University of Birmingham A novel approach for designing large-scale river temperature monitoring networks

Water temperature is an important control on processes in aquatic systems and particularly for freshwater ﬁ sh, affecting growth, survival and demographic characteristics. In recognition of this importance, the Scottish Government has prioritised developing a robust national river temperature monitoring network. Advances in geographical information systems, spatial statistics and ﬁ eld data loggers make large-scale river temperature monitoring increasingly possible. However, duplication of environmental and thermal characteristics among monitoring sites means many networks have lower than expected statistical power. This paper describes a novel methodology for network design, illustrated by the development of the Scotland River Temperature Monitoring Network. A literature review identi ﬁ ed processes controlling stream temperature and associated landscape controls. Metrics indicative of these landscape controls were calculated for points every 500 m along the river network. From these points, sites were chosen to cover the full range of observed environmental gradients and combinations of controlling variables. The resulting network contains sites with unique characteristics covering the range of relevant environmental characteristics observed in Scottish salmon rivers. The network will thus have minimal redundancy, often not seen in large networks, and high statistical power to separate the relative importance of predictor variables thereby allowing large-scale water temperature predictions.

Large-scale Tw networks are required to characterise and understand temperature variability and make predictions of current and future Tw in monitored and unmonitored rivers (Hrachowitz et al. ; Deweber et al. ). However, globally there are relatively few quality controlled long-term networks and even fewer large-scale (at least regional and >100 km 2 ) planned and coordinated river temperature monitoring networks (Table 1). Many monitoring networks are produced ad hoc, evolving over time with poorly defined objectives or represent aggregations of numerous data sets, spanning multiple regions (e.g., US Geological Survey (USGS), NorWest) or countries (e.g., GEMS/Water). Data are often collected with a range of earlier aims, at varying sampling frequencies, with varying deployment approaches and equipment (Table 1). This can result in spatial and temporal biases. As such, aggregated networks arguably do not provide the consistency necessary for use over wider spatial domains. This is especially important in the context of understanding environmental change where temperature trends may be small relative to measurement bias.
A lack of strategic planning can potentially limit the value of networks if sites are not representative of the parameter of interest or the processes or landscape characteristics that control the parameter (Parr et al. ; Deweber et al. ). Where a network contains numerous monitoring sites with similar characteristics, or where a network provides incomplete coverage of process or landscape controls (including spatial coverage) then highly uncertain or biased model fits and predictions will result (Marsh & Anderson ; Deweber et al. ). However, large-scale statistical modelling of Tw using landscape characteristics that are proxies for energy exchange processes or controls show significant potential to inform effective environmental management (Isaak & Hubert ; Hrachowitz et al. ; Chang & Psaris ). Landscape data provide a cost-effective method of generating environmental data across large spatial scales (Wehrly et al. ).

Recent advances in geographical information
GIS analysis can be used to determine landscape characteristics at any point on a river network, without the expense of field survey. Furthermore, the availability of inexpensive data loggers has dramatically increased Tw monitoring (Sowder & Steel ) to the extent that staff time, quality control and appropriate data storage are greater constraints on logger deployment than the cost of instrumentation.
Despite these advances, relatively few studies have modelled temperature distributions across whole basins in relation to environmental and landscape controls (Hrachowitz et al. ). Additionally, as far as the authors are aware, there have been no attempts to establish a large-scale strategically designed network that meets the requirements for modern spatio-temporal statistical modelling, which include appropriate coverage relative to landscape predictors (covariates), calibration, quality control and data storage. Such a network and associated modelling have the potential to answer critical management questions about the spatial variability in river temperature and its controls, the effects of changing landuse and the likely impacts of climate change. The current paucity of such networks demonstrates a challenge to understanding thermal regimes at multiple spatial and temporal scales (Garner et al. ) and to informing appropriate management of rivers. This paper aims to develop a novel methodology for the design of a large-scale water quality monitoring network using the Scotland River Temperature Monitoring Network (SRTMN) as a case study. This initiative aims to produce a network which avoids common limitations exhibited by large-scale networks and has the potential to provide data appropriate for spatio-temporal analysis.   To address these objectives, it was crucial that the sites cover the environmental range and combinations of landscape controls observed in Scotland's rivers. Given the importance of Atlantic salmon as a target species for management and conservation, the environmental range was constrained to accessible rivers using the map of Atlantic salmon distribution originally developed by Gardiner & Egglishaw (). In practice, this constrained the altitudinal range from 0 to ca. 700 m, above which any long-term monitoring would also have been impractical. As the SRTMN is a new network, strategically planned from the start, sites could be selected to cover landscape attributes appropriate to the objectives. By covering the range and  covariates that had been identified as useful proxies for these processes and controls. The literature review focused on identifying GIS covariates that were significant in previous regression-based stream temperature models and that had underlying physical meaning (Table 2). These landscape controls reflect the physical processes that influence Tw at nested spatial scales ( Figure 2, Table 2). Because the landscape controls represent physical process drivers this should ensure that the observed relationships are genuine and transferable to unmonitored locations. The nesting of spatial scales and controls is indicated in Figure 2 and reflected below.

