scholarly journals A crash-testing framework for predictive uncertainty assessment when forecasting high flows in an extrapolation context

2020 ◽  
Vol 24 (4) ◽  
pp. 2017-2041
Author(s):  
Lionel Berthet ◽  
François Bourgin ◽  
Charles Perrin ◽  
Julie Viatgé ◽  
Renaud Marty ◽  
...  
Keyword(s):  
Statistical Treatment ◽  
Data Transformation ◽  
Flood Forecasting ◽  
Large Set ◽  
Significant Drop ◽  
Predictive Uncertainty ◽  
Testing Framework ◽  
Crash Testing ◽  
High Flows ◽  
Hydrologic Uncertainty

Abstract. An increasing number of flood forecasting services assess and communicate the uncertainty associated with their forecasts. While obtaining reliable forecasts is a key issue, it is a challenging task, especially when forecasting high flows in an extrapolation context, i.e. when the event magnitude is larger than what was observed before. In this study, we present a crash-testing framework that evaluates the quality of hydrological forecasts in an extrapolation context. The experiment set-up is based on (i) a large set of catchments in France, (ii) the GRP rainfall–runoff model designed for flood forecasting and used by the French operational services and (iii) an empirical hydrologic uncertainty processor designed to estimate conditional predictive uncertainty from the hydrological model residuals. The variants of the uncertainty processor used in this study differ in the data transformation they use (log, Box–Cox and log–sinh) to account for heteroscedasticity and the evolution of the other properties of the predictive distribution with the discharge magnitude. Different data subsets were selected based on a preliminary event selection. Various aspects of the probabilistic performance of the variants of the hydrologic uncertainty processor, reliability, sharpness and overall quality were evaluated. Overall, the results highlight the challenge of uncertainty quantification when forecasting high flows. They show a significant drop in reliability when forecasting high flows in an extrapolation context and considerable variability among catchments and across lead times. The increase in statistical treatment complexity did not result in significant improvement, which suggests that a parsimonious and easily understandable data transformation such as the log transformation or the Box–Cox transformation can be a reasonable choice for flood forecasting.

scholarly journals A crash-testing framework for predictive uncertainty assessment when forecasting high flows in an extrapolation context

2019 ◽  
Author(s):  
Lionel Berthet ◽  
François Bourgin ◽  
Charles Perrin ◽  
Julie Viatgé ◽  
Renaud Marty ◽  
...  
Keyword(s):  
Statistical Treatment ◽  
Data Transformation ◽  
Flood Forecasting ◽  
Large Set ◽  
Significant Drop ◽  
Predictive Uncertainty ◽  
Testing Framework ◽  
Crash Testing ◽  
High Flows ◽  
Hydrologic Uncertainty

Abstract. An increasing number of flood forecasting services assess and communicate the uncertainty associated with their forecasts. While obtaining reliable forecasts is a key issue, it is a challenging task, especially when forecasting high flows in an extrapolation context, i.e. when the event magnitude is larger than what was observed before. In this study, we present a crash-testing framework that evaluates the quality of hydrological forecasts in an extrapolation context. The experiment setup is based on (i) a large set of catchments in France, (ii) the GRP rainfall-runoff model designed for flood forecasting and used by the French operational services and (iii) an empirical hydrologic uncertainty processor designed to estimate conditional predictive uncertainty from the hydrological model residuals. The variants of the uncertainty processor used in this study differ in the data transformation they used (log, Box–Cox and log–sinh) to account for heteroscedasticity. Different data subsets were selected based on a preliminary event selection. Various aspects of the probabilistic performance of the variants of the hydrologic uncertainty processor, reliability, sharpness and overall quality, were evaluated. Overall, the results highlight the challenge of uncertainty quantification when forecasting high flows. They show a significant drop in reliability when forecasting high flows in an extrapolation context and considerable variability among catchments and across lead times. The increase in statistical treatment complexity did not result in significant improvement, which suggests that a parsimonious and easily understandable data transformation such as the log transformation or the Box–Cox transformation with a parameter between 0.1 and 0.3 can be a reasonable choice for flood forecasting.

