# Impact of Climate Change Parameters on Groundwater Level: Implications for Two Subsidence Regions in Iran Using Geodetic Observations and Artificial Neural Networks (ANN)

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Background and Methodology

#### 2.1. Tropospheric Correction to InSAR Data

_{0}is the surface elevation, z is the highest altitude of the troposphere, R

_{v}(461.495 Jkg

^{−1}K

^{−1}) and R

_{d}(287.05 Jkg

^{−1}K

^{−1}) are the specific gas constants for water vapor and dry air, respectively, e is the water vapor pressure, P(z

_{0}) is the surface pressure, T is the temperature in K and g

_{m}is the averaged gravitational acceleration over the troposphere layer. To reduce the tropospheric effect from the radar phase, it is necessary to convert the zenith tropospheric delay to the line-of-sight direction. Based on the previous studies, this step is performed using a mapping function [29]. Using mapping functions can cause errors in the calculations and results. This error can even lead to a misdiagnosis of the displacement signal in the InSAR processing [29,34]. Therefore, in this study, an advanced but simple integration technique based on direct numerical integration on the line-of-sight direction is used to prevent errors related to the mapping function. As can be seen in Figure 1, this technique, like the simple integration method along the zenith direction, requires meteorological data in 3D space. The meteorological data on the signal path need to be determined using the interpolation approaches. In this study, the kriging and spline interpolations have been used in the horizontal and vertical directions, respectively [35]. More details about this technique can be found in references [29,34].

#### 2.2. Downscaling and Long-Term Prediction

1-. Predictor and predictand data and their relationships are not affected by human-caused climate change and can be transferred to the next decades.

2-. All downscaled parameters are obtained using the available data in the base period because it is assumed that, during the decades belonging to this time period, the data are of a higher quality and are more accessible than in other periods and, also, that the risk of instability in the relationship between predictor variables is small.

3-. In climate simulations, it is assumed that the data follow the normal distribution.

#### 2.3. ANN for Groundwater Level Prediction

#### 2.4. PWV as a Key Indicator

_{V}, ${{K}_{2}}^{\u2019}$ and ${K}_{3}$ are constants with values of 461.50 (JK

^{−1}kg

^{−1}), 16.48 (KhPa

^{−1}) and 3.776 × 105 (K

^{2}hPa

^{−1}), respectively, T

_{m}(K) is the atmospheric weighted mean temperature, P is the surface pressure (hPa), and $\phi $ and H represent the latitude and the ellipsoidal height (km) of the GPS station, respectively.

#### 2.5. Evapotranspiration Estimation

_{s}is saturation vapor pressure, U

_{2}is the wind speed at 2 m height, e

_{a}is actual vapor pressure, ∆ is slope vapor pressure curve and γ is psychrometric constant. It should be mentioned that this parameter is not equal to groundwater evapotranspiration. Due to the dependence of this relationship on a large number of meta-indicators that are often not published by databases or are accessible with different temporal and spatial resolutions, the use of this relationship is usually difficult. Unlike the PM equation, the TH method can estimate the amount of evapotranspiration based on air temperature [51].

_{0}–a

_{2}and b

_{0}–b

_{2}are the model coefficients, which can be determined based on the least squares method. PWV

_{1}and PWV

_{2}are the PWV at the moment of $T\ge 0$ °C and $T<0$ °C, respectively. Finally, the more accurate value of the evapotranspiration index can be calculated using the following equation:

_{Accurate}can be computed at different times based on the differential model and without dependence on many meteorological indices. If there is a need to calculate this index as a two-dimensional map, it is also possible to enter the geographic location into the fitted model. Considering that it is necessary to calculate this index at the location of the station, the differential model is considered only in time. More information about how to use the differential model in temporal and spatial dimensions can be found in [52].

#### 2.6. Effective Rainfall

#### 2.7. Validation Methods

^{2}) have been used to compare and validate the results [54]. The Pearson correlation coefficient was used to determine the correlation between water level changes and meteorological indicators. In addition, the significance of the estimated correlation coefficient is determined based on the given confidence interval. The statistical tests have a null hypothesis. For most tests, the null hypothesis is that there is no relationship between your variables of interest or that there is no difference between groups.

_{0}): there is no significant difference between the two sets of data

_{1}): there is a significant difference between the two sets of data

## 3. Study Area and Data Set

## 4. Processing Results and Discussions

#### 4.1. Subsidence Detection

#### 4.2. Downscaling and Prediction of Meteorological Parameters Using SDSM

#### 4.3. Groundwater Level Prediction from 2013 to 2020

#### 4.4. Groundwater Level Prediction from 2021 to 2030

## 5. Summary and Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 4.**The geographical location of the study areas. The squares show the position of synoptic and GPS stations and the circles represent the location of groundwater well observations.

**Figure 5.**Monthly average of precipitation and temperature at the location of the synoptic station from 1979 to 2021, extracted from ERA5 data.

