inflow from adjacent karst aquifer, return flows and riverbed infiltration.

In the second part of this thesis, a nonlinear optimization modeling technique developed to solve groundwater management problems. In this method, indicator functions were used in order to partition the physical space of aquifer. This method allows deriving expressions for the means, variances and covariances of the groundwater heads and approximating the reliability level. In order to demonstrate the efficiency of the proposed optimization models, several applications have been considered. In the first case study, we developed an explicit stochastic optimization model for finding the optimal crop patterns and the amount of groundwater extraction in Firouzabad aquifer. In this case study, uncertainties in groundwater recharge and withdrawals are considered. The main advantage of this type of modeling is that the average amount of groundwater extraction can be computed where the irrigation demands are conjunctively satisfied by both groundwater and surface water resources.

Furthermore, we proposed a stochastic multiple cell aquifer model. In order to investigate the applicability of the formulation for large-scale groundwater management problems, a synthetic unconfined aquifer with 80 cells was employed. By solving the management problem, optimal groundwater extractions from nine wells were determined and the means, variances and covariances of the groundwater heads were approximated. It is worth pointing out the outcomes were in agreement with those obtained from Monte Carlo simulation.

دسته: زبان خارجه, زبان خارجه برچسب: GROUNDWATER RESOURCE MANAGEMENT UNDER UNCERTAINTY

توضیحات

Table of Contents

Chapter 1

Introduction. 1

1.1 General 1

1.2 Objectives of the Study. 3

Chapter 2

Literature Review.. 5

2.1 Introduction. 5

2.2 Simulation Models. 6

2.3 Uncertainty in the field of environmental modeling. 8

2.3.1 Sources of model uncertainty. 8

2.4 Optimization Techniques. 10

2.5 Type of groundwater management models: 12

2.6 Optimization under uncertainty: 14

2.7 An overview of the methods for estimating groundwater recharge. 18

2.8 Summary. 20

Chapter 3

Reducing uncertainty associated with groundwater recharge estimation in an alluvial-karst system… 21

3.1 Introduction. 21

3.2 Development of conceptual model 22

3.3 Development of mathematical model 24

3.3.1 Water balance in the soil layer 24

3.3.2 Water balance in the alluvial aquifer 27

3.3.3 Water balance in the adjacent karstic aquifer 28

3.4 Model calibration. 29

3.5 Study area. 30

3.5.1 Geological Setting. 31

3.5.2 Hydrgeological Setting. 32

3.6 Model application. 35

3.6.1 Organizing data and develop the conceptual model 35

3.6.2 The parameters of the surface soil layer 35

3.6.3 The parameters of the multicell aquifer model 35

3.6.4 The parameters of the karst compartment 38

3.7 Groundwater recharge estimation by the proposed model 41

3.8 Summary. 48

Chapter 4

Formulation of new stochastic optimization models for groundwater management 50

4.1 Introduction. 50

4.2 Single cell model 50

4.2.1 Introducing new random withdrawal policy. 53

4.3 Multi-cell model 55

4.3.1 Two-cell stochastic model 56

4.3.2 Multiple cell stochastic model: 62

Chapter 5

Results and applications. 66

5.1 Introduction. 66

5.2 Optimal crop planning and conjunctive use of groundwater and surface water and resources. 66

5.2.1 Case study. 67

5.2.2 Formulation of optimization model 68

5.2.3 Constraints. 68

5.2.4 Model parameters. 72

5.2.5 Optimization using Genetic Algorithm (GA) 74

5.2.6 Simulation Model 75

5.2.7 Results and discussion. 76

5.3 Multi-cell model 80

5.3.1 Example 1: Two-cell model 80

5.3.2 Example 2: Multiple cell model 82

Chapter 6

Summary and Conclusions. 89

6.1 Future work. 91

Appendix A: MATLAB code for the multi-cell model 92

References. 99

List of Figures

Fig. 2.1 Sources of model uncertainty; Source: Li and Wu, 2006. 9

Fig. 2.2 Classification of optimization model for groundwater management 12

Fig. 3.1 Schematic diagram of the development procedure for the groundwater conceptual model. CE is the acronym for Consistency Evaluation. It means that if any inconsistency (or conflict) is found between specified steps, it is re-considered to resolve the conflict with regards to controlling observations. [Source: Izady et al. (2013)] 23

