scholarly journals A stochastic space-time rainfall forecasting system for real time flow forecasting II: Application of SHETRAN and ARNO rainfall runoff models to the Brue catchment

2000 ◽  
Vol 4 (4) ◽  
pp. 617-626 ◽  
Author(s):  
D. Mellor ◽  
J. Sheffield ◽  
P. E. O’Connell ◽  
A. V. Metcalfe
Keyword(s):  
Real Time ◽  
Rainfall Runoff ◽  
Rainfall Forecasting ◽  
Ensemble Forecasts ◽  
Flood Warning ◽  
Distributed Modelling ◽  
Time Flow ◽  
Physically Based ◽  
The Impact ◽  
Modelling System

Abstract. Key issues involved in converting MTB ensemble forecasts of rainfall into ensemble forecasts of runoff are addressed. The physically-based distributed modelling system, SHETRAN, is parameterised for the Brue catchment, and used to assess the impact of averaging spatially variable MTB rainfall inputs on the accuracy of simulated runoff response. Averaging is found to have little impact for wet antecedent conditions and to lead to some underestimation of peak discharge under dry catchment conditions. The simpler ARNO modelling system is also parameterised for the Brue and SHETRAN and ARNO calibration and validation results are found to be similar. Ensemble forecasts of runoff generated using both SHETRAN and the simpler ARNO modelling system are compared. The ensemble is more spread out with the SHETRAN model, and a likely explanation is that the ARNO model introduces too much smoothing. Nevertheless, the forecasting performance of the simpler model could be adequate for flood warning purposes. Keywords: SHETRAN, ARNO, HYREX, rainfall-runoff model, Brue, real-time flow forecasting

Real-Time Flow Forecasting in a Watershed Using Rainfall Forecasting Model and Updating Model

2019 ◽  
Vol 33 (14) ◽  
pp. 4799-4820 ◽  
Author(s):  
P. Shirisha ◽  
K. Venkata Reddy ◽  
Deva Pratap
Keyword(s):  
Real Time ◽  
Forecasting Model ◽  
Rainfall Forecasting ◽  
Time Flow

scholarly journals Statistical post-processing of ensemble forecasts of the height of new snow

2019 ◽  
Vol 26 (3) ◽  
pp. 339-357 ◽  
Author(s):  
Jari-Pekka Nousu ◽  
Matthieu Lafaysse ◽  
Matthieu Vernay ◽  
Joseph Bellier ◽  
Guillaume Evin ◽  
...  
Keyword(s):  
Real Time ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Evaluation Criteria ◽  
Training Dataset ◽  
Post Processing ◽  
Probabilistic Forecasting ◽  
Ensemble Forecasts ◽  
Heterogeneous Model ◽  
The Impact

Abstract. Forecasting the height of new snow (HN) is crucial for avalanche hazard forecasting, road viability, ski resort management and tourism attractiveness. Météo-France operates the PEARP-S2M probabilistic forecasting system, including 35 members of the PEARP Numerical Weather Prediction system, where the SAFRAN downscaling tool refines the elevation resolution and the Crocus snowpack model represents the main physical processes in the snowpack. It provides better HN forecasts than direct NWP diagnostics but exhibits significant biases and underdispersion. We applied a statistical post-processing to these ensemble forecasts, based on non-homogeneous regression with a censored shifted Gamma distribution. Observations come from manual measurements of 24 h HN in the French Alps and Pyrenees. The calibration is tested at the station scale and the massif scale (i.e. aggregating different stations over areas of 1000 km2). Compared to the raw forecasts, similar improvements are obtained for both spatial scales. Therefore, the post-processing can be applied at any point of the massifs. Two training datasets are tested: (1) a 22-year homogeneous reforecast for which the NWP model resolution and physical options are identical to the operational system but without the same initial perturbations; (2) 3-year real-time forecasts with a heterogeneous model configuration but the same perturbation methods. The impact of the training dataset depends on lead time and on the evaluation criteria. The long-term reforecast improves the reliability of severe snowfall but leads to overdispersion due to the discrepancy in real-time perturbations. Thus, the development of reliable automatic forecasting products of HN needs long reforecasts as homogeneous as possible with the operational systems.

