More companies are assessing water risks, either voluntarily or in response to regulatory requirements. However, navigating the complex and diverse water challenges across global portfolios while under pressure from external stakeholders is complex. Global-level data is useful for a broad view, yet assessing water risks effectively also requires local insights.
Companies rely on tools like Aqueduct’s Water Risk Atlas to identify which parts of their operations and value chain face significant water risks. Aqueduct’s indicators are based on peer-reviewed, open-source, globally consistent data and methods, allowing for global benchmarking and screening. Water risks in Chicago can be directly compared to water risks in Cape Town and Cairo, all at the click of a button.
However, it’s important to recognize that Aqueduct presents a high-level view, not a ground truth assessment of water risks. Once global data is collected, companies should validate the results internally through staff conversations and externally through outreach as they finalize their priorities.
WRI partnered with Unilever to share a few real-world examples of how Unilever enhanced its water stewardship prioritization exercise by complementing the global Aqueduct results with targeted local feedback and insights.
Bridging the Global-to-Local Gap
A prioritization exercise is an essential part of the water stewardship journey. It helps companies make informed decisions on where to act, given that water is local and companies cannot act everywhere.
In Unilever’s case, the company must assess more than 300 production sites, offices and warehouses across 100 countries. It’d be nearly impossible for Unilever to run a water risk assessment of this scale using only local data. It could lead to data quality issues, data definition discrepancies and even data scarcity. By using Aqueduct’s global indicators, Unilever can spend fewer resources assessing water risks and more resources on taking action.
After this first step, Unilever contacts facility managers whose sites fall within a high-risk sub-basin, giving them the opportunity to provide feedback. If a manager disputes the water stress status — for example, if the Aqueduct data doesn’t reflect the reality on the ground — Unilever often brings in expert consultants to investigate the location using local data, such as details on the facility’s water source, provider and key water infrastructure.
When Global and Local Data Align
Using Aqueduct’s Baseline Water Stress (BWS) indicator, which measures the competition over water resources, Unilever found that the global results resonated with local understanding the vast majority of the time. This holds true across different geographies and levels of water stress.
Unilever found that on sites exposed to Aqueduct’s most extreme water stress — where water demand outpaces natural replenishment — facility managers felt the challenge on the ground.
For example, in Konya, Turkey, Aqueduct showed extremely high levels of water stress, and local staff confirmed this challenge.
Turkey is a highly water-stressed country. It’s been plagued by recurring droughts in recent years that have impacted both people and nature. In Konya, competition over water resources is extremely high. Agriculture uses around 90%, leaving very little for domestic and industrial demand. According to Aqueduct, water demand exceeds the renewable, available water supply for three months of the year.
Unilever found through ground truthing that the region faces major water challenges, including sharply declining groundwater tables, erosion and even sinkholes in the landscape. This kind of corroborating local feedback, and alignment between global and on-the-ground data, reinforces the site’s priority status and creates momentum for action internally throughout the company, from the management team to the local facility team.
When Global and Local Data Mismatch
Despite its utility, a global model will never replace local knowledge. Aqueduct’s indicators are built using global assumptions, resulting in unique limitations that users must consider when evaluating the data. Specifically, Aqueduct is limited in its ability to model the local management of water.
Unilever found through its validation process with site managers that for a few facilities, the lived experience is different from what Aqueduct’s Baseline Water Stress indicator reported.
Reporting water stress in freshwater-abundant areas
Aqueduct reported high water stress in a few sub-basins next to the Great Lakes in North America — a region with abundant freshwater. How is this possible? This mismatch is due to how Aqueduct allocates water supply from Lake Michigan, routing water through natural discharge points rather than the human-made intake points.
To begin with, water demand is extremely high in the region. Illinois ranks as the fourth largest industrial water user in the world.
However, high water demand does not necessarily mean high water stress. In the case of the Great Lakes, human engineering enables water users to access its supply from multiple intake locations across the shoreline — a dynamic that no global hydrological model can capture because there is no global dataset on the human transfer and management of water.
Instead, Aqueduct models the outflow from Lake Michigan through the drainage network based on elevation data. In Unilever’s case, we found that Aqueduct models the outflow from Lake Michigan through the sub-basin adjacent to where it operates.
Because Aqueduct only uses a sub-basin’s internal water supply to calculate its risk, agnostic to what neighboring basins have, Unilever’s resulting water stress score is high. Based on feedback from the local facility manager and consultations with the Aqueduct team, Unilever removed the facility from their prioritization exercise but kept it on the list for regulatory disclosures for consistency.
Reporting water abundance in areas of high stress
In a sub-basin near São Paulo in Brazil, Unilever encountered a different kind of discrepancy. This time, the local facility managers disagreed with Aqueduct’s low water stress result. They reported to Unilever’s sustainability team that the facility experienced water shortages in the past and had even resorted to trucking in water at times.
Indeed, São Paulo and its surrounding area have faced many water challenges in recent years. In 2014, the city nearly ran out of water. A major driver of the region’s water stress is poor water quality, something not captured by Aqueduct’s indicator. If the water is too polluted to use, then there will be less water available to use than Aqueduct models suggest. As a result, local water stress will be intensified.
In this area, water quality issues are largely driven by land degradation. Forests, for example, help to filter out pollutants, reduce heat and stabilize rainfall and soil moisture. Unfortunately, over three-quarters of the forest in São Paulo’s headwaters have been lost.
Compounding the water quality issue are large water transfers to the city of São Paulo, which are not modeled by Aqueduct.
Unilever understood that information shouldn’t only flow from the sustainability team to local facilities. Rather, the company took the local reports seriously and commissioned a regional analysis through external consultants to better understand the water challenges and potential solutions. As a result, Unilever added this facility to its prioritization exercise.
How to Reflect Local Realities in Water Risk Assessments
Unilever’s case study shows that integrating local feedback into global risk assessments helps the company manage water risks more effectively by improving the prioritization process. Here are our key takeaways for companies to enhance their global water risk assessments with local feedback to ensure they reflect real-world conditions on the ground:
1) Use global data as a starting point
After using Aqueduct to run a water risk assessment, use the results to start conversations with your facility managers and other internal stakeholders. Allow information to flow in all directions. Create a list of locations to investigate for further analysis. These may include:
- Places with the highest levels of risk.
- Places with high risk and high business materiality metrics, such as financial value or product volume.
- Places where the Aqueduct result does not align with the local perception, whether it’s an overestimation or underestimation of risk.
2) Document any global-local deviations
Clearly document all conversations, sources and research used to supplement the Aqueduct indicator. This is especially important when reporting to regulatory disclosure frameworks.
3) Review and revise often
Water stewardship is an iterative process. Periodically check in on your priority sites (or potential priority sites) to assess whether any should be reclassified based on changing conditions. For example, a water risk assessment should be rerun when Aqueduct is updated every few years or when a company adds new facilities.
4) Communicate and share internally and externally
At its core, water stewardship must be a collaborative exercise. The more companies talk publicly and share experiences with global-to-local data integration, the faster the community can connect, learn from one another and work toward water-secure outcomes. Communication should happen both internally and externally. Internally, communicate with teams outside the sustainability department, such as procurement, to enhance corporation action. Externally, share lessons with peer organizations and others operating in priority locations to foster collective action.
Using the Data for the Right Purpose
The integration of global and local data can strengthen the prioritization exercise, enrich the narrative and generate buy-in, ultimately improving the company’s water stewardship interventions.
What’s important is understanding how to supplement global data with local knowledge and understanding, whether you are using Aqueduct or any other global data product. Global data is a good place to start, but real change on the ground cannot be achieved without ground truth data.