Real-Time and Automatic River Discharge Measurement With UHF Radar

2020 ◽  
Vol 17 (11) ◽  
pp. 1851-1855
Author(s):  
Yonghuai Yang ◽  
Biyang Wen ◽  
Caijun Wang ◽  
Yidong Hou
2014 ◽  
Vol 18 (11) ◽  
pp. 4467-4484 ◽  
Author(s):  
B. Revilla-Romero ◽  
J. Thielen ◽  
P. Salamon ◽  
T. De Groeve ◽  
G. R. Brakenridge

Abstract. One of the main challenges for global hydrological modelling is the limited availability of observational data for calibration and model verification. This is particularly the case for real-time applications. This problem could potentially be overcome if discharge measurements based on satellite data were sufficiently accurate to substitute for ground-based measurements. The aim of this study is to test the potentials and constraints of the remote sensing signal of the Global Flood Detection System for converting the flood detection signal into river discharge values. The study uses data for 322 river measurement locations in Africa, Asia, Europe, North America and South America. Satellite discharge measurements were calibrated for these sites and a validation analysis with in situ discharge was performed. The locations with very good performance will be used in a future project where satellite discharge measurements are obtained on a daily basis to fill the gaps where real-time ground observations are not available. These include several international river locations in Africa: the Niger, Volta and Zambezi rivers. Analysis of the potential factors affecting the satellite signal was based on a classification decision tree (random forest) and showed that mean discharge, climatic region, land cover and upstream catchment area are the dominant variables which determine good or poor performance of the measure\\-ment sites. In general terms, higher skill scores were obtained for locations with one or more of the following characteristics: a river width higher than 1km; a large floodplain area and in flooded forest, a potential flooded area greater than 40%; sparse vegetation, croplands or grasslands and closed to open and open forest; leaf area index > 2; tropical climatic area; and without hydraulic infrastructures. Also, locations where river ice cover is seasonally present obtained higher skill scores. This work provides guidance on the best locations and limitations for estimating discharge values from these daily satellite signals.


2015 ◽  
Vol 56 (5) ◽  
pp. 900-906 ◽  
Author(s):  
V.B. Ovodenko ◽  
V.V. Trekin ◽  
N.A. Korenkova ◽  
M.V. Klimenko

2011 ◽  
Vol 184 (10) ◽  
pp. 6423-6436 ◽  
Author(s):  
Yen-Chang Chen ◽  
Tsung-Ming Yang ◽  
Nien-Sheng Hsu ◽  
Ting-Ming Kuo

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1697 ◽  
Author(s):  
Stefan Kollet ◽  
Fabian Gasper ◽  
Slavko Brdar ◽  
Klaus Goergen ◽  
Harrie-Jan Hendricks-Franssen ◽  
...  

Operational weather and flood forecasting has been performed successfully for decades and is of great socioeconomic importance. Up to now, forecast products focus on atmospheric variables, such as precipitation, air temperature and, in hydrology, on river discharge. Considering the full terrestrial system from groundwater across the land surface into the atmosphere, a number of important hydrologic variables are missing especially with regard to the shallow and deeper subsurface (e.g., groundwater), which are gaining considerable attention in the context of global change. In this study, we propose a terrestrial monitoring/forecasting system using the Terrestrial Systems Modeling Platform (TSMP) that predicts all essential states and fluxes of the terrestrial hydrologic and energy cycles from groundwater into the atmosphere. Closure of the terrestrial cycles provides a physically consistent picture of the terrestrial system in TSMP. TSMP has been implemented over a regional domain over North Rhine-Westphalia and a continental domain over Europe in a real-time forecast/monitoring workflow. Applying a real-time forecasting/monitoring workflow over both domains, experimental forecasts are being produced with different lead times since the beginning of 2016. Real-time forecast/monitoring products encompass all compartments of the terrestrial system including additional hydrologic variables, such as plant available soil water, groundwater table depth, and groundwater recharge and storage.


