Öz
Stream temperature is a critical characteristic for aquatic ecosystems. Many physical, chemical and biological components are influenced by this environmental variable; therefore, it is crucial to understand the factors that take place in thermodynamic processes in these ecosystems. Regression models are useful tools that help us comprehend and explain the drivers of these thermal processes since they can be used for quantifying the magnitude and the type of the relationship between the independent variables (i.e., air temperature, discharge) and the response variable (i.e., stream temperature). However, selection of data granularity (or time aggregation) of data may often be a key decision for modelers. Although granularity of data is selected based on the ecological relevance of data to the question of interest in many cases, it may arbitrarily be selected by the researchers in many other cases. However, data granularity can substantially influence model coefficients, can affect the model predictions, and influence evaluation of model fitness and interpretation of model outputs. In this article, we adopted regression models and applied different data granularity scenarios to investigate the consequences of data granularity selection in modeling approaches. Our findings showed that using different data granularities resulted in considerable changes in regression coefficients in the models. Our results also revealed that overall model fitness increased with coarser-scale data granularity and model selection was influenced by the type of data granularity. This study might be helpful for modelers and environmental managers since it highlights the significance of selection of data granularity, and proposes a different point of view in model design, evaluation and application from the perspective of data selection.
Anahtar Kelimeler
stream temperature linear regression models data granularity data aggregation temporal scale
Kaynakça
- Akaike, H. (1973). Maximum likelihood identification of Gaussian autoregressive moving average models. Biometrika. https://doi.org/10.1093/biomet/60.2.255
- Akossou, A. Y. J. (2013). Impact of data structure on the estimators R-square and adjusted R-square in linear regression. International Journal of Mathematics and Computation.
- Andrews, R. (2019). Effects of flow reduction on thermal dynamics of streams: improving an important link in Michigan’s water withdrawal assessment tool. M.S. Thesis, Michigan State University, East Lansing, MI . Arismendi, I., Safeeq, M., Dunham, J. B., & Johnson, S. L. (2014). Can air temperature be used to project influences of climate change on stream temperature? Environmental Research Letters. https://doi.org/10.1088/1748-9326/9/8/084015
- Bender, R. (2009). Introduction to the use of regression models in epidemiology. Methods in Molecular Biology. https://doi.org/10.1007/978-1-59745-416-2_9
- Caldwell, P., Segura, C., Gull Laird, S., Sun, G., Mcnulty, S. G., Sandercock, M., … Vose, J. M. (2015). Short-term stream water temperature observations permit rapid assessment of potential climate change impacts. Hydrological Processes. https://doi.org/10.1002/hyp.10358
- Chang, H., Watson, E., & Strecker, A. (2018). Climate Change and Stream Temperature in the Willamette River Basin: Implications for Fish Habitat. World Scientific Series on Asia-Pacific Weather and Climate. https://doi.org/10.1142/9789813235663_0008
- Chen, Y. D., McCutcheon, S. C., Norton, D. J., & Nutter, W. L. (1998). Stream Temperature Simulation of Forested Riparian Areas: II. Model Application. Journal of Environmental Engineering. https://doi.org/10.1061/(asce)0733-9372(1998)124:4(316)
- Cheng, S. T., & Wiley, M. J. (2016). A Reduced Parameter Stream Temperature Model (RPSTM) for basin-wide simulations. Environmental Modeling and Software. https://doi.org/10.1016/j.envsoft.2016.04.015
- Dertli, H. I. (2021). The Impact of Data Granularity and Stream Classification on Temperature Gradient Modeling in Michigan’s Streams. Michigan State University.
