# Climate Variability and Groundwater Response: A Case Study in Burkina Faso (West Africa)

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

^{2}between longitudes 4°08′ W and 4°36′ W and latitudes 10°55′ N and 11°32′ N. It contains the water system including the perennial River Kou, its temporary tributaries, and the three Nasso-Guinguette springs (>6000 m

^{3}/h) (Figure 1). This river is the first major tributary of the right bank of the Mouhoun River (formerly the Black Volta). The Mouhoun is, itself, a tributary of the Volta River, one of the largest West African rivers [32].

^{−5}and 10

^{−3}m

^{2}/s, with an average value of 4 × 10

^{−4}m

^{2}/s and an average value of the storage coefficient of 10

^{−4}[38]. The values of these parameters vary from one layer of the aquifer to another and sometimes within the same layer. The thickness of the unsaturated zone varies between 0 and 20 m, but in a few places it can reach 60 m. In the downstream part of the catchment, in the alluvial plain, the water table is very close to the surface (less than 3 m). In the Kou catchment, all the piezometers are drilled in the top layers of the aquifer (less than 200 m) and a single piezometric map is generally drawn, which reflects the hydraulic continuity of the water table over the entire area. Previous studies have generally considered a single layer in a multilayered aquifer [40,41,42]. The results of isotopic analysis confirm this hypothesis [43]. Located upstream of a large sedimentary basin, the groundwater flow is SW to NE between 499 m and 286 m.a.s.l., with a gradient of 3‰. The faults play an important role in groundwater recharge and in the resurgence of water at spring locations [37,43].

^{3}/d in 2014). The rest of the water needs are met by the installed boreholes or wells (>40,000 m

^{3}/d in 2014).

#### 2.2. Database

^{2}). The Penmann-Monteith method [44] was used to calculate the potential evapotranspiration.

^{2}) on the River Kou located downstream of the three Nasso-Guinguette springs, and those at the Samendéni station on the Mouhoun (Figure 1). At the Samendéni gauging station (catchment area: 4425 km

^{2}), daily data are available from 1955 to 2014 and at the Nasso gauging station, from 1961 to 1997, although with gaps lasting several years.

#### 2.3. Data Processing

#### 2.3.1. Rainfall Data

_{max}) was set at 130 mm based on the results of the area’s soil analysis [36]. It corresponds to the average, weighted with the area of the different types of soil encountered in the catchment. The balance is calculated on a monthly time step basis. For a given month j, the effective rainfall calculation principle can be summarized as follows:

_{j}= min(max(AWC

_{j-1}+ P

_{j}− ETP

_{j},0), AWC

_{max})

_{j}= max(AWC

_{j-1}+ P

_{j}− ETP

_{j}− AWC

_{max},0)

#### 2.3.2. Flow Data

#### 2.4. Statistical Analysis

#### 2.4.1. Break and Trend Test

#### 2.4.2. Correlation Analysis

_{t}is the value at time t, $\overline{x}$. is the mean of the events, and k is a time lag ranging from 0 to m. The cutting point m determines the interval in which the analysis is carried out. For m ≤ n/3, optimum results are found and the usual value of m is n/3 [11]. The inertia of the system is quantified through the memory effect, which is the influential time an event has on a time series. To compare the inertia between different systems, [11] proposes to consider the time lag k corresponding to the r(k) value of 0.2.

_{t}and an output time series y

_{t}. If the input time series is random, the cross-correlation function r

_{xy}(k) corresponds to the system’s impulse response [55]. The cross-correlation function is not symmetrical: r

_{xy}(k) ≠ r

_{yx}(k). It provides information on the causal relation between the input and the output [54]. For k > 0:

_{xy}(k) is a cross-correlogram, and ${\mathsf{\sigma}}_{\mathrm{x}}$ and ${\mathsf{\sigma}}_{\mathrm{y}}$ are the standard deviations of the time series. The cross-correlation function is used to determine the response time of the system between input and output. The lag at which the cross-correlation function takes its maximum corresponds to the response time.

#### 2.4.3. Principal Component Analysis

## 3. Results and Discussion

#### 3.1. The 1970 Drought and Its Hydrological Impacts

#### 3.2. Trend after the 1970 Break

^{3}/s between 1971 and 1997, withdrawals increased only by 0.13 m

^{3}/s. The explanation of the bulk of the decrease is to be grounded on other factors such as rainfall and/or evapotranspiration. The slight increase in rainfall (6%, about 62 mm, between two periods 1971–1990 and 1991–1997), drew downward by the increasing evapotranspiration (57 mm), was insufficient to induce a substantial increase of the flow. Furthermore, given the small size of the catchment and the large groundwater flow contribution (77%) to the total flow (Table 4), the return to a situation similar to that before 1970 implies an increase in the base flow, while it had a slower response to rainfall. The replenishment of aquifers, essential to return to previous hydrological conditions, exceeds one year in duration and might require a succession of several wet years [63]. The hypothesis that groundwater response time may be longer than several years, leading to a delayed flow recovery, could be put forward, but not confirmed by the results given the lack of data.

