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| Titel |
Assessing the performance of eight real-time updating models and procedures for the Brosna River |
| VerfasserIn |
M. Goswami, K. M. O'Connor, K. P. Bhattarai, A. Y. Shamseldin |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 9, no. 4 ; Nr. 9, no. 4 (2005-10-07), S.394-411 |
| Datensatznummer |
250006971
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| Publikation (Nr.) |
 copernicus.org/hess-9-394-2005.pdf |
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| Zusammenfassung |
| The flow forecasting performance of eight updating models, incorporated in
the Galway River Flow Modelling and Forecasting System (GFMFS), was assessed
using daily data (rainfall, evaporation and discharge) of the Irish Brosna
catchment (1207 km2), considering their one to six days lead-time discharge
forecasts. The Perfect Forecast of Input over the Forecast Lead-time scenario
was adopted, where required, in place of actual rainfall forecasts. The eight
updating models were: (i) the standard linear Auto-Regressive (AR) model,
applied to the forecast errors (residuals) of a simulation (non-updating)
rainfall-runoff model; (ii) the Neural Network Updating (NNU) model, also
using such residuals as input; (iii) the Linear Transfer Function (LTF)
model, applied to the simulated and the recently observed discharges; (iv)
the Non-linear Auto-Regressive eXogenous-Input Model (NARXM), also a neural
network-type structure, but having wide options of using recently observed
values of one or more of the three data series, together with non-updated
simulated outflows, as inputs; (v) the Parametric Simple Linear Model (PSLM),
of LTF-type, using recent rainfall and observed discharge data; (vi) the
Parametric Linear perturbation Model (PLPM), also of LTF-type, using recent
rainfall and observed discharge data, (vii) n-AR, an AR model applied to the
observed discharge series only, as a naïve updating model; and (viii)
n-NARXM, a naive form of the NARXM, using only the observed discharge data,
excluding exogenous inputs. The five GFMFS simulation (non-updating) models
used were the non-parametric and parametric forms of the Simple Linear Model
and of the Linear Perturbation Model, the Linearly-Varying Gain Factor Model,
the Artificial Neural Network Model, and the conceptual Soil Moisture
Accounting and Routing (SMAR) model. As the SMAR model performance was found
to be the best among these models, in terms of the Nash-Sutcliffe R2 value,
both in calibration and in verification, the simulated outflows of this model
only were selected for the subsequent exercise of producing updated discharge
forecasts. All the eight forms of updating models for producing lead-time
discharge forecasts were found to be capable of producing relatively good
lead-1 (1-day ahead) forecasts, with R2 values almost 90% or above. However,
for higher lead time forecasts, only three updating models, viz., NARXM, LTF,
and NNU, were found to be suitable, with lead-6 values of R2 about 90% or
higher. Graphical comparisons were made of the lead-time forecasts for the
two largest floods, one in the calibration period and the other in the
verification period. |
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