The PDM rainfall-runoff model

Incorporation of groundwater losses and well level data in rainfall- runoff models illustrated using the PDM

Intermittent streamflow is a common occurrence in permeable catchments, especially where there are pumped abstractions to water supply. Many rainfall-runoff models are not formulated so as to represent ephemeral streamflow behaviour or to allow for the possibility of negative recharge arising from groundwater pumping. A groundwater model component is formulated here for use in extending existin...

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...

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

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...

Artificial Neural Network Model for Rainfall-Runoff Relationship

Conceptual models have been widely used and are considered to be the best choice for describing the runoff process in a watershed. In most cases, the solution accuracy is mainly based on the topographic and hydrologic information subject to certain requirements for model calibration. Thus, these types of model are inappropriate for watershed area with little hydrologic data. Artificial neural n...