Science.gov

Sample records for national flood forecasting

  1. Experiences from coordinated national-level landslide and flood forecasting in Norway

    NASA Astrophysics Data System (ADS)

    Krøgli, Ingeborg; Fleig, Anne; Glad, Per; Dahl, Mads-Peter; Devoli, Graziella; Colleuille, Hervé

    2015-04-01

    While flood forecasting at national level is quite well established and operational in many countries worldwide, landslide forecasting at national level is still seldom. Examples of coordinated flood and landslide forecasting are even rarer. Most of the time flood and landslide forecasters work separately (investigating, defining thresholds, and developing models) and most of the time without communication with each other. One example of coordinated operational early warning systems (EWS) for flooding and shallow landslides is found at the Norwegian Water Resources and Energy Directorate (NVE) in Norway. In this presentation we give an introduction to the two separate but tightly collaborative EWSs and to the coordination of these. The two EWSs are being operated from the same office, every day using similar hydro-meteorological prognosis and hydrological models. Prognosis and model outputs on e.g. discharge, snow melt, soil water content and exceeded landslide thresholds are evaluated in a web based decision-making tool (xgeo.no). The experts performing forecasts are hydrologists, geologists and physical geographers. A similar warning scale, based on colors (green, yellow, orange and red) is used for both EWSs, however thresholds for flood and landslide warning levels are defined differently. Also warning areas may not necessary be the same for both hazards and depending on the specific meteorological event, duration of the warning periods can differ. We present how knowledge, models and tools, but also human and economic resources are being shared between the two EWSs. Moreover, we discuss challenges faced in the communication of warning messages using recent flood and landslide events as examples.

  2. A Methodology for Forecasting Damage & Economic Consequences to Floods: Building on the National Flood Interoperability Experiment (NFIE)

    NASA Astrophysics Data System (ADS)

    Tootle, G. A.; Gutenson, J. L.; Zhu, L.; Ernest, A. N. S.; Oubeidillah, A.; Zhang, X.

    2015-12-01

    The National Flood Interoperability Experiment (NFIE) held June 3-July 17, 2015 at the National Water Center (NWC) in Tuscaloosa, Alabama sought to demonstrate an increase in flood predictive capacity for the coterminous United States (CONUS). Accordingly, NFIE-derived technologies and workflows offer the ability to forecast flood damage and economic consequence estimates that coincide with the hydrologic and hydraulic estimations these physics-based models generate. A model providing an accurate prediction of damage and economic consequences is a valuable asset when allocating funding for disaster response, recovery, and relief. Damage prediction and economic consequence assessment also offer an adaptation planning mechanism for defending particularly valuable or vulnerable structures. The NFIE, held at the NWC on The University of Alabama (UA) campus led to the development of this large scale flow and inundation forecasting framework. Currently, the system can produce 15-hour lead-time forecasts for the entire coterminous United States (CONUS). A concept which is anticipated to become operational as of May 2016 within the NWC. The processing of such a large-scale, fine resolution model is accomplished in a parallel computing environment using large supercomputing clusters. Traditionally, flood damage and economic consequence assessment is calculated in a desktop computing environment with a ménage of meteorology, hydrology, hydraulic, and damage assessment tools. In the United States, there are a range of these flood damage/ economic consequence assessment software's available to local, state, and federal emergency management agencies. Among the more commonly used and freely accessible models are the Hydrologic Engineering Center's Flood Damage Reduction Analysis (HEC-FDA), Flood Impact Assessment (HEC-FIA), and Federal Emergency Management Agency's (FEMA's) United States Multi-Hazard (Hazus-MH). All of which exist only in a desktop environment. With this

  3. A national framework for flood forecasting model assessment for use in operations and investment planning over England and Wales

    NASA Astrophysics Data System (ADS)

    Moore, Robert J.; Wells, Steven C.; Cole, Steven J.

    2016-04-01

    It has been common for flood forecasting systems to be commissioned at a catchment or regional level in response to local priorities and hydrological conditions, leading to variety in system design and model choice. As systems mature and efficiencies of national management are sought, there can be a drive towards system rationalisation, gaining an overview of model performance and consideration of simplification through model-type convergence. Flood forecasting model assessments, whilst overseen at a national level, may be commissioned and managed at a catchment and regional level, take a variety of forms and be large in number. This presents a challenge when an integrated national assessment is required to guide operational use of flood forecasts and plan future investment in flood forecasting models and supporting hydrometric monitoring. This contribution reports on how a nationally consistent framework for flood forecasting model performance has been developed to embrace many past, ongoing and future assessments for local river systems by engineering consultants across England & Wales. The outcome is a Performance Summary for every site model assessed which, on a single page, contains relevant catchment information for context, a selection of overlain forecast and observed hydrographs and a set of performance statistics with associated displays of novel condensed form. One display provides performance comparison with other models that may exist for the site. The performance statistics include skill scores for forecasting events (flow/level threshold crossings) of differing severity/rarity, indicating their probability and likely timing, which have real value in an operational setting. The local models assessed can be of any type and span rainfall-runoff (conceptual and transfer function) and flow routing (hydrological and hydrodynamic) forms. Also accommodated by the framework is the national G2G (Grid-to-Grid) distributed hydrological model, providing area

  4. Advancing the cyberinfrastructure for sustaining high resolution, real-time streamflow and flood forecasts at a national scale

    NASA Astrophysics Data System (ADS)

    Arctur, D. K.; Maidment, D. R.; Clark, E. P.; Gochis, D. J.; Somos-Valenzuela, M. A.; Salas, F. R.; Nelson, J.

    2015-12-01

    In just the last year, it has become feasible to generate and refresh national 15-hour forecasts of streamflow and flood inundation, every hour at high resolution (average 3km stream segments), based on a workflow integrating US National Weather Service forecasts, the WRF-Hydro land surface model, the RAPID streamflow routing model, and other models. This capability has come about through a collaboration of numerous agencies, academic research and data centers, and commercial software vendors. This presentation provides insights and lessons learned for the development and evolution of a scalable architecture for water observations and forecasts that should be sustained operationally.

  5. Real-time flood forecasting

    USGS Publications Warehouse

    Lai, C.; Tsay, T.-K.; Chien, C.-H.; Wu, I.-L.

    2009-01-01

    Researchers at the Hydroinformatic Research and Development Team (HIRDT) of the National Taiwan University undertook a project to create a real time flood forecasting model, with an aim to predict the current in the Tamsui River Basin. The model was designed based on deterministic approach with mathematic modeling of complex phenomenon, and specific parameter values operated to produce a discrete result. The project also devised a rainfall-stage model that relates the rate of rainfall upland directly to the change of the state of river, and is further related to another typhoon-rainfall model. The geographic information system (GIS) data, based on precise contour model of the terrain, estimate the regions that were perilous to flooding. The HIRDT, in response to the project's progress, also devoted their application of a deterministic model to unsteady flow of thermodynamics to help predict river authorities issue timely warnings and take other emergency measures.

