STOCHASTIC MODELING OF WATER TEMPERATURES IN A SMALL STREAM USING AIR-TO-WATER RELATIONS

Stream water temperature is a very important parameter when assessing aquatic ecosystem dynamics. For instance, cold-water fishes such as sa lmon can be adversely affected by maximum summer temperatures or by th ose exaggerated by land-use activities such as deforestation. The pres ent study deals with the modelling of stream water temperatures using a stochastic approach to relate air and water temperatures in Catamara n Brook, a small stream in New Brunswick where long-term multidiscipli nary habitat research is being carried out. The first step in the mode lling approach was to establish the long-term annual component (patter n) in stream water temperatures. This was possible by fitting a Fourie r series to stream water temperatures. The. short-term residual temper atures (departure from the long-term annual component) were modelled u sing different air to water relations, namely a multiple regression an alysis, a second-order Markov process, and a Box-Jenkins time-series m odel. The results indicated that it was possible to predict daily wate r temperatures for small streams using air temperatures and that the t hree models produced similar results in predicting stream temperatures . The root mean square error (RSME) varied between 0.59 degrees C and 1.68 degrees C on an annual basis from 1990 to 1995, with the warmest year (1994) showing the highest RMSE. Although 1992 was an exceptional ly cold summer (coldest in 30 years), good predictions of stream water temperature were obtained, with an RMSE of approximately 1.24 degrees C. Of the three models. the second-order Markov process was preferred based on its performance and its simplicity in development.