Flexible Regression Models Over River Networks

O'Donnell, David; Rushworth, Alastair; Bowman, Adrian; Scott, E. Marian; Hallard, Mark

doi:10.1111/rssc.12024

Cited by 37 publications

(65 citation statements)

References 37 publications

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“…Spatial structure in the data, which was not accounted for by the covariates, was considered by fitting a smoother over the river network, using a modified version of the methods described by O'Donnell, Rushworth, Bowman, Scott, and Hallard (); herein referred to as the river network smoother (RNS). O'Donnell decomposes a river network into small stream segments and models changes in the response variable between connected segments using penalized regression splines.…”

Section: Methodsmentioning

confidence: 99%

“…A unit increase in river order corresponded to a doubling of flow (Hughes, Kaufmann, & Weber, 2011). River order provided a pragmatic and readily derived weighting and was proposed by Ver Hoef, Peterson, and Theobald (2006) and O'Donnell et al (2014) for application when discharge data are unavailable. Second, dimension reduction techniques were used so that the models could be fitted using the GAM function in the R "mgcv" package (Wood, 2001).…”

Section: Model Fitting and River Network Smoothersmentioning

confidence: 99%

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Development of spatial regression models for predicting summer river temperatures from landscape characteristics: Implications for land and fisheries management

et al. 2017

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There is increasing demand for models that can accurately predict river temperature at the large spatial scales appropriate to river management. This paper combined summer water temperature data from a strategically designed, quality controlled network of 25 sites, with recently developed flexible spatial regression models, to understand and predict river temperature across a 3,000 km 2 river catchment. Minimum, mean and maximum temperatures were modelled as a function of nine potential landscape covariates that represented proxies for heat and water exchange processes. Generalised additive models were used to allow for flexible responses. Spatial structure in the river network data (local spatial variation) was accounted for by including river network smoothers. Minimum and mean temperatures decreased with increasing elevation, riparian woodland and channel gradient. Maximum temperatures increased with channel width. There was greater between-river and between-reach variability in all temperature metrics in lowerorder rivers indicating that increased monitoring effort should be focussed at these smaller scales.The combination of strategic network design and recently developed spatial statistical approaches employed in this study have not been used in previous studies of river temperature.The resulting catchment scale temperature models provide a valuable quantitative tool for understanding and predicting river temperature variability at the catchment scales relevant to land use planning and fisheries management and provide a template for future studies.

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Section: Methodsmentioning

confidence: 99%

Section: Model Fitting and River Network Smoothersmentioning

confidence: 99%

Development of spatial regression models for predicting summer river temperatures from landscape characteristics: Implications for land and fisheries management

et al. 2017

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“…A variety of statistical models have been developed to account for spatial correlations in dendritic networks. Additionally, block Kriging has been used for spatial averaging (Isaak et al 2017) and splines accounting for network topology and confluence points have been used effectively to model nonlinear trends in stream networks (Donnell et al 2014). Some models also include "tail-up," "taildown," or "two-tail" correlations to account for directional autocorrelation (Peterson and.…”

Section: Introductionmentioning

confidence: 99%

A geostatistical state‐space model of animal densities for stream networks

Hocking

Thorson

O'Neil

et al. 2018

Ecological Applications

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Population dynamics are often correlated in space and time due to correlations in environmental drivers as well as synchrony induced by individual dispersal. Many statistical analyses of populations ignore potential autocorrelations and assume that survey methods (distance and time between samples) eliminate these correlations, allowing samples to be treated independently. If these assumptions are incorrect, results and therefore inference may be biased and uncertainty underestimated. We developed a novel statistical method to account for spatiotemporal correlations within dendritic stream networks, while accounting for imperfect detection in the surveys. Through simulations, we found this model decreased predictive error relative to standard statistical methods when data were spatially correlated based on stream distance and performed similarly when data were not correlated. We found that increasing the number of years surveyed substantially improved the model accuracy when estimating spatial and temporal correlation coefficients, especially from 10 to 15 yr. Increasing the number of survey sites within the network improved the performance of the nonspatial model but only marginally improved the density estimates in the spatiotemporal model. We applied this model to brook trout data from the West Susquehanna Watershed in Pennsylvania collected over 34 yr from 1981 to 2014. We found the model including temporal and spatiotemporal autocorrelation best described young of the year (YOY) and adult density patterns. YOY densities were positively related to forest cover and negatively related to spring temperatures with low temporal autocorrelation and moderately high spatiotemporal correlation. Adult densities were less strongly affected by climatic conditions and less temporally variable than YOY but with similar spatiotemporal correlation and higher temporal autocorrelation.

