6. _Aquifer transmissivity_ : for a given power, the greater the transmissivity, the higher the volume pumped by a water pumping system and thus the risk of over exploitation. Therefore, the lowest transmissivity value is allocated a 1 and the highest value a 5.

## **Classifying the datasets between each other’s**

The next step of the AHP is to classify the datasets based on their impact on the pumped volume (i.e. abstraction level) and thus on the risk of over exploitation. We choose to consider the annual average of GHI as the most important data as this study focuses on PVWPS and because GHI directly impacts the pumped volume (Meunier, 2019). Then, we consider the renewable groundwater resources as it provides information about the sustainably extractible resource. We consider that storage is coming next as it is the amount of groundwater that is still available in the event of over-abstraction of renewable resources. Then comes the population density as it significantly influences the water demand and thus has a potentially important impact on groundwater abstraction. Subsequently, we consider the static water level because it is directly linked to the resource accessibility. Finally, we consider the transmissivity, which is less directly linked to the pumped volume than the static water level. To assess the robustness of our choices, we compare the outcomes derived from these choices with the results obtained when assigning equal importance to each dataset (see Section 4).

## **Calculating the weights**

Following the AHP approach from (Saaty, 1977) and the classification detailed in the previous paragraph, the chosen pair wise comparison matrix is presented in Table 2. The consistency ratio for this pair wise comparison matrix is 0.027, which is acceptable (Saaty, 1977). The weights for each dataset drawn from this pair wise comparison matrix are summarized in Table 3.

||**Annual**<br>**average of GHI**|**Renewable groundwater**<br>**resources**|**Aquifer**<br>**storage**|**Population**<br>**density**|**Static**<br>**water level**|**Aquifer**<br>**transmissivity**|
|---|---|---|---|---|---|---|
|**Annual average of GHI**|1|2|3|4|5|6|
|**Renewable groundwater**<br>**resources**|0.5|1|2|3|4|5|
|**Aquifer storage**|0.33|0.5|1|2|3|4|
|**Population density**|0.25|0.33|0.5|1|2|3|
|**Static water level**|0.2|0.25|0.33|0.5|1|2|
|**Aquifer transmissivity**|0.16|0.2|0.25|0.33|0.5|1|

_Table 2 - Pair wise comparison matrix for the risk of over exploitation of groundwater resources._

|**Dataset**|**Weight**|
|---|---|
|Annual average of GHI|38 %|
|Renewable groundwater resources|25 %|
|Aquifer storage|16 %|
|Population density|10 %|
|Static water level|6.5 %|
|Aquifer transmissivity|4.5 %|

_Table 3 – Weight matrix for each dataset._

# 4. Results

The results using the weights of Table 3 are shown in Figure 2(a) and in Table 4. In Table 4, we classified the risk of over exploitation into 4 levels (the lower the level, the less risky). Results indicate that the risk of over exploitation is between 2 and 3 for 8% of the GDEs while 91% of them have a risk between 3 and 4. We see in Figure 2(a) that the major risk of over exploitation occurs in GDEs of western South Africa, Namibia, Kenya, Niger, and Somalia while the minor risk occurs in the GDEs of Congo, Gabon and southern Nigeria. This is due to the important difference in terms of annual average of GHI between these areas (see Figure 1(a)). Indeed, the annual average of GHI in western South Africa and Namibia is allocated a 4 while the one in Congo and Gabon is allocated ~1.75. This is also attributed to the significant difference in terms of renewable groundwater resources. Indeed, 6