**Figure 8.**A typical example of computed tropospheric delay using the advanced integration technique on 2018-01-04.

**Figure 9.**Line-of-sight direction displacement velocity maps of two areas. The black star and circles show the location of the reference point and wells, respectively, and the blue square indicates the location of GPS and synoptic stations.

**Figure 10.**Difference between monthly average of predicted temperature from 2013 to 2020 and base period.

**Figure 11.**Difference between monthly average of predicted precipitation from 2013 to 2020 and base period.

**Figure 12.**Comparison between groundwater level simulation from different input sets and observation in 2020.

**Figure 13.**Heatmap plot of difference between monthly average of predicted groundwater level depth from 2013 to 2020 and base period.

**Figure 14.**Heatmap plot of difference between monthly average of predicted groundwater level depth over 2021 to 2030 and base period.

Parameter | Area | Jan | Feb | Mar | Apr | may | Jun | Jul | Aug | Sep | Oct | Nov | Dec |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Average precipitation (cm) | Qazvin | 0.50 | 0.62 | 0.78 | 0.75 | 0.41 | 0.09 | 0.05 | 0.05 | 0.05 | 0.33 | 0.56 | 0.49 |

Yazd | 0.28 | 0.26 | 0.37 | 0.30 | 0.16 | 0.01 | 0.01 | 0.00 | 0.00 | 0.05 | 0.17 | 0.22 | |

Average temperature (k) | Qazvin | 268.8 | 271.9 | 276.9 | 277.0 | 281.8 | 285.9 | 287.2 | 285.9 | 281.0 | 274.2 | 268.1 | 263.8 |

Yazd | 282.3 | 285.7 | 289.8 | 289.1 | 294.4 | 298.3 | 298.7 | 296.8 | 292.3 | 285.7 | 280.3 | 276.6 |

Area | Mission | Product | Track | Flight Direction | Beam Mode | Time Span |
---|---|---|---|---|---|---|

Qazvin | Sentinel-1A | S1A-IW-SLC | 108 | Descending | IW | 2015–2020 |

Yazd | Sentinel-1A | S1A-IW-SLC | 130 | Ascending | IW | 2015–2020 |

Year | Area | Min (m) | Max (m) | Mean (m) | Median (m) |
---|---|---|---|---|---|

2013 2014 2015 2016 2017 2018 2019 2020 | Qazvin (2 wells) | 0.49 0.51 0.58 0.47 0.59 0.57 0.60 0.62 | 1.86 1.98 2.23 2.39 2.64 2.63 2.71 2.81 | 1.31 1.29 1.13 1.39 1.28 1.41 1.53 1.61 | 1.24 1.15 1.11 1.20 1.39 1.47 1.43 1.50 |

2013 2014 2015 2016 2017 2018 2019 2020 | Yazd (2 wells) | 0.50 0.48 0.59 0.63 0.58 0.61 0.67 0.71 | 2.54 2.64 2.87 2.86 3.12 3.25 3.39 3.31 | 1.42 1.49 1.69 1.87 1.88 2.01 2.25 2.19 | 1.31 1.28 1.54 1.63 2.01 2.15 2.11 2.40 |

**Table 4.**Statistical measures of observed and downscaled mean monthly temperature during validation period from 2011 to 2012.

Area | Variable (°C) | RMSE (°C) | NSE | R^{2} |
---|---|---|---|---|

Qazvin | Tmax | 0.41 | 0.77 | 0.85 |

Tmin | 0.31 | 0.83 | 0.87 | |

Yazd | Tmax | 0.36 | 0.79 | 0.85 |

Tmin | 0.43 | 0.84 | 0.89 |

**Table 5.**Statistical measures of observed and downscaled mean monthly precipitation during validation period from 2011 to 2012.

Area | Variable (mm) | RMSE (mm) | NSE | R^{2} |
---|---|---|---|---|

Qazvin | Precipitation | 2.21 | 0.75 | 0.84 |

Yazd | Precipitation | 2.02 | 0.71 | 0.82 |

Set Number | Inputs |
---|---|

1 | Precipitation, Tmin, Tmax, SR, D |

2 | Precipitation, Tmin, Tmax, SR, D, PWV |

3 | Precipitation, Tmin, Tmax, SR, D, ET |

4 | Precipitation, Tmin, Tmax, SR, D, ET, PWV |

Area | Set Number | RMSE (m) | NSE | Correlation |
---|---|---|---|---|

Qazvin | 1 | 0.989 | 0.71 | 0.79 |

Qazvin | 2 | 0.615 | 0.75 | 0.82 |

Qazvin | 3 | 0.361 | 0.81 | 0.89 |

Qazvin | 4 | 0.189 | 0.86 | 0.93 |

Yazd | 1 | 1.023 | 0.70 | 0.74 |

Yazd | 2 | 0.084 | 0.73 | 0.81 |

Yazd | 3 | 0.598 | 0.80 | 0.87 |

Yazd | 4 | 0.241 | 0.84 | 0.91 |

**Table 8.**Correlation coefficient between changes in groundwater level and meteorological parameters. Green color refers to p-Value smaller than 0.05 and red color shows p-Value greater than 0.05.

