An extended STIRPAT model-based methodology for evaluating the driving forces affecting carbon emissions in existing public building sector: evidence from China in 2000–2015

In recent decades, population growth associated with unplanned urban occupation has increased the vulnerability of the Brazilian population to natural disasters. In susceptible regions, early flood forecasting is essential for risk management. Still, in Brazil, most flood forecast and warning systems are based either on simplified models of flood wave propagation through the drainage network or on stochastic models. This paper presents a methodology for flood forecasting aiming to an operational warning system that proposes to increase the lead time of a warning through the use of an ensemble of meteorological forecasts. The chosen configuration was chosen so it would be feasible for an operational flood forecast and risk management. The methodology was applied to the flood forecast for the Itajaí-Açu River basin, a region which comprises a drainage area of approximately 15,500 km² in the state of Santa Catarina, Brazil, historically affected by floods. Ensemble weather forecasts were used as input to the MHD-INPE hydrological model, and the performance of the methodology was assessed through statistical indicators. Results suggest that flood warnings can be issued up to 48 h in advance, with a low rate of false warnings. Streamflow forecasting through the use of hydrological ensemble prediction systems is still scarce in Brazil. To the best of our knowledge, this is the first time this methodology aiming to an operational flood risk management system has been tested in Brazil.     

Flood Forecasting and Flood Warning in the Firth of Clyde, UK

Coastal flooding has caused significant damage to a number of communities around the Firth of Clyde in south-west Scotland, UK. The Firth of Clyde is an enclosed embayment affected by storm surge generated in the Northern Atlantic and propagated through the Irish Channel. In recent years, the worst flooding occurred on 5th January 1991 with the estimated damage of approximately £7M. On average, some £0.5M damage is caused each year by coastal flooding. With the latest climate change predictions suggesting increased storm activity and the expected increase in mean sea levels, these damages are likely to increase. In line with the expansion of flood warning provision in Scotland, the Scottish Environment Protection Agency (SEPA) has developed a flood warning system to provide local authorities and emergency services with up to 24 h warning of coastal flooding within the Firth of Clyde and River Clyde Estuary up to Glasgow City Centre. The Firth of Clyde flood warning system consists of linked 1-D and 2-D mathematical models of the Firth of Clyde and Clyde Estuary, and other software tools for data processing, viewing and generating warning messages. The general methodology adopted in its implementation was developed following extensive consultation with the relevant authorities, including local councils and police. The warning system was launched in October 1999 and has performed well during four winter flood seasons. The system currently makes forecasts four times a day and is the only operational coastal flood warning system in Scotland.This paper summarises the development of the warning system, gives a review of its operation since its launch in 1999 and discusses future developments in flood warning in Scotland.     

An operational flood warning system for poorly gauged basins: demonstration in the Guadalhorce basin (Spain)

This paper deals with the presentation of a flood warning system (GFWS) developed for the specific characteristics of the Guadalhorce basin (3,200 km², SE of Spain), which is poorly gauged and often affected by flash and plain floods. Its complementarity with the European flood alert system (EFAS) has also been studied. At a lower resolution, EFAS is able to provide a flood forecast several days in advance. The GFWS is adapted to the use of distributed rainfall maps (such as radar rainfall estimates), and discharge forecasts are computed using a distributed rainfall–runoff model. Due to the lack of flow measurements, the model parameters calibrated on a small watershed have been transferred in most of the basin area. The system is oriented to provide distributed warnings and fulfills the requirements of ungauged basins. This work reports on the performance of the system on two recent rainfall events that caused several inundations. These results show how the GFWS performed well and was able to forecast the location and timing of flooding. It demonstrates that despite its limitations, a simple rainfall–runoff model and a relatively simple calibration could be useful for event risk management. Moreover, with low resolution and long anticipation, EFAS appears as a good complement tool to improve flood forecasting and compensate for the short lead times of the GFWS.     