2020 ◽  
Author(s):  
Shaun Harrigan ◽  
Ervin Zoster ◽  
Hannah Cloke ◽  
Peter Salamon ◽  
Christel Prudhomme

Abstract. Operational global-scale hydrological forecasting systems are widely used to help manage hydrological extremes such as floods and droughts. The vast amounts of raw data that underpin forecast systems and the ability to generate information on forecast skill have, until now, not been publicly available. As part of the Global Flood Awareness System (GloFAS; https://www.globalfloods.eu/) service evolution, in this paper daily ensemble river discharge reforecasts and real-time forecast datasets are made free and openly available through the Copernicus Climate Change Service (C3S) Climate Data Store (CDS). They include real-time forecast data starting on 1 January 2020 updated operationally every day and a 20-year set of reforecasts and associated metadata, available through the dedicated GloFAS FTP service. This paper describes the model components and configuration used to generate the real-time river discharge forecasts and the reforecasts. An evaluation of ensemble forecast skill using the Continuous Ranked Probability Skill Score (CRPSS) was also undertaken for river points around the globe. Results show that GloFAS is skilful in over 93thinsp;% of catchments in the short- (1- to 3-days) and medium-range (5- to 15-days) against a persistence benchmark forecast, and skilful in over 80 % of catchments out to the extended-range (16- to 30-days) against a climatological benchmark forecast. However, the strength of skill varies considerably by location with GloFAS found to have no or negative skill at longer lead times in broad hydroclimatic regions in tropical Africa, western coast of South America, and catchments dominated by snow and ice in high northern latitudes. Forecast skill is summarised as a new headline skill score to be added as a layer on the GloFAS forecast Web Map Viewer at the next GloFAS cycle release, expected Autumn 2020, to aid user’s interpretation and understanding of forecast quality.


Author(s):  
Stefan Kollet ◽  
Fabian Gasper ◽  
Slavko Brdar ◽  
Klaus Goergen ◽  
Harrie-Jan Hendricks-Franssen ◽  
...  

Operational weather and also flood forecasting has been performed successfully for decades and is of great socioeconomic importance. Up to now, forecast products focus on atmospheric variables, such as precipitation, air temperature and, in hydrology, on river discharge. Considering the full terrestrial system from groundwater across the land surface into the atmosphere, a number of important hydrologic variables are missing especially with regard to the shallow and deeper subsurface (e.g. groundwater), which are gaining considerable attention in the context of global change. In this study, we propose a terrestrial monitoring/forecasting system using the Terrestrial Systems Modeling Platform (TSMP) that predicts all essential states and fluxes of the terrestrial hydrologic and energy cycles from groundwater into the atmosphere. Closure of the terrestrial cycles provides a physically consistent picture of the terrestrial system in TSMP. TSMP has been implemented over a regional domain over North Rhine-Westphalia and a continental domain over European in a real-time forecast/monitoring workflow. Applying a real-time forecasting/monitoring workflow over both domains, experimental forecasts are being produced with different lead times since the beginning of 2016. Real-time forecast/monitoring products encompass all compartments of the terrestrial system including additional hydrologic variables, such as plant available soil water, groundwater table depth, and groundwater recharge and storage.


Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 114
Author(s):  
Giampaolo Crotti ◽  
Jorge Leandro ◽  
Punit Kumar Bhola

Operational real-time flood forecast is often done on the prediction of discharges at specific gauges using hydrological models. Hydrodynamic models, which can produce inundation maps, are computationally demanding and often cannot be used directly for that purpose. The FloodEvac framework has been developed in order to enable 2D flood inundations map to be forecasted at real-time. The framework is based on a database of pre-recorded synthetic events. In this paper, the framework is improved by generating a database based on rescaled historical river discharge events. This historical database includes a wider variety of runoff curves, including non-Gaussian and multi-peak shapes that better reflect the characteristics and the behavior of the natural streams. Hence, a hybrid approach is proposed by joining the historical and the existing synthetic database. The increased number of scenarios in the hybrid database allows reliable predictions, thus improving the robustness and applicability of real-time flood forecasts.


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