HYDROLOGICAL REGIMES, SAMPLING STRATEGIES, AND ASSESSMENT OF ERRORS IN MASS LOAD ESTIMATES FOR UNITED-KINGDOM RIVERS

A computational framework is presented for heuristic investigation of the performance of different algorithms and sampling frequencies for e stimating river mass loads. The approach adopted is to generate a time series of synthetic concentration, from a time series of observed str eamflow, on the basis of available information on the covariation of n ow and concentration for the determinand and site of interest. A refer ence mass load for the whole, or any part, of the time series is calcu lated from the flow and synthetic concentration time series. Combinati ons of different estimation algorithms and (periodic) sampling interva ls can be applied and the resultant mass load estimates compared with the reference value. For a chosen estimation algorithm, the distributi on of mass load estimates derived from replicated samples leads to mea sures of accuracy (bias) and precision (random error). A qualitative c omparison of the performance of two mass load estimation algorithms, s pecified by the Paris Commission for monitoring fluvial inputs to the North Sea, is presented using the hydrological regime of a 20-km(2) ca tchment in southwest England and two general cases of hysteretic conce ntration behaviour: concentration increases with flow and concentratio n decreases with now. In each case, a better estimate of river mass lo ad is obtained when the variation in now between concentration samples is taken into account.