Evaluation of real-time flash flood forecasts for Haiti during the passage of Hurricane Tomas, November 4–6, 2010

The January 2010 earthquake that devastated Haiti left its population ever more vulnerable to rainfall-induced flash floods. A flash flood guidance system has been implemented to provide real-time information on the potential of small (~70 km²) basins for flash flooding throughout Haiti. This system has components for satellite rainfall ingest and adjustment on the basis of rain gauge information, dynamic soil water deficit estimation, ingest of operational mesoscale model quantitative precipitation forecasts, and estimation of the times of channel flow at bankfull. The result of the system integration is the estimation of the flash flood guidance (FFG) for a given basin and for a given duration. FFG is the amount of rain of a given duration over a small basin that causes minor flooding in the outlet of the basin. Amounts predicted or nowcasted that are higher than the FFG indicate basins with potential for flash flooding. In preparation for Hurricane Tomas’ landfall in early November 2010, the FFG system was used to generate 36-h forecasts of flash flood occurrence based on rainfall forecasts of the nested high-resolution North American Model of the National Centers for Environmental Prediction. Assessment of the forecast flood maps and forecast precipitation indicates the utility and value of the forecasts in understanding the spatial distribution of the expected flooding for mitigation and disaster management. It also highlights the need for explicit uncertainty characterization of forecast risk products due to large uncertainties in quantitative precipitation forecasts on hydrologic basin scales.     

基于FloodArea模型的小流域山洪灾害临界雨量阈值初探

针对缺乏水位流量资料的山区小流域地区山洪临界雨量难以确定的问题,以四川省南江河上游流域作为研究区域,基于德国Geomer公司开发的二维非恒定流水动力模型"FloodArea ",利用流域逐时降雨资料,地形高程数据以及土地利用数据,重现南江" 6·28"山洪暴发的动态演进过程,对模拟得到的逐时淹没深度与1~24 h累积流域面雨量求相关,选取预警点淹没深度与累积面雨量的相关系数最高的时效作为预警点致灾临界雨量阈值的预报时效,通过建立预警点淹没深度与预报时效累积面雨量的回归方程,从而获取预警点不同风险等级的临界雨量阈值。结果表明：FloodArea模型能够较好地呈现出此次典型山洪的暴发过程,通过对不同地势预警点临界雨量阈值的对比,最终选取地势较低,位于河流汇口地带、风险等级较高的上两九义校作为南江河上游流域山洪风险预警点。     

河道洪水实时概率预报模型与应用

通过数据同化方法合理地将实时水文观测数据融入到洪水预报模型中,可提高洪水预报模型的实时性和精确度。选取沿程断面流量、水位和糙率系数作为代表水流状态的基本粒子,以监测断面实测水位数据作为观测信息,建立了基于粒子滤波数据同化算法的河道洪水实时概率预报模型。模型应用于黄河中下游河道洪水预报计算的结果表明,采用粒子滤波方法同化观测水位后,不仅可以直接校正水位,同时也可以有效地校正流量和糙率,为未来时刻模型预报计算提供更准确的水流初始条件和糙率取值区间,进而有效地提高模型预报结果的精度,给出合理的概率预报区间。不同预报期的预报结果表明,随着预报期的增长,同化效果减弱,模型预报结果的精度会有所降低,水位概率预报结果受粒子间糙率不同的影响不确定性增加,而流量概率预报结果受给定模型边界条件的影响不确定性降低。所提出模型可以有效同化真实水位观测数据,适合应用于实际的洪水预报工作中。     

Decision support system based on the history of flood and flash flood events in Romania

A lightweight decision support system is presented, oriented also to statistics, useful for assisting weather forecasters and other parties interested in hazard assessment associated with extreme weather. The system can be used in enhancing the warning procedures, ahead of a flood or a flash flood whose probability of occurrence is based on the history of such events in a particular region. A software application has been built that integrates meteorological data with Geographical Information Systems procedures, in a unified informational aggregate. This system stores various types of data related to flood and flash flood events, so it is able to provide the user with any piece of information related to a documented event. It also catalogues any information that users provide it with, to further document a past, or an ongoing event. The system can be used to raise awareness of forecasters over a particular context, before a possibly hazardous situation, and it can also offer automatic warnings and suggestions to those interested in disaster mitigation.     