  6. A global flash flood forecasting system

    NASA Astrophysics Data System (ADS)

    Baugh, Calum; Pappenberger, Florian; Wetterhall, Fredrik; Hewson, Tim; Zsoter, Ervin

    2016-04-01

    The sudden and devastating nature of flash flood events means it is imperative to provide early warnings such as those derived from Numerical Weather Prediction (NWP) forecasts. Currently such systems exist on basin, national and continental scales in Europe, North America and Australia but rely on high resolution NWP forecasts or rainfall-radar nowcasting, neither of which have global coverage. To produce global flash flood forecasts this work investigates the possibility of using forecasts from a global NWP system. In particular we: (i) discuss how global NWP can be used for flash flood forecasting and discuss strengths and weaknesses; (ii) demonstrate how a robust evaluation can be performed given the rarity of the event; (iii) highlight the challenges and opportunities in communicating flash flood uncertainty to decision makers; and (iv) explore future developments which would significantly improve global flash flood forecasting. The proposed forecast system uses ensemble surface runoff forecasts from the ECMWF H-TESSEL land surface scheme. A flash flood index is generated using the ERIC (Enhanced Runoff Index based on Climatology) methodology [Raynaud et al., 2014]. This global methodology is applied to a series of flash floods across southern Europe. Results from the system are compared against warnings produced using the higher resolution COSMO-LEPS limited area model. The global system is evaluated by comparing forecasted warning locations against a flash flood database of media reports created in partnership with floodlist.com. To deal with the lack of objectivity in media reports we carefully assess the suitability of different skill scores and apply spatial uncertainty thresholds to the observations. To communicate the uncertainties of the flash flood system output we experiment with a dynamic region-growing algorithm. This automatically clusters regions of similar return period exceedence probabilities, thus presenting the at-risk areas at a spatial

  7. Forecaster priorities for improving probabilistic flood forecasts

    NASA Astrophysics Data System (ADS)

    Wetterhall, Fredrik; Pappenberger, Florian; Alfieri, Lorenzo; Cloke, Hannah; Thielen, Jutta

    2014-05-01

    Hydrological ensemble prediction systems (HEPS) have in recent years been increasingly used for the operational forecasting of floods by European hydrometeorological agencies. The most obvious advantage of HEPS is that more of the uncertainty in the modelling system can be assessed. In addition, ensemble prediction systems generally have better skill than deterministic systems both in the terms of the mean forecast performance and the potential forecasting of extreme events. Research efforts have so far mostly been devoted to the improvement of the physical and technical aspects of the model systems, such as increased resolution in time and space and better description of physical processes. Developments like these are certainly needed; however, in this paper we argue that there are other areas of HEPS that need urgent attention. This was also the result from a group exercise and a survey conducted to operational forecasters within the European Flood Awareness System (EFAS) to identify the top priorities of improvement regarding their own system. They turned out to span a range of areas, the most popular being to include verification of an assessment of past forecast performance, a multi-model approach for hydrological modelling, to increase the forecast skill on the medium range (>3 days) and more focus on education and training on the interpretation of forecasts. In light of limited resources, we suggest a simple model to classify the identified priorities in terms of their cost and complexity to decide in which order to tackle them. This model is then used to create an action plan of short-, medium- and long-term research priorities with the ultimate goal of an optimal improvement of EFAS in particular and to spur the development of operational HEPS in general.

  8. STORM3: a new flood forecast management and monitoring system in accordance with the recent Italian national directive

    NASA Astrophysics Data System (ADS)

    Burastero, A.; Pintus, F.; Rossi, L.; Versace, C.

    2005-09-01

    The effectiveness of alert systems for civil protection purposes, defined as the ability to minimize the level of risk in a region subjected to an imminent flood event, strongly depends on availability and exploitability of information. It also depends on technical expertise and the ability to easily manage the civil protection actions through the organization into standardized procedures. Hydro-geologic and hydraulic risk estimation, based on the combination of different technical issues (in this case meteorological, hydro-geological, hydraulic matters), but also socio-economic ones, requires the integration between quasi-static and time-varying information within the same operative platform. Beside the real-time data exchange, a Decision Support System must provide tools which enable knowledge sharing among the civil protection centres. Moreover, due to the amount and heterogeneity of information, quality procedures become necessary to handle all forecasting and monitoring routines within operative centres, according to the latest national directive. In Italy procedures on the civil protection matter have been condensed into the Prime Minister's Directive (27 February 2004. STORM3, an innovative management and monitoring System for real-time flood forecasting and warning, takes in the Directive, supporting the operator step by step within the different phases of civil protection activities.

  9. Preparing for floods: flood forecasting and early warning

    NASA Astrophysics Data System (ADS)

    Cloke, Hannah

    2016-04-01

    Flood forecasting and early warning has continued to stride ahead in strengthening the preparedness phases of disaster risk management, saving lives and property and reducing the overall impact of severe flood events. For example, continental and global scale flood forecasting systems such as the European Flood Awareness System and the Global Flood Awareness System provide early information about upcoming floods in real time to various decisionmakers. Studies have found that there are monetary benefits to implementing these early flood warning systems, and with the science also in place to provide evidence of benefit and hydrometeorological institutional outlooks warming to the use of probabilistic forecasts, the uptake over the last decade has been rapid and sustained. However, there are many further challenges that lie ahead to improve the science supporting flood early warning and to ensure that appropriate decisions are made to maximise flood preparedness.

  10. Advances in Global Flood Forecasting Systems

    NASA Astrophysics Data System (ADS)

    Thielen-del Pozo, J.; Pappenberger, F.; Burek, P.; Alfieri, L.; Kreminski, B.; Muraro, D.

    2012-12-01

    A trend of increasing number of heavy precipitation events over many regions in the world during the past century has been observed (IPCC, 2007), but conclusive results on a changing frequency or intensity of floods have not yet been established. However, the socio-economic impact particularly of floods is increasing at an alarming trend. Thus anticipation of severe events is becoming a key element of society to react timely to effectively reduce socio-economic damage. Anticipation is essential on local as well as on national or trans-national level since management of response and aid for major disasters requires a substantial amount of planning and information on different levels. Continental and trans-national flood forecasting systems already exist. The European Flood Awareness System (EFAS) has been developed in close collaboration with the National services and is going operational in 2012, enhancing the national forecasting centres with medium-range probabilistic added value information while at the same time providing the European Civil Protection with harmonised information on ongoing and upcoming floods for improved aid management. Building on experiences and methodologies from EFAS, a Global Flood Awareness System (GloFAS) has now been developed jointly between researchers from the European Commission Joint Research Centre (JRC) and the European Centre for Medium-Range Weather Forecast (ECWMF). The prototype couples HTESSEL, the land-surface scheme of the ECMWF NWP model with the LISFLOOD hydrodynamic model for the flow routing in the river network. GloFAS is set-up on global scale with horizontal grid spacing of 0.1 degree. The system is driven with 51 ensemble members from VAREPS with a time horizon of 15 days. In order to allow for the routing in the large rivers, the coupled model is run for 45 days assuming zero rainfall after day 15. Comparison with observations have shown that in some rivers the system performs quite well while in others the hydro

  11. Evaluation of Flood Forecast and Warning in Elbe river basin - Impact of Forecaster's Strategy

    NASA Astrophysics Data System (ADS)

