# Impact of Soil Surface Temperature on Changes in the Groundwater Level

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## Abstract

**:**

## 1. Introduction

- Increased risk of waterborne diseases—as water levels decrease, water becomes more stagnant and can become contaminated with bacteria and other microorganisms [28]. This can increase the risk of waterborne diseases such as cholera, typhoid fever, and diarrhea.

## 2. Materials and Methods

#### 2.1. Case Study Area

^{2}[31]. The current population is approximately 1.97 million (as of 2022), with around 63% residing in rural regions [32].

^{3}of Amudarya river water [34].

- (i)
- Sandy and desert sandy soils—Most soils in the western and southern parts of the Bukhara region (including in the Kyzyl-Kum desert and the Uzbek–Turkmen border area) have low levels of humus (0.5%) and nitrogen (0.04–0.05%);
- (ii)
- Grey-brown soils—The most common soil types in the northern and eastern parts of the region are grey-brown soils, which are used for intensive irrigation farming under arid climates. These soils have a variety of textures, from sandy–loamy to medium loamy. The humus content in the top layer of soil varies in the range 0.6–0.9%, but it is higher in traditionally irrigated areas (1.2–1.8%). The nitrogen content in these soils is between 0.05% and 0.16%, and the total phosphorus content is between 0.09% and 0.11% [35];
- (iii)
- Takyr soils—These rare soil types are found mostly in the central Bukhara region, mixed with grey-brown and sandy soils. They form in shallow, clay-rich depressions that collect water. As the water evaporates, salt crusts form on the surface;
- (iv)
- Meadow soils—Meadow soils are found along the Zarafshan River and its former riverbed, extending to the Amu Darya River. They have higher humus (1.1–1.4%) and nitrogen (0.08–0.12%) contents than other types of desert soils.

#### 2.2. Linear Regression Model

_{1}+ cx

_{2}+ dx

_{3}+…, where a, b, c, d… determine how the equation’s parameters, x

_{1}, x

_{2}, x

_{3}…, relate to the y-function [38].

## 3. Results and Discussion

#### ANOVA Test

- Source—This column identifies the source of variation in the data. In this case, the source of variation is the location of the groundwater level measurements;
- DF—This column stands for degrees of freedom. Degrees of freedom are a measure of the variability in the data. In this case, there are 1.0 degrees of freedom for the model and 115.0 degrees of freedom for the error;
- Sum of squares—This column shows the amount of variation in the data that is explained by the model. In this case, the model explains 1.077 of the variation in the data;
- Mean squares—This column is calculated by dividing the sum of squares by the degrees of freedom. In this case, the mean square for the model is 1.077;
- F—This column is the F statistic. The F statistic is a measure of the significance of the model. In this case, the F statistic is 132.506;
- Pr > F—This column is the p-value. The p-value is a measure of the probability of obtaining the observed results if the null hypothesis is true. In this case, the p-value is <0.0001;
- Signification codes—This column shows the significance of the results. A significance code of * indicates that the results are significant at the 0.05 level. A significance code of ** indicates that the results are significant at the 0.01 level, and a significance code of *** indicates that the results are significant at the 0.001 level.

- Source—This column identifies the source of the parameter. In this case, the source of the parameters is the model;
- Value—This column shows the value of the parameter. In this case, the value of the intercept is 1.464 and the value of soil surface temperature (SST), °C is 0.019;
- Standard error—This column shows the standard error of the parameter. In this case, the standard error of the intercept is 0.052 and the standard error of SST, °C is 0.002;
- t—This column is the t-statistic for the parameter. The t-statistic is a measure of the significance of the parameter. In this case, the t-statistic for the intercept is 28.324 and the t-statistic for SST, °C is 11.511;
- Pr > |t|—This column is the p-value for the parameter. The p-value is a measure of the probability of obtaining the observed results if the null hypothesis is true. In this case, the p-value for the intercept is <0.0001 and the p-value for SST, °C is <0.0001;
- Signification codes—This column shows the significance of the results. A significance code of * indicates that the results are significant at the 0.05 level. A significance code of ** indicates that the results are significant at the 0.01 level, and a significance code of *** indicates that the results are significant at the 0.001 level.

Source | Value | Standard Error | t | Pr > |t| | Lower Bound (95%) | Upper Bound (95%) | p-Values Signification Codes |
---|---|---|---|---|---|---|---|

Intercept | 1.464 | 0.052 | 28.324 | <0.0001 | 1.362 | 1.566 | *** |

Soil surface temperature (SST), °C | 0.019 | 0.002 | 11.511 | <0.0001 | 0.016 | 0.023 | *** |

^{2}value of 0.428 in other research [24]. Moreover, the correlation between the soil temperature and groundwater level was R

^{2}= 0.49 when the soil temperature was measured at 5 cm, and R

^{2}= 0.59 when the soil temperature was measured at 10 cm [46].

