Estimation of different uncertainties in simulated streamflow from hydrological models

Streamflow simulated from hydrological models is associated with uncertainty from a variety of sources. The chief sources of uncertainty are: i) errors in the measurement of the observed inputs to the hydrologic models, say precipitation, discharge, and temperature observations, ii) calibration uncertainty associated with algorithms used to estimate the parameters of the model iii) structural uncertainty, associated with incomplete or approximate representation of the catchment with hydrologic models. Operational forecasts generally ignore these uncertainties for important management decisions in water resources, for example, in issuing flood warnings. However, several works have shown that these uncertainties can substantially impact large streamflow forecasts made through hydrologic models. In this work, we explore different error models for estimating the relative contribution of individual error sources to overall uncertainty in the streamflow simulations. Four hydrologic models are used to estimate error distributions at various flow quantiles due to individual sources. The strategy can be adopted to improve the sources contributing to these uncertainties for future predictions from these systems. The approach may be used to reduce the major sources of uncertainty, which will help in reducing the computational efforts in estimating the uncertainties in streamflow simulations.