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“…The RNS is included to account for spatial structure in the data that cannot be explained by the covariates. The RNS is a modified version of that developed by O'Donnell et al (2014), with the amount of smoothness at a confluence controlled by the proportional influence of upstream tributaries weighted by Strahler river order (Strahler, 1957) and fitted using a set of "reduced rank" basis functions; see Jackson et al (2017b) for full details. The RNS was allowed up to 7 df based on knowledge of RNS complexity for the Spey (Jackson et al, 2017b).…”

Section: Single-catchment Modelsmentioning

confidence: 99%

“…The development of affordable, reliable, accurate T w data loggers has led to a rapid increase in T w monitoring (Sowder and Steel, 2012), to the point that staff time, data storage and quality control are often now the greatest limitations on data collection . At the same time, there have been substantial developments in spatial statistical modelling approaches (Ver Hoef et al, 2006Ver Hoef and Peterson, 2010;Isaak et al, 2014;Jackson et al, 2017b;O'Donnell et al, 2014;Peterson et al, 2013;Rushworth et al, 2015), monitoring network design (Dobbie et al, 2008;Jackson et al, 2016;Som et al, 2014), spatial datasets (e.g. shapefiles incorporating covariates such as in "The National Stream Internet Project" (Isaak et al, 2011) or gridded air temperature datasets (Perry and Hollis, 2005a, b)) and spatial analysis tools (Isaak et al, 2011(Isaak et al, , 2014Peterson et al, 2013;Peterson and Ver Hoef, 2014).…”

mentioning

confidence: 99%

Can spatial statistical river temperature models be transferred between catchments?

Jackson

Fryer

Hannah

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. There has been increasing use of spatial statistical models to understand and predict river temperature (T w ) from landscape covariates. However, it is not financially or logistically feasible to monitor all rivers and the transferability of such models has not been explored. This paper uses T w data from four river catchments collected in August 2015 to assess how well spatial regression models predict the maximum 7-day rolling mean of daily maximum T w (T w max ) within and between catchments. Models were fitted for each catchment separately using (1) landscape covariates only (LS models) and (2) landscape covariates and an air temperature (T a ) metric (LS_T a models). All the LS models included upstream catchment area and three included a river network smoother (RNS) that accounted for unexplained spatial structure. The LS models transferred reasonably to other catchments, at least when predicting relative levels of T w max . However, the predictions were biased when mean T w max differed between catchments. The RNS was needed to characterise and predict finer-scale spatially correlated variation. Because the RNS was unique to each catchment and thus non-transferable, predictions were better within catchments than between catchments. A single model fitted to all catchments found no interactions between the landscape covariates and catchment, suggesting that the landscape relationships were transferable. The LS_T a models transferred less well, with particularly poor performance when the relationship with the T a metric was physically implausible or required extrapolation outside the range of the data. A single model fitted to all catchments found catchment-specific relationships between T w max and the T a metric, indicating that the T a metric was not transferable. These findings improve our understanding of the transferability of spatial statistical river temperature models and provide a foundation for developing new approaches for predicting T w at unmonitored locations across multiple catchments and larger spatial scales.Copyright statement. The works published in this journal are distributed under the Creative Commons Attribution 3.0 License. This license does not affect the Crown copyright work, which is re-usable under the Open Government Licence (OGL). The Creative Commons Attribution 3.0 License and the OGL are interoperable and do not conflict with, reduce or limit each other.

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Flexible Regression Models Over River Networks

Cited by 37 publications

References 37 publications

Development of spatial regression models for predicting summer river temperatures from landscape characteristics: Implications for land and fisheries management

Development of spatial regression models for predicting summer river temperatures from landscape characteristics: Implications for land and fisheries management

A geostatistical state‐space model of animal densities for stream networks

Can spatial statistical river temperature models be transferred between catchments?

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