Water Journal : Water Journal April 2012
catchment management water APRIL 2012 101 of known E. coli genes to match the gene found in the water sample with an origin (i.e. an animal or human). As the library grows, along with the development of complementary gene markers, the precision and confidence in this process can only improve. Seqwater has developed a condition assessment tool to aid in the understanding of catchment and storage condition and impacts. The condition assessment tool is separated into two components: a catchment condition assessment and a storage condition assessment. The Catchment Condition Assessment is based on the following analysis: Adoption of best management practice As a suitable forecast of future improvements in water quality as a result of catchment condition; this score is calculated through measurement of the following indicators: • % of grazing properties with property management plans; • % of grazing properties with greater than 90% median long-term groundcover; • % of woody vegetation with protection status; • % of agricultural properties on <30% slope; • % of sewered urban properties. Catchment land-use and sediment mobility Catchment land-use is a key determinant of diffuse source pollutants, through sediment mobility. Sediment mobility through waterways and into storages is a key system driver of reduced drinking water quality. Sediment mobility is generally described as a function of geology and soil type, slope and vegetation to stabilise soils. This score is calculated through measurement of the following indicators: • % of catchment within lowest 90th percentile for likelihood of containing pollutants (EMSS index); • % of catchment vegetated; • % of catchment within lowest 90th percentile for Universal Soil Loss Equation (USLE) erosion hazard. Riparian condition Riparian condition provides a filter for runoff from lands suffering poor management practice, which causes sediment mobility and other pollutants. Of particular interest with regard to the riparian condition is the vegetation adjacent to the waterway, and the condition of the streambank. The indicators selected to reflect riparian condition are: • % of riparian area with vegetated cover; • % of stream length within vegetated cover. The storage condition assessment Scores are determined based on the state of the following attributes that reflect a 'healthy' storage: • Good catchment condition with any land use appropriately managed; • Minimal cyanobacterial (blue-green algae) blooms; • Minimal incidence of bacteria and pathogens; • Low suspended sediment and nutrient levels; • Suitability for primary human contact; and • Healthy ecological condition. In practical terms, this is represented by the comparison of 17 water quality indicators with set guidelines (derived from state policy documents and local water quality objectives). These indicators are divided into categories that reflect the "healthy" storage philosophy, nine of which fall under the Water Quality Index category and four each under Toxicant/Pathogens and Biological indices. An indicator score is generated for each indicator based on non-compliance and amplitude scores (Maxwell et al., 2010). Non-compliance is the probability of exceeding the recommended guideline and amplitude is a measure of the distance from the recommended guideline. The indicator scores for each of the three indices (Water Quality, Toxicant/Pathogens and Biological) are averaged to give a single score for each index. The average of the indices gives the final condition assessment score, which is then converted into a condition assessment scorecard (Figure 2). Extensive work has also been conducted to develop an index of vulnerability to poor water quality and cyanobacterial blooms based on simple measures of reservoir and catchment characteristics (Leigh et al., 2010). The index of vulnerability (VI) to poor water quality and cyanobacterial blooms in the subtropical reservoirs examined in this study was based on the percentage of agricultural land use in catchments, catchment area relative to reservoir volume, and physical characteristics of reservoirs. This VI has been successfully validated using water quality and cyanobacteria data collected from 15 drinking water reservoirs in subtropical South-East Queensland. Strong correlations were observed with increased cyanobacterial cell densities in summer months, as well as their proportional contribution to the total algal density. The index has the capability to predict vulnerability to poor water quality and summer blooms of cyanobacteria in subtropical and, potentially, tropical and temperate-zone reservoirs. Additionally, a new method was developed to detect synchronous change points in densities of cyanobacteria along the gradient of percentage grazing land cover in catchments (Leigh et al., 2010b). Figure 2. Example matrix for calculation of storage grade for representative storage. A score of 1 indicates very poor water quality and a score of 0 indicates very high water quality.
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