Spatiotemporal and Economic Impacts of Media on Water Conservation During Drought: An Explainable AI Approach

Abstract Reduced public engagement in water conservation during droughts can exacerbate water scarcity and its associated impacts. However, capturing these coupled human‐natural dynamics in hydrologic models remains a persistent challenge. Here, we develop an AI model framework to estimate household water‐saving patterns during the 2022–2023 drought in South Korea, integrating both drought conditions and social factors such as news coverage. Results indicate that a spike in drought‐related news coverage is associated with higher water‐saving rates in metropolitan areas, but not in rural areas. The trained AI model shows that a scenario with an additional 100 news articles per month during the drought onset and caution stages corresponds to an average increase of 13.6 percentage points in model‐simulated household water‐saving rates in metropolitan areas. This corresponds to potential avoided production costs of KRW 1.14 billion (USD 0.82 million), with more modest gains in rural areas (about 4.0 percentage points and KRW 0.46 billion (USD 0.33 million)). However, the modeled water‐saving response in metropolitan areas sharply declines during the drought alert stage when the water saving is already high (above +10%). Our results suggest that drought‐related news patterns can serve as a proxy to inform potential region and stage‐specific water‐saving targets, reflecting differences between metropolitan and rural areas. This study underscores the value of explainable AI in providing a quantitative framework to capture human‐nature interactions and support the design of targeted drought communication strategies in a timely manner.