The concept of climate tipping points in socio-environmental systems is increasingly being used to describe nonlinear climate change impacts and encourage social transformations in response to climate change. However, the processes that lead to these tipping points and their impacts are highly complex and deeply uncertain. This is due to numerous interacting environmental and societal system components, constant system evolution, and uncertainty in the relationships between events and their consequences. In the face of this complexity and uncertainty, this research presents a conceptual framework that describes systemic processes that could lead to tipping points socio-environmental systems, with a focus on coastal communities facing sea level rise. Within this context, we propose an organizational framework for system description that consists of elements, state variables, links, internal processes, and exogenous influences. This framework is then used to describe three mechanisms by which socio-environmental tipping could occur: feedback processes, cascading linkages, and nonlinear relationships. We presented this conceptual framework to an expert panel of coastal practitioners and found that it has potential to characterize the effects of secondary climatic impacts that are rarely the focus of coastal risk analyses. Finally, we identify salient areas for further research that can build upon the proposed conceptual framework to inform practical efforts that support climate adaptation and resilience.

Accurate models of water withdrawal are crucial in anticipating the potential water use impacts of drought and climate change. Machine-learning methods are increasingly used in water withdrawal prediction due to their ability to model the complex, nonlinear relationship between water use and potential explanatory factors. However, most machine learning methods do not explicitly address the hierarchical nature of water use data, where multiple observations are typically available for multiple facilities, and these facilities can be grouped an organized in a variety of different ways. This work presents a novel approach for prediction of water withdrawals across multiple usage sectors using an ensemble of models fit at different hierarchical levels. A dataset of over 300,000 records of water withdrawal was used to fit models at the facility and sectoral grouping levels, as well as across facility clusters defined by temporal water use characteristics. Using repeated holdout cross validation, it demonstrates that ensemble predictions based on models learned from different data groupings improve withdrawal predictions for 63% of facilities relative to facility-level models. The relative improvement gained by ensemble modeling was greatest for facilities with fewer observations and higher variance, indicating its potential value in predicting withdrawal for facilities with relatively short data records or data quality issues. Inspection of the ensemble weights indicated that cluster level weights were often higher than sector level weights, pointing towards the value of learning from the behavior of facilities with similar water use patterns, even if they are in a different sector.

Many states in US follow strict regulations on water discharge into the streams to enforce water quality standards, however water withdrawal restrictions from the streams are limited and inadequate in water management at the time of low flows. In states such as Virginia (VA), Virginia Department of Environmental Quality (VDEQ) requires a Virginia Water Protection (VWP) permit for all water withdrawals made from Virginia’s surface waters. However, under certain provisions of VWP regulations, users are exempted from having a permit (e.g., water withdrawal in existence before 1989) allowing unrestricted access for water withdrawals. Such permit exemptions are in existence in many states and present a severe challenge to the management of water supplies. Still, little research exists that quantifies the impact they could have on water availability. This study was conducted to compare the impact of permit exemptions on surface water availability and drought flows and compares these impacts to the relatively well-studied risks presented by climate change and demand growth in Virginia (VA). This study makes use of VaHydro, a comprehensive, modular flow model to examine the impacts of exempt users’ withdrawals, demand growth, climate change and compare with the base scenario representing current precipitation and temperature conditions and current withdrawals. While the reduction in flows was widespread in climate change scenario, the impacts were more localized in exempt users and demand growth scenarios. It was observed that permit exemptions existed in 90% of the counties in VA and impacts on flows exceeded than climate change scenario in certain regions and at the low flows. Higher reduction in flows was observed during winter months in climate change scenarios while reductions were observed higher in summer months in demand and exempt user scenarios.