    Danhelka, Jan; Vlasak, Tomas

    2010-05-01

    Czech Hydrometeorological Institute (CHMI) is responsible for flood forecasting and warning in the Czech Republic. To meet that issue CHMI operates hydrological forecasting systems and publish flow forecast in selected profiles. Flood forecast and warning is an output of system that links observation (flow and atmosphere), data processing, weather forecast (especially NWP's QPF), hydrological modeling and modeled outputs evaluation and interpretation by forecaster. Forecast users are interested in final output without separating uncertainties of separate steps of described process. Therefore an evaluation of final operational forecasts was done for profiles within Elbe river basin produced by AquaLog forecasting system during period 2002 to 2008. Effects of uncertainties of observation, data processing and especially meteorological forecasts were not accounted separately. Forecast of flood levels exceedance (peak over the threshold) during forecasting period was the main criterion as flow increase forecast is of the highest importance. Other evaluation criteria included peak flow and volume difference. In addition Nash-Sutcliffe was computed separately for each time step (1 to 48 h) of forecasting period to identify its change with the lead time. Textual flood warnings are issued for administrative regions to initiate flood protection actions in danger of flood. Flood warning hit rate was evaluated at regions level and national level. Evaluation found significant differences of model forecast skill between forecasting profiles, particularly less skill was evaluated at small headwater basins due to domination of QPF uncertainty in these basins. The average hit rate was 0.34 (miss rate = 0.33, false alarm rate = 0.32). However its explored spatial difference is likely to be influenced also by different fit of parameters sets (due to different basin characteristics) and importantly by different impact of human factor. Results suggest that the practice of interactive

  12. Flood forecasting for Tucurui Hydroelectrical Plant, Brazil

    SciTech Connect

    Solomon, S.I.; Basso, E.; Osorio, C.; Melo de Moraes, H.; Serrano, A.

    1986-04-01

    The construction of the Tucurui Hydroelectric Plant on the Tocantins River basin in Brazil requires flood forecasting to ensure the safety of the cofferdam. The latter has been initially designed for a flood with a return frequency of one in 25 years. Lack of adequate forecasting facilities during the earlier stages of construction has resulted in significant damages and construction delays. Statistical forecasting models were developed by Projeto de Hidrologia y Climatologie da Amazonia (PHCA) for the purpose of preventing further damage at the site. The application of these models during the 1980 flood season, when the highest flood on record occurred at the Tucurui site, proved of great assistance in preventing the flooding of the cofferdam. In conjunction with the development of these models a number of data collection platforms using data transmission through the GOES system were installed to provide the data required for forecasting.

  13. The use of MOGREPS ensemble rainfall forecasts in operational flood forecasting systems across England and Wales

    NASA Astrophysics Data System (ADS)

    Schellekens, J.; Weerts, A. H.; Moore, R. J.; Pierce, C. E.; Hildon, S.

    2011-03-01

    Operational flood forecasting systems share a fundamental challenge: forecast uncertainty which needs to be considered when making a flood warning decision. One way of representing this uncertainty is through employing an ensemble approach. This paper presents research funded by the Environment Agency in which ensemble rainfall forecasts are utilised and tested for operational use. The form of ensemble rainfall forecast used is the Met Office short-range product called MOGREPS. It is tested for operational use within the Environment Agency's National Flood Forecasting System (NFFS) for England and Wales. Currently, the NFFS uses deterministic forecasts only. The operational configuration of the NFFS for Thames Region is extended to trial the use of the new ensemble rainfall forecasts in support of probabilistic flood forecasting. Evaluation includes considering issues of model performance, configuration (how to fit the ensemble forecasts within the current configurations), data volumes, run times and options for displaying probabilistic forecasts. Although ensemble rainfall forecasts available from MOGREPS are not extensive enough to fully verify product performance, it is concluded that their use within current Environment Agency regional flood forecasting systems can provide better information to the forecaster than use of the deterministic forecasts alone. Of note are the small number of false alarms of river flow exceedance generated when using MOGREPS as input and that small flow events are also forecasted rather well, notwithstanding the rather coarse resolution of the MOGREPS grid (24 km) compared to the studied catchments. In addition, it is concluded that, with careful configuration in NFFS, MOGREPS can be used in existing systems without a significant increase in system load.

  14. Coupling flood forecasting and social media crowdsourcing

    NASA Astrophysics Data System (ADS)

    Kalas, Milan; Kliment, Tomas; Salamon, Peter

    2016-04-01

    Social and mainstream media monitoring is being more and more recognized as valuable source of information in disaster management and response. The information on ongoing disasters could be detected in very short time and the social media can bring additional information to traditional data feeds (ground, remote observation schemes). Probably the biggest attempt to use the social media in the crisis management was the activation of the Digital Humanitarian Network by the United Nations Office for the Coordination of Humanitarian Affairs in response to Typhoon Yolanda. The network of volunteers performing rapid needs & damage assessment by tagging reports posted to social media which were then used by machine learning classifiers as a training set to automatically identify tweets referring to both urgent needs and offers of help. In this work we will present the potential of coupling a social media streaming and news monitoring application ( GlobalFloodNews - www.globalfloodsystem.com) with a flood forecasting system (www.globalfloods.eu) and the geo-catalogue of the OGC services discovered in the Google Search Engine (WMS, WFS, WCS, etc.) to provide a full suite of information available to crisis management centers as fast as possible. In GlobalFloodNews we use advanced filtering of the real-time Twitter stream, where the relevant information is automatically extracted using natural language and signal processing techniques. The keyword filters are adjusted and optimized automatically using machine learning algorithms as new reports are added to the system. In order to refine the search results the forecasting system will be triggering an event-based search on the social media and OGC services relevant for crisis response (population distribution, critical infrastructure, hospitals etc.). The current version of the system makes use of USHAHIDI Crowdmap platform, which is designed to easily crowdsource information using multiple channels, including SMS, email

  15. Optimized Flood Forecasts Using a Statistical Enemble

    NASA Astrophysics Data System (ADS)

    Silver, Micha; Fredj, Erick

    2016-04-01

    The method presented here assembles an optimized flood forecast from a set of consecutive WRF-Hydro simulations by applying coefficients which we derive from straightforward statistical procedures. Several government and research institutions that produce climate data offer ensemble forecasts, which merge predictions from different models to gain a more accurate fit to observed data. Existing ensemble forecasts present climate and weather predictions only. In this research we propose a novel approach to constructing hydrological ensembles for flood forecasting. The ensemble flood forecast is created by combining predictions from the same model, but initiated at different times. An operative flood forecasting system, run by the Israeli Hydrological Service, produces flood forecasts twice daily with a 72 hour forecast period. By collating the output from consecutive simulation runs we have access to multiple overlapping forecasts. We then apply two statistical procedures to blend these consecutive forecasts, resulting in a very close fit to observed flood runoff. We first employ cross-correlation with a time lag to determine a time shift for each of the original, consecutive forecasts. This shift corrects for two possible sources of error: slow or fast moving weather fronts in the base climate data; and mis-calibrations of the WRF-Hydro model in determining the rate of flow of surface runoff and in channels. We apply this time shift to all consecutive forecasts, then run a linear regression with the observed runoff data as the dependent variable and all shifted forecasts as the predictor variables. The solution to the linear regression equation is a set of coefficients that corrects the amplitude errors in the forecasts. These resulting regression coefficients are then applied to the consecutive forecasts producing a statistical ensemble which, by design, closely matches the observed runoff. After performing this procedure over many storm events in the Negev region

  16. National Flood Interoperability Experiment

    NASA Astrophysics Data System (ADS)

    Maidment, D. R.

