Science Inventory

Spatial Statistical Network Models for Stream and River Temperatures in the Chesapeake Bay Watershed


Detenbeck, N., A. Morrison, AND R. Abele. Spatial Statistical Network Models for Stream and River Temperatures in the Chesapeake Bay Watershed. American Water Resources Association (AWRA) Summer Specialty Conference GIS and Water Resources, Sacramento, CA, July 11 - 13, 2016.


The purpose of this research is to develop predictive models for thermal class and selected thermal metrics for the Chesapeake Bay Watershed.


Numerous metrics have been proposed to describe stream/river thermal regimes, and researchers are still struggling with the need to describe thermal regimes in a parsimonious fashion. Regional temperature models are needed for characterizing and mapping current stream thermal regimes, establishing reference condition and aquatic life use categories, assessing and prioritizing past impacts as well as predicting future impacts and identifying critical thermal refugia. While mechanistically-based heat models can predict stream temperatures within a few tenths of a degree, the ability to use these models for regional applications is prohibitive due to the time required for both preprocessing and computer simulation runs. Statistical models provide an alternative approach and are generally easier to implement at a regional scale. While most regional regression models of stream temperature have prediction errors of 2-3°C at best, spatial statistical models have been developed to improve regression modeling techniques by taking into account the spatial covariance structure inherent in stream networks. Unlike the earlier approaches, spatial statistical models describe spatial autocorrelation as part of the error structure based on distance along the flow network as well as standard Euclidean distances between observation points. Our goal for this study is to develop predictive models for a parsimonious set of metrics describing the thermal regime of streams/rivers across the Chesapeake Bay Watershed using watershed and waterbody attributes as well as meteorological variables. To accomplish these objectives, we developed a database of Chesapeake Bay monitoring stations using existing stream/river temperature time series data obtained from state, federal, and nongovernmental organization sources. After filtering the database by applying quality control criteria, we described thermal regimes of Chesapeake Bay rivers and streams based on a set of reduced metrics chosen through principal component analysis of 78 candidate variables. We are developing spatial statistical models for each of these metrics, incorporating spatial autocorrelation based on both distance along the flow network and Euclidean distance. We used a variety of GIS tools, such as geographic weighted regression, zonal statistics, solar radiation and the ArcHydro package to develop the parameters used as independent variables in our spatial statistical models. In addition, we utilized GIS-based tools such as NHDPlus Basin Delineator to facilitate watershed processing. Landscape networks were developed using the NHDPlus hydrographic dataset. The STARS (Spatial Tools for the Analysis of Rivers) geoprocessing toolset was used to calculate spatial information needed to fit the spatial statistical models to the stream network.We predicted monthly median July stream temperatures as a function of air temperature, drainage density, land cover, main channel slope, watershed storage (percent lake and wetland area), percent coarse-grained surficial deposits, stream flow and velocity and watershed area, with an overall root-mean-square prediction error of 1.5○ C. Growing season maximum varied as a function of air temperature, local channel slope, shaded August solar radiation, imperviousness, and watershed storage. Predictive models for the remaining variables such as daily temperature range, growing season maximum, maximum daily rate of change, and timing of growing season maximum are being developed.

Record Details:

Product Published Date: 07/20/2016
Record Last Revised: 07/21/2016
OMB Category: Other
Record ID: 321930