National scale
At the largest spatial scale it was important that the network covered the main climatological, hydrological and geological controls on Tw ( Figure 2). Consequently, target catchments were chosen to span the whole of Scotland         Sites were chosen from each variable plotted against the x and y coordinate which ensured a broad geographic spread of landscape characteristics. The chosen sites were those closest to the grid node, shown by triangles in Figure 5.
Where no points were within half the distance between one node to the next, this environmental combination was ignored to avoid duplication of similar characteristics. (2) The resulting data set was visually assessed to ensure that the sites chosen in (1)   Similarly, there was a desire to make use of existing telemetry infrastructure even where temperature monitoring did not currently exist. For example, the location of SEPA gauging stations was also overlain on the selection grid and used to replace SRTMN selected sites where they lay within the defined point radius. The addition of gauging station locations to the network had the added benefit of providing discharge data that are potentially useful for understanding river thermal regimes, even at a small number of sites.
Stage 3: practical considerations for field deployment The logistics and health and safety involved in maintaining a continuous monitoring site must be considered (Laize ).
Costs are increased if data loggers are in areas of limited accessibility, involving time-consuming hikes to isolated streams for downloading. This can create problems in maintaining that particular site long-term and requires additional safety considerations. In addition, the loss of equipment, for example, through vandalism, can also affect the long-term viability of a network and the quality of data collected from it. Consequently, local knowledge from collaborators was used to assess the risk of loss, drying out or vandalism.
Where this was likely, alternative locations for the originally chosen point were found from other points within a radius (diamonds in Figure 5) of the desired characteristics (black grid points in Figure 5).

RESULTS AND DISCUSSION
A perfectly gridded coverage, as exemplified in Figure 4, could not be expected for the chosen sites. However, Figure 6 demonstrates a good coverage of the environmental range across all combinations of potential controlling variables. If a network were biased to particular characteristics, uncertainty will be increased for any extrapolation from monitored to unmonitored locations (Wagner et al. ).
As the selected sites for SRTMN cover the environmental range and combinations of variables (Figure 6)

CONCLUSIONS AND RECOMMENDATIONS
This paper described and evaluated a potential methodology for the design of a new monitoring network. The approach was illustrated using the SRTMN as a real-world, practical case study. The method characterised the environmental characteristics of potential monitoring sites to cover the environmental range of controlling variables, required to: (1) characterise spatial and temporal variability in thermal regimes across Scotland; (2) identify climatically sensitive locations; (3) improve understanding of controls on Tw; (4) develop models to predict future river temperatures and predict thermal regimes in unmonitored rivers; (5) assess mitigation and adaptation strategies for high temperature; and (6) provide long-term monitoring of thermal regimes. The network is strategically planned to ensure the desired coverage of controlling characteristics rather than spatially balanced or randomly located sites which are often the focus of previous networks (Isaak et al.  considers). It is therefore anticipated that the network will have minimal redundancy and high levels of statistical power and meet the objectives identified at the start of the network design process.
From the development of this method for the SRTMN, the following key recommendations can be made for designing other large-scale monitoring networks: • Begin with clear network aims and objectives that identify data requirements.
• Where large-scale spatial statistical models are required, undertake a literature review to determine process drivers and more readily obtained proxies (e.g., GIS or remote sensing data) to represent these processes.
• Assess the amount of resource available and consequently the number of sampling sites or samples that can be planned.
• Select sites to cover the range of environmental characteristics which influence the parameter of interest; an adaptation of Latin squares principles may be used.
• Develop comprehensive standard operating procedures and data storage facilities for data quality control.
• Where possible, integrate current monitoring sites and existing infrastructure to make best use of collective resource.
The merits of this network design will be tested further when data are returned and analysis undertaken. Further research could involve implementing the principles of this approach in other large-scale network designs with different research objectives and target parameters (e.g., water chemistry, fish abundance). The principles identified here are likely to be applicable across different large-scale monitoring networks, due to the values of GIS for assessing landscape characteristics at large spatial scales. Upscaling process-based knowledge to larger spatial scales is a major challenge across disciplines and is required to inform appropriate management, but critically requires large-scale high quality monitoring networks such as SRTMN. Finally, adjustments to this methodology could also be used to assess and revise current monitoring networks that have grown organically and potentially contain redundancy.