| 1. |
- Fuentes–Andino, Diana, 1984-, et al.
(författare)
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Reproducing an extreme flood with uncertain post-event information
- 2017
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Ingår i: Hydrology and Earth System Sciences Discussions. - : Copernicus GmbH. - 1812-2108 .- 1812-2116 .- 1607-7938. ; 21:7, s. 3597-3618
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Tidskriftsartikel (refereegranskat)abstract
- Studies for the prevention and mitigation of floods require information on discharge and extent of inundation, commonly unavailable or uncertain, especially during extreme events. This study was initiated by the devastating flood in Tegucigalpa, the capital of Honduras, when Hurricane Mitch struck the city. In this study we hypothesized that it is possible to estimate, in a trustworthy way considering large data uncertainties, this extreme 1998 flood discharge and the extent of the inundations that followed from a combination of models and post-event measured data. Postevent data collected in 2000 and 2001 were used to estimate discharge peaks, times of peak, and high-water marks. These data were used in combination with rain data from two gauges to drive and constrain a combination of well-known modelling tools: TOPMODEL, Muskingum-Cunge-Todini routing, and the LISFLOOD-FP hydraulic model. Simulations were performed within the generalized likelihood uncertainty estimation (GLUE) uncertainty-analysis framework. The model combination predicted peak discharge, times of peaks, and more than 90% of the observed highwater marks within the uncertainty bounds of the evaluation data. This allowed an inundation likelihood map to be produced. Observed high-water marks could not be reproduced at a few locations on the floodplain. Identifications of these locations are useful to improve model set-up, model structure, or post-event data-estimation methods. Rainfall data were of central importance in simulating the times of peak and results would be improved by a better spatial assessment of rainfall, e. g. from radar data or a denser rain-gauge net-work. Our study demonstrated that it was possible, considering the uncertainty in the post-event data, to reasonably reproduce the extreme Mitch flood in Tegucigalpa in spite of no hydrometric gauging during the event. The method proposed here can be part of a Bayesian framework in which more events can be added into the analysis as they become available.
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| 2. |
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| 3. |
- Reynolds, Eduardo, et al.
(författare)
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Robustness of flood-model calibration using single and multiple events
- 2019
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Ingår i: Hydrological Sciences Journal. - Oxon : Taylor & Francis. - 0262-6667 .- 2150-3435. ; 65:5, s. 842-853
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Tidskriftsartikel (refereegranskat)abstract
- Lack of discharge data for model calibration is challenging for flood prediction in ungauged basins. Since establishment and maintenance of a permanent discharge station is resource demanding, a possible remedy could be to measure discharge only for a few events. We tested the hypothesis that a few flood-event hydrographs in a tropical basin would be sufficient to calibrate a bucket-type rainfall-runoff model, namely the HBV model, and proposed a new event-based calibration method to adequately predict floods. Parameter sets were chosen based on calibration of different scenarios of data availability, and their ability to predict floods was assessed. Compared to not having any discharge data, flood predictions improved already when one event was used for calibration. The results further suggest that two to four events for calibration may considerably improve flood predictions with regard to accuracy and uncertainty reduction, whereas adding more events beyond this resulted in small performance gains.
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| 4. |
- Reynolds, Eduardo, et al.
(författare)
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Sub-daily runoff predictions using parameters calibrated on the basis of data with a daily temporal resolution
- 2017
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Ingår i: Journal of Hydrology. - : Elsevier BV. - 0022-1694 .- 1879-2707. ; 550, s. 399-411
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Tidskriftsartikel (refereegranskat)abstract
- Concentration times in small and medium-sized basins (similar to 10-1000 km(2)) are commonly less than 24 h. Flood-forecasting models are thus required to provide simulations at high temporal resolutions (1 h-6 h), although time-series of input and runoff data with sufficient lengths are often only available at the daily temporal resolution, especially in developing countries. This has led to study the relationships of estimated parameter values at the temporal resolutions where they are needed from the temporal resolutions where they are available. This study presents a methodology to treat empirically model parameter dependencies on the temporal resolution of data in two small basins using a bucket-type hydrological model, HBV-light, and the generalised likelihood uncertainty estimation approach for selecting its parameters. To avoid artefacts due to the numerical resolution or numerical method of the differential equations within the model, the model was consistently run using modelling time steps of one-hour regardless of the temporal resolution of the rainfall-runoff data. The distribution of the parameters calibrated at several temporal resolutions in the two basins did not show model parameter dependencies on the temporal resolution of data and the direct transferability of calibrated parameter sets (e.g., daily) for runoff simulations at other temporal resolutions for which they were not calibrated (e.g., 3 h or 6 h) resulted in a moderate (if any) decrease in model performance, in terms of Nash-Sutcliffe and volume-error efficiencies. The results of this study indicate that if sub-daily forcing data can be secured, flood forecasting in basins with sub-daily concentration times may be possible with model-parameter values calibrated from long time series of daily data. Further studies using more models and basins are required to test the generality of these results.
