Northeast Daily Stream Temperature

Analysis of daily stream temperature data for the northeastern US using the conteStreamTemperature package

View the Project on GitHub Conte-Ecology/conteStreamTemperature_northeast

Northeast Daily Stream Temperature

Daniel J Hocking


Coming Soon


Our objective was to develop novel statistical model of daily stream temperature that incorporates features of stochastic models and extends the Letcher et al. (2016) framework to large geographic areas. The model needed to (1) handle time series data of widely varying duration from many sites accounting for autocorrelation at specific locations within watersheds, (2) while also including information about catchment, landscape, and meteorological conditions for explanatory and predictive purposes. (3) For a daily temperature model we needed to include an autoregressive function to account for temporal autocorrelation in the time series, a challenge with other statistical models at fine temporal resolution. (4) We also wanted to account for uncertainty at various levels and propagate this through to predictions.

We use the model to predict daily stream temperature across the northeastern United States over a 34-year time record.


Statistical models of stream temperature often rely on the close relationship between air temperature and water temperature. However, this relationship breaks down during the winter in temperature zones, particularly as streams freeze, thereby changing their thermal and properties. Many researchers and managers are interested in the non-winter effects of temperature. The winter period, when phase change and ice cover alter the air-water relationship, differs in both time (annually) and space. We developed an index of air-water synchrony ($Index_{sync}$) so we can model the portion of the year that it not affected by freezing properties. The index is the difference between air and observed water temperatures divided by the water temperature plus 0.000001 to avoid division by zero.

We calculate the $Index_{sync}$ for each day of the year at each reach for each year with observed data. We then calculate the 99.9% confidence interval of $Index_{sync}$ for days between the 125 and 275 days of the year (05 May and 02 October). Then moving from the middle of the year (day 180) to the beginning of the year, we searched for the first time when 10 consecutive days were not within the 99.9% CI. This was selected as the spring breakpoint. Similarly moving from the middle to the end of the year, the first event with fewer than 16 consecutive days within the 99.9% CI was assigned as the autumn breakpoint. Independent breakpoints were estimated for each reach-year combination. More details regarding the identification of the synchronized period can be found in Letcher et al. (2016). The portion of the year between the spring and autumn breakpoints was used for modeling the non-winter, approximately ice-free stream temperatures.

We used a generalized linear mixed model to account for correlation in space (stream reach nested within HUC8). This allowed us to incorporate short time series as well as long time series from different reaches and disjunct time series from the same reaches without risk of pseudoreplication (Hurlbert 1984). By limited stream drainage area to <200 $km^2$ and only modeling the synchronized period of the year, we were able to use a linear model, avoiding the non-linearities that occur at very high temperatures due to evaporative cooling and near 0 C due to phase change (Mohseni 1999).

Stream Temperature Data (Dependent Data)

Summary of data by states, contributors (agencies), and locations.

state agency_name n_records n_years n_locations n_streams
CT CTDEEP 4806258 17 495 397
MA CTDEEP 16261 5 3 2
MA MADEP 446338 6 115 104
MA MAFW 24127 5 48 40
MA USFS 506434 5 40 22
MA USGS_Conte 510755 19 10 5
MD MBSS 43278 11 474 382
ME BSP 52197 8 7 5
ME HBM 5025 1 1 1
ME MEDEP 564133 17 91 64
ME MEDMR 525688 16 65 51
ME MEDOT 85848 2 3 3
ME MEIFW 55398 7 10 10
ME NOAA 45434 8 1 1
ME USFWS 931190 8 19 11
NH MADEP 4654 1 1 1
NH NHDES 1641 3 18 18
NH NHFG 2831 3 25 23
VA USFS_SRS 4039 1 1 1
VA VAHUDY 140682 1 34 34
VT USFS 2775 3 7 7
VT VTFWS 11551 11 46 45
WV WVDNR 835882 8 214 185
Totals: 9622419 21 1728 1386

Predictor Variables

Variable Description Source Processing GitHub Repository
Total Drainage Area The total contributing drainage area from the entire upstream network The SHEDS Data project The individual polygon areas are summed for all of the catchments in the contributing network NHDHRDV2
Riparian Forest Cover The percentage of the upstream 200ft riparian buffer area that is covered by trees taller than 5 meters The National LandCover Database (NLCD) All of the NLCD forest type classifications are combined and attributed to each riparian buffer polygon using GIS tools. All upstream polygon values are then aggregated. nlcdLandCover
Daily Precipitation The daily precipitation record for the individual local catchment Daymet Daily Surface Weather and Climatological Summaries Daily precipitation records are spatially assigned to each catchment based on overlapping grid cells using the zonalDaymet R package daymet
Upstream Impounded Area The total area in the contributing drainage basin that is covered by wetlands, lakes, or ponds that intersect the stream network U.S. Fish & Wildlife Service (FWS) National Wetlands Inventory All freshwater surface water bodies are attributed to each catchment using GIS tools. All upstream polygon values are then aggregated. fwsWetlands
Percent Agriculture The percentage of the contributing drainage area that is covered by agricultural land (e.g. cultivated crops, orchards, and pasture) including fallow land. The National LandCover Database All of the NLCD agricutlural classifications are combined and attributed to each catchment polygon using GIS tools. All upstream polygon values are then aggregated. nlcdLandCover
Percent High Intensity Developed The percentage of the contributing drainage area covered by places where people work or live in high numbers (typically defined as areas covered by more than 80% impervious surface) The National LandCover Database The NLCD high intensity developed classification is attributed to each catchment polygon using GIS tools. All upstream polygon values are then aggregated. nlcdLandCover

General Results

The model results are summarized in this coefficients table. More results will be added in the future.

Parameter Mean SD LCRI UCRI
Intercept 17.051 0.123 16.81 17.2980
AirT 2.029 0.020 1.99 2.0694
7-day AirT 1.551 0.025 1.50 1.5997
2-day Precip 0.051 0.002 0.05 0.0541
30-day Precip 0.001 0.006 -0.01 0.0130
Drainage Area 0.371 0.058 0.26 0.4849
Impounded Area 0.181 0.067 0.05 0.3092
Riparian Forest Cover -0.249 0.050 -0.35 -0.1504
High Development -0.002 0.032 -0.06 0.0615
Agriculture -0.139 0.047 -0.23 -0.0461
AirT x 2-day Precip 0.019 0.002 0.02 0.0228
AirT x 30-day Precip -0.033 0.003 -0.04 -0.0261
AirT x Drainage 0.027 0.019 -0.01 0.0662
AirT x Impounded Area -0.047 0.023 -0.09 -0.0003
AirT x Forest -0.016 0.017 -0.05 0.0181
AirT x High Development -0.001 0.011 -0.02 0.0212
AirT x Agriculture, -0.017 0.016 -0.05 0.0147
2-day Precip x Drainage -0.039 0.002 -0.04 -0.0353
30-day Precip x Drainage -0.044 0.006 -0.06 -0.0336
AirT x 2-day Precip x Drainage -0.015 0.002 -0.02 -0.0118
AirT x 30-day Precip x Drainage -0.005 0.003 -0.01 0.0017
AR1 0.813 0.002 0.81 0.8159
Group Coef SD Variance
Site Intercept 1.09 1.186
AirT 0.34 0.113
7-day AirT 0.36 0.132
HUC8 Intercept 0.64 0.412
AirT 0.24 0.059
7-day AirT 0.28 0.080
Year Intercept 0.27 0.076
  Intercept AirT 7-day AirT
AirT 0.553
7-day AirT 0.306 0.126