Valerie Isham abstract
Stochastic modelling in hydrology
Rainfall is the driving force for many hydrological processes. As has been all too apparent in recent months, rainfall that cannot be absorbed or drained away causes major flooding disasters and flood defences must be designed to cope with extreme events. Soil moisture, for which rainfall is the input, provides the dynamic link between climate, soil and vegetation, and impacts plant dynamics as well as other processes at a range of spatial scales. Historical rainfall data are, perhaps surprisingly, often not available at the temporal and spatial resolution needed for hydrological modelling and design. The talk will describe some stochastic models for rainfall fields in continuous space-time that can be used to provide artificial rainfall data at arbitrary temporal and spatial scales, together with some simple soil moisture models for which these rainfall models form the input. Climate change poses an additional challenge, as rainfall under future climate scenarios is needed for hydrological design, and a way to allow for this in model fitting will be described.