scholarly journals Multiscale error analysis, correction, and predictive uncertainty estimation in a flood forecasting system

2011 ◽  
Vol 47 (7) ◽  
Author(s):  
K. Bogner ◽  
F. Pappenberger
Keyword(s):  
Error Analysis ◽  
Flood Forecasting ◽  
Uncertainty Estimation ◽  
Predictive Uncertainty ◽  
Forecasting System

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

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 Estimation of predictive hydrological uncertainty using quantile regression: examples from the national flood forecasting system (England and Wales)

2010 ◽  
Vol 7 (4) ◽  
pp. 5547-5575 ◽  
Author(s):  
A. H. Weerts ◽  
H. C. Winsemius ◽  
J. S. Verkade
Keyword(s):  
Quantile Regression ◽  
Water Level ◽  
Uncertainty Estimation ◽  
Water Levels ◽  
Forecast Errors ◽  
Rainfall Runoff ◽  
England And Wales ◽  
Predictive Uncertainty ◽  
Hydrological Characteristics ◽  
Forecasting System

Abstract. In this paper, a is presented for assessing the predictive uncertainty of rainfall-runoff and hydraulic forecasts that conditions forecasts 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 discharges and 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 an extremely simple, efficient and robust means for uncertainty estimation of deterministic forecasts.

Predictive Uncertainty Estimation Using Deep Learning for Soft Robot Multimodal Sensing

2021 ◽  
Vol 6 (2) ◽  
pp. 951-957
Author(s):  
Ze Yang Ding ◽  
Junn Yong Loo ◽  
Vishnu Monn Baskaran ◽  
Surya Girinatha Nurzaman ◽  
Chee Pin Tan
Keyword(s):  
Deep Learning ◽  
Uncertainty Estimation ◽  
Soft Robot ◽  
Predictive Uncertainty ◽  
Multimodal Sensing

scholarly journals A pan-African medium-range ensemble flood forecast system

2015 ◽  
Vol 19 (8) ◽  
pp. 3365-3385 ◽  
Author(s):  
V. Thiemig ◽  
B. Bisselink ◽  
F. Pappenberger ◽  
J. Thielen
Keyword(s):  
Large Scale ◽  
Practical Implication ◽  
River Basins ◽  
Flood Forecasting ◽  
Small Scale ◽  
Flood Forecast ◽  
Flood Events ◽  
Forecast System ◽  
Pan African ◽  
Forecasting System

Abstract. The African Flood Forecasting System (AFFS) is a probabilistic flood forecast system for medium- to large-scale African river basins, with lead times of up to 15 days. The key components are the hydrological model LISFLOOD, the African GIS database, the meteorological ensemble predictions by the ECMWF (European Centre for Medium-Ranged Weather Forecasts) and critical hydrological thresholds. In this paper, the predictive capability is investigated in a hindcast mode, by reproducing hydrological predictions for the year 2003 when important floods were observed. Results were verified by ground measurements of 36 sub-catchments as well as by reports of various flood archives. Results showed that AFFS detected around 70 % of the reported flood events correctly. In particular, the system showed good performance in predicting riverine flood events of long duration (> 1 week) and large affected areas (> 10 000 km2) well in advance, whereas AFFS showed limitations for small-scale and short duration flood events. The case study for the flood event in March 2003 in the Sabi Basin (Zimbabwe) illustrated the good performance of AFFS in forecasting timing and severity of the floods, gave an example of the clear and concise output products, and showed that the system is capable of producing flood warnings even in ungauged river basins. Hence, from a technical perspective, AFFS shows a large potential as an operational pan-African flood forecasting system, although issues related to the practical implication will still need to be investigated.

Development of a Real-Time Flood Forecasting System by Hydraulic Flood Routing

2001 ◽  
Author(s):  
Joo Heon Lee ◽  
Do Hun Lee ◽  
Sang Man Jeong ◽  
Eun Tae Lee
Keyword(s):  
Real Time ◽  
Flood Forecasting ◽  
Flood Routing ◽  
Forecasting System

Measuring the skill of an operational ice jam flood forecasting system

2021 ◽  
Vol 52 ◽  
pp. 102001
Author(s):  
Brandon S. Williams ◽  
Apurba Das ◽  
Peter Johnston ◽  
Bin Luo ◽  
Karl-Erich Lindenschmidt
Keyword(s):  
Flood Forecasting ◽  
Ice Jam ◽  
Forecasting System
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