Temporal variation and assessment of trophic state indicators in missouri reservoirs: Implication for lake monitoring and management

Abstract

The magnitude and management implications of temporal variability in trophic state metrics was simulated by measuring mean values of total phosphorus (TP), total nitrogen (TN), chlorophyll (Chl) and Secchi depth (SD) in summer (May-August) and detecting trends in these variables in a virtual lake undergoing gradual (doubling over 20 years) and abrupt (doubling over two years) change. Numbers of samples required (samples per summer over number of summers) to adequately detect these rates of change were used to show the size and management implications of temporal variability. Long-term data from 116 Missouri reservoirs, including eight summer data sets based on daily sampling, provided estimates of autocorrelation and variation within and among summers (seasonal and year-to-year variance) used in Monte Carlo simulations to evaluate sampling requirements. In simulations based on median variance, obtaining long-term means with 95% confidence intervals spanning less than a factor of two took from three years (TN) to eight years (Chl) with monthly samples (n=3 per summer). For a lake with mean values doubling every 20 years, linear regression had >75% chance of detecting the trend after 13 years of monthly samples for TN, but Chl required >20 years. For a lake with Chl doubling over two years, at least six years of pre-change data and 11 years of post-change data were required before monthly sampling gave >75% probability of detecting the trend. Increasing sampling to weekly frequency (n=16 per summer) in most scenarios reduced required duration of sampling by <2 years. Variability data from lakes in other regions fall in the range exhibited by Missouri reservoirs. Results emphasize the need for long-term data to fulfill lake management needs and suggest that ordinary lake monitoring typically will not detect trends in individual lakes. © 2006 Taylor & Francis Group, LLC.