Estimation of instantaneous peak flows from maximum mean daily flows using the HBV hydrological model

Ding, Jie; Wallner, Markus; Müller, Hannes; Haberlandt, Uwe

doi:10.1002/hyp.10725

Cited by 18 publications

(34 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, investigations without subsequent steps to implement spatial consistence exist as well (Ding et al, 2016) and have been included in this investigation (named V1). The hypothesis tested in this study is that these different rainfall data 10 sets lead to differences in the derived runoff statistics as well.…”

Section: Discussionmentioning

confidence: 99%

“…However, Ding et al (2016) also used disaggregated time series for their rainfall-runoff analyzes with a focus on instantaneous peak flows, but without any subsequent changes to the disaggregated time series. Neither a systematic over-or underestimation of simulated discharge and flood peaks can be found in both investigations.…”

Section: Introductionmentioning

confidence: 99%

“…The hypothesis of this study is that 10 rainfall data sets with different degrees of spatial consistence will result in different areal rainfall intensities and hence influence runoff statistics derived from simulated runoff time series. Therefore, three different rainfall data sets are used as input for rainfall runoff modelling: disaggregated time series with (Müller and Haberlandt, 2015) and without (Ding et al, 2016) implemented spatial consistence, and thirdly time series with an "overestimated spatial consistence" by parallelization (Haberlandt and Radtke, 2014). A systematic comparison is carried out including rainfall-runoff simulations with and 15 without calibration, differing station densities and different rainfall-runoff models.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Rainfall disaggregation for hydrological modeling: Is there a need for spatial consistence?

Müller¹,

Wallner²,

Förster³

et al. 2017

Preprint

View full text Add to dashboard Cite

Abstract.In this investigation, the influence of disaggregated rainfall data sets with different degrees of spatial consistence on rainfall runoff modeling results is analyzed for three meso-scale catchments in Lower Saxony, Germany. For the disaggregation of daily rainfall time series into hourly values a multiplicative random cascade model is applied. The disaggregation is applied on a per station basis without consideration of surrounding stations, hence subsequent steps are then required to implement 15 spatial consistence. Spatial consistence is here represented by three bivariate spatial rainfall characteristics, complementing each other. A resampling algorithm and a parallelization approach are evaluated against the disaggregated time series without any subsequent steps. With respect to rainfall, clear differences between these three approaches can be identified regarding bivariate spatial rainfall characteristics, areal rainfall intensities and extreme values. The resampled time series lead to the best agreement with the observed ones. Using these different rainfall data sets as input to hydrological modeling, 20 we hypothesize that derived runoff statistics are subject to similar differences as well. However, an impact on the runoff statistics summer and winter peak flows, monthly average discharge and flow duration curve of the simulated runoff time series cannot be detected. Several modifications of the investigation using rainfall runoff models with and without parameter calibration or using different rain gauge densities lead to similar results in runoff statistics. Only if the spatially highly resolved rainfall-runoff WaSiM-model is applied instead of the semi-distributed HBV-IWW-model, slight differences 25 regarding the seasonal peak flows can be identified. Hence, the hypothesis formulated before is rejected in this case study. These findings suggest that (i) simple model structures might compensate for deficiencies in spatial representativeness through parameterization and (ii) highly resolved hydrological models benefit from improved spatial modeling of rainfall.Hydrol. Earth Syst. Sci. Discuss., https://doi

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Rainfall disaggregation for hydrological modeling: Is there a need for spatial consistence?

Müller¹,

Wallner²,

Förster³

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…Therefore, a multiplicative, micro-canonical cascade model [55] was used for the rainfall disaggregation. The model has been used before for rainfall disaggregation over a wide range of scales, e.g., from monthly to daily values [56], as well as from daily to hourly values [57] and to 5 min values [58] to generate input for rainfall-runoff-modelling [58,59]. The rainfall amount of one time step is divided into b finer time steps of equal length (with b = branching number, see Figure 2 for a schematic illustration).…”

Section: Cascade Modelmentioning

confidence: 99%

Uncertainty of Rainfall Products: Impact on Modelling Household Nutrition from Rain-Fed Agriculture in Southern Africa