    2014-12-01

    The National Flood Interoperability Experiment is led by the academic community in collaboration with the National Weather Service through the new National Water Center recently opened on the Tuscaloosa campus of the University of Alabama. The experiment will also involve the partners in IWRSS (Integrated Water Resources Science and Services), which include the USGS, the Corps of Engineers and FEMA. The experiment will address the following questions: (1) How can near-real-time hydrologic forecasting at high spatial resolution, covering the nation, be carried out using the NHDPlus or next generation geofabric (e.g. hillslope, watershed scales)? (2) How can this lead to improved emergency response and community resilience? (3) How can improved an improved interoperability framework support the first two goals and lead to sustained innovation in the research to operations process? The experiment will run from September 2014 through August 2015, in two phases. The mobilization phase from September 2014 until May 2015 will assemble the components of the interoperability framework. A Summer Institute to integrate the components will be held from June to August 2015 at the National Water Center involving faculty and students from the University of Alabama and other institutions coordinated by CUAHSI. It is intended that the insight that arises from this experiment will help lay the foundation for a new national scale, high spatial resolution, near-real-time hydrologic simulation system for the United States.

  17. Flood Forecasting in River System Using ANFIS

    NASA Astrophysics Data System (ADS)

    Ullah, Nazrin; Choudhury, P.

    2010-10-01

    The aim of the present study is to investigate applicability of artificial intelligence techniques such as ANFIS (Adaptive Neuro-Fuzzy Inference System) in forecasting flood flow in a river system. The proposed technique combines the learning ability of neural network with the transparent linguistic representation of fuzzy system. The technique is applied to forecast discharge at a downstream station using flow information at various upstream stations. A total of three years data has been selected for the implementation of this model. ANFIS models with various input structures and membership functions are constructed, trained and tested to evaluate efficiency of the models. Statistical indices such as Root Mean Square Error (RMSE), Correlation Coefficient (CORR) and Coefficient of Efficiency (CE) are used to evaluate performance of the ANFIS models in forecasting river flood. The values of the indices show that ANFIS model can accurately and reliably be used to forecast flood in a river system.

  18. Flood Forecasting in River System Using ANFIS

    SciTech Connect

    Ullah, Nazrin; Choudhury, P.

    2010-10-26

    The aim of the present study is to investigate applicability of artificial intelligence techniques such as ANFIS (Adaptive Neuro-Fuzzy Inference System) in forecasting flood flow in a river system. The proposed technique combines the learning ability of neural network with the transparent linguistic representation of fuzzy system. The technique is applied to forecast discharge at a downstream station using flow information at various upstream stations. A total of three years data has been selected for the implementation of this model. ANFIS models with various input structures and membership functions are constructed, trained and tested to evaluate efficiency of the models. Statistical indices such as Root Mean Square Error (RMSE), Correlation Coefficient (CORR) and Coefficient of Efficiency (CE) are used to evaluate performance of the ANFIS models in forecasting river flood. The values of the indices show that ANFIS model can accurately and reliably be used to forecast flood in a river system.

  19. The Flood Forecasting Centre (FFC) in the UK

    NASA Astrophysics Data System (ADS)

    Davies, P.

    2009-09-01

    The Met Office and the Environment Agency in the UK have set up a joint Flood Forecasting Centre (FFC), based at the London offices of the Met Office. This partnership will improve the UK's ability to respond to flooding events by providing an earlier national forecasting and alert service to central and local government departments so as to give them more time to prepare for floods and reduce the risk of loss of life and damage to property. The creation of the centre is in response to a key recommendation of Sir Michael Pitt's Review following the summer 2007 floods over the UK. For the first time, the FFC combines the Environment Agency's expertise in flood risk management and the Met Office's expertise in weather forecasting under one roof. My presentation will describe the benefits it will bring to the emergency responder community. It will also cover the tools available to the centre such as the new generation of high resolution weather models now coming on line. As a result, flood forecasting and warning systems, (which historically have been based on the lack of sufficiently fine scale rainfall information), need to be revisited in the light of the new meteorological modelling capabilities. This is particularly true for surface water flooding, where these new capabilities offer, for the first time, the possibility of providing credible alerts.

  20. Development and application of an atmospheric-hydrologic-hydraulic flood forecasting model driven by TIGGE ensemble forecasts

    NASA Astrophysics Data System (ADS)

    Bao, Hongjun; Zhao, Linna

    2012-02-01

    A coupled atmospheric-hydrologic-hydraulic ensemble flood forecasting model, driven by The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) data, has been developed for flood forecasting over the Huaihe River. The incorporation of numerical weather prediction (NWP) information into flood forecasting systems may increase forecast lead time from a few hours to a few days. A single NWP model forecast from a single forecast center, however, is insufficient as it involves considerable non-predictable uncertainties and leads to a high number of false alarms. The availability of global ensemble NWP systems through TIGGE offers a new opportunity for flood forecast. The Xinanjiang model used for hydrological rainfall-runoff modeling and the one-dimensional unsteady flow model applied to channel flood routing are coupled with ensemble weather predictions based on the TIGGE data from the Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), the UK Met Office (UKMO), and the US National Centers for Environmental Prediction (NCEP). The developed ensemble flood forecasting model is applied to flood forecasting of the 2007 flood season as a test case. The test case is chosen over the upper reaches of the Huaihe River above Lutaizi station with flood diversion and retarding areas. The input flood discharge hydrograph from the main channel to the flood diversion area is estimated with the fixed split ratio of the main channel discharge. The flood flow inside the flood retarding area is calculated as a reservoir with the water balance method. The Muskingum method is used for flood routing in the flood diversion area. A probabilistic discharge and flood inundation forecast is provided as the end product to study the potential benefits of using the TIGGE ensemble forecasts. The results demonstrate satisfactory flood forecasting with clear signals of probability of floods up to a

  1. Flood Forecasting in Wales: Challenges and Solutions

    NASA Astrophysics Data System (ADS)

    How, Andrew; Williams, Christopher

    2015-04-01

    With steep, fast-responding river catchments, exposed coastal reaches with large tidal ranges and large population densities in some of the most at-risk areas; flood forecasting in Wales presents many varied challenges. Utilising advances in computing power and learning from best practice within the United Kingdom and abroad have seen significant improvements in recent years - however, many challenges still remain. Developments in computing and increased processing power comes with a significant price tag; greater numbers of data sources and ensemble feeds brings a better understanding of uncertainty but the wealth of data needs careful management to ensure a clear message of risk is disseminated; new modelling techniques utilise better and faster computation, but lack the history of record and experience gained from the continued use of more established forecasting models. As a flood forecasting team we work to develop coastal and fluvial forecasting models, set them up for operational use and manage the duty role that runs the models in real time. An overview of our current operational flood forecasting system will be presented, along with a discussion on some of the solutions we have in place to address the challenges we face. These include: • real-time updating of fluvial models • rainfall forecasting verification • ensemble forecast data • longer range forecast data • contingency models • offshore to nearshore wave transformation • calculation of wave overtopping

  2. Flood Warning and Forecasting System in Slovakia

    NASA Astrophysics Data System (ADS)

    Leskova, Danica

    2016-04-01

    In 2015, it finished project Flood Warning and Forecasting System (POVAPSYS) as part of the flood protection in Slovakia till 2010. The aim was to build POVAPSYS integrated computerized flood forecasting and warning system. It took a qualitatively higher level of output meteorological and hydrological services in case of floods affecting large territorial units, as well as local flood events. It is further unfolding demands on performance and coordination of meteorological and hydrological services, troubleshooting observation, evaluation of data, fast communication, modeling and forecasting of meteorological and hydrological processes. Integration of all information entering and exiting to and from the project POVAPSYS provides Hydrological Flood Forecasting System (HYPOS). The system provides information on the current hydrometeorological situation and its evolution with the generation of alerts and notifications in case of exceeding predefined thresholds. HYPOS's functioning of the system requires flawless operability in critical situations while minimizing the loss of its key parts. HYPOS is a core part of the project POVAPSYS, it is a comprehensive software solutions based on a modular principle, providing data and processed information including alarms, in real time. In order to achieve full functionality of the system, in proposal, we have put emphasis on reliability, robustness, availability and security.

