Effective Lead Time Research Articles

Improving categorical and continuous accuracy of precipitation forecasts by integrating Empirical Quantile Mapping and Bernoulli-Gamma-Gaussian distribution

Statistical post-processing is a pivotal approach in enhancing the statistical accuracy and applicability of precipitation forecasts from numerical weather prediction models. The existing methods primarily focus on correcting errors in either categorical or quantitative aspects individually, failing to address both simultaneously. Furthermore, the benefits of post-processing in extending the effective lead time (ELT) have not been quantitatively examined. In this study, an integrated statistical post-processing method by combining the Empirical Quantile Mapping (EQM) and the Bernoulli-Gamma-Gaussian model (BGG) (EQM-BGG for short) is constructed, and a comprehensive accuracy metric for evaluating the ELT is also proposed. Taking the Bengbu Basin located in the upper and middle reaches of the Huai River Basin as the study area, the statistical post-processing using the three methods (EQM, BGG, EQM-BGG) is performed on the multi-model weighted integration forecast precipitation (MWIFP), which are derived from three numerical models, namely ECMWF, NCEP and CMA. The results demonstrate that the integration of EQM-BGG capitalizes on the combined strengths of EQM and BGG, facilitating a dual correction of categorical and quantitative errors. The forecast accuracy (FA) and mean absolute error (MAE) of the post-processed basin-average forecasts are enhanced by more than 10%, and the ELTs for both the entire basin and sub-basins are extended by 12–78 h. At the grid scale, EQM-BGG significantly reduces the decay rate of forecast accuracy and the expansion speed of low-accuracy regions as the lead time increases, the improvement of FAs and MAEs surpasses 10% and 20%, respectively, across all grids within the lead time of 96–102 h. Furthermore, certain grids in the northwestern part show an extension of ELTs by 18 to 54 h. These findings highlight that EQM-BGG significantly enhances both categorical and quantitative accuracy simultaneously, resulting in precipitation forecasts that exhibit greater reliability over longer lead times. This advancement enriches the methodologies for hydro-meteorological forecasts.

Post-Processing Ensemble Precipitation Forecasts and Their Applications in Summer Streamflow Prediction over a Mountain River Basin

Ensemble precipitation forecasts (EPFs) can help to extend lead times and provide reliable probabilistic forecasts, which have been widely applied for streamflow predictions by driving hydrological models. Nonetheless, inherent biases and under-dispersion in EPFs require post-processing for accurate application. It is imperative to explore the skillful lead time of post-processed EPFs for summer streamflow predictions, particularly in mountainous regions. In this study, four popular EPFs, i.e., the CMA, ECMWF, JMA, and NCEP, were post-processed by two state of art methods, i.e., the Bayesian model averaging (BMA) and generator-based post-processing (GPP) methods. These refined forecasts were subsequently integrated with the Xin’anjiang (XAJ) model for summer streamflow prediction. The performances of precipitation forecasts and streamflow predictions were comprehensively evaluated before and after post-processing. The results reveal that raw EPFs frequently deviate from ensemble mean forecasts, particularly underestimating torrential rain. There are also clear underestimations of uncertainty in their probabilistic forecasts. Among the four EPFs, the ECMWF outperforms its peers, delivering skillful precipitation forecasts for 1–7 lead days and streamflow predictions for 1–4 lead days. The effectiveness of post-processing methods varies, yet both GPP and BMA address the under-dispersion of EPFs effectively. The GPP method, recommended as the superior method, can effectively improve both deterministic and probabilistic forecasting accuracy. Moreover, the ECMWF post-processed by GPP extends the effective lead time to seven days and reduces the underestimation of peak flows. The findings of this study underscore the potential benefits of adeptly post-processed EPFs, providing a reference for streamflow prediction over mountain river basins.

