Current Status of Time Series Analysis in Hydrological Sciences - Hydrologic Time Series Analysis: Theory and Practice - page 125

sediment flux took place for the Narmada River (-2.07 × 10 6 t/yr) due to the
construction of dam.
6.4 Application of Time Series Analysis in
Groundwater Hydrology
6.4.1 Groundwater Flow
Molénat et al. (1999) viewed the catchment as a system that converts the
rainfall to the stream discharge through a transfer function (TF). By comparing
the observed TF with the simulated TF, the hydrological processes and their
time scales were identified. The simulated transfer functions were developed
using the Dupuit's assumptions and linear representation of the aquifer. The
identification of the TF was based on the stochastic method using a spectral
representation of the rainfall and streamflow time series. The novelty of this
work is to extend the stochastic approach to the one-order catchment hydrology
and to develop a model, which takes into account both the aquifer discharge
and rapid flows. The proposed method was applied to three first-order
agricultural catchments located in different regions of France. For each site,
the obtained results were in good agreement with reality. These results indicated
that the streamflow is dominated by the aquifer flow, which is the fast transfer
accounting for 3-8% of the total discharge depending on the catchment. The
stochastic approach based on the spectral analysis of temporal variations in
global observations was emphasized to be useful for extracting significant
information about the dominant processes occurring in the catchment and
their characteristic time scales.
6.4.2 Groundwater Quality
Chang (1988) developed a modelling technique that includes the homogeneity
test of data and the best model selection to fit the water loss series by a
stochastic process. The results of this study revealed nonhomogeneities in the
annual water loss time series from the Ohio River basin, and hence the
adjustments were required before the model fitting by a stochastic process.
The best model selected based on the criterion of the parsimony of parameters
was successfully used to forecast the regional water losses.
Wilson et al. (1992) established groundwater quality changes caused by
anthropogenic activities with the help of a time-series analysis of well water
quality data from a 1964-1965 survey. In all cases, Ca +2 and Fe +3 were found
to increase with depth due to the dissolution of Ca minerals as water moves
downgradient, and due to a change from oxidizing to reducing conditions
downgradient, respectively. NO 3 - and Cl - concentrations were found higher in
the recharge areas possibly due to surface pollution sources. The significant
variability of chemical constituents was attributed to the recharge events,