scholarly journals Automation and human expertise in operational river forecasting

2016 ◽  
Vol 3 (5) ◽  
pp. 692-705 ◽  
Author(s):  
Thomas C. Pagano ◽  
Florian Pappenberger ◽  
Andrew W. Wood ◽  
Maria-Helena Ramos ◽  
Anders Persson ◽  
...  
Keyword(s):  
River Forecasting

Design of operational precipitation and streamflow networks for river forecasting

1979 ◽  
Vol 15 (6) ◽  
pp. 1823-1832 ◽  
Author(s):  
R. Uwe Jettmar ◽  
G. Kenneth Young ◽  
Richard K. Farnsworth ◽  
John C. Schaake
Keyword(s):  
River Forecasting

scholarly journals Rapid changes in snow cover at low elevations in the Sierra Nevada, California, U.S.A.

1997 ◽  
Vol 25 ◽  
pp. 367-370 ◽  
Author(s):  
Richard Kattelmann
Keyword(s):  
Snow Cover ◽  
Sierra Nevada ◽  
Mountain Range ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
Distributed Models ◽  
Rapid Changes ◽  
Spatially Distributed ◽  
Precipitation Type ◽  
River Forecasting

Snow cover in the intermittent snow zone of the Sierra Nevada can occupy more than 10 000 km2 of the mountain range, but it has received relatively little attention in river forecasting. Snow is deposited at lower elevations only during the cold storms of winter, and remains there only for a few days or weeks. When cold storms have created a thin snow cover at low elevations, a subsequent warm storm can melt this snow in just a few hours and increase the runoff response dramatically. Operational hydrological models and river-forecasting procedures have tended to overlook contributions from the intermittent-snow zone, focusing instead on rainfall-runoff or melt from the snowpack zone at higher elevations. Data-collection efforts are minimal in this zone, too. Ideally, spatially distributed models of snowmelt and runoff generation are needed to account for the typically large differences in snow cover on different aspects in the intermittent snow zone. Although aircraft and satellite imagery would be most desirable to monitor the distribution of snow cover in the intermittent-snow zone, even a few climate stations that report precipitation type and snow presence would be a major improvement over the present situation in the Sierra Nevada.

Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting

2012 ◽  
Vol 36 (4) ◽  
pp. 480-513 ◽  
Author(s):  
Robert J. Abrahart ◽  
François Anctil ◽  
Paulin Coulibaly ◽  
Christian W. Dawson ◽  
Nick J. Mount ◽  
...  
Keyword(s):  
Neural Network ◽  
Emerging Themes ◽  
River Forecasting

Application of Forecast Verification Science to Operational River Forecasting in the U.S. National Weather Service

2009 ◽  
Vol 90 (6) ◽  
pp. 779-784 ◽  
Author(s):  
Julie Demargne ◽  
Mary Mullusky ◽  
Kevin Werner ◽  
Thomas Adams ◽  
Scott Lindsey ◽  
...  
Keyword(s):  
Forecast Verification ◽  
National Weather Service ◽  
The U.S ◽  
Weather Service ◽  
River Forecasting

scholarly journals Proof of Concept of an Altimeter-Based River Forecasting System for Transboundary Flow Inside Bangladesh

2014 ◽  
Vol 7 (2) ◽  
pp. 587-601 ◽  
Author(s):  
Faisal Hossain ◽  
A. H. Siddique-E-Akbor ◽  
Liton Chandra Mazumder ◽  
Sardar M. ShahNewaz ◽  
Sylvain Biancamaria ◽  
...  
Keyword(s):  
Proof Of Concept ◽  
Forecasting System ◽  
River Forecasting

scholarly journals Legitimising neural network river forecasting models: a new data-driven mechanistic modelling framework

2013 ◽  
Vol 10 (1) ◽  
pp. 145-187 ◽  
Author(s):  
N. J. Mount ◽  
C. W. Dawson ◽  
R. J. Abrahart
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Model Development ◽  
Relative Sensitivity ◽  
Data Driven ◽  
Neural Network Models ◽  
Core Element ◽  
Mechanistic Modelling ◽  
Mechanistic Understanding ◽  
River Forecasting

Abstract. In this paper we address the difficult problem of gaining an internal, mechanistic understanding of a neural network river forecasting (NNRF) model. Neural network models in hydrology have long been criticised for their black-box character, which prohibits adequate understanding of their modelling mechanisms and has limited their broad acceptance by hydrologists. In response, we here present a new, data-driven mechanistic modelling (DDMM) framework that incorporates an evaluation of the legitimacy of a neural network's internal modelling mechanism as a core element in the model development process. The framework is exemplified for two NNRF modelling scenarios, and uses a novel adaptation of first order, partial derivate, relative sensitivity analysis methods as the means by which each model's mechanistic legitimacy is explored. The results demonstrate the limitations of standard, goodness-of-fit validation procedures applied by NNRF modellers, by highlighting how the internal mechanisms of complex models that produce the best fit scores can have much lower legitimacy than simpler counterparts whose scores are only slightly inferior. The study emphasises the urgent need for better mechanistic understanding of neural network-based hydrological models and the further development of methods for elucidating their mechanisms.

scholarly journals Rapid changes in snow cover at low elevations in the Sierra Nevada, California, U.S.A.

1997 ◽  
Vol 25 ◽  
pp. 367-370 ◽  
Author(s):  
Richard Kattelmann
Keyword(s):  
Snow Cover ◽  
Sierra Nevada ◽  
Mountain Range ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
Distributed Models ◽  
Rapid Changes ◽  
Spatially Distributed ◽  
Precipitation Type ◽  
River Forecasting

Snow cover in the intermittent snow zone of the Sierra Nevada can occupy more than 10 000 km2of the mountain range, but it has received relatively little attention in river forecasting. Snow is deposited at lower elevations only during the cold storms of winter, and remains there only for a few days or weeks. When cold storms have created a thin snow cover at low elevations, a subsequent warm storm can melt this snow in just a few hours and increase the runoff response dramatically. Operational hydrological models and river-forecasting procedures have tended to overlook contributions from the intermittent-snow zone, focusing instead on rainfall-runoff or melt from the snowpack zone at higher elevations. Data-collection efforts are minimal in this zone, too. Ideally, spatially distributed models of snowmelt and runoff generation are needed to account for the typically large differences in snow cover on different aspects in the intermittent snow zone. Although aircraft and satellite imagery would be most desirable to monitor the distribution of snow cover in the intermittent-snow zone, even a few climate stations that report precipitation type and snow presence would be a major improvement over the present situation in the Sierra Nevada.

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