Enhancement of Cruise Boat Resilience to Strong Convective Gusts with Global Model Cumulus Variable Prediction

Ship pilots and maritime safety administration have an urgent need for more accurate and earlier warnings for strong wind gusts. This study firstly investigated the “Oriental Star” cruise ship capsizing event in 2015, one of the deadliest shipwreck events in recent years, and explored all related hydro-meteorological components in a global mesoscale model. It was found that rather than the missing signal in raw surface-wind prediction, the cumulus precipitation variable (CP) increased dramatically during the accident occurrence, which significantly corresponds to a sub-grid strong wind gust. The effective lead time could be extended from 24 h (deterministic model) to 48 h (ensemble model). This finding was then verified in another two recent deadly cruise boat accidents. The introduction of the new variable aims to improve the current maritime safeguard system in predicting sub-grid strong wind gusts for small-sized cruise boats offshore and in inland rivers. Finally, an automatic response system was developed to provide economical convection prediction via INMARSAT email communication, aiming to explore operational severe convective gust early warning and appropriate numerical mesoscale model applications.

Application of Variational Optical Flow Forecasting Technique Based on Precipitation Spectral Decomposition to Three Case Studies of Heavy Precipitation Events during Rainy Season in Hebei Province

Short-term heavy precipitation is a crucial factor that triggers urban waterlogging and flash flood disasters, which impact human production and livelihood. Traditional short-term forecasting methods have time- and scale-based limitations. To achieve timely, location-specific, and quantitative precipitation forecasting, this study applies the precipitation spectral decomposition algorithm, along with variational echo tracking and autoregressive AR2 extrapolation techniques, to forecast three cases of heavy precipitation events during the rainy season in Hebei Province. The variational optical flow extrapolation forecasting based on precipitation spectral decomposition has a forecasting lead time of up to 3 h. However, noticeable discrepancies in forecast accuracy can be observed around 2 h, and the forecasting skill gradually weakens with longer lead times. For 3 h lead time forecasts, substantial variability occurs among different performance metrics, lacking clear comparability. The effective forecast lead time for variational optical flow forecasting based on precipitation spectral decomposition is up to 1.6 h for severe convective weather systems and up to 2.2 h for stratiform cloud weather systems. Overall, the forecast effect of this method is good in the three rainfalls—the highest CSI is up to 0.74, the highest POD is up to 0.87, and the forecast accuracy and success rate are high.

Can a Machine Learning–Enabled Numerical Model Help Extend Effective Forecast Range through Consistently Trained Subgrid-Scale Models?

Abstract The development of machine learning (ML) techniques enables data-driven parameterizations, which have been investigated in many recent studies. Some investigations suggest that a priori-trained ML models exhibit satisfying accuracy during training but poor performance when coupled to dynamical cores and tested. Here we use the evolution of the barotropic vorticity equation (BVE) with periodically reinforced shear instability as a prototype problem to develop and evaluate a model-consistent training strategy, which employs a numerical solver supporting automatic differentiation and includes the solver in the loss function for training ML-based subgrid-scale (SGS) turbulence models. This approach enables the interaction between the dynamical core and the ML-based parameterization during the model training phase. The BVE model was run at low, high, and ultrahigh (truth) resolutions. Our training dataset contains only a short period of coarsened high-resolution simulations. However, given initial conditions long after the training dataset time, the trained SGS model can still significantly increase the effective lead time of the BVE model running at the low resolution by up to 50% relative to the BVE simulation without an SGS model. We also tested using a covariance matrix to normalize the loss function and found it can notably boost the performance of the ML parameterization. The SGS model’s performance is further improved by conducting transfer learning using a limited number of discontinuous observations, increasing the forecast lead-time improvement to 73%. This study demonstrates a potential pathway to using machine learning to enhance the prediction skills of our climate and weather models. Significance Statement Numerical weather prediction is performed at limited resolution for computational feasibility, and the schemes to estimate unresolved processes are called parameterization. We propose a strategy to develop better deep learning–based parameterization in which an automatic differentiable numerical solver is employed as the dynamic core and interacts with the parameterization scheme during its training. Such a numerical solver enables consistent deep learning, because the parameterization is trained with a direct target of making the numerical model (dynamic core and parameterization together) forecast match observations as much as possible. We demonstrate the feasibility and effectiveness of such a strategy using a surrogate model and advocate that such machine learning–enabled numerical models provide a promising pathway to developing next-generation weather forecast and climate models.

