Sensitivity of flood events to global climate change

Panagoulia, Dionysia; Dimou, George

doi:10.1016/s0022-1694(96)03056-9

Cited by 72 publications

(38 citation statements)

References 19 publications

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“…are examined after a threshold is defined (e.g. Panagoulia & Dimou, 1997b;Kim & Barros, 2001). In this study, the daily time step in the modelled time series was kept for exploration of high and low flows at a preliminary stage in investigation of model transferability for high/low flows to varied climate conditions.…”

Section: Runoff Modelling In Climatically Varied Regimesmentioning

confidence: 99%

Artificial neural networks and high and low flows in various climate regimes

Panagoulia

2006

Hydrological Sciences Journal

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An algorithm coupling linear least squares and simplex optimization (LLSSIM) is used to examine the ability of a three-layer feedforward artificial neural network (ANN) to simulate the high and low flows in various climate regimes over a mountainous catchment (the Mesochora catchment in central Greece). The plot of the long-term annual catchment pseudo-precipitation (rain plus snowmelt) simulated by the snow accumulation and ablation model (SAA) of the US National Weather Service (US NWS) showed trends of three climatically distinct periods, described by clearly descending, rising and moderately descending segments in pseudo-precipitation. The ANN model was calibrated for each of the three climate types and each was validated against the others. A set of statistical measures and graphs adapted for high and low flows showed the robustness of the ANN model under various climates and transient conditions. The ANN model proved capable of simulating well the daily high and low flows when it is calibrated for increasing pseudo-precipitation and validated for moderately decreasing pseudo-precipitation. For the entire period, the ANN model provided a better simulation of high and low flows than the conceptual soil moisture accounting (SMA) model of the US NWS, which was also employed in this study. Because the ANN is not a physically-based model, it is by no means a substitute for the SMA model. However, it is concluded that the ANN approach is an effective alternative for daily high-and low-flow simulation and forecasting in climatically varied regimes, particularly in cases where the internal dynamics of the catchment do not require an explicit representation.Key words artificial neural network; conceptual modelling; high flows; linear least squares; low flows; simplex optimization Réseaux de neurones artificiels et crues et étiages en régimes climatiques variésRésumé Un algorithme couplé de moindres carrés linéaires et d'optimisation simplex (LLSSIM) a été utilisé afin d'examiner l'aptitude d'un réseau de neurones artificiel (RNA) sans rétroaction à trois niveaux à simuler les crues et les étiages selon les régimes climatiques variés d'un bassin versant montagneux (le bassin versant de Mesochora en Grèce centrale). Les graphiques de la pseudoprécipitation (pluie plus fonte nivale) à long terme, simulée par le modèle d'accumulation et d'ablation de la neige (SAA) du service national météorologique des Etats Unis (US NWS), ont révélé les tendances de trois périodes climatiques distinctes, correspondant à des segments de décroissance forte, croissance et décroissance modérée. Le modèle RNA a été calé pour chacun des trois types climatiques et validé par rapport aux deux autres. Un ensemble d'indices statistiques et de graphiques adaptés aux crues et aux étiages a montré la robustesse du modèle RNA selon différents climats ainsi qu'en conditions transitoires. Il est apparu que le modèle RNA est apte à bien simuler les crues et les étiages journaliers lorsqu'il est calé en période de pseudo-précipitation croissan...

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Section: Runoff Modelling In Climatically Varied Regimesmentioning

confidence: 99%

Artificial neural networks and high and low flows in various climate regimes

Panagoulia

2006

Hydrological Sciences Journal

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“…GCM-implied changes were applied to the observed rainfall and potential evaporation to generate inputs for conceptual or semi-distributed rainfall-runoff hydrological simulation models (e.g., [1][2][3][4][5]). The change values that are derived from averaging multi-year monthly GCM outputs are applied to daily or even hourly observed data [6].…”

Section: Introductionmentioning

confidence: 99%

Possible Impacts of Climate Change on Daily Streamflow and Extremes at Local Scale in Ontario, Canada. Part II: Future Projection