- Dingman, S. L. (1972). Equilibrium temperatures of water surfaces as related to air temperature and solar radiation. Water Resources Research. https://doi.org/10.1029/WR008i001p00042
- Du, X., Goss, G., & Faramarzi, M. (2020). Impacts of hydrological processes on stream temperature in a cold region watershed based on the SWAT equilibrium temperature model. Water (Switzerland). https://doi.org/10.3390/W12041112
- Ducharne, A. (2008). Importance of stream temperature to climate change impact on water quality. Hydrology and Earth System Sciences. https://doi.org/10.5194/hess-12-797-2008
- Dugdale, S. J., Malcolm, I. A., Kantola, K., & Hannah, D. M. (2018). Stream temperature under contrasting riparian forest cover: Understanding thermal dynamics and heat exchange processes. Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2017.08.198
- Ficklin, D. L., Stewart, I. T., & Maurer, E. P. (2013). Effects of climate change on stream temperature, dissolved oxygen, and sediment concentration in the Sierra Nevada in California. Water Resources Research. https://doi.org/10.1002/wrcr.20248
- Guo, D., Lintern, A., Webb, J. A., Ryu, D., Liu, S., Bende-Michl, U., … Western, A. W. (2019). Key Factors Affecting Temporal Variability in Stream Water Quality. Water Resources Research. https://doi.org/10.1029/2018WR023370
- Hamid, A., Bhat, S. U., & Jehangir, A. (2020). Local determinants influencing stream water quality. Applied Water Science. https://doi.org/10.1007/s13201-019-1043-4
- Iversen, T.M. (1971). The ecology of a mosquito population (A~des communis ) in a temporary pool in a Danish beech wood. Arch. Hydrobiol., 69: 309-332.
- Jackson, H. M., Gibbins, C. N., & Soulsby, C. (2007). Role of discharge and temperature variation in determining invertebrate community structure in a regulated river. River Research and Applications. https://doi.org/10.1002/rra.1006
- Kroll, C. N., & Song, P. (2013). Impact of multicollinearity on small sample hydrologic regression models. Water Resources Research. https://doi.org/10.1002/wrcr.20315
- Magnusson, J., Jonas, T., & Kirchner, J. W. (2012). Temperature dynamics of a proglacial stream: Identifying dominant energy balance components and inferring spatially integrated hydraulic geometry. Water Resources Research. https://doi.org/10.1029/2011WR011378
- Mantua, N., Tohver, I., & Hamlet, A. (2010). Climate change impacts on streamflow extremes and summertime stream temperature and their possible consequences for freshwater salmon habitat in Washington State. Climatic Change. https://doi.org/10.1007/s10584-010-9845-2
- Mason, C. H., & Perreault, W. D. (1991). Collinearity, Power, and Interpretation of Multiple Regression Analysis. Journal of Marketing Research. https://doi.org/10.2307/3172863
- Neumann, D. W., Rajagopalan, B., & Zagona, E. A. (2003). Regression Model for Daily Maximum Stream Temperature. Journal of Environmental Engineering. https://doi.org/10.1061/(asce)0733-9372(2003)129:7(667)
- Nuhfer, A. J., Zorn, T. G., & Wills, T. C. (2017). Effects of reduced summer flows on the brook trout population and temperatures of a groundwater-influenced stream. Ecology of Freshwater Fish. https://doi.org/10.1111/eff.12259
- Pilgrim, J. M., Fang, X., & Stefan, H. G. (1998). Stream temperature correlations with air temperatures in Minnesota: Implications for climate warming. Journal of the American Water Resources Association. https://doi.org/10.1111/j.1752-
- Seber, G. A. F., & Wild, C. J. (1989). Autocorrelated Errors. In Nonlinear Regression. https://doi.org/10.1002/0471725315.ch6
- Sinokrot, B. A., & Stefan, H. G. (1993). Stream temperature dynamics: Measurements and modeling. Water Resources Research. https://doi.org/10.1029/93WR00540
- Sridhar, V., Sansone, A. L., LaMarche, J., Dubin, T., & Lettenmaier, D. P. (2004). Prediction of stream temperature in forested watersheds. Journal of the American Water Resources Association. https://doi.org/10.1111/j.1752-1688.2004.tb01019.x
- Stefan, H. G., & Preud’homme, E. B. (1993). STREAM TEMPERATURE ESTIMATION FROM AIR TEMPERATURE. JAWRA Journal of the American Water Resources Association. https://doi.org/10.1111/j.1752-1688.1993.tb01502.x
- Webb, B. W., Clack, P. D., & Walling, D. E. (2003). Water-air temperature relationships in a Devon river system and the role of flow. Hydrological Processes. https://doi.org/10.1002/hyp.1280
- Zorn, T.G., Seelbach, P.W., and Wiley, M.J. (2004). Utility of Species-Specific, Multiple Linear Regression Models for Prediction ofFish Assemblages in Rivers of Michigan’s Lower Peninsula.Michigan Department of Natural Resources, Fisheries Research Report 2072, Ann Arbor, Michigan. http://www.michigandnr.com/PUBLICATIONS/PDFS/ifr/ifrlibra/Research/reports/2072rr.pdf
- Zorn, T. G., P. W. Seelbach, E. S. Rutherford, T. C. Wills, S. Cheng, and M. J. Wiley. (2008). A landscape-scale habitat suitability model to evaluate effects of flow reduction on fish assemblages in Michigan streams. Michigan Department of Natural Resources, Fisheries Research Report 2089. Ann Arbor. https://www2.dnr.state.mi.us/Publications/pdfs/ifr/ifrlibra/Research/reports/2089/RR2089.pdf
Öz
Kaynakça
- Akaike, H. (1973). Maximum likelihood identification of Gaussian autoregressive moving average models. Biometrika. https://doi.org/10.1093/biomet/60.2.255
- Akossou, A. Y. J. (2013). Impact of data structure on the estimators R-square and adjusted R-square in linear regression. International Journal of Mathematics and Computation.
