scholarly journals Peer Review #2 of "A hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags (v0.1)"

2016 ◽  
Author(s):  
C Frissell
Keyword(s):  
Water Temperature ◽  
Peer Review ◽  
Hierarchical Model ◽  
Stream Temperature ◽  
Time Lags

scholarly journals A hierarchical model of daily stream temperature for regional predictions

2018 ◽  
Author(s):  
Daniel J Hocking ◽  
Kyle O'Neil ◽  
Benjamin H Letcher
Keyword(s):  
Water Temperature ◽  
Air Temperature ◽  
Hierarchical Model ◽  
Mean Squared Error ◽  
Thermal Sensitivity ◽  
Stream Temperature ◽  
Eastern United States ◽  
Validation Data ◽  
Temperature Records ◽  
Use Factors

Stream temperature is an important exogenous factor influencing populations of stream organisms such as fish, amphibians, and invertebrates. Many states regulate stream protections based on temperature. Therefore, stream temperature models are important, particularly for estimating thermal regimes in unsampled space and time. To help meet this need, we developed a hierarchical model of daily stream temperature and applied it across the eastern United States. Our model accommodates many of the key challenges associated with daily stream temperature models including the lagged response of water temperature to changes in air temperature, incomplete and widely-varying observed time series, spatial and temporal autocorrelation, and the inclusion of predictors other than air temperature. We used 248,517 daily stream temperature records from 1,352 streams to fit our model and 100,909 records were withheld for model validation. Our model had a root mean squared error of 0.61 C for the fitted data and 2.03 C for the validation data, indicating excellent fit and good predictive power for understanding regional temperature patterns. We then used our model to predict daily stream temperatures from 1980 - 2015 for all streams <200 km2 from Maine to Virginia. From these, we calculated derived stream metrics including mean July temperature, mean summer temperature, and the thermal sensitivity of each stream reach to changes in air temperature. Although generally water temperature follows similar latitudinal and altitudinal patterns as air temperature, there are considerable differences at the reach scale based on landscape and land-use factors.

scholarly journals A robust hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags

2015 ◽  
Author(s):  
Benjamin H Letcher ◽  
Daniel J Hocking ◽  
Kyle O'Neill ◽  
Andrew R Whiteley ◽  
Keith H Nislow ◽  
...  
Keyword(s):  
Missing Data ◽  
Water Temperature ◽  
Spatial Scales ◽  
Warming Trend ◽  
Stream Temperature ◽  
Time Lags ◽  
Stream Network ◽  
Modeling Framework ◽  
Small Stream ◽  
Yearly Period

Water temperature is a primary driver of stream ecosystems and commonly forms the basis of stream classifications. Robust models of stream temperature are critical as the climate changes, but estimating daily stream temperature poses several important challenges. We developed a statistical model that accounts for many challenges that can make stream temperature estimation difficult. Our model identifies the yearly period when air and water temperature are synchronized, accommodates hysteresis, incorporates time lags, deals with missing data and autocorrelation and can include external drivers. In a small stream network, the model performed well (RMSE = 0.59 °C), identified a clear warming trend (0.063 °C · y-1) and a widening of the synchronized period (2.9 d · y-1). We also carefully evaluated how missing data influenced predictions. Missing data within a year had a small effect on performance (~ 0.05% average drop in RMSE with 10% fewer days with data). Missing all data for a year decreased performance (~ 0.6 °C jump in RMSE), but this decrease was moderated when data were available from other streams in the network. Straightforward incorporation of external drivers (e.g. land cover, basin size) should allow this modeling framework to be readily applied across multiple sites and at multiple spatial scales.

scholarly journals A robust hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags

2015 ◽  
Author(s):  
Benjamin H Letcher ◽  
Daniel J Hocking ◽  
Kyle O'Neill ◽  
Andrew R Whiteley ◽  
Keith H Nislow ◽  
...  
Keyword(s):  
Missing Data ◽  
Water Temperature ◽  
Spatial Scales ◽  
Warming Trend ◽  
Stream Temperature ◽  
Time Lags ◽  
Stream Network ◽  
Modeling Framework ◽  
Small Stream ◽  
Yearly Period

Water temperature is a primary driver of stream ecosystems and commonly forms the basis of stream classifications. Robust models of stream temperature are critical as the climate changes, but estimating daily stream temperature poses several important challenges. We developed a statistical model that accounts for many challenges that can make stream temperature estimation difficult. Our model identifies the yearly period when air and water temperature are synchronized, accommodates hysteresis, incorporates time lags, deals with missing data and autocorrelation and can include external drivers. In a small stream network, the model performed well (RMSE = 0.59 °C), identified a clear warming trend (0.063 °C · y-1) and a widening of the synchronized period (2.9 d · y-1). We also carefully evaluated how missing data influenced predictions. Missing data within a year had a small effect on performance (~ 0.05% average drop in RMSE with 10% fewer days with data). Missing all data for a year decreased performance (~ 0.6 °C jump in RMSE), but this decrease was moderated when data were available from other streams in the network. Straightforward incorporation of external drivers (e.g. land cover, basin size) should allow this modeling framework to be readily applied across multiple sites and at multiple spatial scales.

