Baseflow separation tools play a significant role in understanding and modelling the relationship between rainfall and runoff generation. The tools estimate the groundwater contribution to the streamflow and by that help in the representation of other streamflow components as well. In this research two baseflow separation tools, namely the WETSPRO and a Neural Network based tool are employed.
The SUPERFLEX hydrological model is used to mimic various natures of catchments. The model generates quick flow, baseflow and total flow simulations at an hourly and daily time step. The baseflow separation tools are then employed on the model simulations. The WETSPRO tool works on both the daily and hourly time series, but the NN tool is developed to work only on hourly time series. Performance of the tools is assessed using goodness of fit statistics and hydrograph comparisons.
The analysis from the WETSPRO is further used to improve process representation in the SUPERFLEX model. Initially baseflow influencing parameters are calibrated and those parameters were then transferred to recalibrate the SUPERFLEX model. Model simulations are evaluated using total flow hydrograph comparisons, goodness of fit statistics, and cumulative volume graphs. The cumulative volume graphs are plotted for the volume of water before and after recalibration with an intension to close the water balance.
The results show that, two of the SUPERFLEX model structures are more suitable to the catchment studied. From the two baseflow separation tools, the WETSPRO provided better estimates of baseflow. It was also shown that the additional information from the WETSPRO tool helped moderately in the improvement of model simulation. |
