Reducing uncertainty in flow simulations for lake catchments using SWAT +

ABSTRACT Accurate land cover data and inflow estimates are important for hydrological modelling in lake catchments to enable efficient management. Flow model calibration is challenging in data-limited regions or where discharge measurements are difficult, such as braided river systems. This study applies the SWAT+ model to simulate daily flows in the Lake Opuha catchment, New Zealand, characterised by braided rivers flowing into a lake with a controlled outlet. The aim is to assess how flow simulations are influenced by improved land cover classification and calibration using different flow datasets. Objectives include updating bare land classes, developing a lake water balance model, and evaluating the effect of model parameters when using flow gauging station data and lake inflows for calibration. Results show that calibration with both flow gauge and lake water balance data provided reasonable streamflow simulations (calibration period: NSE = 0.63 and 0.58; validation period: NSE = 0.43 and 0.49). However, the model calibrated with lake water balance data achieved a greater reduction in flow prediction uncertainty. This study demonstrates that the lake water balance approach is a suitable method for model calibration in catchments with limited data.