November 2016, La Paz in Bolivia experienced Day Zero: this is the day that drinking water reserves in the system of reservoirs were almost depleted due to extreme droughts and citizens had to rely on drinking water supply by tanker trucks operated by the Bolivian army. This water crisis took the government and the Bolivian water company by surprise and industries and schools needed to close down. A national state of emergency was declared.
Instead of proposing to build additional dams, our experts proposed a method in which the existing infrastructure network of 25 dams and reservoirs was optimised using a decision support system.
The first step in this project was to improve the monitoring system. As this used to be executed by hand throughout an extensive area of the Andes, this was very time-consuming. We proposed to change this into a semi-online monitoring network fed by solar panels that communicate to a control room. In the second step, an advanced hydrological model of the area will be built. And finally, a smart software solution will link the hydrological model to the monitoring data and weather forecasts and this will generate different possible scenarios. Based on the scenarios, the software solution will advise the operators in the control room in their daily business operations. As the tool continuously calculates scenarios, it will also serve as an early warning system to forecast another Day Zero within the next 1.5 years and define mitigating measures.
This new monitoring system will provide the following benefits:
With this project the water system operations will be robust and professionalised and most importantly will prevent supply interruptions to a 2.2 million people city. This can be achieved without expanding the current water system with additional dams that have a big impact on the ecosystem.
We cannot look into the future and prevent extreme dry periods with the optimisations, but the number of interruptions will decrease and if a Day Zero is predicted by the system, there will be enough time to prepare adaptive measures.