For several hydrological modelling tasks, precipitation time-series with a high (i.e. sub-daily) resolution are indispensable. This data is, however, not always available and thus model simulations are used to compensate. A canonical class of stochastic models for sub-daily precipitation are Poisson-cluster processes, with the Bartlett-Lewis rectangular pulse model (BLRPM) as a prominent representative. The BLRPM has been shown to well reproduce certain characteristics found in observations. Our focus is on intensity-duration-frequency relationship (IDF), which are of particular interest in risk assessment. Based on a high resolution precipitation time-series (5-min) from Berlin-Dahlem, BLRPM parameters are estimated and IDF curves are obta...
This paper explores the use of a class of stochastic point process models, based on doubly stochasti...
Many hydrological applications, such as flood studies, require the use of long rainfall data at fine...
High-resolution space-time stochastic models for precipitation are crucial for hydrological applicat...
For several hydrological modelling tasks, precipitation time-series with a high (i.e. sub- daily) re...
For several hydrological modelling tasks precipitation time series with a high (sub-daily) resoluti...
To simulate the impacts of within-storm rainfall variabilities on fast hydrological processes, long ...
In a recent development in the literature, a new temporal rainfall model, based on the Bartlett-Lewi...
In a recent development in the literature, a new temporal rainfall model, based on the Bartlett-Lewi...
Stochastic point processes for rainfall are known to be able to preserve the temporal variability of...
A cluster point process model is considered for the analysis of fine-scale rainfall time series. The...
Rainfall variability within a storm is of major importance for fast hydrological processes, e.g. sur...
The design and operation of urban drainage systems require long and continuous rain series in a high...
There are a number of stochastic point process models that can be used to generate rainfall data at ...
Graduation date: 1986Mathematical models of the precipitation process are needed to\ud effectively u...
Stochastic rainfall models are widely used in hydrological studies because they provide a framework ...
This paper explores the use of a class of stochastic point process models, based on doubly stochasti...
Many hydrological applications, such as flood studies, require the use of long rainfall data at fine...
High-resolution space-time stochastic models for precipitation are crucial for hydrological applicat...
For several hydrological modelling tasks, precipitation time-series with a high (i.e. sub- daily) re...
For several hydrological modelling tasks precipitation time series with a high (sub-daily) resoluti...
To simulate the impacts of within-storm rainfall variabilities on fast hydrological processes, long ...
In a recent development in the literature, a new temporal rainfall model, based on the Bartlett-Lewi...
In a recent development in the literature, a new temporal rainfall model, based on the Bartlett-Lewi...
Stochastic point processes for rainfall are known to be able to preserve the temporal variability of...
A cluster point process model is considered for the analysis of fine-scale rainfall time series. The...
Rainfall variability within a storm is of major importance for fast hydrological processes, e.g. sur...
The design and operation of urban drainage systems require long and continuous rain series in a high...
There are a number of stochastic point process models that can be used to generate rainfall data at ...
Graduation date: 1986Mathematical models of the precipitation process are needed to\ud effectively u...
Stochastic rainfall models are widely used in hydrological studies because they provide a framework ...
This paper explores the use of a class of stochastic point process models, based on doubly stochasti...
Many hydrological applications, such as flood studies, require the use of long rainfall data at fine...
High-resolution space-time stochastic models for precipitation are crucial for hydrological applicat...