A debris-flow alarm system for the Alpine Illgraben catchment: design and performance

We describe the development, implementation, and first analyses of the performance of a debris-flow warning system for the Illgraben catchment and debris fan area. The Illgraben catchment (9.5 km²), located in the Canton of Valais, Switzerland, in the Rhone River valley, is characterized by frequent and voluminous sediment transport and debris-flow activity, and is one of the most active debris-flow catchments in the Alps. It is the site of an instrumented debris-flow observation station in operation since the year 2000. The residents in Susten (municipality Leuk), tourists, and other land users, are exposed to a significant hazard. The warning system consists of four modules: community organizational planning (hazard awareness and preparedness), event detection and alerting, geomorphic catchment observation, and applied research to facilitate the development of an early warning system based on weather forecasting. The system presently provides automated alert signals near the active channel prior to (5–15 min) the arrival of a debris flow or flash flood at the uppermost frequently used channel crossing. It is intended to provide data to support decision-making for warning and evacuation, especially when unusually large debris flows are expected to leave the channel near populated areas. First-year results of the detection and alert module in comparison with the data from the independent debris-flow observation station are generally favorable. Twenty automated alerts (alarms) were issued, which triggered flashing lights and sirens at all major footpaths crossing the channel bed, for three debris flows and 16 flood flows. Only one false alarm was issued. The major difficulty we encountered is related to the variability and complexity of the events (e.g., events consisting of multiple surges) and can be largely solved by increasing the duration of the alarm. All of the alarms for hazardous events were produced by storms with a rainfall duration and intensity larger than the threshold for debris-flow activity that was defined in an earlier study, supporting our intention to investigate the use of rainfall forecasts to increase the time available for warning and implementation of active countermeasures.     

An integrated system for forecasting Arno River flash floods

A system for effectively forecasting flash floods of the Arno River (Tuscany, Italy) should provide a flood warning with 10–12 h of lead time, primarily in order to evacuate the city of Florence. This goal may be achieved by acquiring and processing meteorological and hydrological data in real-time and, accordingly, by releasing alarms at different levels of reliability and concern. Through the application of both procedural language and expert system techniques, a prototype was developed which can readily handle a variety of relevant information and make predictions on flood hazard in Florence. The system was fairly successfully tested by processing simple meteorological data which enable a 24 hour forewarning to be released.     

Evaluation of the real-time TRMM-based multi-satellite precipitation analysis for an operational flood prediction system in Nzoia Basin,Lake Victoria,Africa

Many researchers seek to take advantage of the recently available and virtually uninterrupted supply of satellite-based rainfall information as an alternative and supplement to the ground-based observations in order to implement a cost-effective flood prediction in many under-gauged regions around the world. Recently, NASA Applied Science Program has partnered with USAID and African-RCMRD to implement an operational water-hazard warning system, SERVIR-Africa. The ultimate goal of the project is to build up disaster management capacity in East Africa by providing local governmental officials and international aid organizations a practical decision-support tool in order to better assess emerging flood impacts and to quantify spatial extent of flood risk, as well as to respond to such flood emergencies more expediently. The objective of this article is to evaluate the applicability of integrating NASA’s standard satellite precipitation product with a flood prediction model for disaster management in Nzoia, sub-basin of Lake Victoria, Africa. This research first evaluated the TMPA real-time rainfall data against gauged rainfall data from the year 2002 through 2006. Then, the gridded Xinanjiang Model was calibrated to Nzoia basin for period of 1985–2006. Benchmark streamflow simulations were produced with the calibrated hydrological model using the rain gauge and observed streamflow data. Afterward, continuous discharge predictions forced by TMPA 3B42RT real-time data from 2002 through 2006 were simulated, and acceptable results were obtained in comparison with the benchmark performance according to the designated statistic indices such as bias ratio (20%) and NSCE (0.67). Moreover, it is identified that the flood prediction results were improved with systematically bias-corrected TMPA rainfall data with less bias (3.6%) and higher NSCE (0.71). Although the results justify to suggest to us that TMPA real-time data can be acceptably used to drive hydrological models for flood prediction purpose in Nzoia basin, continuous progress in space-borne rainfall estimation technology toward higher accuracy and higher spatial resolution is highly appreciated. Finally, it is also highly recommended that to increase flood forecasting lead time, more reliable and more accurate short- or medium-range quantitative precipitation forecasts is a must.     

