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...
Abstract:To advance the science of dam removal, analyses of functions and benefits need to be linked to individual dam attributes and effects on downstream receiving waters. We examined 7550 dams in the New England (USA) region for possible tradeoffs associated with dam removal. Dam removal often generates improvements for safety or migratory fish passage but might increase nitrogen (N) flux and eutrophication in coastal watersheds. We estimated N loading and removal with algorithms using geospatial data on land use, stream flow and hydrography. We focused on dams with reservoirs that increase retention time at specific points of river reaches, creating localized hotspots of elevated N removal. Approximately 2200 dams with reservoirs had potential benefits for N removal based on N loading, retention time and depth. Across stream orders, safety concerns on these N removal dams ranged between 28% and 44%. First order streams constituted the majority of N removal dams (70%), but only 3% of those were classified as high value for fish passage. In cases where dam removal might eliminate N removal function from a particular reservoir, site-specific analyses are warranted to improve N delivery estimates and examine alternatives that retain the reservoir while enhancing fish passage and safety.
Water resource managers seeking to optimize stream ecosystem services and abstractions of water from watersheds need an understanding of the importance of land use, physical and climatic characteristics, and hydrography on different low flow components of stream hydrographs. Within 33 USGS gaged watersheds of southern New England, we assessed relationships between watershed variables and a set of low flow parameters by using an information-theoretical approach. The key variables identified by the Akaike Information Criteria (AIC) weighting factors as generating positive relationships with low flow events included percent stratified drift, mean elevation, drainage area, and mean August precipitation. The extent of wetlands in the watershed was negatively related to low flow magnitudes. Of the various land use variables, the percentage of developed land was found to have the highest importance and a negative relationship on low flow magnitudes, but was less important than wetlands and physical and climatic features. Our results suggest that management practices aimed to sustain low flows in fluvial systems can benefit from attention to specific watershed features. We draw attention to the finding that streams located in watersheds with high proportions of wetlands may require more stringent approaches to withdrawals to sustain fluvial ecosystems during drought periods, particularly in watersheds with extensive development and limited deposits of stratified drift.
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