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Data Based Mechanistic modelling optimal utilisation of raingauge data for rainfall-riverflow modelling of sparsely gauged tropical basin in Ghana

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Data Based Mechanistic modelling optimal utilisation of raingauge data for rainfall-riverflow modelling of sparsely gauged tropical basin in Ghana. / Ampadu, Boateng; Chappell, Nick A.; Tych, Wlodzimierz.

In: Mathematical Theory and Modeling, Vol. 5, No. 8, 08.2015, p. 29-49.

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@article{7f1408fc38d442429b3840cb7bdced03,
title = "Data Based Mechanistic modelling optimal utilisation of raingauge data for rainfall-riverflow modelling of sparsely gauged tropical basin in Ghana",
abstract = "Data-Based Mechanistic (DBM) modelling is a Transfer Function (TF) modelling approach, whereby the data defines the model. The DBM approach, unlike physics-based distributed and conceptual models that fit existing laws to data-series, uses the data to identify the model structure in an objective statistical manner. The approach is parsimonious, in that it requires few spatially-distributed data and is, therefore, suitable for data limited regions like West Africa. Multiple Input Single Output (MISO) rainfall to riverflow modelling approach is the utilization of multiple rainfall time-series as separate input in parallel into a model to simulate a single riverflow time-series in a large scale. The approach is capable of simulating the effects of each rain gauge on a lumped riverflow response.Within this paper we present the application of DBM-MISO modelling approach to 20778 km2 humid tropical rain forest basin in Ghana. The approach makes use of the Bedford Ouse modelling technique to evaluate the non-linear behaviour of the catchment with the input of the model integrated in different ways including into new single-input time-series for subsequent Single Input Single Output (SISO) modelling. The identified MISO models were able to improve the efficiency and understanding of the rainfall-riverflow behaviour within the study catchment. The paper illustrates the potential benefits of the methodology in modelling large catchments with sparse network of rainfall stations.",
keywords = "Ghana, DBM model, Rainfall, MISO, Transfer function",
author = "Boateng Ampadu and Chappell, {Nick A.} and Wlodzimierz Tych",
year = "2015",
month = aug
language = "English",
volume = "5",
pages = "29--49",
journal = "Mathematical Theory and Modeling",
issn = "2224-5804",
number = "8",

}

RIS

TY - JOUR

T1 - Data Based Mechanistic modelling optimal utilisation of raingauge data for rainfall-riverflow modelling of sparsely gauged tropical basin in Ghana

AU - Ampadu, Boateng

AU - Chappell, Nick A.

AU - Tych, Wlodzimierz

PY - 2015/8

Y1 - 2015/8

N2 - Data-Based Mechanistic (DBM) modelling is a Transfer Function (TF) modelling approach, whereby the data defines the model. The DBM approach, unlike physics-based distributed and conceptual models that fit existing laws to data-series, uses the data to identify the model structure in an objective statistical manner. The approach is parsimonious, in that it requires few spatially-distributed data and is, therefore, suitable for data limited regions like West Africa. Multiple Input Single Output (MISO) rainfall to riverflow modelling approach is the utilization of multiple rainfall time-series as separate input in parallel into a model to simulate a single riverflow time-series in a large scale. The approach is capable of simulating the effects of each rain gauge on a lumped riverflow response.Within this paper we present the application of DBM-MISO modelling approach to 20778 km2 humid tropical rain forest basin in Ghana. The approach makes use of the Bedford Ouse modelling technique to evaluate the non-linear behaviour of the catchment with the input of the model integrated in different ways including into new single-input time-series for subsequent Single Input Single Output (SISO) modelling. The identified MISO models were able to improve the efficiency and understanding of the rainfall-riverflow behaviour within the study catchment. The paper illustrates the potential benefits of the methodology in modelling large catchments with sparse network of rainfall stations.

AB - Data-Based Mechanistic (DBM) modelling is a Transfer Function (TF) modelling approach, whereby the data defines the model. The DBM approach, unlike physics-based distributed and conceptual models that fit existing laws to data-series, uses the data to identify the model structure in an objective statistical manner. The approach is parsimonious, in that it requires few spatially-distributed data and is, therefore, suitable for data limited regions like West Africa. Multiple Input Single Output (MISO) rainfall to riverflow modelling approach is the utilization of multiple rainfall time-series as separate input in parallel into a model to simulate a single riverflow time-series in a large scale. The approach is capable of simulating the effects of each rain gauge on a lumped riverflow response.Within this paper we present the application of DBM-MISO modelling approach to 20778 km2 humid tropical rain forest basin in Ghana. The approach makes use of the Bedford Ouse modelling technique to evaluate the non-linear behaviour of the catchment with the input of the model integrated in different ways including into new single-input time-series for subsequent Single Input Single Output (SISO) modelling. The identified MISO models were able to improve the efficiency and understanding of the rainfall-riverflow behaviour within the study catchment. The paper illustrates the potential benefits of the methodology in modelling large catchments with sparse network of rainfall stations.

KW - Ghana

KW - DBM model

KW - Rainfall

KW - MISO

KW - Transfer function

M3 - Journal article

VL - 5

SP - 29

EP - 49

JO - Mathematical Theory and Modeling

JF - Mathematical Theory and Modeling

SN - 2224-5804

IS - 8

ER -