In the context of climate modelling, convective clouds in tropical regions play a major role in determining the climate sensitivity. The vertical transport of mass and energy associated with this type of clouds is often represented with so called mass-flux parameterization schemes. In this work the aim is to evaluate the relationship between mass-flux and large scale environmental conditions using observations in a tropical region, over a period of 13 wet seasons. A uniquely comprehensive data set from the C-band polarimetric radar (CPOL) in Darwin, Australia, is used to estimate vertical velocity inside precipitating convective clouds. Ultimately, mass-flux is derived over a domain size similar to that of a general circulation model (GCM) grid box. Five parameters (RH_500, CAPE, CIN, ­Omega_500 and Chi_crit) are selected to describe environmental conditions and with these, the magnitude and shape of mass-flux is analysed. Chi_crit appears to be the most valid parameter to represent both the shape and magnitude of mass-flux. All other selected parameters strongly influence only one of the two aspects of the profile. Additionally, fractional entrainment is retrieved from mass-flux profiles and partitioned into two terms: one dependent on area fraction and the other on vertical velocity. Under all environmental conditions, the layer between 4.5 and 7 km experiences detrainment. It can be inferred that a stable layer, known as the freezing level, is present at 4.5 km. Below the freezing level, the vertical velocity and the mass-flux shape are most relevant to determine entrainment rates leading to the conclusion that the vertical velocity should not be disregarded when parameterizing convection.

The Meuse river basin covers an area of 33,000 km2, touches five countries and is a major communication route in Europe. It is one of the catchments with longest streamflow records, with daily measures of discharge dating back to the beginning of the previous century. Attempts to model streamflow with standard hydrological models revealed that average streamflow was consistently overestimated by the model in the period 1933-1968. Different attempts to explain such anomaly can be found in the literature. In this work we hypothesise that this anomaly could be resolved by considering a time varying root zone storage capacity, represented by a model parameter (Su,max), which has affected the partitioning between precipitation and streamflow. Vegetation is in fact believed to adjust root zone storage capacity to overcome droughts with a return period of about 20 years. To test our hypothesis, a semi-distributed conceptual model, based on the FLEX modelling approach, was used. A time varying Su,max was obtained with two approaches: by calibration of the model parameters in a moving time window, and by derivation of Su,max directly from climate variables. The results show that adding time dependency to Su,max improves the mean flow simulation, however not to a degree that it fully explains the observed anomaly. Deriving Su,max directly from climate variables delivered a better fit to the average streamflow than calibration, which confirms the feasibility of a climate derived root zone storage capacity in hydrological modelling.