scholarly journals Case Study: A Real-Time Flood Forecasting System with Predictive Uncertainty Estimation for the Godavari River, India

Water ◽  
2016 ◽  
Vol 8 (10) ◽  
pp. 463 ◽  
Author(s):  
Silvia Barbetta ◽  
Gabriele Coccia ◽  
Tommaso Moramarco ◽  
Ezio Todini
Keyword(s):  
Real Time ◽  
Flood Forecasting ◽  
Uncertainty Estimation ◽  
Predictive Uncertainty ◽  
Godavari River ◽  
Forecasting System

Performance assessment of hydrologic uncertainty processor through integration of the principal components analysis

2017 ◽  
Vol 10 (2) ◽  
pp. 373-390 ◽  
Author(s):  
Yi Yao ◽  
Zhongmin Liang ◽  
Weimin Zhao ◽  
Xiaolei Jiang ◽  
Binquan Li
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Lead Time ◽  
Yellow River ◽  
Flood Forecasting ◽  
River Floodplains ◽  
Comparison Results ◽  
Components Analysis ◽  
Routing Methods ◽  
Hydrologic Uncertainty

Abstract Uncertainty analysis is important and should be always considered when using models for flood forecasting. In this paper, the ‘Principal Components Analysis-Hydrologic Uncertainty Processor’ (PCA-HUP) was developed for probabilistic flood forecasting (PFF) and further evaluated in the middle Yellow River, China. Due to the severe sediment erosion, small and medium floods drain in the main channel (normal floods) while large floods would spill over the bank and drain in river floodplains (overbank floods). Thus, the practical routing methods were used to provide the deterministic flood forecasting (DFF) input for PCA-HUP. PCA-HUP quantifies the forecast uncertainty and provides PFF results. The comparison of performance between the DFF and PFF outputs indicated that PFF could also provide a good accuracy of deterministic hydrograph. In order to explore the performance decay of DFF and PFF with lead time increasing, the lead times n = 1, 6 and 10 hours were chosen for comparison. Results suggested that, with the increasing lead time, the performances of both DFF and PFF decayed accordingly. As a consequence, this study proved the practicability of PCA-HUP in the operational forecasting for both normal and overbank floods in the middle reach of Yellow River.

scholarly journals Estimation of predictive hydrological uncertainty using quantile regression: examples from the National Flood Forecasting System (England and Wales)

2011 ◽  
Vol 15 (1) ◽  
pp. 255-265 ◽  
Author(s):  
A. H. Weerts ◽  
H. C. Winsemius ◽  
J. S. Verkade
Keyword(s):  
Quantile Regression ◽  
Water Level ◽  
Flood Forecasting ◽  
Water Levels ◽  
Forecast Errors ◽  
Rainfall Runoff ◽  
England And Wales ◽  
Predictive Uncertainty ◽  
Hydrological Characteristics ◽  
Forecasting System

Abstract. In this paper, a technique is presented for assessing the predictive uncertainty of rainfall-runoff and hydraulic forecasts. The technique conditions forecast uncertainty on the forecasted value itself, based on retrospective Quantile Regression of hindcasted water level forecasts and forecast errors. To test the robustness of the method, a number of retrospective forecasts for different catchments across England and Wales having different size and hydrological characteristics have been used to derive in a probabilistic sense the relation between simulated values of water levels and matching errors. From this study, we can conclude that using Quantile Regression for estimating forecast errors conditional on the forecasted water levels provides a relatively simple, efficient and robust means for estimation of predictive uncertainty.

