Simulating mountain runoff with meso-scale weather model rainfall estimates: a New Zealand experience

Ibbitt, Richard P.; Henderson, Roddy; Copeland, J. H.; Wratt, David

doi:10.1016/s0022-1694(00)00351-6

Cited by 43 publications

(29 citation statements)

References 15 publications

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“…TOPMODEL is a frequently used model, based on simple physical approximations, and is well documented in the literature [for an overview see Beven et al, 1995;Beven, 1997Beven, , 2001. It has been applied to a wide range of catchments, including regionalization studies [e.g., Ibbitt et al, 2001;Bastola et al, 2008]. The choice of TOPMODEL as a good model structure for the hydrology of the páramo ecosystem is based on extensive field experience.…”

Section: Hydrological Modelmentioning

confidence: 99%

Regionalization as a learning process

Buytaert

Beven

2009

Water Resources Research

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[1] This paper deals with the uncertainty involved in geographical migration of hydrological model structures, commonly known as regionalization. Regionalization relies on the hypothesis that calibrated parameter sets from a donor catchment can be useful to predict discharge of an ungauged catchment. However, since every catchment is unique, model parameters need to be adapted for differences between a calibration and a prediction catchment, either by transformation or further selection. This process is inherently uncertain. Model parameters, and therefore the required changes, do not exactly represent quantities that we can measure or calculate. This paper outlines an approach to learn about how model parameters should be transformed between a gauged and an ungauged catchment. The approach consists of an iterative process, in which a model structure is applied successively to gauged catchments. After each step, parameter behavior is evaluated as a function of catchment properties and intercatchment similarities. The method is illustrated with an application of a customized version of TOPMODEL to a set of catchments in the Ecuadorian Andes. First, parameter sets are generated for a donor catchment. This model ensemble is then used to predict the discharge of the other catchments, after applying a stochastic parameter transformation to account for the uncertainty in the model migration. The parameter transformation is then evaluated and improved before further application. The case study shows that accurate predictions can be made for predicted basins. At the same time, knowledge is gained about model behavior and potential model limitations.

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Section: Hydrological Modelmentioning

confidence: 99%

Regionalization as a learning process

Buytaert

Beven

2009

Water Resources Research

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“…This work will link a high resolution atmospheric prediction model (Speer et al, 1996;M€ o olders, 1998;Yu et al, 1999;Ibbitt et al, 2000, Kalma, 2001M€ o olders, 2001) to the dynamic hydrological model. The numerical weather prediction model we will use is The University of New South Wales HIgh RESolution model (HIRES) developed by one of authors (LML) (see, for example, Leslie et al, 1985;Leslie and Purser, 1995;Speer et al, 1996).…”

Section: Possible Explanation Of Fire Effects On Streamflowmentioning

confidence: 99%

The effects of bushfires on hydrological processes using a paired-catchment analysis

Liu

Leslie

Speer

et al. 2004

Meteorology and Atmospheric Physics

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“…To assess that, radar data (and nowcasts) have been used as inputs to a distributed hydrological model. The National Institute of Water and Atmospheric Research (NIWA) has implemented the TOPNET hydrological model (Ibbitt et al, 2001) for Mahurangi, a small rural catchment (8 × 15 km) near Auckland. This catchment is about 20 km from the radar, and therefore is in an ideal location to validate the rainfall estimates and forecasts.…”

Section: Forecasting River Flowmentioning

confidence: 99%