Laboratory analysis of a water quality sample links a lot of data and metadata to a singular point in time and space. However, the objectives for monitoring may span spatial and temporal scales from point sampling (e.g. at an outfall) to watershed assessment (e.g. to characterize waters; identify trends; assess threats; inform pollution control; guide environmental emergency response; and support the development, implementation, and assessment of policies and regulations).
Reconciling data- and metadata-dense analytical results with watershed-scale outcomes is a work-in-progress for many monitoring agencies.
Strategic design of water quality monitoring program networks can facilitate inference about the vast un-sampled space from the sparsely sampled points. Sites can be chosen as ‘index’ sites where the condition of water at that site is indicative of the condition of interest of one, or more, management objectives.
Direct sharing of data is one mechanism that works well for some end-uses. A sophisticated end-user who has specific questions about specific parameters at specific places and times can find the answers they need in discoverable, searchable, and accessible on-line data (e.g. is the presence of a given pesticide responsive to regulatory intervention?). The client ‘merely’ needs to choose exactly which samples, of exactly which parameters (there can be dozens), at exactly which locations, created by exactly which methods (for inter-comparability), and download the data for further analysis.
However, for the end-user who merely wants to find water that is swimmable, fishable, or drinkable the prospect of reviewing every parameter at every location for all recent samples and putting that data in the context of relevant guidelines and thresholds is far too onerous a task. For these users, water quality indices are a very good solution.
A water quality index (WQI) is equivalent to the grade on a school report card. On some scale (often 0 to 100) all of the relevant data within an area of interest can be reduced into a single, easy to interpret number. There are a plethora of different water quality indices to choose from, each one tuned to locally relevant issues, priorities, regulations, and guidelines. Some are tuned to monitoring the natural environment (i.e. base state and influence of human activity), some to human health (i.e. consumption and recreation), and others to competitiveness (i.e. water fitness for purpose for industrial and agricultural uses).
Data sharing and WQI are like bookends to the problem of connecting water quality data to the many and varied societal objectives that demand timely, relevant, and reliable information about the condition of the water resource. At one end you have onerous granularity of information and at the other end you have a smearing of valuable precision in time, space, and parameter-specific dynamics. In both cases you have a lack of timeliness.
Somewhere between these extremes must be a solution that is ‘just right’.
In recent decades water resource managers around the world have embraced real-time reporting of continuous records of water level and discharge. The timeliness of these data has enabled many changes in how water resources are managed. Highly responsive and adaptive decisions based on what is happening as opposed to what has happened, or what could happen, is widely attributed with many improved outcomes for water supply management.
We know that flow and water quality are highly correlated as a result of integration of sources of varying water constituents (i.e. dilution and concentration). The integration of all contributing waters into flow at a specified location inherently connects point-scale observations to watershed-scale events.
Continuous monitoring and real-time communication from multi-probe water quality sensors is helping to bridge the considerable gap between observation-scale data and watershed-scale-outcomes for water quality. For example, specific conductance is a parameter that varies in accordance to the concentration of dissolved ions such as calcium, chloride, fluoride, magnesium, potassium, sodium, and sulphate.
However, increasing deployments of multi-probe sensors is not enough as one must also take into consideration flow.
Following our example above, synchronization of discrete laboratory analyses with continuous monitoring of specific conductivity and discharge calculations from continuous gauging stations can reveal which ions are influential at which flow regimes providing real insight into not only what is happening but why. This improved understanding of spatial and temporal dynamics of water quality parameters improves the inference that can be made across all relevant time and space scales.