scholarly journals Impact of overland flow on soil characteristics in Třebsín experimental plots

2017 ◽  
Vol 12 (No. 3) ◽  
pp. 187-193
Author(s):  
H. Bačinová ◽  
P. Kovář
Keyword(s):  
Overland Flow ◽  
Extreme Rainfall ◽  
Field Capacity ◽  
Rainfall Runoff ◽  
The Czech Republic ◽  
Physically Based ◽  
Runoff Discharge ◽  
The Impact ◽  
First Time ◽  
Experimental Plots

This paper describes the continuation of simulated outcomes from the plots No. 4 and No. 5 with two different soils, using the KINFIL model to assess the runoff from extreme rainfall. The KINFIL model is a physically-based, parameter-distributed 3D model that has been applied to the Třebsín experimental station in the Czech Republic. This model was used for the first time in 2012 to simulate the impact of overland flow caused by natural or sprinkler-made intensive rains on four of the nine experimental plots. This measurement of a rain simulator producing a high-intensity rainfall involves also hydraulic conductivity, soil sorptivity, plot geometry and granulometric curves to be used for the present analysis. However, since 2012, the KINFIL model has been amended to provide a more effective comparison of the measured and computed results using the values of new parameters such as storage suction factor and field capacity on plot 4 and plot 5. The KINFIL model uses all input data mentioned above, and it produces the output data such as gross rainfall, effective rainfall, runoff discharge hydraulic depths, hydraulic velocities and shear velocities as well as shear stress values depending on the soil particle distribution. These processes are innovative, physically based, and both the measured and the computed results fit reliably.  

Ensemble hydrological forecasting for flood warning in small catchments in Saxony, Germany

2020 ◽  
Author(s):  
Jens Grundmann ◽  
Achim Six ◽  
Andy Philipp
Keyword(s):  
Extreme Precipitation ◽  
Disaster Response ◽  
Lead Times ◽  
Rainfall Runoff ◽  
Ensemble Forecasts ◽  
Processing Scheme ◽  
Hydrological Forecasting ◽  
Flood Warning ◽  
Training Concept ◽  
Small Catchments

<p>Reliable warnings and forecasts of extreme precipitation and the resulting floods are an important prerequisite for disaster response. Especially for small catchments, warning and forecasting systems are challenging due to the short response time of the catchments and the uncertainties of the meteorological forecast products. Thus, ensemble forecasts of precipitation are an option to portray these inherent uncertainties. By this contribution, we present our operational processing scheme for ensemble hydrological forecasting. We use the COSMO-D2-EPS product of the German Weather Service, which provides an ensemble of 20 members each three hours, for lead times up to 27 hours. Each member is evaluated regarding specific extreme precipitation thresholds for predefined hydrological warning regions. If these thresholds are exceeded in a specific region, rainfall-runoff models for the associated catchments are started to propagate the meteorological uncertainty into the resulting runoff, followed by statistical post processing and visualization. In addition, a communication and training concept based on a series of workshops with the locally responsible civil protection forces to deal with the uncertainties in the forecast is associated. Results are presented by a re-analysis of the flood in the upper Weiße Elster catchment in May 2018 in the Vogtland region of Saxony. Rainfall amounts larger than 140mm in 6 hours led to the highest flood warning levels in the region. Analysis show that such extreme amounts of precipitation are only predicted by one member of the COSMO-D2-EPS ensemble forecast. The deterministic COSMO-D2 model run does not show this, which underlines the benefit and the potential of the ensemble predictions, but also the need for a suitable communication of the uncertainties.</p>

