Wlodzimierz Tych

Role of rainstorm intensity underestimated by data-derived flood models: Emerging global evidence from subsurface-dominated watersheds

Intense rainstorms are a prevalent feature of current weather. Evidence is presented showing that simulation of flood hydrographs shown to be dominated by subsurface flow requires watershed model parameterisation to vary between periods of different rainstorm intensity, in addition to varying with antecedent basin storage. The data show an emerging global relation between flood response and the intensity of rainstorms. Flood responses are quantified as watershed residence times (strictly time constants of nonlinear transfer-function models) identified directly from information contained within 15-min rainfall and streamflow observations. The emerging monotonic, curvilinear relation indicates that (subsurface) watershed residence time decreases as mean intensity rises, and is seen over a wide range of synoptic conditions from temperate and tropical climates. Projected increases in rainstorm intensity would then result in a greater likelihood of river floods in subsurface-dominated watersheds than is currently simulated by systems models omitting this additional nonlinearity.

Extended State Dependent Parameter modelling with a Data-Based Mechanistic approach to nonlinear model structure identification

Abstract A unified approach to Multiple and single State Dependent Parameter modelling, termed Extended State Dependent Parameters (ESDP) modelling, of nonlinear dynamic systems described by time-varying dynamic models applied to ARX or transfer-function model structures. Crucially, the approach proposes an effective model structure identification method using a novel Information Criterion (IC) taking into account model complexity in terms of the number of states involved. In ESDP, model structure involves not only the model orders, but also selection of the states driving the parameters, which effectively prevents the use of most current IC. This leads to a powerful methodology for investigating nonlinear systems building on the Data-Based Mechanistic (DBM) philosophy of Young and expanding the applications of the existing DBM methods. The methodologies presented are tested and demonstrated on both simulated data and on high frequency hydrological observations, showing how structure identification leads to discovery of dynamic relationships between system variables.