A monthly water balance model for assessing streamflow uncertainty in hydrologic studies

Accurate assessment of streamflow is crucial for operational water resources management projects. The aim of this study was to estimate the uncertainties in the surface runoff simulated by a monthly water balance model in a mountainous watershed of the Portaikos river, a tributary of the Pinios river, Thessaly, Greece. The University of Thessaly (UTHBAL) monthly water balance model was developed in the R statistical computing environment language, named ‘R-UTHBAL’, to estimate surface water balance in data-scarce watersheds. Two sources of uncertainties in hydrological modelling were considered: the uncertainties in input data estimation and in model parameters. The uncertainties were estimated with the use of the R-package ‘hydroPSO’, a global Particle Swarm Optimisation (PSO) algorithm for the calibration of environmental models. The R-UTHBAL was integrated with the hydroPSO algorithm and advanced sensitivity analyses, and user-friendly evaluation plots were estimated to facilitate the interpretation and assessment of the calibration results. Areal input datasets were estimated using typical engineering methods (i.e. precipitation/temperature gradients, Thiessen polygons) and several objective functions, (i.e. Nash-Sutcliffe Efficiency and variations or adaptations), addressing different parts of the hydrograph have been used to assess both the skill and the robustness of the R-UTHBAL model to perform consistent streamflow predictions. Confidence intervals in the simulated runoff due to input data uncertainty, parameter uncertainty and total uncertainty were calculated. Application of R-UTHBAL with the hydroPSO showed that the uncertainty in streamflow estimation should always be accounted for and evaluated in operational water resources management projects.