#### 3.3. Correlation Analysis of the Flows

#### 3.4. Characterization of Groundwater Level Dynamic

#### 3.4.1. Seasonal Variation and Rainfall-Water Level Relationship

#### 3.4.2. Interannual Variation of Groundwater Level

^{2}× S

_{s}/K

_{h}= L

^{2}× S/T

_{s}is the specific storage [L

^{−1}], K

_{h}is the horizontal hydraulic conductivity [LT

^{−1}], S is the storage coefficient [-], and T is the transmissivity [L

^{2}T

^{−1}].

^{−4}m

^{2}/s, a storage coefficient of 10

^{–4}, and a length of 30 km (average size of the catchment) gave a response time on the order of seven years. This is in agreement with the findings from Stoelzle et al. [68] who showed that porous systems such as sandstone have a long-term sensitivity to changes in rainfall and a stronger response to multiyear recharge variability. This slow response of the system was described earlier in Section 3.2 and Section 3.3.

^{3}/d out of 40,700 m

^{3}/d as a total at the catchment scale) than downstream ones, the assumption of high pumping rates is not a strong argument to justify the difference in behavior between upstream and downstream areas. In connection with topography and land use change, the assumption of higher recharge rates in the downstream area through the surface water, as observed in Southwestern Niger [24] was also put forth but is debatable given that some piezometers are located in a high topography area (P31, Pz26).

#### 3.4.3. Factors Explaining the Spatial and Temporal Variation of the Water Table

- •
- The interannual variation of groundwater level determined by the difference of the annual average groundwater levels of two consecutive years.
- •
- The magnitude of seasonal fluctuations determined by the difference between the highest groundwater level and the lowest groundwater level at the annual scale.
- •
- The response time estimated by the rainfall-groundwater level correlation.
- •
- The rainfall-groundwater level maximum correlation (Rk) estimated by cross-correlations.

## 4. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 1.**(

**a**) Study area location in Burkina Faso; (

**b**) Mouhoun and Kou catchments with gauging stations, rainfall stations, and monitoring boreholes used in this study; and (

**c**) Nasso-Guinguette springs area.

**Figure 2.**Monthly average rainfall, air temperature, and potential evapotranspiration from 1961 to 2014 at Bobo-Dioulasso station.

**Figure 3.**Autocorrelation of daily flows measured at (

**a**) Samendéni; and (

**b**) Nasso stations for different years; and (

**c**) example of 1966 hydrograph measured at Samendéni; and (

**d**) Nasso stations.

**Figure 4.**Interannual variations of the groundwater levels (m.a.s.l) from 1995 to 2014, for a representative sample of piezometers.

**Figure 5.**Spatial distribution of (

**a**) the amplitude of seasonal fluctuations; and (

**b**) interannual variation according to the clusters G1, G2, and G3 as aforementioned.

**Figure 6.**Cross-correlation between rainfall and groundwater level based on a monthly time step between 1995 and 1997: (

**a**) upstream Kou catchment piezometers; (

**b**) Nasso-Guinguette spring area piezometers; and (

**c**) downstream Kou catchment piezometers.

**Figure 7.**Average annual rainfall and five-year and seven-year moving averages over the past 25 years. (

**a**) Thiessen average rainfall over the Kou catchment; (

**b**) rainfall at the Bobo-Dioulasso station; and (

**c**) rainfall at the vallée du Kou station (most downstream station).

**Figure 8.**PCA of spatial and temporal heterogeneity of the aquifer behavior with a representative sample of piezometers. Analysis with six active variables (Lag, WTD, R

_{k}, SA, ΔR, and ΔGWL).

