Towards a comprehensive physically-based rainfall-runoff model

Towards a comprehensive physically-based rainfall-runoff model

This paper introduces TOPKAPI (TOPographic Kinematic APproximation and Integration), a new physically-based distributed rainfallrunoff model deriving from the integration in space of the kinematic wave model. The TOPKAPI approach transforms the rainfall-runoff and runoff routing processes into three ‘structurally-similar’ non-linear reservoir differential equations describing different hydrolog...

Guidelines for rainfall-runoff modelling: Towards best practice model application

Rainfall Runoff Modelling Based on Genetic Programming

The runoff formation process is believed to be highly non-linear, time varying, spatially distributed, and not easily described by simple models. Considerable time and effort has been directed to model this process, and many hydrologic models have been built specifically for this purpose. All of them, however, require significant amounts of data for their respective calibration and validation. ...

A Statistical Rainfall-Runoff Mixture Model with Heavy-Tailed Components

1 We present a conditional density model of river runoff given covariate information 2 which includes precipation at four surrounding stations. The proposed model is non3 parametric in the central part of the distribution and relies on Extreme-Value Theory 4 parametric assumptions for the upper tail of the distribution. From the trained con5 ditional density model, we can compute quantiles of v...

Neural network emulation of a rainfall-runoff model

The potential of an artificial neural network to perform simple non-linear hydrological transformations is examined. Four neural network models were developed to emulate different facets of a recognised non-linear hydrological transformation equation that possessed a small number of variables and contained no temporal component. The 5 modeling process was based on a set of uniform random distri...