  3. A operational real time flood forecasting chain

    NASA Astrophysics Data System (ADS)

    Arena, N.; Cavallo, A.; Giannoni, F.; Turato, B.

    2003-04-01

    Extreme floods forecast represent an important modeling challenge for which it is crucial to utilize the simplest model representations that capture the dominant controls of extreme flood response. For extreme floods, the spatio-temporal structure of rainfall and drainage network structure often play a fundamental role. The integrated meteo-hydrologic real time forecasting chain in use at the Hydrometorological Center of Liguria Region is presented with particular regard to a specific case study. The meteorological forecasts are performed through the use of traditional means as Numerical Weather Predictions models at different resolutions and an innovative tool for the now-casting prediction as the meteorological Radar. The elements of the hydrologic model are a Hortonian infiltration model and a GIUH-based network response model. The basin scales of interest range from approximately 50 - 1,000 km2. The case study is the November 23-26, 2002 event.

  4. Medium range flood forecasts at global scale

    NASA Astrophysics Data System (ADS)

    Voisin, N.; Wood, A. W.; Lettenmaier, D. P.; Wood, E. F.

    2006-12-01

    While weather and climate forecast methods have advanced greatly over the last two decades, this capability has yet to be evidenced in mitigation of water-related natural hazards (primarily floods and droughts), especially in the developing world. Examples abound of extreme property damage and loss of life due to floods in the underdeveloped world. For instance, more than 4.5 million people were affected by the July 2000 flooding of the Mekong River and its tributaries in Cambodia, Vietnam, Laos and Thailand. The February- March 2000 floods in the Limpopo River of Mozambique caused extreme disruption to that country's fledgling economy. Mitigation of these events through advance warning has typically been modest at best. Despite the above noted improvement in weather and climate forecasts, there is at present no system for forecasting of floods globally, notwithstanding that the potential clearly exists. We describe a methodology that is eventually intended to generate global flood predictions routinely. It draws heavily from the experimental North American Land Data Assimilation System (NLDAS) and the companion Global Land Data Assimilation System (GLDAS) for development of nowcasts, and the University of Washington Experimental Hydrologic Prediction System to develop ensemble hydrologic forecasts based on Numerical Weather Prediction (NWP) models which serve both as nowcasts (and hence reduce the need for in situ precipitation and other observations in parts of the world where surface networks are critically deficient) and provide forecasts for lead times as long as fifteen days. The heart of the hydrologic modeling system is the University of Washington/Princeton University Variable Infiltration Capacity (VIC) macroscale hydrology model. In the prototype (tested using retrospective data), VIC is driven globally up to the time of forecast with daily ERA40 precipitation (rescaled on a monthly basis to a station-based global climatology), ERA40 wind, and ERA40

  5. Probabilistic Flash Flood Forecasting using Stormscale Ensembles

    NASA Astrophysics Data System (ADS)

    Hardy, J.; Gourley, J. J.; Kain, J. S.; Clark, A.; Novak, D.; Hong, Y.

    2013-12-01

    Flash flooding is one of the most costly and deadly natural hazards in the US and across the globe. The loss of life and property from flash floods could be mitigated with better guidance from hydrological models, but these models have limitations. For example, they are commonly initialized using rainfall estimates derived from weather radars, but the time interval between observations of heavy rainfall and a flash flood can be on the order of minutes, particularly for small basins in urban settings. Increasing the lead time for these events is critical for protecting life and property. Therefore, this study advances the use of quantitative precipitation forecasts (QPFs) from a stormscale NWP ensemble system into a distributed hydrological model setting to yield basin-specific, probabilistic flash flood forecasts (PFFFs). Rainfall error characteristics of the individual members are first diagnosed and quantified in terms of structure, amplitude, and location (SAL; Wernli et al., 2008). Amplitude and structure errors are readily correctable due to their diurnal nature, and the fine scales represented by the CAPS QPF members are consistent with radar-observed rainfall, mainly showing larger errors with afternoon convection. To account for the spatial uncertainty of the QPFs, we use an elliptic smoother, as in Marsh et al. (2012), to produce probabilistic QPFs (PQPFs). The elliptic smoother takes into consideration underdispersion, which is notoriously associated with stormscale ensembles, and thus, is good for targeting the approximate regions that may receive heavy rainfall. However, stormscale details contained in individual members are still needed to yield reasonable flash flood simulations. Therefore, on a case study basis, QPFs from individual members are then run through the hydrological model with their predicted structure and corrected amplitudes, but the locations of individual rainfall elements are perturbed within the PQPF elliptical regions using Monte

  6. HESS Opinions "Forecaster priorities for improving probabilistic flood forecasts"

    NASA Astrophysics Data System (ADS)

    Wetterhall, F.; Pappenberger, F.; Alfieri, L.; Cloke, H. L.; Thielen-del Pozo, J.; Balabanova, S.; Daňhelka, J.; Vogelbacher, A.; Salamon, P.; Carrasco, I.; Cabrera-Tordera, A. J.; Corzo-Toscano, M.; Garcia-Padilla, M.; Garcia-Sanchez, R. J.; Ardilouze, C.; Jurela, S.; Terek, B.; Csik, A.; Casey, J.; Stankūnavičius, G.; Ceres, V.; Sprokkereef, E.; Stam, J.; Anghel, E.; Vladikovic, D.; Alionte Eklund, C.; Hjerdt, N.; Djerv, H.; Holmberg, F.; Nilsson, J.; Nyström, K.; Sušnik, M.; Hazlinger, M.; Holubecka, M.

    2013-11-01

    Hydrological ensemble prediction systems (HEPS) have in recent years been increasingly used for the operational forecasting of floods by European hydrometeorological agencies. The most obvious advantage of HEPS is that more of the uncertainty in the modelling system can be assessed. In addition, ensemble prediction systems generally have better skill than deterministic systems both in the terms of the mean forecast performance and the potential forecasting of extreme events. Research efforts have so far mostly been devoted to the improvement of the physical and technical aspects of the model systems, such as increased resolution in time and space and better description of physical processes. Developments like these are certainly needed; however, in this paper we argue that there are other areas of HEPS that need urgent attention. This was also the result from a group exercise and a survey conducted to operational forecasters within the European Flood Awareness System (EFAS) to identify the top priorities of improvement regarding their own system. They turned out to span a range of areas, the most popular being to include verification of an assessment of past forecast performance, a multi-model approach for hydrological modelling, to increase the forecast skill on the medium range (>3 days) and more focus on education and training on the interpretation of forecasts. In light of limited resources, we suggest a simple model to classify the identified priorities in terms of their cost and complexity to decide in which order to tackle them. This model is then used to create an action plan of short-, medium- and long-term research priorities with the ultimate goal of an optimal improvement of EFAS in particular and to spur the development of operational HEPS in general.