scholarly journals A hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e1727 ◽  
Author(s):  
Benjamin H. Letcher ◽  
Daniel J. Hocking ◽  
Kyle O’Neil ◽  
Andrew R. Whiteley ◽  
Keith H. Nislow ◽  
...  
Keyword(s):  
Missing Data ◽  
Water Temperature ◽  
Warming Trend ◽  
Stream Temperature ◽  
Time Lags ◽  
Stream Network ◽  
Temperature Estimation ◽  
Small Stream ◽  
Primary Driver ◽  
Yearly Period

Water temperature is a primary driver of stream ecosystems and commonly forms the basis of stream classifications. Robust models of stream temperature are critical as the climate changes, but estimating daily stream temperature poses several important challenges. We developed a statistical model that accounts for many challenges that can make stream temperature estimation difficult. Our model identifies the yearly period when air and water temperature are synchronized, accommodates hysteresis, incorporates time lags, deals with missing data and autocorrelation and can include external drivers. In a small stream network, the model performed well (RMSE = 0.59°C), identified a clear warming trend (0.63 °C decade−1) and a widening of the synchronized period (29 d decade−1). We also carefully evaluated how missing data influenced predictions. Missing data within a year had a small effect on performance (∼0.05% average drop in RMSE with 10% fewer days with data). Missing all data for a year decreased performance (∼0.6 °C jump in RMSE), but this decrease was moderated when data were available from other streams in the network.

scholarly journals A hierarchical model of daily stream temperature for regional predictions

2018 ◽  
Author(s):  
Daniel J Hocking ◽  
Kyle O'Neil ◽  
Benjamin H Letcher
Keyword(s):  
Water Temperature ◽  
Air Temperature ◽  
Hierarchical Model ◽  
Mean Squared Error ◽  
Thermal Sensitivity ◽  
Stream Temperature ◽  
Eastern United States ◽  
Validation Data ◽  
Temperature Records ◽  
Use Factors

Stream temperature is an important exogenous factor influencing populations of stream organisms such as fish, amphibians, and invertebrates. Many states regulate stream protections based on temperature. Therefore, stream temperature models are important, particularly for estimating thermal regimes in unsampled space and time. To help meet this need, we developed a hierarchical model of daily stream temperature and applied it across the eastern United States. Our model accommodates many of the key challenges associated with daily stream temperature models including the lagged response of water temperature to changes in air temperature, incomplete and widely-varying observed time series, spatial and temporal autocorrelation, and the inclusion of predictors other than air temperature. We used 248,517 daily stream temperature records from 1,352 streams to fit our model and 100,909 records were withheld for model validation. Our model had a root mean squared error of 0.61 C for the fitted data and 2.03 C for the validation data, indicating excellent fit and good predictive power for understanding regional temperature patterns. We then used our model to predict daily stream temperatures from 1980 - 2015 for all streams <200 km2 from Maine to Virginia. From these, we calculated derived stream metrics including mean July temperature, mean summer temperature, and the thermal sensitivity of each stream reach to changes in air temperature. Although generally water temperature follows similar latitudinal and altitudinal patterns as air temperature, there are considerable differences at the reach scale based on landscape and land-use factors.

scholarly journals Assessing the impacts of hydrologic and land use alterations on water temperature in the Farmington River basin in Connecticut

2019 ◽  
Vol 23 (11) ◽  
pp. 4491-4508 ◽  
Author(s):  
John R. Yearsley ◽  
Ning Sun ◽  
Marisa Baptiste ◽  
Bart Nijssen
Keyword(s):  
Land Use ◽  
Water Temperature ◽  
River Basin ◽  
Land Surface ◽  
Canopy Cover ◽  
River System ◽  
Stream Temperature ◽  
Hydrologic Model ◽  
Riparian Buffers ◽  
Connecticut River

Abstract. Aquatic ecosystems can be significantly altered by the construction of dams and modification of riparian buffers, and the effects are often reflected in spatial and temporal changes to water temperature. To investigate the implications for water temperature of spatially and temporally varying riparian buffers and dam-induced hydrologic alterations, we have implemented a modeling system (DHSVM-RBM) within the framework of the state-space paradigm that couples a spatially distributed land surface hydrologic model, DHSVM, with the distributed stream temperature model, RBM. The basic modeling system has been applied previously to several similar-sized watersheds. However, we have made enhancements to DHSVM-RBM that simulate spatial heterogeneity and temporal variation (i.e., seasonal changes in canopy cover) in riparian vegetation, and we included additional features in DHSVM-RBM that provide the capability for simulating the impacts of reservoirs that may develop thermal stratification. We have tested the modeling system in the Farmington River basin in the Connecticut River system, which includes varying types of watershed development (e.g., deforestation and reservoirs) that can alter the streams' hydrologic regime and thermal energy budget. We evaluated streamflow and stream temperature simulations against all available observations distributed along the Farmington River basin. Results based on metrics recommended for model evaluation compare well to those obtained in similar studies. We demonstrate the way in which the model system can provide decision support for watershed planning by simulating a limited number of scenarios associated with hydrologic and land use alterations.

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