A Comparative Study on the Derivation of Unit Hydrograph for Bharathapuzha River Basin

AbstractSeveral techniques are available for the development of the unit hydrograph. But most of these traditional methods require manual fitting of the unit hydrograph through few points, which does not guarantee the area under the unit hydrograph to be unity. More over most of the stations are ungauged, due to which it becomes difficult to develop the unit hydrograph. So in order to overcome these problems, two methods have been considered in this study for the development of the unit hydrograph for Bharathapuzha river basin. They are the “two parameter Gamma distribution” and “three parameter Beta distribution”, both of which are based on Probability Distribution Functions (pdfs). The unit hydrograph developed by the two parameter Gamma distribution match well with the one developed by CWC method, but the unit hydrograph developed by the three parameter Beta distribution does not match well with the one developed by the CWC method. From the unit hydrograph, runoff hydrograph is convoluted for the year 2008. For this the hourly rainfall are generated from daily rainfall values by disaggregation. But on plotting, the simulated discharge hydrograph is found to be greater than the observed discharge. This may be due to non incorporation of the inflow outflow processes of many hydraulic structures such as dams, irrigation schemes etc, existing in the basin in the model study. The data related to these structures could not be obtained due to certain restriction in acquiring the data from authorized agencies

A novel two-stage multi-step dynamic error correction model for improving streamflow forecast accuracy

The occurrences of floods in the recent past have significantly increased due to climate change and anthropogenic activities. Hence, reliable streamflow forecasts are crucial for minimizing the detrimental effects of flooding. However, forecast accuracy deteriorates besides elevated uncertainty when the lead time increases. Therefore, streamflow forecast should have improved accuracy with simultaneous uncertainty quantification to increase the model confidence for effective decision-making. The study proposes a novel two-stage multi-step dynamic error correction model to forecast up to 7 days ahead of streamflow, with the objective of no significant deterioration in accuracy. The framework is developed by integrating the process-based hydrological HBV model with the Bayesian-based Particle filter (PF) and machine learning-based Random Forest algorithm (RF). This facilitates combining the advantages of each model, i.e., process understanding ability of the HBV model, robust uncertainty quantifying ability of the PF technique, and relatively superior predictive ability of the RF algorithm. The model performance is quantified through several statistical performance error measures and uncertainty indices, with graphical performance indicators. The framework tested on the Beas and Sunkoshi river basins of India and Nepal exemplified the NSE of 0.94 and 0.98 in calibration and 0.95 and 0.99 in validation respectively for the 7-day ahead streamflow forecast. Hence, the proposed dynamic modeling framework can be considered as a potential tool to forecast streamflow without significant deterioration in the model accuracy even at increased lead times