## 4. Conclusions

- By monitoring groundwater levels and soil surface temperatures, it is possible to identify areas where groundwater resources are at risk of depletion. This information can then be used to warn stakeholders and develop mitigation strategies. Farmers could use sensors to monitor soil surface temperature and groundwater levels in their fields. This information can then be used to adjust irrigation practices and other land management practices to conserve groundwater resources;
- Practices such as crop rotation, cover cropping, and reduced tillage can help to reduce soil surface temperature and evaporation rates, which can help to conserve groundwater resources;
- Water managers could develop a network of monitoring stations to track groundwater levels and soil surface temperatures across a region. These data could then be used to create early warning systems for groundwater depletion and to develop more effective management plans;
- The government could provide incentives to farmers and other land users to adopt sustainable land management practices that conserve groundwater resources.

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 4.**Changes in groundwater level during the annual and growing season in Bukhara from 1991 to 2020.

**Figure 5.**The relationship between solar radiation and soil surface temperature in the Bukhara region of Uzbekistan.

**Figure 6.**The relationship between soil surface temperature and groundwater level in the Bukhara region of Uzbekistan.

No. | Administrative Districts | Number of Monitoring Wells |
---|---|---|

1 | Vobkent | 244 |

2 | Gijduvon | 326 |

3 | Jondor | 303 |

4 | Kagan | 130 |

5 | Karavulbazar | 113 |

6 | Karakul | 222 |

7 | Olot | 234 |

8 | Romitan | 263 |

9 | Shofirkon | 268 |

10 | Bukhara | 267 |

11 | Peshku | 220 |

Total for Bukhara region | 2590 |

Variable | Minimum | Maximum | Mean | Std. Deviation |
---|---|---|---|---|

Mean annual air temperature | 14.675 | 17.175 | 16.084 | 0.563 |

Variable | Solar Radiation | Soil Surface Temperature |
---|---|---|

Solar radiation | 1 | 0.840 |

Soil surface temperature | 0.840 | 1 |

Goodness of Fit Statistics (Soil Surface Temperature, °C) | |
---|---|

Observations | 11 |

Sum of weights | 11 |

DF (Degrees of freedom) | 9 |

R² (Regression) | 0.706 |

Adjusted R² | 0.674 |

MSE (Mean squared error) | 0.117 |

RMSE (Root mean squared error) | 0.343 |

MAPE (Mean absolute percentage error) | 1.528 |

DW (Durbin–Watson) [39] | 1.482 |

Cp (Mallows’s Cp coefficient) [40] | 2.000 |

AIC (Akaike information criterion) [41] | −21.777 |

SBC (Schwarz Bayesian criterion) [42] | −20.981 |

PC (Partial correlation) | 0.424 |

Press (Predicted residual error sum of squares) | 1.765 |

Q² (Second quartile) | 0.509 |

Variable | Value | Standard Error | t | Pr > |t| | Lower Bound (95%) | Upper Bound (95%) |
---|---|---|---|---|---|---|

Solar radiation | 0.840 | 0.181 | 4.651 | 0.001 | 0.432 | 1.249 |

Variable | Observations | Minimum | Maximum | Mean | Std. Deviation |
---|---|---|---|---|---|

Groundwater level, m | 117 | 1.720 | 2.29 | 2.051 | 0.132 |

Soil surface temperature, °C | 117 | 18.0 | 39.0 | 30.444 | 4.996 |

Source | DF | Sum of Squares | Mean Squares | F | Pr > F | p-Value Signification Codes |
---|---|---|---|---|---|---|

Model | 1.0 | 1.077 | 1.077 | 132.506 | <0.0001 | *** |

Error | 115.0 | 0.935 | 0.008 | |||

Corrected Total | 116.0 | 2.011 |

Goodness of Fit Statistics (Groundwater Level, m) | |
---|---|

Observations | 117 |

Sum of weights | 117 |

DF (Degrees of freedom) | 115 |

R² | 0.535 |

Adjusted R² | 0.531 |

MSE (Mean squared error) | 0.008 |

RMSE (Root mean squared error) | 0.090 |

MAPE (Mean absolute percentage error) | 3.581 |

DW (Durbin–Watson) | 1.529 |

Cp (Mallows’s Cp coefficient) | 2.000 |

AIC (Akaike information criterion) | −561.094 |

SBC (Schwarz Bayesian criterion) | −555.570 |

PC (Partial correlation coefficient) | 0.481 |

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## Share and Cite

**MDPI and ACS Style**

Khamidov, M.; Ishchanov, J.; Hamidov, A.; Shermatov, E.; Gafurov, Z.
Impact of Soil Surface Temperature on Changes in the Groundwater Level. *Water* **2023**, *15*, 3865.
https://doi.org/10.3390/w15213865

**AMA Style**

Khamidov M, Ishchanov J, Hamidov A, Shermatov E, Gafurov Z.
Impact of Soil Surface Temperature on Changes in the Groundwater Level. *Water*. 2023; 15(21):3865.
https://doi.org/10.3390/w15213865

**Chicago/Turabian Style**

Khamidov, Mukhamadkhan, Javlonbek Ishchanov, Ahmad Hamidov, Ermat Shermatov, and Zafar Gafurov.
2023. "Impact of Soil Surface Temperature on Changes in the Groundwater Level" *Water* 15, no. 21: 3865.
https://doi.org/10.3390/w15213865