# Spatiotemporal Analysis of Groundwater Resources in the Saïss Aquifer, Morocco

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area and Data Collection

^{2}, 95 km length and 30 km width. It occupies a significant part of the Sebou watershed with the two largest cities (Fez and Meknes) and several centers (Figure 1). The Fez–Meknes basin is part of the south Rifin. The study area delimitation was based on the aquifer system extension and not on the watershed limit.

#### 2.2. Methodology

#### 2.2.1. Spatial Variability of the Groundwater Resources

#### Spatial Interpolation

#### Criteria for Evaluating the Performance of Interpolation Methods

#### 2.2.2. Temporal Variability of the Groundwater Resources

#### Reconstruction of Missing Time Series Data

_{t}} a series with mean μ, then, if the series is supposed to follow an ARIMA(p,d,q) (P,D,Q)

^{s}model, we can write:

## 3. Results and Discussion

#### 3.1. Data Processing and Quality Control

- -
- The existence of a spatial autocorrelation;
- -
- A distribution of values approaching the normal distribution;
- -
- The presence of a drift;
- -
- Anisotropic behavior;
- -
- The absence of abnormal values.

#### 3.2. Identification of the Optimal Spatial Interpolation Method

^{2}).

^{2}coefficient. The ranking of the optimal interpolation methods from the most accurate (OCK) to the least accurate (IDW) is given in Table 2.

^{2}= 0.987 demonstrates the existence of a strong correlation between the observed values and those predicted.

^{2}; nevertheless, ordinary kriging is more precise than cokriging from the point of view of the spatial distribution of uncertainty.

#### 3.3. Temporal Groundwater Level Modeling

#### Reconstruction of Time Series

^{2}), is illustrated in Figure 11. The results demonstrate that the model produces acceptable performance in simulating groundwater levels.

#### 3.4. Study of the Variation in the Groundwater Resources between 2005 and 2020

- -
- Category 1 corresponds to piezometers in which the groundwater level did not undergo a significant variation. For example, for piezometer 566/21, the groundwater level varies slightly between +/−4 m.
- -
- Category 2 corresponds to piezometers in which the groundwater level increased by approximately 20 m. These piezometers are mostly located in the irrigated perimeters with surface water. The development of this perimeter made it possible, on the one hand, to reduce the use of groundwater resources for irrigation. On the other hand, it made it possible to have an infiltration of this surface water. At the piezometer 237/15, the water table depth experienced significant fluctuations but with a general upward trend of 25 m.
- -
- Category 3 corresponds to piezometers whose groundwater levels of the aquifer experienced a decrease of approximately 7 m (Piezometer 3362/15). This drop in groundwater resources is due to excessive water pumping. Moreover, the majority of these piezometers are located in agricultural areas irrigated by groundwater resources. In addition, they are far from potential sources of recharge such as rivers.

## 4. Conclusions

^{2}and RMSE indicators). However, the ordinary kriging is more accurate than the cokriging from the point of view of the uncertainty of spatial distribution.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 6.**Control of data quality. (

**a**) Trend tool. (

**b**) Variographic surface: an anisotropic behavior with variability in the 45° direction. (

**c**) Semivariographic cloud: calculated values of the variance γ(h) for all pairs of the samples plotted on the y axis against the distance h separating the two locations, represented on the x axis.

**Figure 7.**(

**a**) Correlation between the groundwater level and the topographic elevation. (

**b**) Correlation between measured and predicted groundwater levels for 2020.

**Figure 8.**Groundwater level map interpolated by ordinary cokriging (OCK), ordinary kriging (OK), and empirical Bayesian kriging (EBK).

**Figure 9.**Groundwater level prediction errors map for ordinary kriging (OK), ordinary cokriging (OCK), and empirical Bayesian kriging (EBK).

**Figure 10.**(

**a**) Original groundwater level time series containing missing values. (

**b**) The reconstructed groundwater levels and gap values filled using ARIMA model for nine selected piezometers.

**Figure 11.**Scatterplot of predicted vs. observed values of groundwater level in six selected piezometric stations.

**Figure 12.**Comparison between measured and reconstructed groundwater level using ARIMA model for the six borehole piezometers.

**Figure 13.**Map of wells according to the value of the groundwater level difference between 2005 and 2020.

**Table 1.**Summary statistics of measurements of the groundwater level of the Saïss aquifer for 2005 and 2020.

Date | Min | Max | Average | Median | Standard Deviation | Skewness | Kurtosis | 1st Quartile | 3rd Quartile |
---|---|---|---|---|---|---|---|---|---|

2005 | 373 | 762 | 528 | 517 | 122 | 0.22 | 1.7 | 406 | 621 |

2020 | 380 | 787 | 536 | 528 | 119 | 0.23 | 1.8 | 419 | 627 |

2020 | RMSE | R^{2} | ME |
---|---|---|---|

OCK | 13.25 | 0.987 | −0.49 |

UCK | 13.28 | 0.984 | −0.48 |

EBK | 13.77 | 0.979 | 0.19 |

OK | 16.04 | 0.978 | −0.86 |

UK | 16.14 | 0.977 | 0.97 |

LPI | 17.71 | 0.910 | −0.94 |

RBF | 37.12 | 0.900 | −1.51 |

IDW | 45.65 | 0.860 | −9.8 |

Piezometer | Valid Measurements | Missing Data | % |
---|---|---|---|

237/15 | 106 | 86 | 44.8 |

787/21 | 114 | 78 | 40.6 |

566/21 | 116 | 76 | 39.6 |

1309/22 | 117 | 75 | 39.1 |

2813/15 | 121 | 71 | 37.0 |

2366/15 | 152 | 40 | 20.8 |

2792/15 | 156 | 36 | 18.8 |

2604/15 | 158 | 34 | 17.7 |

290/22 | 172 | 20 | 10.4 |

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**MDPI and ACS Style**

El Garouani, M.; Radoine, H.; Lahrach, A.; Jarar Oulidi, H. Spatiotemporal Analysis of Groundwater Resources in the Saïss Aquifer, Morocco. *Water* **2023**, *15*, 105.
https://doi.org/10.3390/w15010105

**AMA Style**

El Garouani M, Radoine H, Lahrach A, Jarar Oulidi H. Spatiotemporal Analysis of Groundwater Resources in the Saïss Aquifer, Morocco. *Water*. 2023; 15(1):105.
https://doi.org/10.3390/w15010105

**Chicago/Turabian Style**

El Garouani, Manal, Hassan Radoine, Aberrahim Lahrach, and Hassane Jarar Oulidi. 2023. "Spatiotemporal Analysis of Groundwater Resources in the Saïss Aquifer, Morocco" *Water* 15, no. 1: 105.
https://doi.org/10.3390/w15010105