Detecting the effects of spatial variability of rainfall on hydrological modelling within an uncertainty analysis framework

Younger, P. M.; Freer, J. E.; Beven, Keith

doi:10.1002/hyp.7341

Cited by 68 publications

(54 citation statements)

References 44 publications

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“…An interesting stochastic uncertainty approach for spatial rainfall fields in the dynamic TOPMODEL (Beven and Freer, 2001) was presented by Younger et al (2009). The results were obtained by dividing a catchment into homogeneous irregular zones in which the precipitation was randomly perturbed by large factors.…”

Section: T Berezowski Et Al: Spatial Sensitivity Analysis Of Snow Cmentioning

confidence: 99%

“…Such a method would use a regular grid to quantify the spatial pattern of sensitivity as in Stisen et al (2011); hence it differs from the irregular zonation in Younger et al (2009). Furthermore, the perturbation of spatial input data in a general framework should be realized using a well-established algorithm, e.g.…”

Section: T Berezowski Et Al: Spatial Sensitivity Analysis Of Snow Cmentioning

confidence: 99%

“…8). This approach may be considered imperfect, as irregular, homogenous zonation (Younger et al, 2009) could more directly reference the sensitivity to spatial features of the model. Regular zonation used in this study was similar as in Stisen et al (2011).…”

Section: Spatial Sensitivity Analysismentioning

confidence: 99%

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Spatial sensitivity analysis of snow cover data in a distributed rainfall-runoff model

Berezowski

Nossent

Chormański

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. As the availability of spatially distributed data sets for distributed rainfall-runoff modelling is strongly increasing, more attention should be paid to the influence of the quality of the data on the calibration. While a lot of progress has been made on using distributed data in simulations of hydrological models, sensitivity of spatial data with respect to model results is not well understood. In this paper we develop a spatial sensitivity analysis method for spatial input data (snow cover fraction -SCF) for a distributed rainfall-runoff model to investigate when the model is differently subjected to SCF uncertainty in different zones of the model. The analysis was focussed on the relation between the SCF sensitivity and the physical and spatial parameters and processes of a distributed rainfall-runoff model. The methodology is tested for the Biebrza River catchment, Poland, for which a distributed WetSpa model is set up to simulate 2 years of daily runoff. The sensitivity analysis uses the Latin-Hypercube One-factor-At-a-Time (LH-OAT) algorithm, which employs different response functions for each spatial parameter representing a 4 × 4 km snow zone. The results show that the spatial patterns of sensitivity can be easily interpreted by cooccurrence of different environmental factors such as geomorphology, soil texture, land use, precipitation and temperature. Moreover, the spatial pattern of sensitivity under different response functions is related to different spatial parameters and physical processes. The results clearly show that the LH-OAT algorithm is suitable for our spatial sensitivity analysis approach and that the SCF is spatially sensitive in the WetSpa model. The developed method can be easily applied to other models and other spatial data.

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Section: T Berezowski Et Al: Spatial Sensitivity Analysis Of Snow Cmentioning

confidence: 99%

Section: T Berezowski Et Al: Spatial Sensitivity Analysis Of Snow Cmentioning

confidence: 99%

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Spatial sensitivity analysis of snow cover data in a distributed rainfall-runoff model

Berezowski

Nossent

Chormański

et al. 2015

Hydrol. Earth Syst. Sci.

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“…There are many studies on the sensitivity of hydrological models to rainfall distribution. Some of these focus on the influence of spatial rainfall patterns (Dawdy & Bergman, 1969;Wilson et al, 1979;Troutman, 1983;Berndtsson & Niemczynowicz, 1988;Krajewski et al, 1991; 398 Patrick Arnaud et al Younger et al, 2009), others on the influence of raincell dynamics (Borga et al, 2007), on the shapes of the floods generated and on the time lag between rainfall and the floods (Surkan, 1974).…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of hydrological models to uncertainty in rainfall input

