scholarly journals Potential application of hydrological ensemble prediction in forecasting flood and its components over the Yarlung Zangbo River Basin, China

2018 ◽  
Author(s):  
Li Liu ◽  
Su L. Pan ◽  
Zhi X. Bai ◽  
Yue P. Xu
Keyword(s):  
River Basin ◽  
Lead Time ◽  
Flood Forecasting ◽  
Ensemble Prediction ◽  
Infiltration Capacity ◽  
Weather Forecasts ◽  
Flood Volume ◽  
Yarlung Zangbo River ◽  
Forecasting System

Abstract. In recent year, flood becomes a serious issue in Tibetan Plateau (TP) due to climate change. Many studies have shown that ensemble flood forecasting based on numerical weather predictions can provide early warning with extended lead time. However, the role of hydrological ensemble prediction in forecasting flood volume and its components over the Yarlung Zangbo River Basin (YZR), China has not been systematically investigated. This study adopts Variable Infiltration Capacity (VIC) model to forecast annual maximum floods (MF) and annual first floods (FF) in YZR based on precipitation, maximum and minimum temperature from European Centre for Medium-Range Weather Forecasts (ECMWF). N-simulations is proposed to account for more scenarios of parameters in VIC and shows improved flood simulation. Ensemble flood forecasting system can skilfully predict MF with a lead time of more than10 days, and has skill in forecasting the snowmelt-related components in about 7 days ahead. The accuracy of forecasts for FF is inferior with a lead time of only 5 days. The performance in 7-day accumulated flood volumes is better than the peak flows. The components in baseflow for FF are irrelevant to lead time, whilst for MF an obvious deterioration in performance with lead time can be perceived. The snowmelt-induced surface runoff is the most poorly captured component by the system, and the well-predicted rainfall-related components are the major contributor for good performance. From this study, it is concluded that snowmelt-induced flood volume plays an important role in YZR Basin especially in FF.

scholarly journals Potential application of hydrological ensemble prediction in forecasting floods and its components over the Yarlung Zangbo River basin, China

2019 ◽  
Vol 23 (8) ◽  
pp. 3335-3352 ◽  
Author(s):  
Li Liu ◽  
Yue Ping Xu ◽  
Su Li Pan ◽  
Zhi Xu Bai
Keyword(s):  
Lead Time ◽  
Flood Forecasting ◽  
Base Flow ◽  
Ensemble Prediction ◽  
The Tibetan Plateau ◽  
Infiltration Capacity ◽  
Trade Offs ◽  
Yarlung Zangbo River ◽  
Forecasting System ◽  
Flow Components

Abstract. In recent year, floods becomes a serious issue in the Tibetan Plateau (TP) due to climate change. Many studies have shown that ensemble flood forecasting based on numerical weather predictions can provide an early warning with extended lead time. However, the role of hydrological ensemble prediction in forecasting flood volume and its components over the Yarlung Zangbo River (YZR) basin, China, has not been investigated. This study adopts the variable infiltration capacity (VIC) model to forecast the annual maximum floods and annual first floods in the YZR based on precipitation and the maximum and minimum temperature from the European Centre for Medium-Range Weather Forecasts (ECMWF). N simulations are proposed to account for parameter uncertainty in VIC. Results show that when trade-offs between multiple objectives are significant, N simulations are recommended for better simulation and forecasting. This is why better results are obtained for the Nugesha and Yangcun stations. Our ensemble flood forecasting system can skillfully predict the maximum floods with a lead time of more than 10 d and can predict about 7 d ahead for meltwater-related components. The accuracy of forecasts for the first floods is inferior, with a lead time of only 5 d. The base-flow components for the first floods are insensitive to lead time, except at the Nuxia station, whilst for the maximum floods an obvious deterioration in performance with lead time can be recognized. The meltwater-induced surface runoff is the most poorly captured component by the forecast system, and the well-predicted rainfall-related components are the major contributor to good performance. The performance in 7 d accumulated flood volumes is better than the peak flows.

scholarly journals Assessing the Potential of Upcoming Satellite Altimeter Missions in Operational Flood Forecasting Systems