Analyzing lead time for flood early warning system in the upstream Citarum River

The Citarum River is a staple water source for the people of West Java, irrigating rice fields in West Java, generating electricity in Java and Bali, supplying water to Jakarta, and operating hydroelectric dams. West Java is prone to flooding because of the concave topography of the Upper Citarum. Floods in Majalaya and Sapan are typically of short duration, with water levels increasing rapidly. Therefore, implementing a predictive early warning system is essential for effective flood control. This study examines lead times utilizing Global Satellite Mapping of Precipitation (GSMaP) data, water levels, and discharge information to provide Majalaya and Sapan communities with early flood warnings and enable timely evacuations. The analysis reveals that Majalaya’s lead time is often less than an hour (with 76% incidence and 24% risk), while Sapan's lead time is commonly less than four hours (with 86% occurrence and 14% risk). Utilizing effective lead times enhances the preparation before a flood in the study area. Given the importance of pre-flood evacuation lead times, this research can inform the development of early warning systems in Indonesia, particularly in Majalaya and Sapan.

ENSO‐GTC: ENSO Deep Learning Forecast Model With a Global Spatial‐Temporal Teleconnection Coupler

AbstractContinuing the tropical Pacific multivariate air‐sea coupler proposed by us before, we design the Global spatial‐temporal Teleconnection Coupler (GTC), which is modeled to discover the latent teleconnections among global sea surface temperature (SST). To this end, Pacific, Indian, and Atlantic oceans are divided into small ocean patches that compose a dynamics graph, in which the adjacent relationships are artificially constructed by prior knowledge and the non‐adjacent relationships are learned from the data by deep learning methods. Based on GTC, an El Niño‐Southern Oscillation (ENSO) deep learning forecast model (ENSO‐GTC) is established, where probability graph convolution layers are designed to learn spatial‐temporal teleconnection, that is, non‐adjacent relationships in the dynamics graph. A loss function with a graph total variations penalty term is remarkably proposed to maintain physical consistency. We tune ENSO‐GTC to the optimal, which has Niño3.4 index correlation skills of 0.79/0.66/0.51 at 6‐/12‐/18‐month forecasts with iterative strategy, and above 0.6 within 20‐month forecasts with direct strategy, outperforming the other state‐of‐the‐art models. We explore the forecast skill of ENSO‐GTC on the effective forecast lead time, improvements of persistence barrier, and analysis of forecast errors. Moreover, we find that what GTC has learned exactly matches multiple ENSO theories. The oscillations of Kelvin and Rossby waves make a 6‐month lagged correlation on Pacific SST. The north and south Pacific meridional modes (NPMM and SPMM) are strongly linked to the evolutions of ENSO and should be monitored more. The teleconnections between equatorial Indian/Atlantic and Pacific are very important and usually have 2‐month/8‐month lagged correlations before ENSO.

Predicting malaria outbreaks from sea surface temperature variability up to 9 months ahead in Limpopo, South Africa, using machine learning.