Forecasting for flood warning

Advances in flood forecasting have been constrained by the difficulty of estimating rainfall continuously over space, for catchment-, national- and continental-scale areas. This has had a concomitant impact on the choice of appropriate model formulations for given flood-forecasting applications. Whilst weather radar used in combination with raingauges – and extended to utilise satellite remote-sensing and numerical weather prediction models – have offered the prospect of progress, there have been significant problems to be overcome. These problems have curtailed the development and adoption of more complete distributed model formulations that aim to increase forecast accuracy. Advanced systems for weather radar display and processing, and for flood forecast construction, are now available to ease the task of implementation. Applications requiring complex networks of models to make forecasts at many locations can be undertaken without new code development and be readily revised to take account of changing requirements. These systems make use of forecast-updating procedures that assimilate data from telemetry networks to improve flood forecast performance, at the same time coping with the possibility of data loss. Flood forecasting systems that integrate rainfall monitoring and forecasting with flood forecasting and warning are now operational in many areas. Present practice in flood modelling and forecast updating is outlined from a UK perspective. Challenges for improvement are identified, particularly against a background of greater access to spatial datasets on terrain, soils, geology, land-cover, and weather variables. Representing the effective runoff production and translation processes operating at a given grid or catchment scale may prove key to improved flood simulation, and robust application to ungauged basins through physics-based linkages with these spatial datasets. The need to embrace uncertainty in flood-warning decision-making is seen as a major challenge for the future. To cite this article: R.J. Moore et al., C. R. Geoscience 337 (2005).     

Using value engineering to optimize flood forecasting and flood warning systems: Golestan and Golabdare watersheds in Iran as case studies

Flood occurrence has always been one of the most important natural phenomena, which is often associated with disaster. Consequently, flood forecasting (FF) and flood warning (FW) systems, as the most efficient non-structural measures in reducing flood loss and damage, are of prime importance. These systems are low cost and the time required for their implementation is relatively short. It is emphasized that for designing the components of these systems for various rivers, climatic conditions and geographical settings different methods are required. One of the major difficulties during implementing these systems in different projects is the fact that sometimes the main functions of these systems are ignored. Based on a systematic and practical approach and considering the components of these systems, it would be possible to extract the most essential key functions of the system and save time, effort and money by this way. For instance, in a small watershed with low concentration and small lead time, the main emphasis should be on predicting and monitoring weather conditions. In this article, different components of flood forecasting and flood warning systems have been introduced. Then analysis of the FF and FW system functions has been undertaken based on the value engineering (VE) technique. Utilizing a functional view based on function analysis system technique (FAST), the total trend of FF and FW functions has been identified. The systematic trend and holistic view of this technique have been used in optimizing FF and FW systems of the Golestan province and Golabdare watersheds in Iran as the case studies.     

陆气耦合模型在实时暴雨洪水预报中的应用

采用加拿大区域性中尺度大气模式MC2(Canadian Mesoscale Compressible Community)和新安江模型单向耦合模型系统,对2005年7月4～15日发生在淮河流域的一场暴雨洪水,进行了实时预报.采用王家坝以上流域的实测降水和王家坝断面的实测洪水资料,对MC2预报降水的时空分布和陆气耦合模型预报的洪水过程进行了分析.结果表明,MC2对该场强降水过程具有很好的预报能力,陆气耦合模型有效地增长了洪水预报的预见期,具有很好的应用前景.     

