Search for: All records

Rapid climate change and invasive species introductions threaten ecological communities across the globe. Freshwaters are particularly vulnerable and impacted, especially when these stresses coincide. We document the migration of an invasive piscine predator, the Sacramento pikeminnow (Ptychocheilus grandis), within its introduced range, the South Fork Eel River, California, USA. Snorkel surveys and temperature monitoring in 2015–2019 showed that pikeminnow migrate upstream during spring and early summer, with earlier migration in warmer years. We developed a statistical temperature model to forecast the timing and extent of upstream migration by pikeminnow under varying combinations of discharge and air temperature. Modeled river temperature increased with air temperature and downstream and decreased with discharge. In years with low discharge and high air temperature, we predict pikeminnow will move upstream earlier, increasing spatial and temporal overlap in their summer range with native fishes. Managing conditions that reduce pikeminnow co-occurrence with native fishes (i.e., decreasing river temperature) could increase amount and duration of predator-free habitat for native fishes. We predict invasive pikeminnow will have larger impacts on invaded riverine communities with global warming and increasing drought severity. Knowledge of life history and phenology, for pikeminnow and other organisms, can guide effective management as conditions change and help to limit adverse impacts of introduced organisms on native species.

 

Abstract. A common parameter in hydrological modeling frameworks is root zone water storage capacity (SR[L]), which mediates plant water availability during dry periods as well as the partitioning of rainfall between runoff and evapotranspiration. Recently, a simple flux-tracking-based approach was introduced to estimate the value of SR (Wang-Erlandsson et al., 2016). Here, we build upon this original method, which we argue may overestimate SR in snow-dominated catchments due to snow melt and evaporation processes. We propose a simple extension to the method presented by Wang-Erlandsson et al. (2016) and show that the approach provides a lower estimate of SR in snow-dominated watersheds. This SR dataset is available at a 1 km resolution for the continental USA, along with the full analysis code, on the Google Colab and Earth Engine platforms. We highlight differences between the original and new methods across the rain–snow transition in the Southern Sierra Nevada, California, USA. As climate warms and precipitation increasingly arrives as rain instead of snow, the subsurface may be an increasingly important reservoir for storing plant-available water between wet and dry seasons; therefore, improved estimates of SR will better clarify the future role of the subsurface as a storage reservoir that can sustain forests during seasonal dry periods and episodic drought. 

Water budgets are essential for characterizing water supplies from snow‐dominated upland catchments where data are sparse, groundwater systems are complex, and measurements are prone to error (ε). One solution is imposing water budget closure (CWB) by ignoring difficult‐to‐measure variables, including inter‐basin groundwater fluxes (G) andε. However, conventional CWB‐based analyses, which derive evapotranspiration (ET) from precipitation (P) and streamflow (Q) (e.g., the Budyko hypothesis), are limited in their ability to take advantage of recent advances inETproducts, physically‐based frameworks for improving inferences aboutG, or tools to statistically characterizeε(Triple Collocation [TC]); all of which offer promise for improved water supply predictions via open water budgets (OWB). We clarify the value of these advances in upland settings by comparing standard land surface model, Ensemble Mean, and TC‐MergedPandETproducts in 114 upland catchments. When compared against a long‐term OWB, we find that the CWB assumptions are unsupported in 75%–100% of our 114 catchments, depending on the product. We then show how applying these CWB assumptions in snowy, steep catchments whereεis large can inflate inferences about streamflow response to climate change by up 9 times more than independent (OWB) estimates ofETusing TC. Finally, we demonstrate how advances in OWB analysis reveal that high, arid settings with deep permeable substrate are groundwater exporters while most other basins are groundwater importers. Our results highlight the advantages of OWB analyses that harness new products, tools, and frameworks for characterizing inter‐basin groundwater fluxes in critical upland settings.

 

A growing empirical literature associates climate anomalies with increased risk of violent conflict. This association has been portrayed as a bellwether of future societal instability as the frequency and intensity of extreme weather events are predicted to increase. This paper investigates the theoretical foundation of this claim. A seminal microeconomic model of opportunity costs—a mechanism often thought to drive climate–conflict relationships—is extended by considering realistic changes in the distribution of climate-dependent agricultural income. Results advise caution in using empirical associations between short-run climate anomalies and conflicts to predict the effect of sustained shifts in climate regimes: Although war occurs in bad years, conflict may decrease if agents expect more frequent bad years. Theory suggests a nonmonotonic relation between climate variability and conflict that emerges as agents adapt and adjust their behavior to the new income distribution. We identify 3 measurable statistics of the income distribution that are each unambiguously associated with conflict likelihood. Jointly, these statistics offer a unique signature to distinguish opportunity costs from competing mechanisms that may relate climate anomalies to conflict.