Type of Data | Station Name | Data | Period | Gap Rate (%) | Annual Average ^{1} | Standard Deviation ^{1} |
---|---|---|---|---|---|---|

Climatic data | Bobo-Dioulasso | Daily rainfall | 1961–2014 | 0.2 | 1065 | 205 |

Monthly potential evapotranspiration | 1961–2014 | 0.4 | 1958 | 115 | ||

Monthly average temperature | 1961–2014 | 2 | 27.2 | 0.5 | ||

Farakoba | Monthly rainfall | 1953–2014 | 5.6 | 1048 | 160 | |

Nasso | Monthly rainfall | 1953–2012 | 4.7 | 1042 | 177 | |

Vallée du Kou | Monthly rainfall | 1986–2014 | 7.5 | 938 | 142 | |

Samoroguan | Monthly rainfall | 1964–2014 | 5.4 | 1031 | 186 | |

Orodara | Monthly rainfall | 1954–2014 | 1 | 1140 | 186 | |

Hydrometric data | Samendéni | Daily flow | 1955–2014 | 18 | 14.4 | 5.5 |

Nasso | Daily flow | 1961–1997 | 29 | 3.7 | 1.4 | |

Piezometric data | 21 piezometers | Monthly water level | 1995–1999 | 13–25 | – | – |

2007–2014 | 28–39 |

^{1}Rainfall unit ”mm” flow unit “m

^{3}/s”, evapotranspiration unit “mm”, temperature unit “°C”.

Corresponding Probability | Category |
---|---|

α < 1% | Very significant break |

1% < α < 5% | Significant break |

5% < α < 20% | Low significant break |

α > 20% | Homogeneous series |

**Table 3.**Application of Pettitt tests on Bobo-Dioulasso rainfall indices over 1961–2014. (

**a**) Annual rainfall; and (

**b**) July to September.

Pettitt Test | Rainfall | ||||||
---|---|---|---|---|---|---|---|

Indices | p-value (%) | Break Date | Break Significance | Before Break ^{1} | After Break ^{1} | Deficit ^{2} (%) | |

(a) | P0 | 6.4 | 1970 | Low significant | 1187 | 993 | 16 |

P10 | 7.5 | 1971 | Low significant | 996 | 824 | 17 | |

P30 | 10.7 | 1971 | Low significant | 496 | 371 | 25 | |

P50 | 12.7 | 1970 | Low significant | 209 | 117 | 44 | |

(b) | P0 | 4.3 | 1970 | Significant | 814 | 635 | 22 |

P10 | 5.7 | 1970 | Low significant | 717 | 541 | 24 | |

P30 | 4.8 | 1971 | Significant | 381 | 257 | 33 | |

P50 | 4.5 | 1970 | Significant | 193 | 83.8 | 57 |

^{1}Rainfall unit “mm”;

^{2}The deficit is the ratio (in %) between the difference of values before and after the break.

**Table 4.**Results of the break test on Samendéni and Nasso station flows over the period from 1961 to 1997.

Station | Variable | Pettitt Test | Rainfall and Flow | ||||
---|---|---|---|---|---|---|---|

p-Value (%) | Break Date | Break Significance | Before Break ^{1} | After Break ^{1} | Deficit ^{2} (%) | ||

Bobo-Dioulasso | Rainfall | 2.11 | 1970 | Significant | 1177 | 984 | 16 |

Samendéni | Catchment rainfall | 2.55 | 1970 | Significant | 1177 | 1017 | 14 |

Total flow | 1.12 | 1970 | Significant | 19.3 | 11.4 | 41 | |

Base flow | 0.47 | 1970 | Very significant | 14.2 | 8.7 | 39 | |

Surface flow | 3.45 | 1970 | Significant | 5.1 | 2.6 | 49 | |

Nasso | Catchment rainfall | 8.16 | 1970 | Low significant | 1122 | 1002 | 11 |

Total flow | 0.01 | (1970–1974) ^{3} | Very significant | 4.8 | 2.7 | 43 | |

Base flow | 0.01 | (1970–1974) ^{3} | Very significant | 3.7 | 2.6 | 31 | |

Surface flow | 0.01 | (1970–1974) ^{3} | Very significant | 1.1 | 0.1 | 88 |

^{1}Rainfall unit “mm” and flow unit “m

^{3}/s”.

^{2}Deficit between the 1961–1970 and 1971–1997 periods.

^{3}The date of the break is in this period but is not accurate because of gaps.

**Table 5.**Results of break test on the Bobo-Dioulasso rainfall station and the Samendéni gauging station from 1971 to 2014, and on the Nasso gauging station

^{1}from 1971 to 1997.