2021 ◽  
Vol 13 (21) ◽  
pp. 4459
Author(s):  
Aline Falck ◽  
Javier Tomasella ◽  
Fabrice Papa
Keyword(s):  
Data Collection ◽  
Lead Time ◽  
Flood Forecasting ◽  
Lead Times ◽  
Latency Time ◽  
Distributed Hydrological Model ◽  
Satellite Altimeter ◽  
Operational Characteristics ◽  
Potential Benefits ◽  
Forecasting System

This study investigates the potential of observations with improved frequency and latency time of upcoming altimetry missions on the accuracy of flood forecasting and early warnings. To achieve this, we assessed the skill of the forecasts of a distributed hydrological model by assimilating different historical discharge time frequencies and latencies in a framework that mimics an operational forecast system, using the European Ensemble Forecasting system as the forcing. Numerical experiments were performed in 22 sub-basins of the Tocantins-Araguaia Basin. Forecast skills were evaluated in terms of the Relative Operational Characteristics (ROC) as a function of the drainage area and the forecasts’ lead time. The results showed that increasing the frequency of data collection and reducing the latency time (especially 1 d update and low latency) had a significant impact on steep headwater sub-basins, where floods are usually more destructive. In larger basins, although the increased frequency of data collection improved the accuracy of the forecasts, the potential benefits were limited to the earlier lead times.

scholarly journals Dynamical Extended-Range Prediction of Early Monsoon Rainfall over India

2009 ◽  
Vol 137 (4) ◽  
pp. 1480-1492 ◽  
Author(s):  
Frédéric Vitart ◽  
Franco Molteni
Keyword(s):  
Lead Time ◽  
Monsoon Rainfall ◽  
Horizontal Resolution ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Variable Resolution ◽  
Sensitivity Experiments ◽  
Extended Range ◽  
Forecasting System

Abstract The 15-member ensembles of 46-day dynamical forecasts starting on each 15 May from 1991 to 2007 have been produced, using the ECMWF Variable Resolution Ensemble Prediction System monthly forecasting system (VarEPS-monthy). The dynamical model simulates a realistic interannual variability of Indian precipitation averaged over the month of June. It also displays some skill to predict Indian precipitation averaged over pentads up to a lead time of about 30 days. This skill exceeds the skill of the ECMWF seasonal forecasting System 3 starting on 1 June. Sensitivity experiments indicate that this is likely due to the higher horizontal resolution of VarEPS-monthly. Another series of sensitivity experiments suggests that the ocean–atmosphere coupling has an important impact on the skill of the monthly forecasting system to predict June rainfall over India.

scholarly journals Recommendations for Improving Integration in National End-to-End Flood Forecasting Systems: An Overview of the FFIR (Flooding From Intense Rainfall) Programme

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 725 ◽  
Author(s):  
David Flack ◽  
Christopher Skinner ◽  
Lee Hawkness-Smith ◽  
Greg O’Donnell ◽  
Robert Thompson ◽  
...  
Keyword(s):  
Surface Water ◽  
Lead Time ◽  
End Users ◽  
Intense Rainfall ◽  
Weather Forecasts ◽  
Propagation Of Uncertainty ◽  
Effective Dissemination ◽  
Forecasting System ◽  
End To End ◽  
The Uk

Recent surface-water and flash floods have caused millions of pounds worth of damage in the UK. These events form rapidly and are difficult to predict due to their short-lived and localised nature. The interdisciplinary Flooding From Intense Rainfall (FFIR) programme investigated the feasibility of enhancing the integration of an end-to-end forecasting system for flash and surface-water floods to help increase the lead time for warnings for these events. Here we propose developments to the integration of an operational end-to-end forecasting system based on the findings of the FFIR programme. The suggested developments include methods to improve radar-derived rainfall rates and understanding of the uncertainty in the position of intense rainfall in weather forecasts; the addition of hydraulic modelling components; and novel education techniques to help lead to effective dissemination of flood warnings. We make recommendations for future advances such as research into the propagation of uncertainty throughout the forecast chain. We further propose the creation of closer bonds to the end users to allow for an improved, integrated, end-to-end forecasting system that is easily accessible for users and end users alike, and will ultimately help mitigate the impacts of flooding from intense rainfall by informed and timely action.

scholarly journals Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology

2018 ◽  
Vol 18 (8) ◽  
pp. 2183-2202 ◽  
Author(s):  
Ekrem Canli ◽  
Martin Mergili ◽  
Benni Thiebes ◽  
Thomas Glade
Keyword(s):  
High Performance ◽  
Weather Forecasting ◽  
Flood Forecasting ◽  
Ensemble Prediction ◽  
Operational Mode ◽  
Physically Based ◽  
Landslide Forecasting ◽  
Forecasting System ◽  
Prediction Systems ◽  
Performance Computing