Luetkemeier

Stein

Drees

et al. 2018

Water

View full text Add to dashboard Cite

Good quality data on precipitation are a prerequisite for applications like short-term weather forecasts, medium-term humanitarian assistance, and long-term climate modelling. In Sub-Saharan Africa, however, the meteorological station networks are frequently insufficient, as in the Cuvelai-Basin in Namibia and Angola. This paper analyses six rainfall products (ARC2.0, CHIRPS2.0, CRU-TS3.23, GPCCv7, PERSIANN-CDR, and TAMSAT) with respect to their performance in a crop model (APSIM) to obtain nutritional scores of a household's requirements for dietary energy and further macronutrients. All products were calibrated to an observed time series using Quantile Mapping. The crop model output was compared against official yield data. The results show that the products (i) reproduce well the Basin's spatial patterns, and (ii) temporally agree to station records (r = 0.84). However, differences exist in absolute annual rainfall (range: 154 mm), rainfall intensities, dry spell duration, rainy day counts, and the rainy season onset. Though calibration aligns key characteristics, the remaining differences lead to varying crop model results. While the model well reproduces official yield data using the observed rainfall time series (r = 0.52), the products' results are heterogeneous (e.g., CHIRPS: r = 0.18). Overall, 97% of a household's dietary energy demand is met. The study emphasizes the importance of considering the differences among multiple rainfall products when ground measurements are scarce.

show abstract

“…Concerning the impact of land use and climate change on extreme runoffs, the flow time series data cannot be used without consistency check. Hydrological modeling with local calibrated parameters has been proven to be a more robust and reliable approach to estimate IPFs from the modeled MDFs when taking into account the dynamic changes in the catchments of interest (Ding, Wallner, Müller, & Haberlandt, ). However, both of the strategies cannot be carried out to predict the IPFs in ungauged areas due to the lack of hydrological data.…”

Section: Introductionmentioning

confidence: 99%

Estimation of instantaneous peak flow from maximum mean daily flow by regionalization of catchment model parameters

Ding

Haberlandt²

2016

Hydrological Processes

View full text Add to dashboard Cite

Regionalization methods have been effectively used in many hydrological studies, such as regional flood frequency analysis and low flows. However, there is no study to estimate the instantaneous peak flow (IPF) from maximum mean daily flow (MDF) using hydrological models with regionalized parameters. In this paper, the semidistributed conceptual hydrological model Hydrologiska Byråns Vattenbalansavdelning is operated on a daily time step for 18 catchments in the Aller‐Leine basin, Germany. The model is calibrated on four different flow statistics, including winter/summer extremes distribution and flow duration curves. The model parameter values are predefined with the associated catchment descriptors by a transfer function. Two different regionalization schemes are investigated: one is carried out for all the catchments in the study area; the other one is only performed for several catchments within a cluster. The k‐means algorithm is used to 12 different catchment characteristics from all 18 catchments as the partitional clustering algorithm. Subsequently, the general extreme value distributions are fitted to the modeled MDFs, which are then transferred into IPF quantiles using a multiple regression model. The results show that (a) the uncertainty resulted from model parameter regionalization for the estimation of IPFs is much smaller than the error when using MDFs instead of IPFs; (b) the hydrological responses of the clustered catchments located in the flat areas are, in general, not as homogeneous as the ones in high elevated regions; and (c) the model with the parameters derived from the same regionalization coefficients within a cluster performs better using the corresponding parameters estimated through all the catchments.

show abstract

Estimation of instantaneous peak flows from maximum mean daily flows using the HBV hydrological model

Cited by 18 publications

References 46 publications

Rainfall disaggregation for hydrological modeling: Is there a need for spatial consistence?

Rainfall disaggregation for hydrological modeling: Is there a need for spatial consistence?

Uncertainty of Rainfall Products: Impact on Modelling Household Nutrition from Rain-Fed Agriculture in Southern Africa

Estimation of instantaneous peak flow from maximum mean daily flow by regionalization of catchment model parameters

Contact Info

Product

Resources

About