Robustness of flood-model calibration using single and multiple events

Reynolds, J. E.; Halldin, Sven; Seibert, Jan; Xu, Chong‐Yu; Grabs, Thomas

doi:10.1080/02626667.2019.1609682

Cited by 14 publications

(15 citation statements)

References 30 publications

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“…More conceptual approaches that aim to overcome this issue by minimizing or even avoiding the use of additional data for calibration have been discussed in previous research (Gharari et al, 2014; Hulsman et al, 2018; Kapangaziwiri et al, 2012; Li et al, 2014). Such approaches suggest new methods to make use of insufficient data sources by, for example, using water level measurements (Hulsman et al, 2018; Jian et al, 2017) or discontinuous observational time series (Reynolds et al, 2019). More conceptual approaches use relational constraints based on export knowledge to specify the parameter space (Gharari et al, 2014) or to constrain hydrological model output and the parameter space based on, for example, the Budyko framework (Budyko, 1974; Bouaziz et al, 2018; Kapangaziwiri et al, 2012; Li et al, 2014).…”

Section: Motivationmentioning

confidence: 99%

Using the Budyko Framework for Calibrating a Global Hydrological Model

Greve

Burek

Wada

2020

Water Resources Research

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Global hydrological models (GHMs) have become an established tool to simulate water resources worldwide. Most of the GHMs are however uncalibrated and typically use a set of basic hydrological parameters, that could potentially lead to unrealistic projections of the terrestrial water cycle. The calibration of hydrological models is usually performed by using and comparing modeled to observed discharge. Accurate station data and reliable time series data of discharge are, however, often not available for many parts of the world and classic calibration approaches are therefore not feasible. In this paper, we aim to develop a new calibration approach that requires no additional data, is easy to implement, and substantially improves model performance, especially in regions where uncalibrated model performance is rather poor. This is achieved by using the Budyko framework, which provides a conceptual representation of the long-term water and energy balance. We use a state-of-the-art GHM and calibrate the model within nine river catchments of different sizes and characteristics. Since observed river discharge is available for these catchments, we are able to compare the Budyko-based calibration approach to a classic discharge-based calibration scheme and the uncalibrated model version. In all catchments, the Budyko-based calibration approach decreases biases and increases model performance compared to the uncalibrated model version although performance improvements obtained through a classic calibration approach are greater. Nonetheless, a Budyko-based calibration is a valuable, intermediate approach between use of a basic set of a priori hydrological parameters and classical calibration against discharge data. 1. Motivation Global hydrological models (GHMs) have become a common tool to assess the large-scale dynamics of surface and subsurface hydrological processes, ranging from more classical hydrologic modeling approaches focusing on small catchment areas, up to continental-basin scales. At smaller scales, models are usually calibrated using a large set of parameters. The majority of GHMs are however uncalibrated, typically using reduced sets of a priori parameters. Focusing on individual or small sets of catchments, calibration is usually performed using observed discharge. Global modeling approaches suffer from multiple issues hindering a reasonable and feasible calibration procedure, most prominently the varying availability of accurate discharge observations in different world regions (Hanasaki et al., 2018; Müller-Schmid et al., 2014). While in North America, Europe, and parts of Asia dense gauging networks enable a comprehensive calibration of model parameters, this is largely impossible in Africa, South America, Australia, and other parts of Asia. In addition, GHMs usually show large uncertainties and biases and do not permit quantitative assessments of past, present, and future hydroclimatological changes, especially in these data-scarce regions. Calibrating GHMs only against observed records that are concen...

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Section: Motivationmentioning

confidence: 99%

Using the Budyko Framework for Calibrating a Global Hydrological Model

Greve

Burek

Wada

2020

Water Resources Research

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“…The events above both thresholds were selected within the period between June 2000 and December 2011. The length of the events was defined as in Reynolds et al (2019). The start of each flood event was the time step at which the precedent rainstorm started, while its end was when the percentage change in the recession decreased by less than 5% for 10 consecutive hourly time steps, or when the percentage change was positive because of the occurrence of a new rainfall event.…”

Section: Study Sitementioning

confidence: 99%

“…Performing field measurements including periods representative of the main hydrological processes could be another option to overcome the lack of discharge data for model calibration Beven 2009, Reynolds et al 2019). In an earlier study (Reynolds et al 2019), we tested the hypothesis that a few highflow events would be sufficient to calibrate a bucket-type rainfallrunoff model and our results indicate that two to four events, compared to the scenario of not having any data, could considerably improve flood predictions with regard to accuracy and uncertainty reduction. These results were encouraging, but the events used in calibration were above an extreme threshold (i.e.…”

Section: Introductionmentioning

confidence: 98%

Flood prediction using parameters calibrated on limited discharge data and uncertain rainfall scenarios

Reynolds

Halldin

Seibert

et al. 2020

Hydrological Sciences Journal

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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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“…These were events that generated a streamflow over 211 m 3 /s. This streamflow threshold was defined following the methodology proposed by Reynolds et al [30], using the annual minimum from monthly from monthly maximum records instead of the annual mean. This obeyed the short time-series available for the streamflow (1 year; Figure 2).…”

Section: Datamentioning

confidence: 99%

“…In the absence of long-term observations, event sampling data may provide sufficient information to perform hydrological and hydrodynamic simulations. In this sense, several studies have found that the degree of hydrological information obtained by processing data from several storm events is comparable to that obtained by processing long-term data series [30][31][32][33][34][35][36]. The implementation of externally coupled models with event sampling data for flood hazard assessment may be quite relevant in zones with high spatial precipitation variability such as those located in the Andes-Amazon transition [37], where monitoring networks are sparse and recently established.…”

Section: Introductionmentioning

confidence: 96%

Flood Hazard Assessment in Data-Scarce Watersheds Using Model Coupling, Event Sampling, and Survey Data

Hurtado-Pidal

Triana

Espitia

et al. 2020

Water

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The application of hydrologic and hydrodynamic models in flash flood hazard assessment is mainly limited by the availability of robust monitoring systems and long-term hydro-meteorological observations. Nevertheless, several studies have demonstrated that coupled modeling approaches based on event sampling (short-term observations) may cope with the lack of observed input data. This study evaluated the use of storm events and flood-survey reports to develop and validate a modeling framework for flash flood hazard assessment in data-scarce watersheds. Specifically, we coupled the hydrologic modeling system (HEC-HMS) and the Nays2Dflood hydrodynamic solver to simulate the system response to several storm events including one, equivalent in magnitude to a 500-year event, that flooded the City of Tena (Ecuador) on 2 September, 2017. Results from the coupled approach showed satisfactory model performance in simulating streamflow and water depths (0.40 ≤ Nash-Sutcliffe coefficient ≤ 0.95; −3.67% ≤ Percent Bias ≤ 23.4%) in six of the eight evaluated events, and a good agreement between simulated and surveyed flooded areas (Fit Index = 0.8) after the 500-year storm. The proposed methodology can be used by modelers and decision-makers for flood impact assessment in data-scarce watersheds and as a starting point for the establishment of flood forecasting systems to lessen the impacts of flood events at the local scale.

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Robustness of flood-model calibration using single and multiple events

Cited by 14 publications

References 30 publications

Using the Budyko Framework for Calibrating a Global Hydrological Model

Using the Budyko Framework for Calibrating a Global Hydrological Model

Flood prediction using parameters calibrated on limited discharge data and uncertain rainfall scenarios

Flood Hazard Assessment in Data-Scarce Watersheds Using Model Coupling, Event Sampling, and Survey Data

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