Improving event-scale and long-term estimates of rainfall interception using a modular analytical modelling framework

Rainfall interception loss (E_i) by vegetation plays a crucial role in the regional and global water cycle, as intercepted rainfall does not enter the terrestrial hydrological cycle. Moreover, E_i can account for up to 10–50% of gross precipitation in forested ecosystems. Accurate estimation of E_i is therefore essential for applications such as drought monitoring and prediction, flood prevention, and river discharge prediction.

Several approaches exist to estimate E_i, including empirical methods, numerical models such as the Rutter model, and analytical models such as the sparse Gash and van Dijk–Bruijnzeel models. The latter have proven particularly useful due to their simplicity and their assumption of one storm per day, which enables application at the global scale with minimal input data. When properly calibrated, these models provide reliable long-term estimates of E_i in forested environments. However, when applied at the scale of individual rainfall events without recalibration, they tend to overestimate (respectively underestimate) E_i for low (respectively high) observed values. These discrepancies may arise from uncertainties in parameter estimation, input data errors, conceptual model errors, or observational uncertainties.

To investigate the potential conceptual model errors in the sparse Gash and van Dijk–Bruijnzeel models, new analytical model formulations are introduced, which explicitly represent canopy drainage, based on the exponential drainage function of the Rutter model. Additionally, to systematically investigate the sources of error, a modular modelling framework is employed. This framework allows for multiple configurations of input data, variable implementations (e.g., canopy cover and storage, precipitation intensity and evaporation rate) and the aforementioned model structures. The objective is to develop a robust interception model capable of providing accurate daily estimates at the global scale using readily available data, while remaining applicable to both long-term observational datasets and individual rainfall events.

All combinations of input data, parameter estimation approaches, and model structures are evaluated using both literature-based parameter values and optimised parameter sets. Model calibration and validation are first conducted on a large subset of long-term observational water balance studies reported in literature. The optimised parameter sets are subsequently validated against an independent subset of long-term datasets, as well as event-based water balance observations. In addition, the modular framework enables a systematic assessment of the epistemic uncertainty associated with each modelling choice.

Finally, a global daily dataset of E_i (1980–present) at 0.1° spatial resolution is presented, derived from the best-performing model configuration. This dataset provides new opportunities to investigate the interactions between E_i and climate- and weather-related processes. Furthermore, this work is framed within the activities of the new ESA CCI Land Evaporation project and is a first example of the usage of the modular framework, which will be expanded towards a multiphysics land evaporation model.