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.

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.

A Storm-Triggered Landslide Monitoring and Prediction System: Formulation and Case Study

2010 ◽  
Vol 14 (12) ◽  
pp. 1-24 ◽  
Author(s):  
Diandong Ren ◽  
Lance M. Leslie ◽  
Rong Fu ◽  
Robert E. Dickinson ◽  
Xiang Xin
Keyword(s):  
Lead Time ◽  
Landslide Monitoring ◽  
Lead Times ◽  
Sensitivity Experiment ◽  
The Changjiang River ◽  
Precipitation Morphology ◽  
Series Of Experiments ◽  
Geologic Data ◽  
Rms Errors

Abstract Predicting the location and timing of mudslides with adequate lead time is a scientifically challenging problem that is critical for mitigating landslide impacts. Here, a new dynamic modeling system is described for monitoring and predicting storm-triggered landslides and their ecosystem implications. The model ingests both conventional and remotely sensed topographic and geologic data, whereas outputs include diagnostics required for the assessment of the physical and societal impacts of landslides. The system first was evaluated successfully in a series of experiments under idealized conditions. In the main study, under real conditions, the system was assessed over a mountainous region of China, the Yangjiashan Creeping (YC) slope. For this data-rich section of the Changjiang River, the model estimated creeping rates that had RMS errors of ∼0.5 mm yr−1 when compared with a dataset generated from borehole measurements. A prediction of the creeping curve for 2010 was made that suggested significant slope movement will occur in the next 5 years, without any change in the current precipitation morphology. However, sliding will become imminent if a storm occurs in that 5-yr period that produces over 150 mm of precipitation. A sensitivity experiment shows that the identified location fails first, triggering domino-effect slides that progress upslope. This system for predicting storm-triggered landslides is intended to improve upon present warning lead times to minimize the impacts of shallow, fast moving, and therefore hazardous landslides.

scholarly journals Combination of different types of ensembles for the adaptive simulation of probabilistic flood forecasts: hindcasts for the Mulde 2002 extreme event

2008 ◽  
Vol 15 (2) ◽  
pp. 275-286 ◽  
Author(s):  
J. Dietrich ◽  
S. Trepte ◽  
Y. Wang ◽  
A. H. Schumann ◽  
F. Voß ◽  
...  
Keyword(s):  
Lead Time ◽  
Flood Control ◽  
Flood Management ◽  
Decision Makers ◽  
Flood Event ◽  
Ensemble Prediction ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Extreme Flood

Abstract. Flood forecasts are essential to issue reliable flood warnings and to initiate flood control measures on time. The accuracy and the lead time of the predictions for head waters primarily depend on the meteorological forecasts. Ensemble forecasts are a means of framing the uncertainty of the potential future development of the hydro-meteorological situation. This contribution presents a flood management strategy based on probabilistic hydrological forecasts driven by operational meteorological ensemble prediction systems. The meteorological ensemble forecasts are transformed into discharge ensemble forecasts by a rainfall-runoff model. Exceedance probabilities for critical discharge values and probabilistic maps of inundation areas can be computed and presented to decision makers. These results can support decision makers in issuing flood alerts. The flood management system integrates ensemble forecasts with different spatial resolution and different lead times. The hydrological models are controlled in an adaptive way, mainly depending on the lead time of the forecast, the expected magnitude of the flood event and the availability of measured data. The aforementioned flood forecast techniques have been applied to a case study. The Mulde River Basin (South-Eastern Germany, Czech Republic) has often been affected by severe flood events including local flash floods. Hindcasts for the large scale extreme flood in August 2002 have been computed using meteorological predictions from both the COSMO-LEPS ensemble prediction system and the deterministic COSMO-DE local model. The temporal evolution of a) the meteorological forecast uncertainty and b) the probability of exceeding flood alert levels is discussed. Results from the hindcast simulations demonstrate, that the systems would have predicted a high probability of an extreme flood event, if they would already have been operational in 2002. COSMO-LEPS showed a reasonably good performance within a lead time of 2 to 3 days. Some of the deterministic very short-range forecast initializations were able to predict the dynamics of the event, but others underpredicted rainfall. Thus a lagged average ensemble approach is suggested. The findings from the case study support the often proposed added value of ensemble forecasts and their probabilistic evaluation for flood management decisions.

scholarly journals Estimating the benefits of single value and probability forecasting for flood warning

2011 ◽  
Vol 8 (4) ◽  
pp. 6639-6681 ◽  
Author(s):  
J. S. Verkade ◽  
M. G. F. Werner
Keyword(s):  
Flood Risk ◽  
Lead Time ◽  
False Alarms ◽  
Lead Times ◽  
Flood Warning ◽  
Probabilistic Forecasts ◽  
Response Systems ◽  
The Cost ◽  
Warning Response

