Process-Based Estimates of Seasonal Catchment Hydrology: Dimensionless Models

Catchment functions (consisting of partition, storage and discharge) are difficult to measure or model, especially considering the wide variety of landscape forms (e.g. plant canopy and soil properties) and variable climate forcing (e.g. precipitation and radiation). After formulating an analytical model to predict the seasonal water balance of the canopy, the root zone, and the saturated zone by using functions of six dimensionless parameters (Woods, 2003, Advances in Water Resources), Woods (2009, Advances in Water Resources) developed a related model for seasonal snowpack dynamics. This presentation will use enhancements of these two simple models to estimate evaporation (E) and changes in water storage (dS/dt) and then the catchment runoff (Q), driven by summary statistics of precipitation (P), temperature and potential evaporation, based on the seasonal water balance (dS/dt= P-E-Q). In this study, we (i) firstly quantify the parameters (e.g. interception capacity relative to rainfall and melt factor) used in this improved model, using an a priori approach; (ii) assess this model in many catchments around the world by using existing global data products; (iv) identify the dominant parameters controlling the water balance; (v) discuss the limitations of this model. As a result, we will find in which situations it is possible to simply and reliably estimate seasonal variation in river flow without flow measurements, and other situations where model refinement is needed. This is important both for improving our understanding of catchment hydrology, and for predicting the seasonal hydrological differences between various hydro-climatic conditions or catchments, especially in locations with sparse measurements.