  7. Interactive Forecasting with the National Weather Service River Forecast System

    NASA Technical Reports Server (NTRS)

    Smith, George F.; Page, Donna

    1993-01-01

    The National Weather Service River Forecast System (NWSRFS) consists of several major hydrometeorologic subcomponents to model the physics of the flow of water through the hydrologic cycle. The entire NWSRFS currently runs in both mainframe and minicomputer environments, using command oriented text input to control the system computations. As computationally powerful and graphically sophisticated scientific workstations became available, the National Weather Service (NWS) recognized that a graphically based, interactive environment would enhance the accuracy and timeliness of NWS river and flood forecasts. Consequently, the operational forecasting portion of the NWSRFS has been ported to run under a UNIX operating system, with X windows as the display environment on a system of networked scientific workstations. In addition, the NWSRFS Interactive Forecast Program was developed to provide a graphical user interface to allow the forecaster to control NWSRFS program flow and to make adjustments to forecasts as necessary. The potential market for water resources forecasting is immense and largely untapped. Any private company able to market the river forecasting technologies currently developed by the NWS Office of Hydrology could provide benefits to many information users and profit from providing these services.

  8. Ensemble flood forecasting on the Tocantins River - Brazil

    NASA Astrophysics Data System (ADS)

    Fan, Fernando; Collischonn, Walter; Jiménez, Karena; Sorribas, Mino; Buarque, Diogo; Siqueira, Vinicius

    2014-05-01

    The Tocantins River basin is located in the northern region of Brazil and has about 300.000 km2 of drainage area upstream of its confluence with river Araguaia, its major tributary. The Tocantins River is intensely used for hydropower production, with seven major dams, including Tucuruí, world's fourth largest in terms of installed capacity. In this context, the use of hydrological streamflow forecasts at this basin is very useful to support the decision making process for reservoir operation, and can produce benefits by reducing damages from floods, increasing dam safety and upgrading efficiency in power generation. The occurrence of floods along the Tocantins River is a relatively frequent event, where one recent example is the year of 2012, when a large flood occurred in the Tocantins River with discharge peaks exceeding 16.000m³/s, and causing damages to cities located along the river. After this flooding event, a hydrological forecasting system was developed and is operationally in use since mid-2012 in order to assist the decision making of dam operation along the river basin. The forecasting system is based on the MGB-IPH model, a large scale distributed hydrological model, and initially used only telemetric data as observed information and deterministic rainfall forecasts from the Brazilian Meteorological Forecasting Centre (CPTEC) with 7-days lead time as input. Since August-2013 the system has been updated and now works with two new features: (i) a technique for merging satellite TRMM real-time precipitation estimative with gauged information is applied to reduce the uncertainty due to the lack of observed information over a portion of the basin, since the total number of rain gages available is scarce compared to the total basin area; (ii) rainfall ensemble forecasts with 16-days lead time provided by the Global Ensemble Forecasting System (GEFs), from the 2nd Generation of NOAA Global Ensemble Reforecast Data Set, maintained by the National Center for

  9. Timetable of an operational flood forecasting system

    NASA Astrophysics Data System (ADS)

    Liechti, Katharina; Jaun, Simon; Zappa, Massimiliano

    2010-05-01

    At present a new underground part of Zurich main station is under construction. For this purpose the runoff capacity of river Sihl, which is passing beneath the main station, is reduced by 40%. If a flood is to occur the construction site is evacuated and gates can be opened for full runoff capacity to prevent bigger damages. However, flooding the construction site, even if it is controlled, is coupled with costs and retardation. The evacuation of the construction site at Zurich main station takes about 2 to 4 hours and opening the gates takes another 1 to 2 hours each. In the upper part of the 336 km2 Sihl catchment the Sihl lake, a reservoir lake, is situated. It belongs and is used by the Swiss Railway Company for hydropower production. This lake can act as a retention basin for about 46% of the Sihl catchment. Lowering the lake level to gain retention capacity, and therewith safety, is coupled with direct loss for the Railway Company. To calculate the needed retention volume and the water to be released facing unfavourable weather conditions, forecasts with a minimum lead time of 2 to 3 days are needed. Since the catchment is rather small, this can only be realised by the use of meteorological forecast data. Thus the management of the construction site depends on accurate forecasts to base their decisions on. Therefore an operational hydrological ensemble prediction system (HEPS) was introduced in September 2008 by the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). It delivers daily discharge forecasts with a time horizon of 5 days. The meteorological forecasts are provided by MeteoSwiss and stem from the operational limited-area COSMO-LEPS which downscales the ECMWF ensemble prediction system to a spatial resolution of 7 km. Additional meteorological data for model calibration and initialisation (air temperature, precipitation, water vapour pressure, global radiation, wind speed and sunshine duration) and radar data are also provided by

  10. Recent Operational Innovations and Future Developments at the Flood Forecasting Centre

    NASA Astrophysics Data System (ADS)

    Millard, Jon; Pilling, Charlie

    2015-04-01

    The Flood Forecasting Centre (FFC) was established in 2009 to give an overview of flood risk across England and Wales and is a partnership between the UK Met Office, the Environment Agency and Natural Resources Wales. Primarily serving the emergency response community, the FFC aims to provide trusted guidance to help protect lives and livelihoods from flooding across England and Wales from its base at the Met Office in Exeter. The flood forecasts consist of an assessment of the likelihood as well as the expected level of impacts of flood events during the next five days. The FFC provide forecasts for all natural sources of flooding, namely; fluvial, coastal, surface water and groundwater but liaise closely with meteorologists at the Met Office and local flood forecasters at the Environment Agency and Natural Resources Wales. Key challenges include providing; forecasts with longer lead times especially for fluvial and coastal events, forecasts at shorter timescales and with more spatial focus for rapid response catchments and surface water events, and also clear communications of forecast uncertainties. As well as operational activities, the FFC run a significant development and improvement programme and are linked in with Met Office and Environment Agency science projects in order to bring new science into operations to try and meet these challenges and improve performance. Latest developments which are now being applied operationally to provide an enhanced flood warning service will be presented. Examples include; the use of the national hydrological model Grid to Grid (G2G) for both fluvial and surface water flooding, extended surge ensembles for coastal flooding, enhancements in the surface water forecasting tool, and improvements to products communicating these forecasts. An overview of the current projects under development will also be provided, including; improvements to data within G2G, surface water hazard impact modelling, 7 day wave ensemble forecasts

  11. Support vector regression for real-time flood stage forecasting

    NASA Astrophysics Data System (ADS)

    Yu, Pao-Shan; Chen, Shien-Tsung; Chang, I.-Fan

    2006-09-01

    SummaryFlood forecasting is an important non-structural approach for flood mitigation. The flood stage is chosen as the variable to be forecasted because it is practically useful in flood forecasting. The support vector machine, a novel artificial intelligence-based method developed from statistical learning theory, is adopted herein to establish a real-time stage forecasting model. The lags associated with the input variables are determined by applying the hydrological concept of the time of response, and a two-step grid search method is applied to find the optimal parameters, and thus overcome the difficulties in constructing the learning machine. Two structures of models used to perform multiple-hour-ahead stage forecasts are developed. Validation results from flood events in Lan-Yang River, Taiwan, revealed that the proposed models can effectively predict the flood stage forecasts one-to-six-hours ahead. Moreover, a sensitivity analysis was conducted on the lags associated with the input variables.