Abstract. Flood risk can be reduced by means of flood forecasting, warning and response systems (FFWRS). These systems include a forecasting sub-system which is imperfect, meaning that inherent uncertainties in hydrological forecasts may result in false alarms and missed floods, or surprises. This forecasting uncertainty decreases the potential reduction of flood risk, but is seldom accounted for in estimates of the benefits of FFWRSs. In the present paper, a method to estimate the benefits of (imperfect) FFWRSs in reducing flood risk is presented. These benefits include not only the reduction of flood losses due to a warning response, but also consider the costs of the warning response itself, as well as the costs associated with forecasting uncertainty. The method allows for estimation of the benefits of FFWRSs that use either deterministic or probabilistic forecasts. Through application to a case study, it is shown that FFWRSs using a probabilistic forecast have the potential to realise higher benefits at all lead-times. However, it is also shown that provision of warning at increasing lead-time does not necessarily lead to an increasing reduction of flood risk, but rather that an optimal lead-time at which warnings are provided can be established as a function of forecast uncertainty and the cost-loss ratio of the user receiving and responding to the warning.

Interactions between Physics Diversity and Multiscale Initial Condition Perturbations for Storm-Scale Ensemble Forecasting

2020 ◽  
Vol 148 (8) ◽  
pp. 3549-3565
Author(s):  
Aaron Johnson ◽  
Xuguang Wang
Keyword(s):  
Spatial Scale ◽  
Lead Time ◽  
Cold Pool ◽  
Small Scale ◽  
Lead Times ◽  
Synoptic Scale ◽  
Convective Initiation ◽  
Forecast Performance ◽  
Forecast Lead Time

Abstract This study investigates impacts on convection-permitting ensemble forecast performance of different methods of generating the ensemble IC perturbations in the context of simultaneous physics diversity among the ensemble members. A total of 10 convectively active cases are selected for a systematic comparison of different methods of perturbing IC perturbations in 10-member convection-permitting ensembles, both with and without physics diversity. These IC perturbation methods include simple downscaling of coarse perturbations from a global model (LARGE), perturbations generated with ensemble data assimilation directly on the multiscale domain (MULTI), and perturbations generated using each method with small scales filtered out as a control. MULTI was found to be significantly more skillful than LARGE at early lead times in all ensemble physics configurations, with the advantage of MULTI gradually decreasing with increasing forecast lead time. The advantage of MULTI, relative to LARGE, was reduced but not eliminated by the presence of physics diversity because of the extra ensemble spread that the physics diversity provided. The advantage of MULTI, relative to LARGE, was also reduced by filtering the IC perturbations to a commonly resolved spatial scale in both ensembles, which highlights the importance of flow-dependent small-scale (<~10 m) IC perturbations in the ensemble design. The importance of the physics diversity, relative to the IC perturbation method, depended on the spatial scale of interest, forecast lead time, and the meteorological characteristics of the forecast case. Such meteorological characteristics include the strength of synoptic-scale forcing, the role of cold pool interactions, and the occurrence of convective initiation or dissipation.

scholarly journals Simultaneous Ensemble Postprocessing for Multiple Lead Times with Standardized Anomalies

2017 ◽  
Vol 145 (7) ◽  
pp. 2523-2531 ◽  
Author(s):  
Markus Dabernig ◽  
Georg J. Mayr ◽  
Jakob W. Messner ◽  
Achim Zeileis
Keyword(s):  
Standard Deviation ◽  
Conventional Method ◽  
Numerical Weather Prediction ◽  
Statistical Models ◽  
Lead Time ◽  
Observation Interval ◽  
Weather Prediction ◽  
Lead Times ◽  
Ensemble Forecasts ◽  
Time Specific

Separate statistical models are typically fit for each forecasting lead time to postprocess numerical weather prediction (NWP) ensemble forecasts. Using standardized anomalies of both NWP values and observations eliminates most of the lead-time-specific characteristics so that several lead times can be forecast simultaneously. Standardized anomalies are formed by subtracting a climatological mean and dividing by the climatological standard deviation. Simultaneously postprocessing forecasts between +12 and +120 h increases forecast coherence between lead times, yields a temporal resolution as high as the observation interval (e.g., up to 10 min), and speeds up computation times while achieving a forecast skill comparable to the conventional method.

Achieving Lean Warehousing Through Value Stream Mapping

2020 ◽  
Vol 9 (3) ◽  
pp. 387-401
Author(s):  
Abhishek P.G. ◽  
Maheshwar Pratap
Keyword(s):  
Lead Time ◽  
Value Stream Mapping ◽  
Lead Times ◽  
Value Stream ◽  
Current State ◽  
Future State ◽  
Lean Tools ◽  
Warehouse Operations ◽  
High Lead

