Assessment of <scp>JULES</scp> Land Surface Model Coupled With <scp>CaMa</scp> ‐Flood for an Operational Streamflow Forecasting Across Australia

ABSTRACT Operational hydrology forecasts provide crucial information to manage the water resources and offer early warnings to prepare for extreme events. Hydrological modelling and forecasting are challenging particularly in Australia due to its high hydro‐climatic variability, numerous intermittent or ephemeral rivers as well as flat terrain. This study has two primary objectives: (i) to implement the Catchment‐based Macro‐scale Floodplain (CaMa‐Flood) model to simulate river hydrodynamics across Australia; (ii) to evaluate the performance of land surface models coupled with CaMa‐Flood in simulating streamflow for an operational forecasting service. For this purpose, CaMa‐Flood is coupled with standalone JULES, the landscape water balance model (AWRA‐L) and two reanalysis datasets (BARRA‐R2 and ERA5‐Land). Analyses against 452 topographically and hydro‐climatically diverse catchments indicate very good results of offline JULES and AWRA‐L at both daily and monthly timescales. However, offline JULES tends to overestimate runoff in central Australia, while AWRA‐L overestimates runoff in Southeast Australia and Eastern Tasmania. BARRA‐R2 and ERA5‐Land show large underestimations across the country, with all models having their lowest performances in ephemeral catchments. Differences in runoff generation processes and forcings can be attributed to the performance differences in the models. A sensitivity analysis of CaMa‐Flood topographic parameters indicates that the default configuration generally produces reliable simulations; however, further improvements maybe achieved in some locations through fine‐tuning relevant parameters.