This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is...

In many semi-arid environments, vegetation is self-organised into spatial patterns. The most striking examples of this are on gentle slopes, where striped patterns are typical, running parallel to the contours. Previously, Klausmeier [1999. Regular and irregular patterns in semiarid vegetation. Science 284, 1826-1828.] has proposed a model for vegetation stripes based on competition for water. ...

[1] The primary advantage of radar observations of precipitation compared with traditional rain gauge measurements is their high spatial and temporal resolution and large areal coverage. Unfortunately, radar data require vigorous quality control before being converted into precipitation products that can be used as input to hydrologic models. In this study we coupled a physically based atmosphe...

The coupling of hydrological distributed models to numerical weather prediction outputs is an important issue for hydrological applications such as forecasting of flood events. Downscaling meteorological predictions to the hydrological scales requires the resolution of two fundamental issues regarding precipitation, namely, (1) understanding the statistical properties and scaling laws of rainfa...

[1] Previous observational studies have identified three different types of diurnal precipitation variation over the conterminous U.S.: localized afternoon rainfall maxima over the Mississippi and Ohio valleys, propagating mesoscale convective systems (MCSs) from the Rocky Mountain region, and propagating MCSs over the Appalachian Mountains. This study focuses on the second type, which involves...

Catchments that do not behave the way the hydrologist expects, expose the frailties of hydrological science, particularly its unduly simplistic treatment of input and model uncertainty. A conceptual rainfall– runoff model represents a highly simplified hypothesis of the transformation of rainfall into runoff. Sub-grid variability and mis-specification of processes introduce an irreducible model...

A hidden Markov model (HMM) is used to describe daily rainfall occurrence at ten gauge stations in the state of Ceará in northeast Brazil during the February–April wet season 1975–2002. The model assumes that rainfall occurrence is governed by a few discrete states, with Markovian daily transitions between them. Four “hidden” rainfall states are identified. One pair of the states represents wet...

The distributed lag model (DLM), used most prominently in air pollution studies, finds application wherever the effect of a covariate is delayed and distributed through time. We specify modified formulations of DLMs to provide computationally attractive, flexible varying-coefficient models that are applicable in any setting in which lagged covariates are regressed on a time-dependent response. ...

Hazard assessment of shallow landslides represents an important aspect of land management in mountainous areas. Among all the methods proposed in the literature, physically based methods are the only ones that explicitly includes the dynamic factors that control landslide triggering (rainfall pattern, land-use). For this reason, they allow forecasting both the temporal and the spatial distribut...

Stochastic weather generators are useful for producing daily sequences that reproduce climatic statistics aggregated to, e.g., a monthly time scale, for use with biological simulation models. This paper describes a stochastic weather generator that disaggregates monthly rainfall by adjusting input parameters or by constraining output to match target rainfall totals, and demonstrates its use wit...