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 The applicability of real-time flood forecasting correction techniques coupled with the Muskingum method

2019 ◽  
Vol 51 (1) ◽  
pp. 17-29 ◽  
Author(s):  
Ruixiang Yang ◽  
Baodeng Hou ◽  
Weihua Xiao ◽  
Chuan Liang ◽  
Xuelei Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Lead Time ◽  
Flood Management ◽  
Flood Forecasting ◽  
Lead Times ◽  
Combination Forecasting ◽  
Permissible Range ◽  
Forecasting Performance ◽  
Muskingum Method ◽  
Better Than

Abstract Improving flood forecasting performance is critical for flood management. Real-time flood forecasting correction techniques (e.g., proportional correction (PC) and Kalman filter (KF)) coupled with the Muskingum method improve the forecasting performance but have limitations (e.g., short lead times and inadequate performance, respectively). Here, particle filter (PF) and combination forecasting (CF) are coupled with the Muskingum method and then applied to 10 flood events along the Shaxi River, China. Two indexes (overall consistency and permissible range) are selected to compare the performances of PC, KF, PF and CF for 3 h lead time. The changes in overall consistency for different lead times (1–6 h) are used to evaluate the applicability of PC, KF, PF and CF. The main conclusions are as follows: (1) for 3 h lead time, the two indexes indicate that the PF performance is optimal, followed in order by KF and PC; CF performance is close to PF and better than KF. (2) The performance of PC decreases faster than that of KF and PF with increases in the lead time. PC and PF are applicable for short (1–2 h) and long lead times (3–6 h), respectively. CF is applicable for 1–6 h lead times; however, it has no advantage over PC and PF for short and long lead times, respectively, which may be due to insufficient training and increase in cumulative errors.

scholarly journals The suitability of remotely sensed soil moisture for improving operational flood forecasting

2013 ◽  
Vol 10 (11) ◽  
pp. 13783-13816 ◽  
Author(s):  
N. Wanders ◽  
D. Karssenberg ◽  
A. de Roo ◽  
S. M. de Jong ◽  
M. F. P. Bierkens
Keyword(s):  
Soil Moisture ◽  
Flood Forecasting ◽  
Remotely Sensed ◽  
Added Value ◽  
Validation Dataset ◽  
Lead Times ◽  
Distributed Hydrological Model ◽  
Danube Basin ◽  
Discharge Data ◽  
Soil Moisture Data

Abstract. We evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the prediction of the timing and height of the flood peak and low flows. EFAS is an operational flood forecasting system for Europe and uses a distributed hydrological model for flood predictions with lead times up to 10 days. For this study, satellite-derived soil moisture from ASCAT, AMSR-E and SMOS is assimilated into the EFAS system for the Upper Danube basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into EFAS, an Ensemble Kalman Filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure optimal performance of the EnKF. For the validation, additional discharge observations not used in the EnKF, are used as an independent validation dataset. Our results show that the accuracy of flood forecasts is increased when more discharge observations are assimilated; the Mean Absolute Error (MAE) of the ensemble mean is reduced by 65%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of base flows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the Continuous Ranked Probability Score (CRPS) shows a performance increase of 5–10% on average, compared to assimilation of discharge only. When soil moisture data is used, the timing errors in the flood predictions are decreased especially for shorter lead times and imminent floods can be forecasted with more skill. The number of false flood alerts is reduced when more data is assimilated into the system and the best performance is achieved with the assimilation of both discharge and satellite observations. The additional gain is highest when discharge observations from both upstream and downstream areas are used in combination with the soil moisture data. These results show the potential of remotely sensed soil moisture observations to improve near-real time flood forecasting in large catchments.

scholarly journals Extending flood forecasting lead time in large watershed by coupling WRF QPF with distributed hydrological model

2016 ◽  
Author(s):  
Ji Li ◽  
Yangbo Chen ◽  
Huanyu Wang ◽  
Jianming Qin ◽  
Jie Li
Keyword(s):  
Lead Time ◽  
Hydrological Model ◽  
Southern China ◽  
Weather Forecast ◽  
Forecast Model ◽  
Flood Forecasting ◽  
Rain Gauge ◽  
Model Parameters ◽  
Distributed Hydrological Model ◽  
Flood Warning

Abstract. Long lead time flood forecasting is very important for large watershed flood mitigation as it provides more time for flood warning and emergency responses. Latest numerical weather forecast model could provide 1–15 days quantitative precipitation forecasting products at grid format, by coupling this product with distributed hydrological model could produce long lead time watershed flood forecasting products. This paper studied the feasibility of coupling the Liuxihe Model with the WRF QPF for a large watershed flood forecasting in southern China. The QPF of WRF products has three lead time, including 24 hour, 48 hour and 72 hour, the grid resolution is 20 km × 20 km. The Liuxihe Model is set up with freely downloaded terrain property, the model parameters were previously optimized with rain gauge observed precipitation, and re-optimized with WRF QPF. Results show that the WRF QPF has bias with the rain gauge precipitation, and a post-processing method is proposed to post process the WRF QPF products, which improves the flood forecasting capability. With model parameter re-optimization, the model's performance improves also, it suggests that the model parameters be optimized with QPF, not the rain gauge precipitation. With the increasing of lead time, the accuracy of WRF QPF decreases, so does the flood forecasting capability. Flood forecasting products produced by coupling Liuxihe Model with WRF QPF provides good reference for large watershed flood warning due to its long lead time and rational results.

