a b s t r a c tThe moisture content of vegetation and litter (fuel moisture) is an important determinant of fire risk, and predictions of dead fine fuel moisture content (fuel with a diameter <25.4 mm) are particularly important. A variety of indices, as well as empirical and mechanistic models, have been proposed to predict fuel moisture, but these approaches have seldom been validated across temporally extensive datasets, or widely contrasting vegetation types. Here, we describe a semi-mechanistic model, based on the exponential decline of fuel moisture content with atmospheric vapor pressure deficit, that predicts daily minimum fuel moisture content. We calibrated the model at one site in New South Wales, Australia, and validated it at three contrasting ecosystem types in California, USA, where 10-h fuel moisture content was continuously measured every 30 min over a year. We found that existing drought indices did not accurately predict fuel moisture, and that empirical and equilibrium models provided biased estimates. The mean absolute error (MAE) of the fuel moisture content predicted by our model across sites and years was 3.7%, which was substantially lower than for other, commonly used models. Our model's MAE dropped to 2.9% when fuel moisture was below 20%, and to 1.8% when fuel moisture was below 10%. Our model's MAE was comparable to instrumental MAE (3.1-2.5%), indicating that further improvement may be limited by measurement error. The simplicity, accuracy and precision of our model makes it suitable for a range of applications, such as operational fire management and the prediction of fire risk in vegetation models, without the need for site-specific calibrations.

Differences in water and carbon fluxes along a climate/elevation gradient within a sagebrush ecosystem are quantified, and inferences are made about climate warming using a network of eddy covariance systems. Sites are located within the Reynolds Creek Critical Zone Observatory in southwestern Idaho, USA, with elevations ranging from 1425 to 2111 m, annual precipitation ranging from 290 to 795 mm and annual temperature ranging from 9.1 to 5.4°C. Annual gross ecosystem production (GEP) for the sites averaged (± uncertainty) 385 ± 6, 549 ± 19, 684 ± 25, and 818 ± 26 gC m -2 from lowest to highest elevation. Annual net ecosystem production indicated that the sites are carbon sinks with annual uptake typically ranging from 100 ± 10 to 200 ± 30 gC m -2 . Exceptions to this are: the lowest elevation site which was carbon neutral (1 ± 16 gC m -2 ) during a year with a summer rainfall respiration pulse, and the highest elevation site, where carbon uptake dropped to 42 ± 20 gC m -2 during a heavy snow year. Carbon flux and evapotranspiration (ET) peaked about a month earlier at the lower elevation sites, but with limited precipitation, these sites encountered water stress for much of the growing season. Model simulations suggest that climate warming will likely have a negligible impact on annual ET and GEP at lower elevations, but rather shift ET and GEP earlier in the season and prolong the period of water stress. ET and GEP may increase with climate warming at higher elevations where precipitation is above a threshold of about 450 mm.

Active 20th century fire suppression in western US forests, and a resulting increase in stem density, is thought to account for a significant fraction of the North American carbon sink. We compared California forest inventories from the 1930s with inventories from the 1990s to quantify changes in aboveground biomass. Stem density in mid‐montane conifer forests increased by 34%, while live aboveground carbon stocks decreased by 26%. Increased stem density reflected an increase in the number of small trees and a net loss of large trees. Large trees contain a disproportionate amount of carbon, and the loss of large trees accounts for the decline in biomass between surveys. 20th century fire suppression and increasing stand density may have decreased, rather than increased, the amount of aboveground carbon in western US forests.

Ecohydrologic fluxes within atmosphere, vegetation, and soil systems exhibit a joint variability that arises from forcing and feedback interactions. These interactions cause fluctuations to propagate between variables at many time scales. In an ecosystem, this connectivity dictates responses to climate change, land-cover change, and weather events and must be characterized to understand resilience and sensitivity. We use an information theory-based approach to quantify connectivity in the form of information flow associated with the propagation of fluctuations between variables. We apply this approach to study ecosystems that experience changes in dry-season moisture availability due to rainfall and drought conditions. We use data from two transects with flux towers located along elevation gradients and quantify redundant, synergistic, and unique flow of information between lagged sources and targets to characterize joint asynchronous time dependencies. At the Reynolds Creek Critical Zone Observatory in Idaho, a dry-season rainfall pulse leads to increased connectivity from soil and atmospheric variables to heat and carbon fluxes. At the Southern Sierra Critical Zone Observatory in California, separate sets of dominant drivers characterize two sites at which fluxes exhibit different drought responses. For both cases, our information flow-based connectivity characterizes dominant drivers and joint variability before, during, and after disturbances. This approach to gauge the responsiveness of ecosystem fluxes under multiple sources of variability furthers our understanding of complex ecohydrologic systems.