  12. Impact of rainfall spatial variability on Flash Flood Forecasting

    NASA Astrophysics Data System (ADS)

    Douinot, Audrey; Roux, Hélène; Garambois, Pierre-André; Larnier, Kevin

    2014-05-01

    According to the United States National Hazard Statistics database, flooding and flash flooding have caused the largest number of deaths of any weather-related phenomenon over the last 30 years (Flash Flood Guidance Improvement Team, 2003). Like the storms that cause them, flash floods are very variable and non-linear phenomena in time and space, with the result that understanding and anticipating flash flood genesis is far from straightforward. In the U.S., the Flash Flood Guidance (FFG) estimates the average number of inches of rainfall for given durations required to produce flash flooding in the indicated county. In Europe, flash flood often occurred on small catchments (approximately 100 km2) and it has been shown that the spatial variability of rainfall has a great impact on the catchment response (Le Lay and Saulnier, 2007). Therefore, in this study, based on the Flash flood Guidance method, rainfall spatial variability information is introduced in the threshold estimation. As for FFG, the threshold is the number of millimeters of rainfall required to produce a discharge higher than the discharge corresponding to the first level (yellow) warning of the French flood warning service (SCHAPI: Service Central d'Hydrométéorologie et d'Appui à la Prévision des Inondations). The indexes δ1 and δ2 of Zoccatelli et al. (2010), based on the spatial moments of catchment rainfall, are used to characterize the rainfall spatial distribution. Rainfall spatial variability impacts on warning threshold and on hydrological processes are then studied. The spatially distributed hydrological model MARINE (Roux et al., 2011), dedicated to flash flood prediction is forced with synthetic rainfall patterns of different spatial distributions. This allows the determination of a warning threshold diagram: knowing the spatial distribution of the rainfall forecast and therefore the 2 indexes δ1 and δ2, the threshold value is read on the diagram. A warning threshold diagram is

  13. Delft FEWS: An open shell flood forecasting platform

    NASA Astrophysics Data System (ADS)

    Reggiani, P.; Kwadijk, J. C. J.; Werner, M. G. F.; van Dijk, M. J.; Schellekens, J.; van Kappel, R. R.; Sprokkereef, E.

    2003-04-01

    DELFT FEWS is a flood forecasting system developed over several years at Delft Hydraulics. The main philosophy underlying the system is to provide an open shell tool, that allows integration of arbitrary hydrological and river routing models with meteorological data and numerical weather forecasts. In its actual form DELFT-FEWS constitutes a collection of platform-independent software modules, linked to a central database. The database is used to store historical runoff data from gauging stations, and meteorological data from local and synoptic meteorological stations. These can be updated on-line through direct access to national weather services, weather forecast centres and hydro-meteorological services. In addition, the platform is designed to import and convert numerical weather forecasts produced by weather agencies, and interface them with the database. The system incorporates a wide range of algorithms for data verification, interpolation, model updating and data assimilation. These can be employed for data verification and reconstruction of missing values, as well as for pre processing of meteorological data, such that are made ready for use in hydrological models. The various hydrological and routing models are included into the system via appropriate model adapters, that convert data in the database to specific model data formats and vice versa. In this manner a concatenation of various operational and already tested models into model cascades is facilitated within a single and consistent computational framework. To date the system has been successfully tested with various numerical weather forecasts, including deterministic and ensemble forecasts provided by national weather forecast centres and the European Centre for Medium-Range Weather Forecast. The hydrodynamic river routing module SOBEK, the LISFLOOD suite of raster-based hydrology and hydraulic codes and the well-known HBV hydrological model were included for the computation of the hydrologic

  14. A hydrometeorological approach for probabilistic flood forecast

    NASA Astrophysics Data System (ADS)

    Siccardi, F.; Boni, G.; Ferraris, L.; Rudari, R.

    2005-03-01

    We propose a new methodology for evaluating predictive cumulative distribution functions (CDF) of ground effects for flood forecasting in mountainous environments. The methodology is based on the proper nesting of models suitable for probabilistic meteorological forecast, downscaling of rainfall, and hydrological modeling in order to provide a probabilistic prediction of ground effects of heavy rainfall events. Different ways of nesting are defined as function of the ratio between three typical scales: scales at which rainfall processes are satisfactory represented by meteorological models, scales of the hydrological processes, and scales of the social response. Two different examples of the application of the methodology for different hydrological scales are presented. Predictive CDFs are evaluated, and the motivations that lead to a different paths for CDFs derivation are highlighted.

  15. Real-time application of meteorological ensembles for Danube flood forecasting

    NASA Astrophysics Data System (ADS)

    Csík, A.; Gauzer, B.; Gnandt, B.; Balint, G.

    2009-04-01

    demonstration experiment was that the NHFS (VITUKI National Hydrological Forecasting Service of Hungary) system can be used for such a purpose like real-time usage. The relative large number of model runs could be performed within reasonable time. Suggestions are given to adjust appropriate decision support rules to utilise the array of flood forecasts for flood management and warning purposes. The proper estimation of the contribution to forecast error by different modules of the system may help to better understand expected current uncertainty of the forecast. The given research has been partly supported by EC under INTEGRATED PROJECT PREVIEW PREVention, Information and Early Warning Proposal/Contract: 516172. Keywords: real time flood forecast, hydrological ensembles, meteorological ensembles, River Danube, quantitative precipitation forecast, gridded fields, semi-distributed.

  16. Accounting for uncertainty in distributed flood forecasting models

    NASA Astrophysics Data System (ADS)

    Cole, Steven J.; Robson, Alice J.; Bell, Victoria A.; Moore, Robert J.; Pierce, Clive E.; Roberts, Nigel

    2010-05-01

    Recent research investigating the uncertainty of distributed hydrological flood forecasting models will be presented. These findings utilise the latest advances in rainfall estimation, ensemble nowcasting and Numerical Weather Prediction (NWP). The hydrological flood model that forms the central focus of the study is the Grid-to-Grid Model or G2G: this is a distributed grid-based model that produces area-wide flood forecasts across the modelled domain. Results from applying the G2G Model across the whole of England and Wales on a 1 km grid will be shown along with detailed regional case studies of major floods, such as those of summer 2007. Accounting for uncertainty will be illustrated using ensemble rainfall forecasts from both the Met Office's STEPS nowcasting and high-resolution (~1.5 km) NWP systems. When these rainfall forecasts are used as input to the G2G Model, risk maps of flood exceedance can be produced in animated form that allow the evolving flood risk to be visualised in space and time. Risk maps for a given forecast horizon (e.g. the next 6 hours) concisely summarise a wealth of spatio-temporal flood forecast information and provide an efficient means to identify ‘hot spots' of flood risk. These novel risk maps can be used to support flood warning in real-time and are being trialled operationally across England and Wales by the new joint Environment Agency and Met Office Flood Forecasting Centre.

  17. Using ensemble rainfall predictions in a countrywide flood forecasting model in Scotland

    NASA Astrophysics Data System (ADS)

    Cranston, M. D.; Maxey, R.; Tavendale, A. C. W.; Buchanan, P.

