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Spatial statistical network models for stream and river temperature in New England, USA
Citation:
Detenbeck, N., A. Morrison, R. Abele, AND D. Kopp. Spatial statistical network models for stream and river temperature in New England, USA. WATER RESOURCES RESEARCH. American Geophysical Union, Washington, DC, 52:6018–6040, (2016).
Impact/Purpose:
Assessing stream temperature impacts from stormwater runoff is a necessary precursor to 1) diagnosing causes of biological impairment and mitigating impacts in response to applications of the Clean Water Act Residual Designation Authority, and 2) meeting provisions of Section 438 of the Energy Independence and Security Act of 2007 to "maintain or restore, to the maximum extent technically feasible, the predevelopment hydrology ... with regard to the temperature, rate, volume, and duration of flow. In New England, streams and rivers have been included on the 303d list due to biological impairments, leading in some cases to the development of hydrologically-based TMDLs. Pursuant to the Clean Water Act 40 C.F.R. §122.26(a), a “Residual Designation Authority” has been exercised at both Federal (EPA Region 1) and state (Maine, Vermont) levels to implement solutions to improve biological condition through a combination of MS4 permits and RDA permits. Impacts to stream thermal regimes has been cited as a possible cause for recent findings of low thresholds of biological impairment (<5% impervious cover). In order to assess the contribution of thermal impacts to biological impairment or to measure the degree of alteration from natural thermal regimes, it is necessary to describe and predict natural variation in stream and river thermal regimes, as well as the impacts of anthropogenic factors on natural regimes. This paper presents a state-of-the-art approach to develop a Region-wide model of stream and river temperature regimes which includes the influence of both natural and anthropogenic features. By incorporating the spatial autocorrelation structure of stream networks into the modeling process, we were able to predict monthly median July or August stream temperatures with an overall root-mean-square prediction error of 1.4 and 1.5○ C, respectively, a significant improvement over previous statistical modeling approaches.
Description:
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 1.5°C, 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).