Malaria is the cause of nearly half a million deaths worldwide each year, posing a great socioeconomic burden. Despite recent progress in understanding the influence of climate on malaria infection rates, climatic sources of predictability remain poorly understood and underexploited. Local weather variability alone provides predictive power at short lead times of 1–2 months, too short to adequately plan intervention measures. Here, we show that tropical climatic variability and associated sea surface temperature over the Pacific and Indian Oceans are valuable for predicting malaria in Limpopo, South Africa, up to three seasons ahead. Climatic precursors of malaria outbreaks are first identified via lag-regression analysis of climate data obtained from reanalysis and observational datasets with respect to the monthly malaria case count data provided from 1998–2020 by the Malaria Institute in Tzaneen, South Africa. Out of 11 sea surface temperature sectors analyzed, two regions, the Indian Ocean and western Pacific Ocean regions, emerge as the most robust precursors. The predictive value of these precursors is demonstrated by training a suite of machine-learning classification models to predict whether malaria case counts are above or below the median historical levels and assessing their skills in providing early warning predictions of malaria incidence with lead times ranging from 1 month to a year. Through the development of this prediction system, we find that past information about SST over the western Pacific Ocean offers impressive prediction skills (~80% accuracy) for up to three seasons (9 months) ahead. SST variability over the tropical Indian Ocean is also found to provide good skills up to two seasons (6 months) ahead. This outcome represents an extension of the effective prediction lead time by about one to two seasons compared to previous prediction systems that were more computationally costly compared to the machine learning techniques used in the current study. It also demonstrates the value of climatic information and the prediction framework developed herein for the early planning of interventions against malaria outbreaks.

ENSO-ASC 1.0.0: ENSO deep learning forecast model with a multivariate air–sea coupler

Abstract. The El Niño–Southern Oscillation (ENSO) is an extremely complicated ocean–atmosphere coupling event, the development and decay of which are usually modulated by the energy interactions between multiple physical variables. In this paper, we design a multivariate air–sea coupler (ASC) based on the graph using features of multiple physical variables. On the basis of this coupler, an ENSO deep learning forecast model (named ENSO-ASC) is proposed, whose structure is adapted to the characteristics of the ENSO dynamics, including the encoder and decoder for capturing and restoring the multi-scale spatial–temporal correlations, and two attention weights for grasping the different air–sea coupling strengths on different start calendar months and varied effects of physical variables in ENSO amplitudes. In addition, two datasets modulated to the same resolutions are used to train the model. We firstly tune the model performance to optimal and compare it with the other state-of-the-art ENSO deep learning forecast models. Then, we evaluate the ENSO forecast skill from the contributions of different predictors, the effective lead time with different start calendar months, and the forecast spatial uncertainties, to further analyze the underlying ENSO mechanisms. Finally, we make ENSO predictions over the validation period from 2014 to 2020. Experiment results demonstrate that ENSO-ASC outperforms the other models. Sea surface temperature (SST) and zonal wind are two crucial predictors. The correlation skill of the Niño 3.4 index is over 0.78, 0.65, and 0.5 within the lead time of 6, 12, and 18 months respectively. From two heat map analyses, we also discover the common challenges in ENSO predictability, such as the forecasting skills declining faster when making forecasts through June–July–August and the forecast errors being more likely to show up in the western and central tropical Pacific Ocean in longer-term forecasts. ENSO-ASC can simulate ENSO with different strengths, and the forecasted SST and wind patterns reflect an obvious Bjerknes positive feedback mechanism. These results indicate the effectiveness and superiority of our model with the multivariate air–sea coupler in predicting ENSO and analyzing the underlying dynamic mechanisms in a sophisticated way.

Study of Different Dispensing Patterns of No-Flow Underfill Using Numerical and Experimental Methods

Abstract The conventional capillary underfill process has been a common practice in the industry, somehow the process is costly and time-consuming. Thus, no-flow underfill process is developed to increase the effective lead time production since it integrates the simultaneous reflow and cure of the solder interconnect and underfill. This paper investigates the effect of different dispense patterns of no-flow underfill process by mean of numerical and experimental method. Finite volume method (FVM) was used for the three-dimensional (3D) simulation to simulate the compression flow of the no-flow underfill. Experiments were carried out to complement the simulation validity and the results from both studies have reached a good agreement. The findings show that of all three types of dispense patterns, the combined shape dispense pattern shows better chip filling capability. The dot pattern has the highest velocity and pressure distribution with values of 0.0172 m/s and 813 Pa, respectively. The high-pressure region is concentrated at the center of the chip and decreases out toward the edge. Low in pressure and velocity flow factor somehow lead to issue associated with possibility of incomplete filling or void formation. Dot dispense pattern shows less void formation since it produces high-pressure underfill flow within the ball grid array (BGA). This paper provides reliable insight into the industry to choose the best dispense pattern of recently favorable no-flow underfill process.