Abstract. Landslide forecasting and early warning has a long tradition in landslide research and is primarily carried out based on empirical and statistical approaches, e.g., landslide-triggering rainfall thresholds. In the last decade, flood forecasting started the operational mode of so-called ensemble prediction systems following the success of the use of ensembles for weather forecasting. These probabilistic approaches acknowledge the presence of unavoidable variability and uncertainty when larger areas are considered and explicitly introduce them into the model results. Now that highly detailed numerical weather predictions and high-performance computing are becoming more common, physically based landslide forecasting for larger areas is becoming feasible, and the landslide research community could benefit from the experiences that have been reported from flood forecasting using ensemble predictions. This paper reviews and summarizes concepts of ensemble prediction in hydrology and discusses how these could facilitate improved landslide forecasting. In addition, a prototype landslide forecasting system utilizing the physically based TRIGRS (Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability) model is presented to highlight how such forecasting systems could be implemented. The paper concludes with a discussion of challenges related to parameter variability and uncertainty, calibration and validation, and computational concerns.

scholarly journals An experimental seasonal hydrological forecasting system over the Yellow River basin – Part 1: Understanding the role of initial hydrological conditions

2016 ◽  
Vol 20 (6) ◽  
pp. 2437-2451 ◽  
Author(s):  
Xing Yuan ◽  
Feng Ma ◽  
Linying Wang ◽  
Ziyan Zheng ◽  
Zhuguo Ma ◽  
...  
Keyword(s):  
River Basin ◽  
Rainy Season ◽  
Yellow River ◽  
Yellow River Basin ◽  
The Yellow River ◽  
Hydrological Forecasting ◽  
Hydrological Extremes ◽  
Forecasting System ◽  
The Yellow River Basin

Abstract. The hydrological cycle over the Yellow River has been altered by the climate change and human interventions greatly during past decades, with a decadal drying trend mixed with a large variation of seasonal hydrological extremes. To provide support for the adaptation to a changing environment, an experimental seasonal hydrological forecasting system is established over the Yellow River basin. The system draws from a legacy of a global hydrological forecasting system that is able to make use of real-time seasonal climate predictions from North American Multimodel Ensemble (NMME) climate models through a statistical downscaling approach but with a higher resolution and a spatially disaggregated calibration procedure that is based on a newly compiled hydrological observation dataset with 5 decades of naturalized streamflow at 12 mainstream gauges and a newly released meteorological observation dataset including 324 meteorological stations over the Yellow River basin. While the evaluation of the NMME-based seasonal hydrological forecasting will be presented in a companion paper to explore the added values from climate forecast models, this paper investigates the role of initial hydrological conditions (ICs) by carrying out 6-month Ensemble Streamflow Prediction (ESP) and reverse ESP-type simulations for each calendar month during 1982–2010 with the hydrological models in the forecasting system, i.e., a large-scale land surface hydrological model and a global routing model that is regionalized over the Yellow River. In terms of streamflow predictability, the ICs outweigh the meteorological forcings up to 2–5 months during the cold and dry seasons, but the latter prevails over the former in the predictability after the first month during the warm and wet seasons. For the streamflow forecasts initialized at the end of the rainy season, the influence of ICs for lower reaches of the Yellow River can be 5 months longer than that for the upper reaches, while such a difference drops to 1 month during the rainy season. Based on an additional ESP-type simulation without the initialization of the river routing model, it is found that the initial surface water state is the main source of streamflow predictability during the first month, beyond which other sources of terrestrial memory become more important. During the dry/wet periods, the dominance of ICs on the streamflow predictability can be extended by a month even in the rainy season, suggesting the usefulness of the ESP forecasting approach after the onset of the hydrological extreme events. Similar results are found for the soil moisture predictability but with longer influences from ICs. And the simulations indicate that the soil moisture memory is longer over the middle reaches than those over the upper and lower reaches of the Yellow River. The naturalized hydrological predictability analysis in this study will provide a guideline for establishing an operational hydrological forecasting system as well as for managing the risks of hydrological extremes over the Yellow River basin.

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