- Andrews, R. (2019). Effects of flow reduction on thermal dynamics of streams: improving an important link in Michigan’s water withdrawal assessment tool. M.S. Thesis, Michigan State University, East Lansing, MI . Arismendi, I., Safeeq, M., Dunham, J. B., & Johnson, S. L. (2014). Can air temperature be used to project influences of climate change on stream temperature? Environmental Research Letters. https://doi.org/10.1088/1748-9326/9/8/084015
- Bender, R. (2009). Introduction to the use of regression models in epidemiology. Methods in Molecular Biology. https://doi.org/10.1007/978-1-59745-416-2_9
- Caldwell, P., Segura, C., Gull Laird, S., Sun, G., Mcnulty, S. G., Sandercock, M., … Vose, J. M. (2015). Short-term stream water temperature observations permit rapid assessment of potential climate change impacts. Hydrological Processes. https://doi.org/10.1002/hyp.10358
- Chang, H., Watson, E., & Strecker, A. (2018). Climate Change and Stream Temperature in the Willamette River Basin: Implications for Fish Habitat. World Scientific Series on Asia-Pacific Weather and Climate. https://doi.org/10.1142/9789813235663_0008
- Chen, Y. D., McCutcheon, S. C., Norton, D. J., & Nutter, W. L. (1998). Stream Temperature Simulation of Forested Riparian Areas: II. Model Application. Journal of Environmental Engineering. https://doi.org/10.1061/(asce)0733-9372(1998)124:4(316)
- Cheng, S. T., & Wiley, M. J. (2016). A Reduced Parameter Stream Temperature Model (RPSTM) for basin-wide simulations. Environmental Modeling and Software. https://doi.org/10.1016/j.envsoft.2016.04.015
- Dertli, H. I. (2021). The Impact of Data Granularity and Stream Classification on Temperature Gradient Modeling in Michigan’s Streams. Michigan State University.
- Dingman, S. L. (1972). Equilibrium temperatures of water surfaces as related to air temperature and solar radiation. Water Resources Research. https://doi.org/10.1029/WR008i001p00042
- Du, X., Goss, G., & Faramarzi, M. (2020). Impacts of hydrological processes on stream temperature in a cold region watershed based on the SWAT equilibrium temperature model. Water (Switzerland). https://doi.org/10.3390/W12041112
- Ducharne, A. (2008). Importance of stream temperature to climate change impact on water quality. Hydrology and Earth System Sciences. https://doi.org/10.5194/hess-12-797-2008
- Dugdale, S. J., Malcolm, I. A., Kantola, K., & Hannah, D. M. (2018). Stream temperature under contrasting riparian forest cover: Understanding thermal dynamics and heat exchange processes. Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2017.08.198
- Ficklin, D. L., Stewart, I. T., & Maurer, E. P. (2013). Effects of climate change on stream temperature, dissolved oxygen, and sediment concentration in the Sierra Nevada in California. Water Resources Research. https://doi.org/10.1002/wrcr.20248
- Guo, D., Lintern, A., Webb, J. A., Ryu, D., Liu, S., Bende-Michl, U., … Western, A. W. (2019). Key Factors Affecting Temporal Variability in Stream Water Quality. Water Resources Research. https://doi.org/10.1029/2018WR023370
- Hamid, A., Bhat, S. U., & Jehangir, A. (2020). Local determinants influencing stream water quality. Applied Water Science. https://doi.org/10.1007/s13201-019-1043-4
- Iversen, T.M. (1971). The ecology of a mosquito population (A~des communis ) in a temporary pool in a Danish beech wood. Arch. Hydrobiol., 69: 309-332.