The window fill rate in a periodic review inventory system with order crossover

Many periodic review inventory studies make the simplifying assumption that orders do not crossover, that is, that they are delivered in the same sequence as they were issued. In many real-life situations, however, long international shipping routes result with frequent crossovers. Accordingly, we investigate a periodic review inventory system with backlogged compound Poisson demand in which order may crossover. We develop an exact formula for the window fill rate, i.e. the probability for customers to receive service within their tolerable wait. Evaluating the window fill rate using the exact formula is very time consuming and therefore an efficient approximation formula that assumes that orders do not crossover is considered. Lead times of actual global supply lines demonstrate that this approximation results in considerable overstocking when customers' tolerable wait is high, or understocking when the tolerable wait is low. Replacing the actual lead times distribution with the effective lead times distribution improves the approximation formula's accuracy considerably. This result allows us to show how the window fill rate depends on the model parameters and make practical observations about the trade-off between the tolerable wait and stock levels needed to maintain a required level of window fill rate.

Intercomparison of numerical atmospheric dispersion prediction models for emergency response to emissions of radionuclides with limited source information in the Fukushima Dai-ichi nuclear power plant accident

The utilization of numerical atmospheric dispersion prediction (NDP) models for assisting the emergency response to emission of radionuclides has been recommended by a working group of the Meteorological Society of Japan. This paper verifies the feasibility of the recommendation through NDP model intercomparison with limited emission source information for the case of the Fukushima Dai-ichi Nuclear Power Plant accident caused by the Great East Japan Earthquake in 2011. According to the recommendation of the working group, the NDP models are run under the assumption of a constant rate of emission during the whole forecast period. This is the worst-case scenario when limited source information is available. Generally, no information is provided on the temporal variability and strength of the emissions, while the source location is known. Surface air radionuclide forecasts are utilized for providing warnings of the risk of inhaling radioactive substances suspended in the low-level atmosphere, whereas column-integrated radionuclide forecasts are utilized for estimating the potential maximum wet deposition of radioactive materials on the ground due to precipitation. The NDP model short-range forecasts were validated with observational data for three locations, at the times when the most serious contamination events occurred at each of the three monitoring stations. The NDP models successfully predicted the risk of surface air contamination and/or ground surface contamination caused by wet deposition in these cases. Particularly, the NDP model forecasts allow us to disseminate warnings at effective lead times before exposure to radiation. The different NDP models gradually deviate their forecasts as the lead time progresses. The deviations may indicate the magnitude of forecast errors. Thus, the use of multi-model forecasts is of greater benefit than the single model forecasts, because forecast error information is suggested.

Error correction-based forecasting of reservoir water levels: Improving accuracy over multiple lead times

Providing reliable reservoir water level forecasts is a challenge because of the accumulative errors in hydrological and reservoir routing models. We present a novel forecasting model that addresses these issues. The model consists of a hydrological model to simulate inflow, a reservoir routing model to simulate water levels, and an autoregressive model for error correction. The parameters for the hydrological model were calibrated with the objective of forecasting water levels over multiple lead times, while a back-fitting algorithm was used to recalibrate the parameters sequentially for the hydrological and autoregressive models. The results show that: (1) the forecasting performance of effective lead times can be enhanced by minimizing the difference between the forecasted and observed water levels for multiple lead times; (2) the most recent errors method is better than the one-step-ahead recursive prediction method; and (3) the back-fitting algorithm is superior to the joint inference method.

