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 km 2 ), 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 naïve 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 R 2 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 leadtime discharge forecasts were found to be capable of producing relatively good lead-1 (1-day ahead) forecasts, with R 2 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 R 2 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.
Citation Goswami, M. & O'Connor, K. M. (2010) A "monster" that made the SMAR conceptual model "right for the wrong reasons". Hydrol. Sci. J. 55(6), 913-927.Abstract In earlier studies involving simulation of the Fergus River flows in Ireland, the conceptual Soil Moisture Accounting and Routing (SMAR) model had been found to consistently outperform a number of black-box models. Subsequently, in investigating any loss of flow through this catchment's subsurface karstic features, it was verified from the overall long-term water balance that such losses were substantial. This raised the awkward question of why the volume-conservative SMAR model had performed so well on this considerably non-conservative catchment. Further analyses revealed that, to compensate for the excess volume of total runoff generated by the model's conservative water balance component, the memory length of the surface runoff response function had been unrealistically curtailed in the optimization process, effectively truncating that function and thereby violating the conservation property of the routing process. This embarrassing revelation called for reconsideration of the model structure to account more sensibly for actual losses, while still achieving high model efficiency. This paper highlights not only the discovery of the karstic Fergus catchment as a "hydrological monster", in the context of the SMAR model, but also why conservative models perform poorly in such cases. In an attempt to "tame the monster", better simulation of the observed flows was achieved by conceptually adapting the SMAR model, in a pragmatic empirical manner, by simply modifying its water balance component.Un "monstre" qui rendit le modèle conceptuel SMAR "juste pour de mauvaises raisons" Résumé Lors d'études antérieures portant sur la simulation des débits de la rivière Fergus en Irlande, il avait été trouvé que le modèle conceptuel SMAR (Soil Moisture Accounting and Routing) surpassait nettement un certain nombre de modèles boîte noire. Par la suite, en examinant de possibles pertes dues aux formations karstiques de ce bassin, il a été vérifié en établissant le bilan en eau à long terme que ces pertes étaient substantielles. Cela souleva la délicate question de savoir pourquoi le modèle conservatif SMAR avait donné de si bonnes performances sur ce bassin notoirement non-conservatif. Des analyses complémentaires ont révélé que, pour compenser l'excès d'eau produit par la fonction de production conservative du modèle, la longueur de la mémoire de la fonction de routage de surface avait été réduite de façon irréaliste lors de l'optimisation, tronquant effectivement cette fonction et violant ainsi les propriétés conservatives du processus de routage. Cette découverte embarrassante nécessita de reconsidérer la structure du modèle pour tenir compte de manière plus sensée des pertes réelles tout en conservant un haut niveau d'efficacité. Cet article souligne non seulement la découverte du bassin karstique de la rivière Fergus comme un "monstre hydrologique", dans le conte...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.