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| 5. |
- Reynolds, J. Eduardo, et al.
(författare)
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Definitions of climatological and discharge days : do they matter in hydrological modelling?
- 2018
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Ingår i: Hydrological Sciences Journal. - : Informa UK Limited. - 0262-6667 .- 2150-3435. ; 63:5, s. 836-844
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Tidskriftsartikel (refereegranskat)abstract
- The performance of hydrological models is affected by uncertainty related to observed climatological and discharge data. Although the latter has been widely investigated, the effects on hydrological models from different starting times of the day have received little interest. In this study, observational data from one tropical basin were used to investigate the effects on a typical bucket-type hydrological model, the HBV, when the definitions of the climatological and discharge days are changed. An optimization procedure based on a genetic algorithm was used to assess the effects on model performance. Nash-Sutcliffe efficiencies varied considerably between day definitions, with the largest dependence on the climatological-day definition. The variation was likely caused by how storm water was assigned to one or two daily rainfall values depending on the definition of the climatological day. Hydrological models are unlikely to predict high flows accurately if rainfall intensities are reduced because of the day definition.
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| 6. |
- Reynolds, J. Eduardo, et al.
(författare)
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Flood prediction using parameters calibrated on limited discharge data and uncertain rainfall scenarios
- 2020
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Ingår i: Hydrological Sciences Journal. - : Taylor & Francis. - 0262-6667 .- 2150-3435. ; 65:9, s. 1512-1524
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Tidskriftsartikel (refereegranskat)abstract
- Discharge observations and reliable rainfall forecasts are essential for flood prediction but their availability and accuracy are often limited. However, even scarce data may still allow adequate flood forecasts to be made. Here, we explored how far using limited discharge calibration data and uncertain forcing data would affect the performance of a bucket-type hydrological model for simulating floods in a tropical basin. Three events above thresholds with a high and a low frequency of occurrence were used in calibration and 81 rainfall scenarios with different degrees of uncertainty were used as input to assess their effects on flood predictions. Relatively similar model performance was found when using calibrated parameters based on a few events above different thresholds. Flood predictions were sensitive to rainfall errors, but those related to volume had a larger impact. The results of this study indicate that a limited number of events can be useful for predicting floods given uncertain rainfall forecasts.
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| 7. |
- Reynolds Puga, José Eduardo, 1982-
(författare)
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Flood Prediction in data-scarce basins : Maximising the value of limited hydro-meteorological data
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Floods pose a threat to society that can cause large socio-economic damages and loss of life in many parts of the world. Flood-forecasting models are required to provide simulations at temporal resolutions higher than a day in basins with concentration times smaller than 24 h. However, data at such resolutions are commonly limited or not available, especially in developing or low-income countries. This thesis covers issues related to the scarcity and lack of high temporal-resolution hydro-meteorological data and explores methods where the value of existing data is maximised to improve flood prediction.By varying the starting time of daily records (the day definition), it was shown that this definition had large implications on model calibration and runoff simulation and therefore, should be considered in regionalisation and flood-forecasting applications. A method was developed to treat empirically model-parameter dependencies on the temporal resolution of data. Model parameters seemed to become independent of the temporal resolution of data when the modelling time-step was sufficiently small. Thus, if sub-daily forcing data can be secured, flood forecasting in basins with sub-daily concentration times may be possible using model-parameter values calibrated from time series of daily data. A new calibration method using only a few event hydrographs could improve flood prediction compared to a scenario with no discharge data. Two event hydrographs may be sufficient for calibration, but accuracy and reduction in uncertainty may improve if data on more events can be acquired. Using flood events above a threshold with a high frequency of occurrence for calibration may be as useful for flood prediction as using only extreme events with a low frequency of occurrence. The accuracy of the rainfall forecasts strongly influenced the predictive performance of a flood model calibrated with limited discharge data. Between volume and duration errors of the rainfall forecast, the former had the larger impact on model performance.The methods previously described proved to be useful for predicting floods and are expected to support flood-risk assessment and decision making during the occurrence of floods in data-scarce regions. Further studies using more models and basins are required to test the generality of these results.
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