Cheng¹,

Li²,

Li³

et al. 2012

ACS

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The paper forms the second part of an introduction to possible impacts of climate change on daily streamflow and extremes in the Province of Ontario, Canada. Daily streamflow simulation models developed in the companion paper (Part I) were used to project changes in frequency of future daily streamflow events. To achieve this goal, future climate information (including rainfall) at a local scale is needed. A regression-based downscaling method was employed to downscale eight global climate model (GCM) simulations (scenarios A2 and B1) to selected weather stations for various meteorological variables (except rainfall). Future daily rainfall quantities were projected using daily rainfall simulation models with downscaled future climate information. Following these projections, future daily streamflow volumes can be projected by applying daily streamflow simulation models.The frequency of future daily high-streamflow events in the warm season (May-November) was projected to increase by about 45% -55% late this century from the current condition, on average of eight-GCM A2 projections and four selected river basins. The corresponding increases for future daily low-streamflow events and future daily mean streamflow volume could be about 25% -90% and 10% -20%, respectively. In addition, the return values of annual one-day maximum streamflow volume for various return periods were projected to increase by 20% -40%, 20% -50%, and 30% -80%, respectively for the periods 2001-50, 2026-75, and 2051-2100. Inter-GCM and interscenario uncertainties of future streamflow projections were quantitatively assessed. On average, the projected percentage increases in frequency of future daily high-streamflow events are about 1.4 -2.2 times greater than inter-GCM and interscenario uncertainties.

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“…Increased flooding risks from heavy rainfall events are recognized as the most important threat from climate change in many regions of the world (e.g., [1][2][3][4][5][6]). In Canada, the number of flood disasters has significantly risen in the past three decades.…”

Section: Introductionmentioning

confidence: 99%

Possible Impacts of Climate Change on Daily Streamflow and Extremes at Local Scale in Ontario, Canada. Part I: Historical Simulation

Cheng¹,

Li²,

Li³

et al. 2012

ACS

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The paper forms the first part of an introduction to possible impacts of climate change on daily streamflow and extremes in the Province of Ontario, Canada. In this study, both conceptual and statistical streamflow simulation modeling theories were collectively applied to simulate daily streamflow volumes. Based on conceptual rainfall-runoff modeling principle, the predictors were selected to take into account several physical factors that affect streamflow, such as 1) current and previous quantities of rainfall over the watershed; 2) an index of pre-storm moisture conditions; 3) an index of pre-storm evapotranspiration capacities; and 4) a seasonal factor representing seasonal variation of streamflow volume. These rainfall-runoff conceptual factors were applied to an autocorrelation correction regression procedure to develop a daily streamflow simulation model for each of the four selected river basins. The streamflow simulation models were validated using a leave-one-year-out cross-validation scheme. The simulation models identified that the explanatory predictors are consistent with the physical processes typically associated with high-streamflow events. Daily streamflow simulation models show that there are significant correlations between daily streamflow observations and model validations, with model R 2 s of 0.68 -0.71, 0.61 -0.62, 0.71 -0.74, and 0.95 for Grand, Humber, Upper Thames, and Rideau River Basins, respectively. The major reason for the model performance varying across the basins might be that rainfall-runoff response time and physical characteristics differ significantly among the selected river basins. The results suggest that streamflow simulation models can be used to assess possible impacts of climate change on daily streamflow and extremes at a local scale, which is major objective of a companion paper (Part II).

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Sensitivity of flood events to global climate change

Cited by 72 publications

References 19 publications

Artificial neural networks and high and low flows in various climate regimes

Artificial neural networks and high and low flows in various climate regimes

Possible Impacts of Climate Change on Daily Streamflow and Extremes at Local Scale in Ontario, Canada. Part II: Future Projection

Possible Impacts of Climate Change on Daily Streamflow and Extremes at Local Scale in Ontario, Canada. Part I: Historical Simulation

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