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The rapid growth of demand in agricultural production has created water scarcity issues worldwide. Simultaneously, climate change scenarios have projected that more frequent and severe droughts are likely to occur. Adaptive water resources management has been suggested as one strategy to better coordinate surface water and groundwater resources (i.e., conjunctive water use) to address droughts. In this study, we enhanced an aggregated water resource management tool that represents integrated agriculture, water, energy, and social systems. We applied this tool to the Yakima River Basin (YRB) in Washington State, USA. We selected four indicators of system resilience and sustainability to evaluate four adaptation methods associated with adoption behaviors in alleviating drought impacts on agriculture under RCP4.5 and RCP 8.5 climate change scenarios. We analyzed the characteristics of four adaptation methods, including greenhouses, crop planting time, irrigation technology, and managed aquifer recharge as well as alternating supply and demand dynamics to overcome drought impact. The results show that climate conditions with severe and consecutive droughts require more financial and natural resources to achieve well-implemented adaptation strategies. For long-term impact analysis, managed aquifer recharge appeared to be a cost-effective and easy-to-adopt option, whereas water entitlements are likely to get exhausted during multiple consecutive drought events. Greenhouses and water-efficient technologies are more effective in improving irrigation reliability under RCP 8.5 when widely adopted. However, implementing all adaptation methods together is the only way to alleviate most of the drought impacts projected in the future. The water resources management tool helps stakeholders and researchers gain insights in the roles of modern inventions in agricultural water cycle dynamics in the context of interactive multi-sector systems.
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This content will become publicly available on August 28, 2026
Modeling material flow dynamics in coupled natural‐industrial ecosystems for resilience to climate change: A case study on a soybean‐based industrial ecosystem
Abstract Industrial ecosystems are coupled with natural systems, which causes the material flow dynamics in the network to be affected by the mechanistic dynamics of each node. However, current material flow dynamics studies do not capture these mechanistic and nonlinear dynamics to evaluate material flows in networks, thus missing its role in designing resilient industrial ecosystems. In this work, we present a methodology to overcome this limitation and model material flow dynamics in a coupled natural‐industrial network by accounting for underlying nonlinear dynamics at each node. We propose a three‐step methodology: first, creating accurate surrogate models using liquid time‐constant (LTC) neural networks to capture node‐specific behavior; second, coupling these individual node models to simulate material flow dynamics in the network; and third, evaluating resilience by measuring the system's ability to maintain production levels under climate stress. Applied to a soybean‐based biodiesel production network in Champaign County, Illinois (2006–2096), our analysis reveals significant vulnerability differences between climate scenarios, with the RCP 8.5 scenario triggering production failures approximately 10 years earlier than RCP 4.5 (2016 vs. 2026), exhibiting higher failure frequency and requiring longer recovery periods. Smaller farms (450 ha) demonstrated substantially higher import dependency, while medium farms (500 ha) reached a critical bifurcation point around 2050 under RCP 8.5, indicating a systemic tipping point. These findings provide insights for policymakers and industrial managers to implement targeted interventions, supply chain diversification, and adaptive management strategies, thereby enhancing system resilience while offering industrial ecology practitioners a methodology for modeling material flow dynamics in a coupled natural‐industrial network.
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- Award ID(s):
- 2229250
- PAR ID:
- 10637129
- Publisher / Repository:
- WILEY
- Date Published:
- Journal Name:
- Journal of Industrial Ecology
- ISSN:
- 1088-1980
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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