A Mathematical Modelling System for Flood Forecasting

1984 ◽  
Vol 15 (4-5) ◽  
pp. 305-316 ◽  
Author(s):  
T. Jønch-Clausen ◽  
J. Chr. Refsgaard
Keyword(s):  
Mathematical Modelling ◽  
Real Time ◽  
West Bengal ◽  
Simulation Models ◽  
Flood Routing ◽  
Rainfall Runoff ◽  
Reservoir Operations ◽  
River Catchment ◽  
Damodar River ◽  
Modelling System

In this paper comprehensive simulation models are presented which can forecast the streamflow in real time at various points in river systems and provide a tool for identifying improvements of the reservoir operations during flood situations while taking into consideration the conditions downstream. The mathematical modelling has involved rainfall-runoff predictions as well as flood routing. The application of the modelling system to a part of the 22,000 square kilometres Damodar River Catchment located in Bihar and West Bengal in India, is described.

scholarly journals Physically-based modelling, uncertainty, and pragmatism – Comment on: ‘Système Hydrologique Europeén (SHE): review and perspectives after 30 years development in distributed physically-based hydrological modelling’ by Jens Christian Refsgaard, Børge Storm and Thomas Clausen

2012 ◽  
Vol 43 (6) ◽  
pp. 945-947 ◽  
Author(s):  
John Ewen ◽  
Enda O'Connell ◽  
James Bathurst ◽  
Steve J. Birkinshaw ◽  
Chris Kilsby ◽  
...  
Keyword(s):  
Hydrological Modelling ◽  
Modelling Uncertainty ◽  
Distributed Modelling ◽  
Physically Based ◽  
Physically Based Modelling ◽  
Modelling System

The Système Hydrologique Europeén (SHE) modelling system and physically-based distributed modelling (PBDM) were discussed in Refsgaard et al.'s Système Hydrologique Europeén (SHE): review and perspectives after 30 years development in distributed physically-based hydrological modelling (Hydrology Research41, pp. 355–377). The opportunity is taken here to correct some oversights and potentially misleading perspectives in that paper and mount a more robust defence of PBDM.

scholarly journals Statistical post-processing of ensemble forecasts of the height of new snow

2019 ◽  
Author(s):  
Jari-Pekka Nousu ◽  
Matthieu Lafaysse ◽  
Matthieu Vernay ◽  
Joseph Bellier ◽  
Guillaume Evin ◽  
...  
Keyword(s):  
Real Time ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Evaluation Criteria ◽  
Training Dataset ◽  
Post Processing ◽  
Probabilistic Forecasting ◽  
Ensemble Forecasts ◽  
Heterogeneous Model ◽  
The Impact

Abstract. Forecasting the height of new snow (HN) is crucial for avalanche hazard forecasting, roads viability, ski resorts management and tourism attractiveness. Meteo-France operates the PEARP-S2M probabilistic forecasting system including 35 members of the PEARP Numerical Weather Prediction system, where the SAFRAN downscaling tool is refining the elevation resolution, and the Crocus snowpack model is representing the main physical processes in the snowpack. It provides better HN forecasts than direct NWP diagnostics but exhibits significant biases and underdispersion. We applied a statistical post-processing to these ensemble forecasts, based on Nonhomogeneous Regression with a censored shifted Gamma distribution. Observations come from manual measurements of 24-hour HN in French Alps and Pyrenees. The calibration is tested at the station-scale and the massif-scale (i.e. aggregating different stations over areas of 1000 km2). Compared to the raw forecasts, similar improvements are obtained for both spatial scales. Therefore, the post-processing can be applied at any point of the massifs. Two training datasets are tested: (1) a 22-year homogeneous reforecast for which the NWP model resolution and physical options are identical to the operational system but without the same initial perturbations; (2) 3-year real-time forecasts with a heterogeneous model configuration but the same perturbation methods. The impact of the training dataset depends on lead time and on the evaluation criteria. The long-term reforecast improves the reliability of severe snowfall but leads to overdispersion due to the discrepancy in real-time perturbations. Thus, the development of reliable automatic forecasting products of HN needs long reforecasts as homogeneous as possible with the operational systems.