This article applies value stream mapping (VSM) in a distribution warehouse after identifying and categorizing different warehousing wastes. The study suggests solutions for the reduction of each type of waste and employs lead time as the metric to understand the overall effectiveness of the suggested remedies. The distribution warehouse faced severe stock-out situations and high lead time for all deliveries. Current state and future state maps were utilized for mapping the current and revamped system, respectively. While existing studies on lean warehousing have utilized VSM to study a specific type of waste, this study extends it to include all types of warehousing waste, classifies them into seven types and provides a real case study along with evidence for improvement schemes for each category. This research, employing a case study, suggests an integrated lean warehousing method for design and operation of distribution warehouses. Dilemma/research question/purpose: Can the warehouse avoid stock-outs and decrease the lead time by identifying and reducing or eliminating the seven types of wastes in the warehouse operations? Theory: Lean management principle applied to a warehouse using value stream mapping to eliminate wastes. Type of the case: A problem-solving using lean tools carried out in a warehouse. Protagonist: Not needed. Options: Allow the current state to continue causing stock-outs and high lead times, or identify and reduce the wastes in the warehouse operations and avoid stock-outs, decrease lead times, and improve the overall efficiency of the warehouse. Discussions and case questions: Is it possible for the firm to go one step further than what the future state map has shown in the study? What other lean tools do you see fit to reduce or eliminate the types of waste outlined in this article?

scholarly journals Baipenzhu Reservoir Inflow Flood Forecasting Based on a Distributed Hydrological Model

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 272
Author(s):  
Shichao Xu ◽  
Yangbo Chen ◽  
Lixue Xing ◽  
Chuan Li
Keyword(s):  
Flood Control ◽  
Hydrological Model ◽  
Forecast Accuracy ◽  
Pso Algorithm ◽  
Flood Forecasting ◽  
Data Sources ◽  
Model Parameters ◽  
Distributed Hydrological Model ◽  
Support Mechanism ◽  
Reservoir Basin

For reservoir basins, complex underlying surface conditions, short flood confluence times, and concentrated water volumes make inflow flood forecasting difficult and cause forecast accuracies to be low. Conventional flood forecasting models can no longer meet the required forecast accuracy values for flood control operations. To give full play to the role of reservoirs in flood control and to maximize the use of reservoir flood resources, high-precision inflow flood forecasting is urgently needed as a support mechanism. In this study, the Baipenzhu Reservoir in Guangdong Province was selected as the study case, and an inflow flood forecast scheme was designed for the reservoir by a physically based distributed hydrological model, the Liuxihe model. The results show that the Liuxihe model has strong applicability for flood forecasting in the studied reservoir basin and that the simulation results are very accurate. This study also found that the use of different Digital Elevation Model (DEM) data sources has a certain impact on the structure of the Liuxihe model, but the constructed models can both simulate the inflow flood process of the Baipenzhu Reservoir well. At the same time, the Liuxihe model can reflect the spatial variation in rainfall well, and using the Particle swarm optimization (PSO) algorithm to optimize the initial model parameters can greatly reduce the uncertainty of the model forecasts. According to China’s hydrological information forecast standards, the Liuxihe model forecast schemes constructed by the two data sources are rated as Grade A and can be used for real-time flood forecasting in the Baipenzhu Reservoir basin.

scholarly journals Limits to Flood Forecasting in the Colorado Front Range for Two Summer Convection Periods Using Radar Nowcasting and a Distributed Hydrologic Model

2013 ◽  
Vol 14 (4) ◽  
pp. 1075-1097 ◽  
Author(s):  
Hernan A. Moreno ◽  
Enrique R. Vivoni ◽  
David J. Gochis
Keyword(s):  
Lead Time ◽  
Flood Forecasting ◽  
Hydrologic Model ◽  
Colorado Front Range ◽  
Precipitation Forecast ◽  
Lead Times ◽  
Forecast Errors ◽  
Front Range ◽  
Distributed Hydrologic Model ◽  
Forecast Lead Time

Abstract Flood forecasting in mountain basins remains a challenge given the difficulty in accurately predicting rainfall and in representing hydrologic processes in complex terrain. This study identifies flood predictability patterns in mountain areas using quantitative precipitation forecasts for two summer events from radar nowcasting and a distributed hydrologic model. The authors focus on 11 mountain watersheds in the Colorado Front Range for two warm-season convective periods in 2004 and 2006. The effects of rainfall distribution, forecast lead time, and basin area on flood forecasting skill are quantified by means of regional verification of precipitation fields and analyses of the integrated and distributed basin responses. The authors postulate that rainfall and watershed characteristics are responsible for patterns that determine flood predictability at different catchment scales. Coupled simulations reveal that the largest decrease in precipitation forecast skill occurs between 15- and 45-min lead times that coincide with rapid development and movements of convective systems. Consistent with this, flood forecasting skill decreases with nowcasting lead time, but the functional relation depends on the interactions between watershed properties and rainfall characteristics. Across the majority of the basins, flood forecasting skill is reduced noticeably for nowcasting lead times greater than 30 min. The authors identified that intermediate basin areas [~(2–20) km2] exhibit the largest flood forecast errors with the largest differences across nowcasting ensemble members. The typical size of summer convective storms is found to coincide well with these maximum errors, while basin properties dictate the shape of the scale dependency of flood predictability for different lead times.

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