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A substantial increase in predictive capacity is needed to anticipate and mitigate the widespread change in ecosystems and their services in the face of climate and biodiversity crises. In this era of accelerating change, we cannot rely on historical patterns or focus primarily on long-term projections that extend decades into the future. In this Perspective, we discuss the potential of near-term (daily to decadal) iterative ecological forecasting to improve decision-making on actionable time frames. We summarize the current status of ecological forecasting and focus on how to scale up, build on lessons from weather forecasting, and take advantage of recent technological advances. We also highlight the need to focus on equity, workforce development, and broad cross-disciplinary and non-academic partnerships. 

A growing body of research focuses on climate change and Indigenous peoples. However, relatively little of this work focuses on Native American tribes living in the Atlantic Coastal Plain of the United States. The Lumbee Tribe of North Carolina is a large (60,000 member) Native American tribe located on the Coastal Plain in present day North Carolina (U.S.). The tribe has deep connections to the Lumbee River, which flows through a watershed dominated by extensive forested wetlands. In this paper, I outline key issues associated with climate change and water in the region, and I use long‐term climatic and hydrologic datasets and analysis to establish context for understanding historical climate change in the Lumbee River watershed. Downscaled climate model outputs for the region show how further changes may affect the hydrologic balance of the watershed. I discuss these changes in terms of environmental degradation and potential impacts on Lumbee culture and persistence, which has remained strong through centuries of adversity and has also experienced a resurgence in recent years. I close by acknowledging the especially vulnerable position of the Lumbee Tribe as a non‐federal tribe that lacks access to certain resources, statutory protections, and policies aimed at helping Native American tribes deal with climate change and other environmental challenges. 

Abstract Linking quickflow response to subsurface state can improve our understanding of runoff processes that drive emergent catchment behaviour. We investigated the formation of non‐linear quickflows in three forested headwater catchments and also explored unsaturated and saturated storage dynamics, and likely runoff generation mechanisms that contributed to threshold formation. Our analyses focused on two reference watersheds at the Coweeta Hydrologic Laboratory (CHL) in western North Carolina, USA, and one reference watershed at the Susquehanna Shale Hills Critical Zone Observatory (SHW) in Central Pennsylvania, USA, with available hourly soil moisture, groundwater, streamflow, and precipitation time series over several years. Our study objectives were to characterise (a) non‐linear runoff response as a function of storm characteristics and antecedent conditions, (b) the critical levels of shallow unsaturated and saturated storage that lead to hourly flow response, and (c) runoff mechanisms contributing to rapidly increasing quickflow using measurements of soil moisture and groundwater. We found that maximum hourly rainfall did not significantly contribute to quickflow production in our sites, in contrast to prior studies, due to highly conductive forest soils. Soil moisture and groundwater dynamics measured in hydrologically representative areas of the hillslope showed that variable subsurface states could contribute to non‐linear runoff behaviour. Quickflow generation in watersheds at CHL were dominated by both saturated and unsaturated pathways, but the relative contributions of each pathway varied between catchments. In contrast, quickflow was almost entirely related to groundwater fluctuations at SHW. We showed that co‐located measurements of soil moisture and groundwater supplement threshold analyses providing stronger prediction and understanding of quickflow generation and indicate dominant runoff processes.