독립형 낙뢰 경보시스템의 성능 개선을 위한 경보 조건

As a result of previous studies [1-2], the warning performances without atmospheric electric field (EF) data are examined by using only intra-cloud (IC) discharges, and cloud-to-ground (CG) strikes data provided by a stand-alone lightning warning system (LWS) during two summers. The warning performances were evaluated by the warning statistics with probability of detection (POD), false alarm ratio (FAR) and critical success index (CSI). Upon conclusion of this study, it was found that removal of EF data from warning conditions decreased FAR to 82%, and increased CSI to 171%, POD to 89% in comparison to the prior study[1]. Therefore, these results mean that IC and CG without EF data were beneficial to an increase in warning performances. However, the stand-alone LWS intended to trigger an only prior warning is not possible to make effective alerts (EA) because there is no effective lead time (LT). Hence, it has still been required to develop the new LWS proposed in the previous study [1] for triggering EA.

An Improved “Dynamic Control Operation Module” for Cascade Reservoirs

Reservoirs are one of the most efficient projects for water resources management, and also play an important role in flood control and conservation. Dynamic control of the reservoir flood limited water level (FLWL) is considered as an effective factor to ensure safety of the flood control during the flood season. The maximum allowed water level of cascade reservoirs is also a fundamental key element for implementing reservoir water level dynamic control operation. In this paper, we discussed and improved “Dynamic Control Operation Module for Cascade Reservoirs” (DCOMR). Therefore, a set of new formulas and new methodologies were proposed (NDCOMR) which considers intermediate variables forecast information, the release of the upstream reservoir and interval flow forecast information in effective lead times, which is applied to the dynamic operation of the maximum allowed water level of cascade reservoirs. The Bikou and Miaojiaba cascade reservoirs were selected as a case study. Based on numerical experiment results, the maximum allowed water level of cascade reservoirs at current time determined by NDCOMR was much safer than that determined by DCOMR. The NDCOMR is more rational and safer than DCOMR for flood control, without the need of reducing flood control standard.

Design and implementation of an operational multimodel multiproduct real-time probabilistic streamflow forecasting platform

Abstract The task of real-time streamflow monitoring and forecasting is particularly challenging for ungauged or sparsely gauged river basins, and largely relies upon satellite-based estimates of precipitation. We present the design and implementation of a state-of-the-art real-time streamflow monitoring and forecasting platform that integrates information provided by cutting-edge satellite precipitation products (SPPs), numerical precipitation forecasts, and multiple hydrologic models, to generate probabilistic streamflow forecasts that have an effective lead time of 9 days. The modular design of the platform enables adding/removing any model/product as may be appropriate. The SPPs are bias-corrected in real-time, and the model-generated streamflow forecasts are further bias-corrected and merged, to produce probabilistic forecasts that are computed via several model averaging techniques. The platform is currently operational in multiple river basins in Africa, and can also be adapted to any new basin by incorporating some basin-specific changes and recalibration of the hydrologic models.

Order Crossover Research: A 60‐Year Retrospective to Highlight Future Research Opportunities

AbstractWe review the literature spanning 60 years of efforts to model the phenomenon of order crossover. The importance of this research has increased due to today's longer ocean supply chains, with their greater inherent uncertainty. The incidence of order crossover is higher in these supply chains, and ignoring it will result in overestimation of safety stock. The literature is grouped into four areas, which are described separately, and is mapped to reveal gaps. Gaps exist for combinations of lead time distributions, demand distributions, and inventory policies where extant methods do not address order crossover. The effective lead time approach has proven flexible enough to be used in a wide range of periodic review situations. However, our results show that its accuracy is undermined by bimodal skewed lead‐time distributions, especially when lower bounds are nonzero. Future research needs to concentrate on exploring nonparametric methods or similar methods robust to different lead time distribution assumptions to provide a method of setting safety stock in “real world” situations where orders cross.