Month | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Precipitation | Yazd Well 1 | 0.39 | 0.56 | 0.49 | 0.54 | 0.58 | 0.61 | 0.68 | 0.51 | 0.51 | 0.68 | 0.73 | 0.62 |

Yazd Well 2 | 0.40 | 0.35 | 0.37 | 0.48 | 0.55 | 0.63 | 0.69 | 0.59 | 0.55 | 0.63 | 0.69 | 0.66 | |

Qazvin Well 1 | 0.39 | 0.46 | 0.51 | 0.59 | 0.50 | 0.48 | 0.62 | 0.51 | 0.68 | 0.63 | 0.75 | 0.72 | |

Qazvin Well 2 | 0.43 | 0.40 | 0.49 | 0.51 | 0.57 | 0.51 | 0.60 | 0.63 | 0.71 | 0.61 | 0.68 | 0.66 | |

Temperature | Yazd Well 1 | −0.41 | −0.37 | −0.47 | −0.55 | −0.59 | −0.69 | −0.73 | −0.75 | −0.62 | −0.54 | −0.48 | −0.44 |

Yazd Well 2 | −0.41 | −0.42 | −0.44 | −0.57 | −0.65 | −0.63 | −0.70 | −0.67 | −0.59 | −0.58 | −0.68 | −0.69 | |

Qazvin Well 1 | −0.36 | −0.43 | −0.59 | −0.55 | −0.63 | −0.66 | −0.62 | −0.76 | −0.71 | −0.66 | −0.59 | −0.51 | |

Qazvin Well 2 | −0.40 | −0.51 | −0.46 | −0.57 | −0.63 | −0.59 | −0.69 | −0.74 | −0.71 | −0.68 | −0.61 | −0.59 |

**Table 9.**Correlation coefficient between changes in groundwater level and meteorological parameters. Green color refers to p-Value smaller than 0.05 and red shows p-Value greater than 0.05.

Month | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Precipitation | Yazd Well 1 | 0.31 | 0.39 | 0.41 | 0.61 | 0.50 | 0.63 | 0.69 | 0.51 | 0.43 | 0.65 | 0.62 | 0.68 |

Yazd Well 2 | 0.31 | 0.35 | 0.37 | 0.47 | 0.51 | 0.55 | 0.50 | 0.61 | 0.52 | 0.58 | 0.63 | 0.68 | |

Qazvin Well 1 | 0.31 | 0.33 | 0.43 | 0.59 | 0.68 | 0.69 | 0.73 | 0.76 | 0.72 | 0.62 | 0.69 | 0.61 | |

Qazvin Well 2 | 0.31 | 0.40 | 0.51 | 0.59 | 0.58 | 0.63 | 0.66 | 0.67 | 0.71 | 0.70 | 0.62 | 0.53 | |

Temperature | Yazd Well 1 | −0.37 | −0.42 | −0.53 | −0.57 | −0.61 | −0.52 | −0.69 | −0.61 | −0.59 | −0.55 | −0.51 | −0.61 |

Yazd Well 2 | −0.40 | −0.33 | −0.61 | −0.55 | −0.51 | −0.61 | −0.68 | −0.53 | −0.48 | −0.54 | −0.49 | −0.57 | |

Qazvin Well 1 | −0.37 | −0.39 | −0.44 | −0.51 | −0.59 | −0.52 | −0.46 | −0.41 | −0.44 | −0.58 | −0.51 | −0.58 | |

Qazvin Well 2 | −0.44 | −0.45 | −0.51 | −0.58 | −0.64 | −0.67 | −0.63 | 0.51 | −0.69 | −0.66 | 0.53 | −0.49 |

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Haji-Aghajany, S.; Amerian, Y.; Amiri-Simkooei, A.
Impact of Climate Change Parameters on Groundwater Level: Implications for Two Subsidence Regions in Iran Using Geodetic Observations and Artificial Neural Networks (ANN). *Remote Sens.* **2023**, *15*, 1555.
https://doi.org/10.3390/rs15061555

**AMA Style**

Haji-Aghajany S, Amerian Y, Amiri-Simkooei A.
Impact of Climate Change Parameters on Groundwater Level: Implications for Two Subsidence Regions in Iran Using Geodetic Observations and Artificial Neural Networks (ANN). *Remote Sensing*. 2023; 15(6):1555.
https://doi.org/10.3390/rs15061555

**Chicago/Turabian Style**

Haji-Aghajany, Saeid, Yazdan Amerian, and Alireza Amiri-Simkooei.
2023. "Impact of Climate Change Parameters on Groundwater Level: Implications for Two Subsidence Regions in Iran Using Geodetic Observations and Artificial Neural Networks (ANN)" *Remote Sensing* 15, no. 6: 1555.
https://doi.org/10.3390/rs15061555