## 5 Discussion
### Geothermal Energy  
The quantification of uncertainties in the probability distribution parameters can be dealt with quite well using the Monte Carlo simulation method (Figure 8, Supplementary Figures S4-S10). Thus, the low, best, and high estimates of AG Rift megawatts can be represented by the probability of P90, P50, and P10, which correspond to 28.385 MWe, 57.729 MWe, and 103.868 MWe, respectively (Supplementary Table S11). This result implies a preliminary assessment of the energy potential of 8.73 MWe/km\({}^{2}\) for a maximum duration of 25 years. However, the prediction of the geothermal potential should be studied further with a more accurate assessment (Supplementary Table S11). In order to assess the uncertainty of the volumetric parameters in the potential geothermal estimate of the Rift AG, it is useful to decipher the degree of influence of these parameters using the sensitivity analysis approach. Figure 8b shows the sensitivity of the AG Rift electric potential estimation for to the main parameters over a range of variation of \(\pm\)50%. It can be noted that the degree of influence of these parameters on the geothermal potential is not the same. For example, when the area, thickness, recovery factor, conversion efficiency, and specific heat capacity parameters of the AG Rift  
Figure 8: (**a**) Probability distribution of Monte Carlo simulation and (**b**) Tornado diagram for sensitivity result of volumetric parameters. by 50%, the total geothermal energy estimate increases from 74.33 MWe to 111.49 MWe. However, the porosity parameter does not directly influence the AG Rift geothermal energy estimate, whether it increases or decreases by 50% (Figure 8b). Further, an overall sensitivity analysis was performed using a \(\pm 10/60\%\) increase and decrease in the mean value of the key parameters (Supplementary Figure S11). In the Monte Carlo simulation, the most significant and positive correlation (0.73) with the geothermal energy potential of the AG rift is found to be with the recovery factor parameter (Figure S12). In other words, the higher the value of the recovery factor, the greater the geothermal potential of the AG Rift. Thus, our result is very consistent with the LCOE proposed by [PERSON] et al. (2016). Furthermore, assuming an average capacity of 2.5 MW per geothermal well at AG Rift, it should be noted that to maintain power generation at a constant level with a single flash plant, two additional 2.5 MW wells may be required for approximately eight years of service. While for a 22.22 MW dry steam plant, ten additional wells would have to be operational for three years of regular electricity production. Therefore, the total cost of 3-11 geothermal wells required to generate electricity for a single flash and a dry steam could be $38,893,581 and $142,609,797, respectively. It should also be noted that in our economic evaluation we have included the cost of drilling one well in the unit cost of electricity supplied by AG Rift's dry steam and single expansion plant (Supplementary Tables S9 and S10). Assuming a liquid inlet state of one atm and a saturated liquid, the reversible specific work provided by the PEME\({}_{\text{m}}\) electrolyzer shows that 324.97 kg of geothermal water is required to produce 1 kg of hydrogen, whereas under non-ideal operation this could be as much as 526.45 kg of AG rift geothermal water. Moreover, we found that 253.72 kg of geothermal water using the HTE electrolyzer could produce 1 kg of hydrogen in the reversible case and can reach 296.61 kg for the non-ideal operation. Furthermore, the annual hydrogen production capacity varies from 451.51 tons H\({}_{2}\)/year to 6453.78 tons H\({}_{2}\)/year (Table 5). The annual O&M and yearly electrical cost for hydrogen production in different conditions (reversible and irreversible) are given in Supplementary Table S12. The unit cost of hydrogen production ranges from $3.31/kg H\({}_{2}\) to $8.16/kg H\({}_{2}\) depending on the electrolyzer models/sizes (Table 5).