Incorporating Order Crossover Information into Service-Oriented Base Stock Policy Decisions

We investigate the impact of order crossover on a periodic, order-up-to (R,S) inventory system where attaining a target service level is the focus. Order crossover, when orders arrive in a sequence different from that in which they were originally placed, is increasingly likely to occur in modern supply chains. One reason for this is the increasing pressure to focus on service performance, which we refer to as the “Amazon Service Effect”. Many service-oriented changes to order placement and fulfillment strategies will increase the chance of order crossover. While the majority of inventory control studies, including crossover-focused studies, tend to be cost-oriented, in practice many inventory systems are driven by service goals. To explore the use of crossover information in inventory policy decision-making we adopt the “effective lead-time” (ELT) approach (Hayya et al., 2008) to representing order lead-times where order crossover is present. We develop a hybrid discrete-event/continuous simulation model that utilizes a unique structure to provide a detailed representation of a stochastic inventory system and collect precise information about order crossover, effective lead-time, service performance, and related operating characteristics. We first address two crossover related assumptions that have not been directly addressed in the literature. We find that order crossover clearly impacts service-oriented inventory system performance (much as it does cost-oriented systems) and that a protection period of R ELT time units is appropriate for an R,S inventory policy under crossover. We then explore five methods of including ELT information into the process of determining the order-up-to level (S) for a periodic, order-up-to (R,S) inventory policy. We perform a full-factorial simulation study under various conditions of target service level, demand variance, and lead-time variance, for a total of 375 scenarios. We find the method used to include effective lead-time information into the decision significantly impacts service performance. We compare and contrast the different methods, discuss issues such as data requirements and the sources of error, and address the research and managerial implications of our findings.

A framework for measuring carbon emissions for inbound transportation and distribution networks

The paper aims at developing a robust model that can capture, calculate and manage emissions activity across a transportation and distribution network to enable corporate social responsibility practices via identifying the carbon intensive activities within the transport and extended enterprise. This is a multi-attribute problem where a number of variables need to be taken into account across the different echelons of the supply chain. A case company “X” from the apparel industry is used for the purposes of the study.The supply chain carbon assessment study requires defining the system boundaries, process mapping of the activities involved, identifying key data requirements, collecting and sampling primary and secondary data and calculating emissions based on fuel or distance factors.The research culminates in creating a scenario-planning tool with cost, effective lead time and carbon emissions as model attributes. As part of the research, literature is reviewed on best practices and methodologies and interviews conducted with functional heads, 3PL managers and experts from the apparel industry. In the end, the created tool enables companies to gauge their emissions, optimize them within their respective supply chains and identify key indicators for carbon reduction in inbound networks.

Evaluating flash-flood warnings at ungauged locations using post-event surveys: a case study with the AIGA warning system

This article presents a comparison between real-time discharges calculated by a flash-flood warning system and post-event flood peak estimates. The studied event occurred on 15 and 16 June 2010 at the Argens catchment located in the south of France. Real-time flood warnings were provided by the AIGA (Adaptation d’Information Géographique pour l’Alerte en Crue) warning system, which is based on a simple distributed hydrological model run at a 1-km2 resolution using radar rainfall information. The timing of the warnings (updated every 15 min) was compared to the observed flood impacts. Furthermore, “consolidated” flood peaks estimated by an intensive post-event survey were used to evaluate the AIGA-estimated peak discharges. The results indicated that the AIGA warnings clearly identified the most affected areas. However, the effective lead-time of the event detection was short, especially for fast-response catchments, because the current method does not take into account any rainfall forecast. The flood peak analysis showed a relatively good correspondence between AIGA- and field-estimated peak values, although some differences were due to the rainfall underestimation by the radar and rainfall–runoff model limitations.Editor Z.W. Kundzewicz; Guest editor R.J. MooreCitation Javelle, P., Demargne, J., Defrance, D., Pansu, J. and Arnaud, P., 2014. Evaluating flash-flood warnings at ungauged locations using post-event surveys: a case study with the AIGA warning system. Hydrological Sciences Journal, 59 (7), 1390–1402. http://dx.doi.org/10.1080/02626667.2014.923970