Benchmarking quantitative precipitation estimation by conceptual rainfall‐runoff modeling

Heistermann, Maik; Kneis, David

doi:10.1029/2010wr009153

Cited by 52 publications

(57 citation statements)

References 41 publications

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“…These results are in line with findings of Bárdossy and Das (2008) regarding network density or of Heistermann and Kneis (2011) with respect to different rainfall data sets and spatial interpolation methods; -the hydrological model works quite well for general conditions, i.e., reproducing the hydrograph on the whole, when it is calibrated on extreme conditions, i.e., using the extreme value distribution of peak flows, than vice versa. This confirms that unusual events or small data sets might be sufficient for model calibration (Singh and Bárdossy, 2012;Seibert and Beven, 2009);…”

Section: Discussionsupporting

confidence: 77%

“…For instance, Bárdossy and Das (2008) show that using different rain gauge networks for calibration and validation of a conceptual hydrologic model leads to significantly poorer performance compared to the case when unique networks are employed. Similar problems will occur if precipitation data from different sources are used for calibration and validation, such as rainfall information from point observations and weather radar (Heistermann and Kneis, 2011). In addition, if a hydrological model is calibrated using observed precipitation and runoff time series of high temporal resolution, e.g., hourly data, which are often available only for very short time periods, the outcome might not be optimal for the simulation of floods with large return periods of 50, 100 or more years.…”

Section: U Haberlandt and I Radtke: Hydrological Model Calibration mentioning

confidence: 99%

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Hydrological model calibration for derived flood frequency analysis using stochastic rainfall and probability distributions of peak flows

Haberlandt

Radtke

2014

Hydrol. Earth Syst. Sci.

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Abstract. Derived flood frequency analysis allows the estimation of design floods with hydrological modeling for poorly observed basins considering change and taking into account flood protection measures. There are several possible choices regarding precipitation input, discharge output and consequently the calibration of the model. The objective of this study is to compare different calibration strategies for a hydrological model considering various types of rainfall input and runoff output data sets and to propose the most suitable approach. Event based and continuous, observed hourly rainfall data as well as disaggregated daily rainfall and stochastically generated hourly rainfall data are used as input for the model. As output, short hourly and longer daily continuous flow time series as well as probability distributions of annual maximum peak flow series are employed. The performance of the strategies is evaluated using the obtained different model parameter sets for continuous simulation of discharge in an independent validation period and by comparing the model derived flood frequency distributions with the observed one. The investigations are carried out for three mesoscale catchments in northern Germany with the hydrological model HEC-HMS (Hydrologic Engineering Center's Hydrologic Modeling System). The results show that (I) the same type of precipitation input data should be used for calibration and application of the hydrological model, (II) a model calibrated using a small sample of extreme values works quite well for the simulation of continuous time series with moderate length but not vice versa, and (III) the best performance with small uncertainty is obtained when stochastic precipitation data and the observed probability distribution of peak flows are used for model calibration. This outcome suggests to calibrate a hydrological model directly on probability distributions of observed peak flows using stochastic rainfall as input if its purpose is the application for derived flood frequency analysis.

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Section: Discussionsupporting

confidence: 77%

Section: U Haberlandt and I Radtke: Hydrological Model Calibration mentioning

confidence: 99%

Hydrological model calibration for derived flood frequency analysis using stochastic rainfall and probability distributions of peak flows

Haberlandt

Radtke

2014

Hydrol. Earth Syst. Sci.

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“…A discussion of the advantages and limitations of this approach is provided by Heistermann and Kneis (2011). We apply this strategy as one option to identify strengths and weaknesses of the radar-based rainfall estimates.…”

Section: Qpe Verification Using Rain Gaugesmentioning

confidence: 99%

“…However, different rainfall products have different error structures which imply different trade-offs. Heistermann and Kneis (2011) presented an MC approach to benchmark different precipitation products by using hydrological modelling. In our case, however, we are not so much interested in the "absolute" quality of the rainfall product.…”

Section: Qpe Verification Using a Hydrological Modelmentioning

confidence: 99%

Evaluating the potential of radar-based rainfall estimates for streamflow and flood simulations in the Philippines

