TY - JOUR
T1 - Stream heat budget modeling with HFLUX
T2 - Model development, evaluation, and applications across contrasting sites and seasons
AU - Glose, Anne Marie
AU - Lautz, Laura K.
AU - Baker, Emily A.
N1 - Publisher Copyright:
© 2017 Elsevier Ltd
PY - 2017
Y1 - 2017
N2 - Process-based models of fluid flow and heat transport in fluvial systems can be used to quantify unknown spatial and temporal patterns of hydrologic fluxes and to predict system response to change. In this study, a deterministic stream heat budget model, the HFLUX Stream Temperature Solver (HFLUX), is developed and evaluated using field studies. Field studies are conducted across two sites with different streamflow rates (0.07 vs 1.4 m3/s), and point sources versus diffuse sources of groundwater discharge, to demonstrate model transferability. A winter versus summer comparison at one site suggests latent heat flux should be derived using energy-based methods in summer and mass transfer approaches during winter. For each field study, HFLUX successfully modeled stream temperatures through space and time with normalized root mean square errors of 3.0–6.2%. Model calibration to observed temperature data in order to quantify groundwater contributions and a sensitivity analysis are demonstrated using HFLUX.
AB - Process-based models of fluid flow and heat transport in fluvial systems can be used to quantify unknown spatial and temporal patterns of hydrologic fluxes and to predict system response to change. In this study, a deterministic stream heat budget model, the HFLUX Stream Temperature Solver (HFLUX), is developed and evaluated using field studies. Field studies are conducted across two sites with different streamflow rates (0.07 vs 1.4 m3/s), and point sources versus diffuse sources of groundwater discharge, to demonstrate model transferability. A winter versus summer comparison at one site suggests latent heat flux should be derived using energy-based methods in summer and mass transfer approaches during winter. For each field study, HFLUX successfully modeled stream temperatures through space and time with normalized root mean square errors of 3.0–6.2%. Model calibration to observed temperature data in order to quantify groundwater contributions and a sensitivity analysis are demonstrated using HFLUX.
KW - Distributed temperature sensing
KW - Matlab
KW - Sensitivity analysis
KW - Stream temperature
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U2 - 10.1016/j.envsoft.2017.02.021
DO - 10.1016/j.envsoft.2017.02.021
M3 - Article
AN - SCOPUS:85014507625
SN - 1364-8152
VL - 92
SP - 213
EP - 228
JO - Environmental Modelling and Software
JF - Environmental Modelling and Software
ER -