Real time flood forecasting in the Upper Danube basin

Nester, Thomas; Komma, Jürgen; Blöschl, Günter

doi:10.1515/johh-2016-0033

Cited by 13 publications

(5 citation statements)

References 31 publications

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“…Conceptual rainfall-runoff (r-r) models are useful tools for a wide range of tasks such as flood forecasting (Nester et al, 2016), reservoir and water management (Farkas et al, 2016), climate studies (Merz et. al., 2011), etc.…”

Section: Introductionmentioning

confidence: 99%

Factors controlling alterations in the performance of a runoff model in changing climate conditions

Sleziak

Szolgay

Hlavčová

et al. 2018

Journal of Hydrology and Hydromechanics

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In many Austrian catchments in recent decades an increase in the mean annual air temperature and precipitation has been observed, but only a small change in the mean annual runoff. The main objective of this paper is (1) to analyze alterations in the performance of a conceptual hydrological model when applied in changing climate conditions and (2) to assess the factors and model parameters that control these changes. A conceptual rainfall-runoff model (the TUW model) was calibrated and validated in 213 Austrian basins from 1981–2010. The changes in the runoff model’s efficiency have been compared with changes in the mean annual precipitation and air temperature and stratified for basins with dominant snowmelt and soil moisture processes. The results indicate that while the model’s efficiency in the calibration period has not changed over the decades, the values of the model’s parameters and hence the model’s performance (i.e., the volume error and the runoff model’s efficiency) in the validation period have changed. The changes in the model’s performance are greater in basins with a dominant soil moisture regime. For these basins, the average volume error which was not used in calibration has increased from 0% (in the calibration periods 1981–1990 or 2001–2010) to 9% (validation period 2001–2010) or –8% (validation period 1981–1990), respectively. In the snow-dominated basins, the model tends to slightly underestimate runoff volumes during its calibration (average volume error = –4%), but the changes in the validation periods are very small (i.e., the changes in the volume error are typically less than 1–2%). The model calibrated in a colder decade (e.g., 1981–1990) tends to overestimate the runoff in a warmer and wetter decade (e.g., 2001–2010), particularly in flatland basins. The opposite case (i.e., the use of parameters calibrated in a warmer decade for a colder, drier decade) indicates a tendency to underestimate runoff. A multidimensional analysis by regression trees showed that the change in the simulated runoff volume is clearly related to the change in precipitation, but the relationship is not linear in flatland basins. The main controlling factor of changes in simulated runoff volumes is the magnitude of the change in precipitation for both groups of basins. For basins with a dominant snowmelt runoff regime, the controlling factors are also the wetness of the basins and the mean annual precipitation. For basins with a soil moisture regime, landcover (forest) plays an important role.

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Section: Introductionmentioning

confidence: 99%

Factors controlling alterations in the performance of a runoff model in changing climate conditions

Sleziak

Szolgay

Hlavčová

et al. 2018

Journal of Hydrology and Hydromechanics

View full text Add to dashboard Cite

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“…Understanding these patterns is extremely important, both for flood forecasting and for flood risk mapping (e.g. Vorogushyn et al, 2010;Viglione et al, 2013;Merz et al, 2014;Nester et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Flood generation: process patterns from the raindrop to the ocean

Blöschl¹

2022

Hydrol. Earth Syst. Sci.

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Abstract. This article reviews river flood generation processes and flow paths across space scales. The scale steps include the pore, profile, hillslope, catchment, regional and continental scales, representing a scale range of a total of 10 orders of magnitude. Although the processes differ between the scales, there are notable similarities. At all scales, there are media patterns that control the flow of water and are themselves influenced by the flow of water. The processes are therefore not spatially random (as in thermodynamics) but organized, and preferential flow is the rule rather than the exception. Hydrological connectivity, i.e. the presence of coherent flow paths, is an essential characteristic at all scales. There are similar controls on water flow and thus on flood generation at all scales but with different relative magnitudes. Processes at lower scales affect flood generation at larger scales, not simply as a multiple repetition of pore-scale processes but through interactions which cause emergent behaviour of process patterns. For this reason, when modelling these processes, the scale transitions need to be simplified in a way that reflects the relevant structures (e.g. connectivity) and boundary conditions (e.g. groundwater table) at each scale. In conclusion, it is argued that upscaling as the mere multiple application of small-scale process descriptions will not capture the larger-scale patterns of flood generation. Instead, there is a need to learn from observed patterns of flood generation processes at all spatial scales.

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“…Understanding these patterns is extremely important both for flood forecasting and for flood risk mapping (e.g. Vorogushyn et al, 2010;Viglione et al, 2013;Merz et al 2014;Nester et al, 2015). 285 As one moves up in scale to an entire continent, one could -similarly as at other aggregate scales -view flood generation simply as the sum of the processes in many pores, profiles, hillslopes, catchments and regions.…”

Section: Introductionmentioning

confidence: 99%

Flood generation: process patterns from the raindrop to the ocean

Blöschl¹

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. This article reviews river flood generation processes and flow paths across space scales. The scale steps include the pore, profile, hillslope, catchment, regional and continental scales, representing a scale range of a total of 10 orders of magnitude. Although the processes differ between the scales, there are notable similarities. At all scales, there are media patterns that control the flow of water, and are themselves influenced by the flow of water. The processes are therefore not spatially random (as in thermodynamics) but organised, and preferential flow is the rule rather than the exception. Hydrological connectivity, i.e. the presence of coherent flow paths, is an essential characteristic at all scales. There are similar controls on water flow and thus on flood generation at all scales, however, with different relative magnitudes. Processes at lower scales affect flood generation at the larger scales not simply as a multiple repetition of pore scale processes, but through interactions, which cause emergent behaviour of process patterns. For this reason, when modelling these processes, the scale transitions need to be simplified in a way that reflects the relevant structures (e.g. connectivity) and boundary conditions (e.g. groundwater table) at each scale. In conclusion, it is argued that upscaling as the mere multiple application of small scale process descriptions will not capture the larger scale patterns of flood generation. Instead, there is a need to learn from observed patterns of flood generation processes at all spatial scales.

show abstract

Real time flood forecasting in the Upper Danube basin

Cited by 13 publications

References 31 publications

Factors controlling alterations in the performance of a runoff model in changing climate conditions

Factors controlling alterations in the performance of a runoff model in changing climate conditions

Flood generation: process patterns from the raindrop to the ocean

Flood generation: process patterns from the raindrop to the ocean

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