scholarly journals Estimation of the impact of climate change-induced extreme precipitation events on floods

2015 ◽  
Vol 45 (3) ◽  
pp. 173-192 ◽  
Author(s):  
Kamila Hlavčová ◽  
Milan Lapin ◽  
Peter Valent ◽  
Ján Szolgay ◽  
Silvia Kohnová ◽  
...  
Keyword(s):  
Climate Change ◽  
Rainfall Runoff ◽  
Mountainous Regions ◽  
Global Circulation Models ◽  
Extreme Precipitation Events ◽  
Air Temperatures ◽  
Physically Based ◽  
Rainfall Runoff Model ◽  
The Impact ◽  
Runoff Model

Abstract In order to estimate possible changes in the flood regime in the mountainous regions of Slovakia, a simple physically-based concept for climate change-induced changes in extreme 5-day precipitation totals is proposed in the paper. It utilizes regionally downscaled scenarios of the long-term monthly means of the air temperature, specific air humidity and precipitation projected for Central Slovakia by two regional (RCM) and two global circulation models (GCM). A simplified physically-based model for the calculation of short-term precipitation totals over the course of changing air temperatures, which is used to drive a conceptual rainfall-runoff model, was proposed. In the paper a case study of this approach in the upper Hron river basin in Central Slovakia is presented. From the 1981–2010 period, 20 events of the basin’s most extreme average of 5-day precipitation totals were selected. Only events with continual precipitation during 5 days were considered. These 5-day precipitation totals were modified according to the RCM and GCM-based scenarios for the future time horizons of 2025, 2050 and 2075. For modelling runoff under changed 5-day precipitation totals, a conceptual rainfall-runoff model developed at the Slovak University of Technology was used. Changes in extreme mean daily discharges due to climate change were compared with the original flood events and discussed.

scholarly journals On selection of the optimal data time interval for real-time hydrological forecasting

2013 ◽  
Vol 17 (9) ◽  
pp. 3639-3659 ◽  
Author(s):  
J. Liu ◽  
D. Han
Keyword(s):  
Real Time ◽  
Case Studies ◽  
Time Interval ◽  
Rainfall Runoff ◽  
Time Intervals ◽  
Hydrological Forecasting ◽  
Forecast Lead Time ◽  
The Future ◽  
The Impact ◽  
Selection Of

Abstract. With the advancement in modern telemetry and communication technologies, hydrological data can be collected with an increasingly higher sampling rate. An important issue deserving attention from the hydrological community is which suitable time interval of the model input data should be chosen in hydrological forecasting. Such a problem has long been recognised in the control engineering community but is a largely ignored topic in operational applications of hydrological forecasting. In this study, the intrinsic properties of rainfall–runoff data with different time intervals are first investigated from the perspectives of the sampling theorem and the information loss using the discrete wavelet transform tool. It is found that rainfall signals with very high sampling rates may not always improve the accuracy of rainfall–runoff modelling due to the catchment low-pass-filtering effect. To further investigate the impact of a data time interval in real-time forecasting, a real-time forecasting system is constructed by incorporating the probability distributed model (PDM) with a real-time updating scheme, the autoregressive moving-average (ARMA) model. Case studies are then carried out on four UK catchments with different concentration times for real-time flow forecasting using data with different time intervals of 15, 30, 45, 60, 90 and 120 min. A positive relation is found between the forecast lead time and the optimal choice of the data time interval, which is also highly dependent on the catchment concentration time. Finally, based on the conclusions from the case studies, a hypothetical pattern is proposed in three-dimensional coordinates to describe the general impact of the data time interval and to provide implications of the selection of the optimal time interval in real-time hydrological forecasting. Although nowadays most operational hydrological systems still have low data sampling rates (daily or hourly), the future is that higher sampling rates will become more widespread, and there is an urgent need for hydrologists both in academia and in the field to realise the significance of the data time interval issue. It is important that more case studies in different catchments with various hydrological forecasting systems are explored in the future to further verify and improve the proposed hypothetical pattern.

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