Station | Variable | Pettitt Test | Rainfall and Flow | ||||
---|---|---|---|---|---|---|---|

p-Value (%) | Break Date | Break Significance | Before Break ^{2} | After Break ^{2} | Deficit or Surplus (%) ^{3} | ||

Bobo-Dioulasso | Rainfall | 43.1 | No break | – | – | – | – |

Samendéni | Catchment rainfall | 13.8 | 1990 | Low significant | 993 | 1075 | 8 |

Total flow | 0.05 | 1993 | Very significant | 10.2 | 15.8 | 55 | |

Base flow | 0.11 | 1993 | Very significant | 8.1 | 12.4 | 53 | |

Surface flow | 0.17 | 1990 | Very significant | 2.1 | 3.4 | 63 | |

Nasso | Catchment rainfall | 74.1 | No break | – | – | – | – |

Total flow | 0.46 | 1989 | Very significant | 3.01 | 2.63 | 13 | |

Base flow | 0.97 | 1983 | Very significant | 2.99 | 2.58 | 14 | |

Surface flow | 0.01 | 1988 | Very significant | 0.22 | 0.09 | 59 |

^{1}The results from Nasso are provided solely for information. Duee to the shortness of the time series (until 1997), the results cannot be deemed to reflect the trend for the recent years.

^{2}Rainfall unit “mm” and flow unit “m

^{3}/s”.

^{3}There is a deficit at Nasso and a surplus at Samendéni.

Year | ΔGWL (m/year) | SA (m) | Lag (month) | R_{k} | WTD (m) | ΔR (mm/year) |
---|---|---|---|---|---|---|

1995 | 0.0 | 0.64 | 2.00 | 0.6 | 11.5 | 0.0 |

1996 | −0.2 | 0.35 | 2.00 | 0.6 | 11.7 | −10.0 |

1997 | −0.2 | 0.73 | 2.00 | 0.6 | 11.8 | −15.3 |

1998 | −0.2 | 0.36 | 2.00 | 0.6 | 12.0 | −32.0 |

1999 | −0.1 | 0.49 | 2.00 | 0.6 | 12.1 | −17.9 |

2007 | −0.5 | 0.47 | 2.00 | 0.5 | 12.6 | −69.0 |

2008 | 0.0 | 0.79 | 2.00 | 0.5 | 12.6 | 35.5 |

2009 | 0.2 | 0.18 | 2.00 | 0.5 | 12.5 | 33.8 |

2012 | 0.1 | 0.61 | 2.00 | 0.5 | 12.4 | 112.2 |

2013 | 0.2 | 0.22 | 2.00 | 0.5 | 12.2 | 2.9 |

2014 | 0.0 | 0.40 | 2.00 | 0.5 | 12.2 | −11.5 |

Factorial axes | F1 | F2 | F3 | F4 | F5 | F6 |
---|---|---|---|---|---|---|

Eigenvalues | ||||||

Eigenvalues | 2.157 | 1.242 | 1.029 | 0.765 | 0.540 | 0.267 |

Proportion (%) | 35.950 | 20.703 | 17.149 | 12.748 | 8.999 | 4.450 |

Cumulative % | 35.950 | 56.654 | 73.803 | 86.551 | 95.550 | 100.000 |

Eigenvectors | ||||||

WTD | −0.597 | −0.010 | 0.269 | 0.042 | −0.115 | 0.746 |

Lag | −0.484 | −0.135 | 0.538 | 0.104 | 0.414 | −0.525 |

R_{k} | 0.522 | −0.146 | 0.316 | −0.001 | 0.665 | 0.404 |

SA | 0.369 | −0.065 | 0.690 | 0.156 | −0.597 | −0.056 |

∆GWL | 0.034 | 0.705 | −0.004 | 0.698 | 0.121 | 0.017 |

∆R | 0.008 | 0.678 | 0.250 | −0.689 | 0.041 | −0.030 |

© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Tirogo, J.; Jost, A.; Biaou, A.; Valdes-Lao, D.; Koussoubé, Y.; Ribstein, P.
Climate Variability and Groundwater Response: A Case Study in Burkina Faso (West Africa). *Water* **2016**, *8*, 171.
https://doi.org/10.3390/w8050171

**AMA Style**

Tirogo J, Jost A, Biaou A, Valdes-Lao D, Koussoubé Y, Ribstein P.
Climate Variability and Groundwater Response: A Case Study in Burkina Faso (West Africa). *Water*. 2016; 8(5):171.
https://doi.org/10.3390/w8050171

**Chicago/Turabian Style**

Tirogo, Justine, Anne Jost, Angelbert Biaou, Danièle Valdes-Lao, Youssouf Koussoubé, and Pierre Ribstein.
2016. "Climate Variability and Groundwater Response: A Case Study in Burkina Faso (West Africa)" *Water* 8, no. 5: 171.
https://doi.org/10.3390/w8050171