scholarly journals Future shifts in extreme flow regimes in Alpine regions

2019 ◽  
Author(s):  
Manuela I. Brunner ◽  
Daniel Farinotti ◽  
Harry Zekollari ◽  
Matthias Huss ◽  
Massimiliano Zappa
Keyword(s):  
Frequency Analysis ◽  
Flow Regimes ◽  
High Flow ◽  
Low Flow ◽  
Large Set ◽  
Wet Season ◽  
Data Set ◽  
Maximum Discharge ◽  
High Flows ◽  
Extreme Flow

Abstract. Extreme low and high flows can have negative economical, societal, and ecological effects and are expected to become more severe in many regions due to climate change. Besides low and high flows, the whole flow regime is subject to changes. Knowledge on future changes in flow regimes is important since regimes contain information on both extremes and conditions prior to the dry and wet season. Changes in individual low- and high-flow characteristics as well as flow regimes under normal conditions have been thoroughly studied. In contrast, little is known about changes in extreme flow regimes. We here propose two methods for the estimation of extreme flow regimes and apply them to simulated discharge time series for future climate conditions in Switzerland. The first method relies on frequency analysis performed on annual flow duration curves. The second approach performs frequency analysis on the discharge sums of a large set of stochastically generated annual hydrographs. Both approaches were found to produce similar 100-year regime estimates when applied to a data set of 19 hydrological regions in Switzerland. Our results show that changes in both extreme low- and high-flow regimes for rainfall-dominated regions are distinct from those in melt-dominated regions. In rainfall-dominated regions, the minimum discharge of low-flow regimes decreases by up to 50 %, whilst the reduction is of 25 % for high-flow regimes. In contrast, the maximum discharge of low- and high-flow regimes increases by up to 50 %. In melt-dominated regions, the changes point into the other direction than those in rainfall-dominated regions. The minimum and maximum discharge of extreme regimes increase by up to 100 % and decrease by less than 50 %, respectively. Our findings provide guidance in water resources planning and management and the extreme regime estimates are a valuable basis for climate impact studies.

scholarly journals Recent developments in predictive uncertainty assessment based on the model conditional processor approach

2010 ◽  
Vol 7 (6) ◽  
pp. 9219-9270 ◽  
Author(s):  
G. Coccia ◽  
E. Todini
Keyword(s):  
Flood Forecasting ◽  
Uncertainty Assessment ◽  
Water Volume ◽  
Data Set ◽  
Predictive Uncertainty ◽  
Emergency Managers ◽  
Physically Based ◽  
Recent Developments ◽  
Future Value ◽  
Truncated Normal

Abstract. The work aims at discussing the role of predictive uncertainty in flood forecasting and flood emergency management, its relevance to improve the decision making process and the techniques to be used for its assessment. Real time flood forecasting requires taking into account predictive uncertainty for a number of reasons. Deterministic hydrological/hydraulic forecasts give useful information about real future events, but their predictions, as usually done in practice, cannot be taken and used as real future occurrences but rather used as pseudo-measurements of future occurrences in order to reduce the uncertainty of decision makers. Predictive uncertainty (PU) is in fact defined as the probability of occurrence of a future value of a predictand (such as water level, discharge or water volume) conditional upon prior observations and knowledge as well as on all the information we can obtain on that specific future value from model forecasts. When dealing with commensurable quantities, as in the case of floods, PU must be quantified in terms of a probability distribution function which will be used by the emergency managers in their decision process in order to improve the quality and reliability of their decisions. After introducing the concept of PU, the presently available processors are introduced and discussed in terms of their benefits and limitations. In this work the Model Conditional Processor has been extended to the possibility of using a joint truncated normal distribution, in order of improving adaptation to low and high flows. The paper concludes by showing the results of the application of the MCP on the Baron Fork River, OK, USA. The data set provided by the NOAA's National Weather Service, within the DMIP 2 Project, allowed two physically based models, the TOPKAPI model and TETIS model, to be calibrated and a data driven model to be implemented using the Artificial Neural Network. The three model forecasts have been combined with the aim of reducing the PU and improving the probabilistic forecast taking advantage of the different capabilities of each model approach.

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