Weather radar and hydrology

The growing concerns about environmental and climate change issues, as well as the emergence of the concept of sustainable development enshrined in the European Water Framework Directive (WFD) legislation, has modified the requirements for future hydrological observation and modelling initiatives and techniques. Originally, the focus at larger catchment scales was on the water stream flow at a limited number of locations, essentially for operational purposes such as flood prediction, dam regulation and hydropower production as well as hydrological process understanding. Water is now considered under its multiple functions such as water supply, hydrological risk, hydropower, irrigation, tourism, ecological value, to name a few. The demand for better hydrological understanding and techniques has thus moved to a more integrated prediction of the water balance components (rainfall, runoff, water storage, transpiration, evaporation, groundwater levels, etc.) at every point within a region [21]. In addition, consideration of land-use and human-induced landscape modifications is now a major concern for water management problems (i.e. for flood forecasting) and impact studies of land use change on stream flow, pollutants and/or sediments transport. For most of these questions, knowledge of the general characteristics of water balance components is not sufficient, more detailed process understanding (i.e. storm responses) throughout the landscape are required. The development of distributed and/or semi-distributed hydrological models, where there is a greater representation of the land-surface heterogeneities and the characterization of distributed inputs, are thus increasingly necessary. The usefulness of distributed hydrological models has been questioned, mainly due to overparameterization problems, parameter estimation and validation limitations [9], [43] and [8]. Such difficulties cannot be ignored but the continuous development of hydrological observation and landscape characterization techniques are likely to offer renewed possibilities in this field. We must recognise there is an inevitable compromise between the complexity of the model proposed, and the spatial data that is available to drive the model (inputs) and robustly evaluate its predictions