Comparison of statistical methods for estimation of nutrient load to surface reservoirs for sparse data set: Application with a modified model for phosphorus availability

Nutrient budget models for lakes and reservoirs critically respond to the i nput pollutant loading, yet little consensus exists on how to estimate the load, particularly for the common but challenging case of sparse nutrient c oncentration measurements and abundant input flow data. A statistical load calculation using cluster (in this case, annual) mean concentration and str atified (monthly) flow was compared to estimates by sample mean and ratio e stimator methods for phosphorus loading to Whitney Reservoir in North Centr al Texas. The results varied considerably for the various estimator methods during the six-year study period with the cluster and stratified mean appr oach estimating extreme high loading periods not captured by the other meth ods. The variable loading patterns were then tested in phosphorus budget mo del simulations for Whitney Reservoir that considered vertical stratificati on of the water column, water-sediment phosphorus interaction, and seasonal variations in water quality. For independently determined settling,interla yer dispersion, recycling rates, and sediment burial rates estimated for th e respective loading calculation, the cluster and stratified mean loading p attern provided a batter statistical fit of phosphorus concentration measur ements in the epilimnion than when ratio estimator load calculations were u sed. The two loading functions described hypolimnion concentration data equ ally well. The lesson of this exercise is that various methods of load esti mation should be examined in order to develop as reliable a management mode l as possible when only a sparse data set is available for calibration. (C) 2000 Elsevier Science Ltd. All rights reserved.