Reconstruction of missing daily streamflow data using dynamic regression models
Abstract
River discharge is one of the most important quantities in hydrology. It provides fundamental
records for water resources management and climate change monitoring. Even very short data-gaps in this
information can cause extremely different analysis outputs. Therefore, reconstructing missing data of
incomplete data sets is an important step regarding the performance of the environmental models, engineering,
and research applications, thus it presents a great challenge. The objective of this paper is to introduce
an effective technique for reconstructing missing daily discharge data when one has access to only
daily streamflow data. The proposed procedure uses a combination of regression and autoregressive integrated
moving average models (ARIMA) called dynamic regression model. This model uses the linear relationship
between neighbor and correlated stations and then adjusts the residual term by fitting an ARIMA
structure. Application of the model to eight daily streamflow data for the Durance river watershed showed
that the model yields reliable estimates for the missing data in the time series. Simulation studies were also
conducted to evaluate the performance of the procedure.
Domains
Statistics [math.ST]
Origin : Files produced by the author(s)
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