Multi-site modeling and prediction of annual and monthly precipitation in the watershed of Cheliff (Algeria)

Abstract

The quantity of precipitation as well as its distribution varies in time, space, and even in small areas. This temporal and/or spatial variability is due to the support of several quite complex climatic and physiographic factors. The description and the prediction of this variability is a fundamental requirement in a wide variety of human activities, as well as in the elaboration and the design of hydraulic projects. The objective of this work is to study the applicability of Kalman filter (KF) technique for the modeling and prediction of annual or monthly rainfall amounts in the Cheliff watershed, as well as the assessment of the prediction error. The major advantage of KF is to provide with the prediction error covariance an indicator of the filter accuracy. In addition, its algorithm works in the temporal domain with a recursive nature and has an optimal estimator in the least squares concept. Another aspect of its optimality is the incorporation of all the available information on the system, measurements and errors in an adaptive operator, which is reset each time as a new measurement is available. For the implementation of this filter, time series of monthly and annual rainfall data registered over a period of 51 years (1959–2009) in 39 precipitation stations are studied in the Cheliff watershed and the obtained results are quite satisfactory.