Flood forecasts based on multi-model ensemble precipitation forecasting using a coupled atmospheric-hydrological modeling system

The recent improvement of numerical weather prediction (NWP) models has a strong potential for extending the lead time of precipitation and subsequent flooding. However, uncertainties inherent in precipitation outputs from NWP models are propagated into hydrological forecasts and can also be magnified by the scaling process, contributing considerable uncertainties to flood forecasts. In order to address uncertainties in flood forecasting based on single-model precipitation forecasting, a coupled atmospheric-hydrological modeling system based on multi-model ensemble precipitation forecasting is implemented in a configuration for two episodes of intense precipitation affecting the Wangjiaba sub-region in Huaihe River Basin, China. The present study aimed at comparing high-resolution limited-area meteorological model Canadian regional mesoscale compressible community model (MC2) with the multiple linear regression integrated forecast (MLRF), covering short and medium range. The former is a single-model approach; while the latter one is based on NWP models [(MC2, global environmental multiscale model (GEM), T213L31 global spectral model (T213)] integrating by a multiple linear regression method. Both MC2 and MLRF are coupled with Chinese National Flood Forecasting System (NFFS), MC2-NFFS and MLRF-NFFS, to simulate the discharge of the Wangjiaba sub-basin. The evaluation of the flood forecasts is performed both from a meteorological perspective and in terms of discharge prediction. The encouraging results obtained in this study demonstrate that the coupled system based on multi-model ensemble precipitation forecasting has a promising potential of increasing discharge accuracy and modeling stability in terms of precipitation amount and timing, along with reducing uncertainties in flood forecasts and models. Moreover, the precipitation distribution of MC2 is more problematic in finer temporal and spatial scales, even for the high resolution simulation, which requests further research on storm-scale data assimilation, sub-grid-scale parameterization of clouds and other small-scale atmospheric dynamics.     

Using the DPFT approach to improve flash flood forecasting models

DPFT is a lumped approach for operational flash flood forecasting, based on the unit hydrograph. Using a multi-event alternating iterative algorithm, it identifies a robust and stable average transfer function and a consistent set of effective rainfall series associated with each event at the same time. This key ingredient allows an objective calibration of different loss functions, relating gauged precipitation and effective rainfall. A case study based on an operational French basin (545 km²) is presented. Three lumped production functions have been calibrated and compared. The results show that more elaborate models of loss functions must be proposed, and some possible directions for this are pointed out.Presently at the Institut de Ciences de la Terra Jaume Almera, Apartat 30102, 08080 Barcelona, Spain.Presently at EDF-DTG. Service de Ressources en Eau, BP 4348, 31029 Toulouse Cedex, France.     

Mountain range specific analog weather forecast model for northwest Himalaya in India

Mountain range specific analog weather forecast model is developed utilizing surface weather observations of reference stations in each mountain range in northwest Himalaya (NW-Himalaya). The model searches past similar cases from historical dataset of reference observatory in each mountain range based on current situation. The searched past similar cases of each mountain range are used to draw weather forecast for that mountain range in operational weather forecasting mode, three days in advance. The developed analog weather forecast model is tested with the independent dataset of more than 717 days (542 days for Pir Panjal range in HP) of the past 4 winters (2003–2004 to 2006–2007). Independent test results are reasonably good and suggest that there is some possibility of forecasting weather in operational weather forecasting mode employing analog method over different mountain ranges in NW-Himalaya. Significant difference in overall accuracy of the model is found for prediction of snow day and no-snow day over different mountain ranges, when weather is predicted under snow day and no-snow day weather forecast categories respectively. In the same mountain range, significant difference is also found in overall accuracy of the model for prediction of snow day and no-snow day for different areas. This can be attributed to their geographical position and topographical differences. The analog weather forecast model performs better than persistence and climatological forecast for day-1 predictions for all the mountain ranges except Karakoram range in NW-Himalaya. The developed analog weather forecast model may help as a guidance tool for forecasting weather in operational weather forecasting mode in different mountain ranges in NW-Himalaya.     

