Intermittent rivers are spatially dynamic, expanding and contracting in response to changes in water availability, but studies that explicitly examine spatial drying patterns are scarce. We used long-term data produced by citizen scientists to map wet and dry reaches of 3 different river systems to investigate mechanisms producing temporal variation in drying patterns. We quantified the total wetted river length in each survey, and calculated ecologically scaled landscape indices that indicate the carrying capacity (population size) and habitat connectivity of large and small fish metapopulations in these systems. We found that the spatial extent of perennial water decreased over the study period in 2 of the 3 study rivers: ∼26% in the Agua Fria River from 2008 to 2016, and ∼14% in Cienega Creek from 2006 to 2016. We also observed an ∼8% decline in habitat connectivity for large fish in the Agua Fria River. We used multivariate structural equation models to infer causal relationships between spatial drying patterns and temperature, precipitation, streamflow, and drought conditions. These models explained 85% of year-to-year variation in the total length of wet reaches, and 63 and 55% of year-to-year variation in habitat connectivity for large and small fish, respectively. With the US Southwest shifting to an even more arid climate, our results suggest that this may reduce habitat connectivity of fish populations in this region.
Watershed managers are challenged by the need for predictive temperature models with sufficient accuracy and geographic breadth for practical use. We described thermal regimes of New England rivers and streams based on a reduced set of metrics for the May-September growing season (July or August median temperature, diurnal rate of change, and magnitude and timing of growing season maximum) chosen through principal component analysis of 78 candidate metrics. We then developed and assessed spatial statistical models for each of these metrics, incorporating spatial autocorrelation based on both distance along the flow network and Euclidean distance between points. Calculation of spatial autocorrelation based on travel or retention time in place of network distance yielded tighter-fitting Torgegrams with less scatter but did not improve overall model prediction accuracy. We predicted monthly median July or August stream temperatures as a function of median air temperature, estimated urban heat island effect, shaded solar radiation, main channel slope, watershed storage (percent lake and wetland area), percent coarse-grained surficial deposits, and presence or maximum depth of a lake immediately upstream, with an overall root-mean-square prediction error of 1.4 and 1.58C, respectively. Growing season maximum water temperature varied as a function of air temperature, local channel slope, shaded August solar radiation, imperviousness, and watershed storage. Predictive models for July or August daily range, maximum daily rate of change, and timing of growing season maximum were statistically significant but explained a much lower proportion of variance than the above models (5-14% of total). PUBLICATIONSHistorically, when developing predictive temperature models for streams, there has been a trade-off between accuracy of model predictions and practical spatial extent of model coverage. Mechanistically based heat budget models such as SNTEMP [Krause et al., 2004] can predict stream temperature within a few tenths of a degree. However, the only mechanistic model that has been linked with a GIS interface to facilitate regional application is BASIN TEMP [Allen 2008], which is not commercially available. One intermediate solution could be the application of WET-Temp [Cox and Bolte, 2007], a spatially explicit networkbased model for continuous temperature simulation. However, combined preprocessing and run times for an entire region would be prohibitive. LeBlanc et al. [1997] identified a second intermediate solution using a simulation model of the effects of urbanization on water temperature in unregulated streams. They determined that model outputs were sensitive to only four of the model inputs: vegetation transmissivity, channel width, sun angle, and groundwater discharge, thus paving the way to development of a much simpler predictive model. This approach has only been applied at the reach scale, however, and needs to be incorporated into a network model to allow examination of cumulative effects on temperature throughout...
1. While climate change is altering ecosystems on a global scale, not all ecosystems are responding in the same way. The resilience of ecological communities may depend on whether food webs are producer-or detritus-based (i.e. 'green' or 'brown' food webs, respectively), or both (i.e. 'multi-channel' food web).
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