Increasing model parsimony without loss of process fidelity: a simplified LASCAM rainfall-runoff model

Abstract. Rainfall-runoff models are widely used to support water resource planning, yet many struggle under prolonged drought. During these periods, cumulative water deficits can strongly affect streamflow response by altering vegetation water use and contributing to multi-annual storage dynamics. The Large Scale Catchment Model (LASCAM) is well suited to representing these processes through its deep groundwater store and flux structure, but its large number of calibrated parameters increases computational cost and limits practical application. This study developed a simplified version of LASCAM by reducing the number of free parameters while retaining the structural features of the original model. Using the latest version of LASCAM (LASCAM22) as the starting point, we applied a staged simplification procedure that combined sensitivity analysis, parameter default-value testing, and stepwise parameter-fixing calibration. The reduced configuration was then compared with GR4J, Sacramento, and LASCAM22 in split-sample testing. Several low-sensitivity parameters were fixed with limited loss of performance, while the stepwise parameter-fixing calibration supported the 15-parameter configuration (LASCAM15) as the best compromise between dimensionality reduction and performance retention. In the split-sample test, LASCAM15 achieved stronger validation performance than LASCAM22 and outperformed GR4J and Sacramento during both calibration and validation. The resulting model is more parsimonious (i.e., fewer calibrated parameters), is more computationally efficient to calibrate, and provides more robust simulations for catchment modelling applications.