T/CSES 03-2019 Lake algal control process water quality target management technical guidelines: dual probability method for determining the nutrient standards of sub-lakes
T/CSES 03-2019 Lake algal control process water quality target management technical guidelines: dual probability method for determining the nutrient standards of sub-lakes
Basic Information
Scope
Main technical content: For processes such as meteorology and hydrology that are generally stable, long-term data can be used to determine the design assurance rate requirements. However, most lakes are significantly affected by human activities, and the background conditions are subject to trend changes, such as the increase in temperature caused by climate warming, the trend reduction in lake inflow due to increased water consumption in the basin, the increase in lake inflow due to cross-basin water replenishment projects, the changes in lake water level caused by changes in hydrological dispatching principles, biological control measures, and changes in fishery development methods. Using data from the past decade may contain information about future trend changes, so the design assurance rate will consider recent dynamic adjustments. By using the average annual chlorophyll-a and nutrient salt concentrations, the standardization of lake nutrient processes can be understood as a two-step sequential double-probability standardization method: First, use functional demand standardization to confirm the acceptable algae density, expressed as CHLa concentration; Second, use background condition change standardization to confirm the nutrient concentration TP (TN) thresholds that meet the CHLa target requirements (threshold and recurrence period) under changing background conditions. In the second step, long-term and short-term (past decade) dual series of data are used for probability standardization, so that the standardization can take into account the possible impact of long-term cycles and also consider the risk of underestimating the risk by using historical data when recent trend changes occur.