Operational wave prediction of extreme storms in Northern Europe

The disastrous effects of numerous winter storms on the marine environment in the North Sea and the Baltic Sea during the last decade show that wind waves generated by strong winds actually represent natural hazards and require high quality wave forecast systems as warning tools to avoid losses due to the impact of rough seas. Hence, the operational wave forecast system running at the German Weather Service including a regional wave model for the North Sea and the Baltic Sea is checked extensively whether it provides reasonable wave forecasts, especially for periods of extraordinary high sea states during winter storms. For two selected extreme storm events that induced serious damage in the area of interest, comprehensive comparisons between wave measurements and wave model forecast data are accomplished. Spectral data as well as integrated parameters are considered, and the final outcome of the corresponding comparisons and statistical analysis is encouraging. Over and above the capability to provide good short-term forecast results, the regional wave model is able to predict extreme events as severe winter storms connected with extraordinary high waves already about 2 days in advance. Therefore, it represents an appropriate warning tool for offshore activities and coastal environment.     

Validating quantitative precipitation forecast for the Flood Meteorological Office,Patna region during 2011–2014

In order to issue an accurate warning for flood, a better or appropriate quantitative forecasting of precipitation is required. In view of this, the present study intends to validate the quantitative precipitation forecast (QPF) issued during southwest monsoon season for six river catchments (basin) under the flood meteorological office, Patna region. The forecast is analysed statistically by computing various skill scores of six different precipitation ranges during the years 2011–2014. The analysis of QPF validation indicates that the multi-model ensemble (MME) based forecasting is more reliable in the precipitation ranges of 1–10 and 11–25 mm. However, the reliability decreases for higher ranges of rainfall and also for the lowest range, i.e., below 1 mm. In order to testify synoptic analogue method based MME forecasting for QPF during an extreme weather event, a case study of tropical cyclone Phailin is performed. It is realized that in case of extreme events like cyclonic storms, the MME forecasting is qualitatively useful for issue of warning for the occurrence of floods, though it may not be reliable for the QPF. However, QPF may be improved using satellite and radar products.     

An Assessment of Flood Forecasting in Bangladesh: The Experience of the 1998 Flood

Floods are among the most costly natural disasters interms of human sufferings and economic losses inBangladesh. Approximately 20% of the countryexperiences normal annual flooding while all thehistorical catastrophic floods inundated more thanfifty percent of the country's total area. The recentflood in 1998 has been found to be more severe thanall previous floods. During the flooding season of1998, the Flood Forecasting and Warning Center (FFWC)of the Bangladesh Water Development Board (BWDB)prepared daily flood bulletin and incorporatedinformation about rainfall, rise/fall of riverwater, flood forecasting for 24 and 48 hr inadvance and warning messages (if any). The FFWCattempted to provide adequate services to the localand national level decision-making process. Theforecasting procedure adapted by the FFWC was based onhydrological information, forecaster's experience, andmodel simulation. This paper primarily examines therole and activities of the FFWC, especially in floodforecasting and warning. Identification of the causesand consequences of 1998 flood is the other pertinentarea of discussion of the paper.Findings of this research revealed that the flood of1998 was caused by heavy downpour in the upstream thatwas drained out through the major rivers inBangladesh. Three major rivers' peak was synchronizedand characterized it as the most prolonged flood inthe history of Bangladesh. It also revealed that,despite various limitations, the flood forecasts ofthe FFWC were reasonably adequate to meet nationaldemand during the crises of 1998.     

我国新疆北部地区雪面雨日数时空变化特征分析

在全球变化背景下,雪面雨发生频次增加,致灾风险加大,认识雪面雨时空变化特征对于防洪减灾具有重要意义。基于我国新疆北部地区42个国家气象站1960—2015年逐日气温、降水、雪深、天气现象等气象观测数据,制定降水类型、地面状态、雪深等共同判定雪面雨事件的参数化方案,进而分析新疆北部地区雪面雨日数时空变化特征及其与气温、海拔的关系。结果表明：近56 a来新疆北部地区雪面雨日数以0.3 d·（10a）^-1的速率呈缓慢增加趋势;空间分布上,新疆北部地区雪面雨主要集中于塔城北部、伊犁河谷、乌鲁木齐河源地区,其中塔城裕民县最多,年平均雪面雨日数12.2 d;相关分析显示雪面雨日数及雪面雨量均与海拔呈显著正相关。该研究有望提升对新疆干旱区雪面雨事件这一诱发雨雪混合洪水重要现象的科学认识,为新疆地区致灾洪水过程分析以及洪水监测预警提供参考。