Emerging Solutions for Coping With Future Water Scarcity

Currently, the hole in the ozone layer over Antarctica could be the smallest in three decades. Such an achievement, despite the slow outcome, was only possible by a concerted global effort from scientists, governments, industries, and consumers following the signing of the Montreal Protocol in 1987.

It should come as no surprise that, in figuring out ways to confront the climate crisis, what humans need is to learn the scope of these issues in order to make proper decisions. Learning is also the basis for algorithms to predict certain behaviors, and for humans, the learning process is comparably similar. For thousands of years, farmers were able to track the weather; weather forecasting had to rely on human empirical knowledge and depended less on scientific data. Nowadays, with so many complex and hard to predict environmental events, ancient methods used to predict the weather, such as determining the growing season by forecasting until when temperatures will remain above freezing, are not enough anymore.

As of 2019, a total of 17 countries suffer from "extremely high" levels of baseline water stress, and 43 of them fall under the “high” water stress category. With climate crisis disasters happening at the rate of one per week, the current environmental scenario is unpredictable and exacerbated. Floods, droughts, and typhoons have devastating consequences for the stability of human life on Earth and are unexpectedly affecting worldwide systems. It also becomes more common for some locations to suffer from problems related to water excess — such as floods — and from water scarcity — such as droughts — demonstrating the interdependence of the environmental system as the balance between human actions and natural cycles. The Red Cross estimates that the number of people in need of assistance will double — from 108 million a year today to 200 million three decades from now.

Today, more than 2 billion people around the world lack access to sources of appropriately managed safe drinking water, while 4.2 billion do not have adequately managed sanitation services, and over 3 billion lack basic hand washing facilities. The consequences of the different forms of water mismanagement are disastrous, as specialists estimate that more than 800 thousand people die annually as a result of a lack of sanitation and clean water. As we continuously pollute and destroy our environment, by 2025, half of the population of the world might be living in water-stressed areas, worsening an already bleak scenario while serving as a reminder of the interconnection between Earth’s equilibrium and the future of our species.

Global action must be taken to remediate or reduce these impacts. Andrew Steer, president and CEO of the World Resources Institute, states that "a new generation of solutions is emerging, but nowhere near fast enough," reminding us that actions must be taken now. If in 2018, the UN warned that we have just 12 years to reduce the likelihood of a climate change catastrophe, then in 2019 there's a growing consensus that the next 18 months will be critical in dealing with the global heating crisis, among other environmental challenges. However, only the development and application of more refined intelligence can help us make better choices to maneuver the climate breakdown.

Artificial intelligence applications, for instance, are evolving; we are now able to actively and quickly understand, predict, and better act on these disasters in ways that were once unimaginable without the support of autonomous and intelligent systems. By modeling and collecting data on carbon emissions, physical characteristics, chemical composition, biological hazards, and other traces, mathematical results could generate analysis and spot-on conclusions for further investigations designed to raise environmental awareness and align actions between governments, industry leaders, and the population at large.

Yet, substantial improvements must come along to allow sophisticated technologies such as Machine Learning Weather Model and Geospatial Data Generation Tool to become widespread and accessible. To store, process, and train algorithms, computers perform deep calculations in data centers across different machine computation rows that require a tremendous amount of energy. Researchers and programmers must discover novel computational power supply alternatives; otherwise, we are simply ignoring an essential aspect of the overall strategy.

These systems, much like humans, require special attention in reshaping behavior. Environmentally-friendly energy consumption is a computational matter that must be considered among the top goals for society to achieve, right up there with remodeling how humans perceive their actions within the ecosystem. Unquestionably, humankind needs support to face the countless environmental disasters occurring across the globe. For that, artificial intelligence must be trained to encompass the Earth’s needs and limitations into its algorithmic structure. This paradox is a stark reminder that we all live in the same ecosystem, including the tools we create.