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 A pan-African Flood Forecasting System

2014 ◽  
Vol 11 (5) ◽  
pp. 5559-5597 ◽  
Author(s):  
V. Thiemig ◽  
B. Bisselink ◽  
F. Pappenberger ◽  
J. Thielen
Keyword(s):  
Large Scale ◽  
Practical Implication ◽  
River Basins ◽  
Flood Forecasting ◽  
Small Scale ◽  
Lead Times ◽  
Flood Events ◽  
Pan African ◽  
Long Duration ◽  
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 of the ECMWF and critical hydrological thresholds. In this paper the predictive capability is investigated in a hindcast mode, by reproducing hydrological predictions for the year 2003 where important floods were observed. Results were verified with ground measurements of 36 subcatchments as well as with 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 "Save flooding" 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.

scholarly journals Improving efficiencies of flood forecasting during lead times: an operational method and its application in the Baiyunshan Reservoir

2018 ◽  
Vol 50 (2) ◽  
pp. 709-724 ◽  
Author(s):  
Pan Liu ◽  
Xiaojing Zhang ◽  
Yan Zhao ◽  
Chao Deng ◽  
Zejun Li ◽  
...  
Keyword(s):  
Objective Function ◽  
Conventional Method ◽  
Lead Time ◽  
Flood Control ◽  
Flood Forecasting ◽  
Lead Times ◽  
Forecast Performance ◽  
Reservoir Basin ◽  
Hydrological Parameter

Abstract Accurate and reliable flood forecasting plays an important role in flood control, reservoir operation, and water resources management. Conventional hydrological parameter calibration is based on an objective function without consideration for forecast performance during lead-time periods. A novel objective function, i.e., minimizing the sum of the squared errors between forecasted and observed streamflow during multiple lead times, is proposed to calibrate hydrological parameters for improved forecasting. China's Baiyunshan Reservoir basin was selected as a case study, and the Xinanjiang model was used. The proposed method provided better results for peak flows, in terms of the value and occurrence time, than the conventional method. Specifically, the qualified rate of peak flow for 4-, 5-, and 6-h lead times in the proposed method were 69.2%, 53.8%, and 38.5% in calibration, and 60%, 40%, and 20% in validation, respectively. This compares favorably with the corresponding values for the conventional method, which were 53.8%, 15.4%, and 7.7% in calibration, and 20%, 20%, and 0% in validation, respectively. Uncertainty analysis revealed that the proposed method caused less parameter uncertainty than the conventional method. Therefore, the proposed method is effective in improving the performance during multiple lead times for flood mitigation.

Ensemble Flood Forecasting of Typhoons Talas and Roke at Hiyoshi Dam Basin

2016 ◽  
Vol 11 (6) ◽  
pp. 1032-1039 ◽  
Author(s):  
Tomoki Ushiyama ◽  
◽  
Takahiro Sayama ◽  
Yoichi Iwami ◽  
◽  
...  
Keyword(s):  
Weather Prediction ◽  
Flood Forecasting ◽  
Quantitative Prediction ◽  
Ensemble Prediction ◽  
Distributed Hydrological Model ◽  
Ensemble Prediction System ◽  
Forecasting System ◽  
Flood Warnings ◽  
Better Than ◽  
Small Basin

In order to be able to issue flood warnings not hours but days in advance, numerical weather prediction (NWP) is essential to the forecasting of flood-producing rainfall. The regional ensemble prediction system (EPS), advanced NWP on a local scale, has a high potential to improve flood forecasting through the quantitative prediction of precipitation. In this study, the predictability of floods using the ensemble flood forecasting system, which is composed of regional EPS and a distributed hydrological model, was investigated. Two flood events which took place in a small basin in Japan in 2010 and which were caused by typhoons Talas and Roke were examined. As the forecasting system predicted the probability of flood occurrence at least 24 h beforehand in the case of both typhoons, these forecasts were better than deterministic forecasts. However, the system underestimated the peak of the flooding in the typhoon Roke event, and it was too early in its prediction of the appearance of the peak of the flooding in the Talas event. Although the system has its limitations, it has proved to have the potential to produce early flood warnings.

scholarly journals Evaluation of a probabilistic hydrometeorological forecast system

2009 ◽  
Vol 13 (7) ◽  
pp. 1031-1043 ◽  
Author(s):  
S. Jaun ◽  
B. Ahrens
Keyword(s):  
Horizontal Resolution ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Limited Area ◽  
Prediction System ◽  
Distributed Hydrological Model ◽  
Probabilistic Forecasting ◽  
Ensemble Prediction System ◽  
Forecasting System ◽  
Ensemble Systems

Abstract. Medium range hydrological forecasts in mesoscale catchments are only possible with the use of hydrological models driven by meteorological forecasts, which in particular contribute quantitative precipitation forecasts (QPF). QPFs are accompanied by large uncertainties, especially for longer lead times, which are propagated within the hydrometeorological model system. To deal with this limitation of predictability, a probabilistic forecasting system is tested, which is based on a hydrological-meteorological ensemble prediction system. The meteorological component of the system is the operational limited-area ensemble prediction system COSMO-LEPS that downscales the global ECMWF ensemble to a horizontal resolution of 10 km, while the hydrological component is based on the semi-distributed hydrological model PREVAH with a spatial resolution of 500 m. Earlier studies have mostly addressed the potential benefits of hydrometeorological ensemble systems in short case studies. Here we present an analysis of hydrological ensemble hindcasts for two years (2005 and 2006). It is shown that the ensemble covers the uncertainty during different weather situations with appropriate spread. The ensemble also shows advantages over a corresponding deterministic forecast, even under consideration of an artificial spread.

Sign in / Sign up

Export Citation Format

Share Document