Prediction of Water Quality in the Danube River Under extreme Hydrological and Temperature Conditions
One of the requirements imposed by the Water Framework Directive (WFD, 2000/60/EC) is to analyze and predict how quality of surface waters will evolve in the future. In assessing the development of a stream's pollution one must consider all sources of pollution and understand how water quality evolves over time. Flow and water temperature regime of a stream or river are the main factors controlling the extent to which deterioration of a stream's water quality can propagate under constant input from pollution sources. In addition, there is ever increasing public concern about the state of the aquatic environment. Decision makers and scientists involved in water management call for studies proposing simulation models of water quality under extreme natural hydrologic and climatic scenarios. Also, human impact on water resources remain an issue for discussion, especially when it comes to sustainability of water resources with respect to water quality and ecosystem health. In the present study we investigate the long-term trends in water quality variables of the Danube River at Bratislava, Slovakia (Chl-a, Ca, EC, SO2-, Cl-, O2, BOD5, N-tot, PO4-P, NO3-N, NO2-N, etc.), for the period 1991-2005. Several SARIMA models were tested for the long-term prediction of selected pollutant concentrations under various flow and water temperature conditions. In order to create scenarios of selected water quality variables with prediction for 12 months ahead, three types of possible hydrologic and water temperature conditions were defined: i) average conditions - median flows and water temperature; ii) low flows and high water temperature; and iii) high flows and low water temperature. These conditions were derived for each month using daily observations of water temperature and daily discharge readings taken in the Danube at Bratislava over the period 1931-2005 in the form of percentiles (1th-percentile, median, 99th-percentile). Once having derived these extreme-case scenarios, we used selected Box-Jenkins models (with two regressors - discharge and water temperature) to simulate the extreme monthly water quality variables. The impact of natural and man-made changes in a stream's hydrology on water quality can be readily well simulated by means of autoregressive models.
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