- Jackson, H. M., Gibbins, C. N., & Soulsby, C. (2007). Role of discharge and temperature variation in determining invertebrate community structure in a regulated river. River Research and Applications. https://doi.org/10.1002/rra.1006
- Kroll, C. N., & Song, P. (2013). Impact of multicollinearity on small sample hydrologic regression models. Water Resources Research. https://doi.org/10.1002/wrcr.20315
- Magnusson, J., Jonas, T., & Kirchner, J. W. (2012). Temperature dynamics of a proglacial stream: Identifying dominant energy balance components and inferring spatially integrated hydraulic geometry. Water Resources Research. https://doi.org/10.1029/2011WR011378
- Mantua, N., Tohver, I., & Hamlet, A. (2010). Climate change impacts on streamflow extremes and summertime stream temperature and their possible consequences for freshwater salmon habitat in Washington State. Climatic Change. https://doi.org/10.1007/s10584-010-9845-2
- Mason, C. H., & Perreault, W. D. (1991). Collinearity, Power, and Interpretation of Multiple Regression Analysis. Journal of Marketing Research. https://doi.org/10.2307/3172863
- Neumann, D. W., Rajagopalan, B., & Zagona, E. A. (2003). Regression Model for Daily Maximum Stream Temperature. Journal of Environmental Engineering. https://doi.org/10.1061/(asce)0733-9372(2003)129:7(667)
- Nuhfer, A. J., Zorn, T. G., & Wills, T. C. (2017). Effects of reduced summer flows on the brook trout population and temperatures of a groundwater-influenced stream. Ecology of Freshwater Fish. https://doi.org/10.1111/eff.12259
- Pilgrim, J. M., Fang, X., & Stefan, H. G. (1998). Stream temperature correlations with air temperatures in Minnesota: Implications for climate warming. Journal of the American Water Resources Association. https://doi.org/10.1111/j.1752-
- Seber, G. A. F., & Wild, C. J. (1989). Autocorrelated Errors. In Nonlinear Regression. https://doi.org/10.1002/0471725315.ch6
- Sinokrot, B. A., & Stefan, H. G. (1993). Stream temperature dynamics: Measurements and modeling. Water Resources Research. https://doi.org/10.1029/93WR00540
- Sridhar, V., Sansone, A. L., LaMarche, J., Dubin, T., & Lettenmaier, D. P. (2004). Prediction of stream temperature in forested watersheds. Journal of the American Water Resources Association. https://doi.org/10.1111/j.1752-1688.2004.tb01019.x
- Stefan, H. G., & Preud’homme, E. B. (1993). STREAM TEMPERATURE ESTIMATION FROM AIR TEMPERATURE. JAWRA Journal of the American Water Resources Association. https://doi.org/10.1111/j.1752-1688.1993.tb01502.x
- Webb, B. W., Clack, P. D., & Walling, D. E. (2003). Water-air temperature relationships in a Devon river system and the role of flow. Hydrological Processes. https://doi.org/10.1002/hyp.1280
- Zorn, T.G., Seelbach, P.W., and Wiley, M.J. (2004). Utility of Species-Specific, Multiple Linear Regression Models for Prediction ofFish Assemblages in Rivers of Michigan’s Lower Peninsula.Michigan Department of Natural Resources, Fisheries Research Report 2072, Ann Arbor, Michigan. http://www.michigandnr.com/PUBLICATIONS/PDFS/ifr/ifrlibra/Research/reports/2072rr.pdf
- Zorn, T. G., P. W. Seelbach, E. S. Rutherford, T. C. Wills, S. Cheng, and M. J. Wiley. (2008). A landscape-scale habitat suitability model to evaluate effects of flow reduction on fish assemblages in Michigan streams. Michigan Department of Natural Resources, Fisheries Research Report 2089. Ann Arbor. https://www2.dnr.state.mi.us/Publications/pdfs/ifr/ifrlibra/Research/reports/2089/RR2089.pdf
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Bölüm | TÜRKİYE SU BİLİMLERİ VE YÖNETİMİ DERGİSİ |
| Yazarlar | |
| Yayımlanma Tarihi | 25 Temmuz 2022 |
| Yayımlandığı Sayı | Yıl 2022 Cilt: 6 Sayı: 2 |
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