Arnaud¹,

Lavabre²,

Fouchier³

et al. 2011

Hydrological Sciences Journal

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Different approaches used in hydrological modelling are compared in terms of the way each one takes the rainfall data into account. We examine the errors associated with accounting for rainfall variability, whether in hydrological modelling (distributed vs lumped models) or in computing catchment rainfall, as well as the impact of each approach on the representativeness of the parameters it uses. The database consists of 1859 rainfall events, distributed on 500 basins, located in the southeast of France with areas ranging from 6.2 to 2851 km 2 . The study uses as reference the hydrographs computed by a distributed hydrological model from radar rainfall. This allows us to compare and to test the effects of various simplifications to the process when taking rainfall information (complete rain field vs sampled rainfall) and rainfall-runoff modelling (lumped vs distributed) into account. The results appear to show that, in general, the sampling effect can lead to errors in discharge at the outlet that are as great as, or even greater than, those one would get with a fully lumped approach. We found that small catchments are more sensitive to the uncertainties in catchment rainfall input generated by sampling rainfall data as seen through a raingauge network. Conversely, the larger catchments are more sensitive to uncertainties generated when the spatial variability of rainfall events is not taken into account. These uncertainties can be compensated for relatively easily by recalibrating the parameters of the hydrological model, although such recalibrations cause the parameter in question to completely lose physical meaning.Key words rainfall distribution; radar rainfall; raingauge network; lumped modelling; distributed modelling; uncertainty Sensibilité des modèles hydrologiques aux incertitudes dues à l'information pluviométrique utiliséeRésumé Cette étude compare différentes approches utilisées en modélisation hydrologique, chaque approche étant différenciée par sa manière de prendre en compte l'information pluviométrique. Nous regardons les erreurs liées à la prise en compte de la variabilité spatiale de la pluie, à travers la modélisation hydrologique (modèles globaux ou distribués) ou à travers le calcul de la pluie de bassin. Nous regardons aussi l'impact de chaque approche sur la représentativité des paramètres qui lui sont associés. Ce travail porte sur 1859 événements pluvieux répartis sur 500 bassins versants situés dans le sud-est de la France, dans une gamme de surface allant de 6.2 à 2851 km 2 . On définit comme étant les hydrogrammes de référence, les crues modélisées par un modèle hydrologique distribué à partir de champs de pluies issus de radars météorologiques. Cela nous permet de travailler de façon relative, et de tester l'effet de différentes simplifications dans la prise en compte des processus, lors de la prise en compte de la pluie (champs de pluie complet ou échantillonnage de la pluie) et/ou lors de la modélisation hydrologique (globale ou distribuée). Les résultats montrent globale...

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“…Precipitation uncertainty due to spatiotemporal distribution strongly influences flood forecasting and warnings associated with incorrect flow simulations (Berndtsson and Niemczynowicz 1988;Morin et al 1995;Johnson et al 1999;Tsintikidis et al 2002;Younger et al 2009;Arnaud et al 2011). In particular, mountainous regions require more reliable precipitation estimation because the interaction between mountainous terrain and the atmosphere increases the variability of precipitation patterns and the precipitation amounts related to the mesoscale precipitation process (Krajewski and Georgakakos 1994;Wheater et al 2000).…”

Section: Introductionmentioning

confidence: 99%

Rain-Gauge Network Evaluations Using Spatiotemporal Correlation Structure for Semi-Mountainous Regions

Jung¹,

Kim²,

Baik³

et al. 2014

Terr. Atmos. Ocean. Sci.

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A reliable network of rain gauges is a crucial component of rainfall estimation in a watershed. To provide a better evaluation method for rain-gauge networks, a new evaluation method using average inter-gauge correlation coefficients (averaged CC) for estimating an effective radius for each rain gauge was developed. In this study, averaged CCs were obtained from the values of inter-gauge correlation coefficients after choosing a minimum number of rainfall data sets as a threshold. The Nam River Basin (2400 km 2 ) and its 24 rain gauges were selected with 8 years (2003 -2010) rainfall data to validate a new evaluation method. In the spatial correlation coefficient fitting process for generating correlation distances, averaged CCs increased fitness accuracy (maximum 37%) in terms of coefficient of determination (R 2 ) compared with a commonly used method (the last value of the inter-gauge correlation coefficient as the number of data sets is increased: last CC). In the evaluation of effective radii for 8 years, the robustness of the averaged CCs was supported by lower standard deviations for all rain gauges. For the optimum coverage of rainfall estimation in terms of effective radius, the Nam River Basin requires 20 rain gauges. Investigation of altitude effects presented that the effective radii were minimally influenced by the altitude of rain-gauge locations for this area.

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Detecting the effects of spatial variability of rainfall on hydrological modelling within an uncertainty analysis framework

Cited by 68 publications

References 44 publications

Spatial sensitivity analysis of snow cover data in a distributed rainfall-runoff model

Spatial sensitivity analysis of snow cover data in a distributed rainfall-runoff model

Sensitivity of hydrological models to uncertainty in rainfall input

Rain-Gauge Network Evaluations Using Spatiotemporal Correlation Structure for Semi-Mountainous Regions

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