Abon

Kneis

Crisologo

et al. 2015

Geomatics, Natural Hazards and Risk

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This case study evaluates the suitability of radar-based quantitative precipitation estimates (QPEs) for the simulation of streamflow in the Marikina River Basin (MRB), the Philippines. Hourly radar-based QPEs were produced from reflectivity that had been observed by an S-band radar located about 90 km from the MRB. Radar data processing and precipitation estimation were carried out using the open source library wradlib. To assess the added value of the radarbased QPE, we used spatially interpolated rain gauge observations (gauge-only (GO) product) as a benchmark. Rain gauge observations were also used to quantify rainfall estimation errors at the point scale. At the point scale, the radarbased QPE outperformed the GO product in 2012, while for 2013, the performance was similar. For both periods, estimation errors substantially increased from daily to the hourly accumulation intervals. Despite this fact, both rainfall estimation methods allowed for a good representation of observed streamflow when used to force a hydrological simulation model of the MRB. Furthermore, the results of the hydrological simulation were consistent with rainfall verification at the point scale: the radar-based QPE performed better than the GO product in 2012, and equivalently in 2013. Altogether, we could demonstrate that, in terms of streamflow simulation, the radar-based QPE can perform as good as or even better than the GO product À even for a basin such as the MRB which has a comparatively dense rain gauge network. This suggests good prospects for using radar-based QPE to simulate and forecast streamflow in other parts of the Philippines where rain gauge networks are not as dense.

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“…Since systematic errors in rainfall input may partly be compensated by the choice of the model's parameters (see, e.g. Heistermann and Kneis, 2011), the evaluation was done with and without (re-)calibration of the hydrological model to the individual rainfall data sets.…”

Section: Evaluation Proceduresmentioning

confidence: 99%

Evaluation of TRMM rainfall estimates over a large Indian river basin (Mahanadi)

Kneis

Chatterjee

Singh

2014

Hydrol. Earth Syst. Sci.

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Abstract. The paper examines the quality of satellite-based precipitation estimates for the lower Mahanadi River basin (eastern India). The considered data sets known as 3B42 and 3B42-RT (version 7/7A) are routinely produced by the tropical rainfall measuring mission (TRMM) from passive microwave and infrared recordings. While the 3B42-RT data are disseminated in real time, the gauge-adjusted 3B42 data set is published with a delay of some months. The quality of the two products was assessed in a two-step procedure. First, the correspondence between the remotely sensed precipitation rates and rain gauge data was evaluated at the subbasin scale. Second, the quality of the rainfall estimates was assessed by analysing their performance in the context of rainfall-runoff simulation.At sub-basin level (4000 to 16 000 km 2 ) the satellite-based areal precipitation estimates were found to be moderately correlated with the gauge-based counterparts (R 2 of 0.64-0.74 for 3B42 and 0.59-0.72 for 3B42-RT). Significant discrepancies between TRMM data and ground observations were identified at high-intensity levels. The rainfall depth derived from rain gauge data is often not reflected by the TRMM estimates (hit rate < 0.6 for ground-based intensities > 80 mm day −1 ). At the same time, the remotely sensed rainfall rates frequently exceed the gauge-based equivalents (false alarm ratios of 0.2-0.6). In addition, the real-time product 3B42-RT was found to suffer from a spatially consistent negative bias.Since the regionalisation of rain gauge data is potentially associated with a number of errors, the above results are subject to uncertainty. Hence, a validation against independent information, such as stream flow, was essential. In this case study, the outcome of rainfall-runoff simulation experiments was consistent with the above-mentioned findings. The best fit between observed and simulated stream flow was obtained if rain gauge data were used as model input (Nash-Sutcliffe index of 0.76-0.88 at gauges not affected by reservoir operation). This compares to the values of 0.71-0.78 for the gaugeadjusted TRMM 3B42 data and 0.65-0.77 for the 3B42-RT real-time data. Whether the 3B42-RT data are useful in the context of operational runoff prediction in spite of the identified problems remains a question for further research.

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Benchmarking quantitative precipitation estimation by conceptual rainfall‐runoff modeling

Cited by 52 publications

References 41 publications

Hydrological model calibration for derived flood frequency analysis using stochastic rainfall and probability distributions of peak flows

Hydrological model calibration for derived flood frequency analysis using stochastic rainfall and probability distributions of peak flows

Evaluating the potential of radar-based rainfall estimates for streamflow and flood simulations in the Philippines

Evaluation of TRMM rainfall estimates over a large Indian river basin (Mahanadi)

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