Fig. 3.2 (a) Schematic Figure of the water balance in an alluvial-karst system. (b) A multi-cell model of the alluvial aquifer (plan view). 25

Fig. 3.3 Geological map of study area. 31

Fig. 3.4 Groundwater level contour lines for Firouzabad alluvial aquifer (Sept. 2008) 32

Fig. 3.5 Groundwater depth contour lines for Firouzabad alluvial aquifer (Sept. 2008) 33

Fig. 3.5 Map showing location of groundwater wells within the Firouzabad plain. 33

Fig. 3.6 Map showing the cells that represent the alluvial aquifer 36

Fig. 3.7 Monthly discharge from Atashkade spring and monthly water table elevation in OW3 for the period of 16 years from October 1992 to September 2008 (15 days lag between time series was omitted) 40

Fig. 3.8 Comparison between observed (Ob) and simulated (Sim) groundwater head in the four cells of the alluvial aquifer 42

Fig. 3.9 Comparison between observed and simulated groundwater heads for the calibration and validation phases. 43

Fig. 3.10 Monthly average precipitation (P) and temperature (T), and estimated actual evapotranspiration (ETa), runoff (RO) and groundwater recharge from precipitation (PE) in Firouzabad plain. 44

Fig. 3.11 Estimated seasonal average groundwater balance. 45

Fig. 3.12 Simulated exchange flow between the alluvial aquifer and Firouzabad River, taken as positive if it is directed into the aquifer 46

Fig. 4.1 The single cell representation of an aquifer 51

Fig. 4.2 Schematic flow configuration between two cells of an unconfined aquifer 57

Fig. 4.3 Representation of two adjacent cells i and j in an aquifer system by polygonal cells 63

Fig. 5.1 Monthly rainfall and average water table elevation in the study area during 1993-2010 67

Fig. 5.2 Average monthly groundwater withdrawal from wells (MCM) and rainfall (mm). 78

Fig. 5.3 A Two-cell example aquifer 80

Fig. 5.4 Means, variances and covariances obtain from proposed models and simulation (the first case study) 82

Fig. 5.5 Plan view of aquifer system and location of pumping wells used in the second case study. 83

Fig. 5.6 Scatter plots of the results of optimization versus simulation obtained from the second case study. and denote expected value of a pair of random variables which are the heads of adjacent cells in a row and in a column respectively. 86

Fig. 5.7 Comparison between the results of the optimization model and those obtained from Monte Carlo simulation for the cells in row 4 of the aquifer domain. 87

Fig. 5.8 The mean of the head in adjacent cells to the constant head boundary (column 9) 88

Fig. 5.9 Reliability of the cells contains extraction well 88

List of Tables

Table 3.1 Main physical and hydrological features of the four cells of the alluvial aquifer 38

Table 3.2 Results of optimum parameters of the multi-compartment model 41

Table 3.3 Results of calibration. 42

Table 3.4 Estimated semi-distributed groundwater balance in Firouzabad alluvial aquifer for the period of 1992 to 2008. 45

Table 3.5 Estimated annual groundwater balance. 47

Table 5.1 Crop data of the study area. 73

Table 5.2 Average crop evapotranspiration and rainfall in the study area (mm) 73

Table 5.3 The results of the optimization model in comparison with the simulation model 77

Table 5.4 Average groundwater withdrawal annually for optimal crop pattern and supplement amount of rainfall obtained from CSF model 78

Table 5.5 Comparison between the crop patterns, net income, allocated groundwater and change in groundwater head (GWH) in existing and proposed conditions in the Firouzabad plain. 79

Table 5.6 The means and standard deviations of groundwater recharge, and the values of abstraction rate (m³) 81

Table 5.7 The values of water demand (m³) 83

Table 5.8 Optimal Groundwater extraction from wells obtained from the optimization model 86

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