    2012-04-01

    Improving flood predictions for all sources of flooding is at the centre of flood risk management policy in Scotland. With the introduction of the Flood Risk Management (Scotland) Act providing a new statutory basis for SEPA's flood warning responsibilities, the pressures on delivering hydrological science developments in support of this legislation has increased. Specifically, flood forecasting capabilities need to develop in support of the need to reduce the impact of flooding through the provision of actively disseminated, reliable and timely flood warnings. Flood forecasting in Scotland has developed significantly in recent years (Cranston and Tavendale, 2012). The development of hydrological models to predict flooding at a catchment scale has relied upon the application of rainfall runoff models utilising raingauge, radar and quantitative precipitation forecasts in the short lead time (less than 6 hours). Single or deterministic forecasts based on highly uncertain rainfall predictions have led to the greatest operational difficulties when communicating flood risk with emergency responders, therefore the emergence of probability-based estimates offers the greatest opportunity for managing uncertain predictions. This paper presents operational application of a physical-conceptual distributed hydrological model on a countrywide basis across Scotland. Developed by CEH Wallingford for SEPA in 2011, Grid-to-Grid (G2G) principally runs in deterministic mode and employs radar and raingauge estimates of rainfall together with weather model predictions to produce forecast river flows, as gridded time-series at a resolution of 1km and for up to 5 days ahead (Cranston, et al., 2012). However the G2G model is now being run operationally using ensemble predictions of rainfall from the MOGREPS-R system to provide probabilistic flood forecasts. By presenting a range of flood predictions on a national scale through this approach, hydrologists are now able to consider an

  18. A Coastal Flood Decision Support Tool for Forecast Operations in Alaska

    NASA Astrophysics Data System (ADS)

    van Breukelen, C. M.; Moore, A.; Plumb, E. W.

    2015-12-01

    ABSTRACT Coastal flooding and erosion poses a serious threat to infrastructure, livelihood, and property for communities along Alaska's northern and western coastline. While the National Weather Service Alaska Region (NWS-AR) forecasts conditions favorable for coastal flooding, an improvement can be made in communicating event impacts between NWS-AR and local residents. Scientific jargon used by NWS-AR to indicate the severity of flooding potential is often misconstrued by residents. Additionally, the coastal flood forecasting process is cumbersome and time consuming due to scattered sources of flood guidance. To alleviate these problems, a single coastal flooding decision support tool was created for the Fairbanks Weather Forecast Office to help bridge the communication gap, streamline the forecast and warning process, and take into account both the meteorological and socioeconomic systems at work during a flood event. This tool builds on previous research and data collected by the Alaska Division of Geological and Geophysical Surveys (DGGS) and the NWS-AR, using high resolution elevation data to model the impacts of storm tide rise above the mean lower low water level on five of the most at-risk communities along the Alaskan coast. Important local buildings and infrastructure are highlighted, allowing forecasters to relate the severity of the storm tide in terms of local landmarks that are familiar to residents. In this way, this decision support tool allows for a conversion from model output storm tide levels into real world impacts that are easily understood by forecasters, emergency managers, and other stakeholders, helping to build a Weather-Ready Nation. An overview of the new coastal flood decision support tool in NWS-AR forecast operations will be discussed. KEYWORDS Forecasting; coastal flooding; coastal hazards; decision support

  19. Operational flood forecasting system of Umbria Region "Functional Centre

    NASA Astrophysics Data System (ADS)

    Berni, N.; Pandolfo, C.; Stelluti, M.; Ponziani, F.; Viterbo, A.

    2009-04-01

    The hydrometeorological alert office (called "Decentrate Functional Centre" - CFD) of Umbria Region, in central Italy, is the office that provides technical tools able to support decisions when significant flood/landslide events occur, furnishing 24h support for the whole duration of the emergency period, according to the national directive DPCM 27 February 2004 concerning the "Operating concepts for functional management of national and regional alert system during flooding and landslide events for civil protection activities purposes" that designs, within the Italian Civil Defence Emergency Management System, a network of 21 regional Functional Centres coordinated by a central office at the National Civil Protection Department in Rome. Due to its "linking" role between Civil Protection "real time" activities and environmental/planning "deferred time" ones, the Centre is in charge to acquire and collect both real time and quasi-static data: quantitative data from monitoring networks (hydrometeorological stations, meteo radar, ...), meteorological forecasting models output, Earth Observation data, hydraulic and hydrological simulation models, cartographic and thematic GIS data (vectorial and raster type), planning studies related to flooding areas mapping, dam managing plans during flood events, non instrumental information from direct control of "territorial presidium". A detailed procedure for the management of critical events was planned, also in order to define the different role of various authorities and institutions involved. Tiber River catchment, of which Umbria region represents the main upper-medium portion, includes also regional trans-boundary issues very important to cope with, especially for what concerns large dam behavior and management during heavy rainfall. The alert system is referred to 6 different warning areas in which the territory has been divided into and based on a threshold system of three different increasing critical levels according

  20. Defining critical thresholds for ensemble flood forecasting and warning

    NASA Astrophysics Data System (ADS)

    Weeink, Werner H. A.; Ramos, Maria-Helena; Booij, Martijn J.; Andréassian, Vazken; Krol, Maarten S.

    2010-05-01

    The use of weather ensemble predictions in ensemble flood forecasting is an acknowledged procedure to include the uncertainty of meteorological forecasts in a probabilistic streamflow prediction system. Operational flood forecasters can thus get an overview of the probability of exceeding a critical discharge or water level, and decide on whether a flood warning should be issued or not. This process offers several challenges to forecasters: 1) how to define critical thresholds along all the rivers under survey? 2) How to link locally defined thresholds to simulated discharges, which result from models with specific spatial and temporal resolutions? 3) How to define the number of ensemble forecasts predicting the exceedance of critical thresholds necessary to launch a warning? This study focuses on this third challenge. We investigate the optimal number of ensemble members exceeding a critical discharge in order to issue a flood warning. The optimal probabilistic threshold is the one that minimizes the number of false alarms and misses, while it optimizes the number of flood events correctly forecasted. Furthermore, in our study, an optimal probabilistic threshold also maximizes flood preparedness: the gain in lead-time compared to a deterministic forecast. Data used to evaluate critical thresholds for ensemble flood forecasting come from a selection of 208 catchments in France, which covers a wide range of the hydroclimatic conditions (including catchment size) encountered in the country. The GRP hydrological forecasting model, a lumped soil-moisture-accounting type rainfall-runoff model, is used. The model is driven by the 10-day ECMWF deterministic and ensemble (51 members) precipitation forecasts for a period of 18 months. A trade-off between the number of hits, misses, false alarms and the gain in lead time is sought to find the optimal number of ensemble members exceeding the critical discharge. These optimal probability thresholds are further explored in

  1. Flash flood warnings using the ensemble precipitation forecasting technique: A case study on forecasting floods in Taiwan caused by typhoons

    NASA Astrophysics Data System (ADS)

    Yang, Tsun-Hua; Yang, Sheng-Chi; Ho, Jui-Yi; Lin, Gwo-Fong; Hwang, Gong-Do; Lee, Cheng-Shang

    2015-01-01

    A flash flood is an event that develops rapidly. Given early warnings with sufficient lead time, flood forecasting can help people prepare disaster prevention measures. To provide this early warning, a statistics-based flood forecasting model was developed to evaluate the flooding potential in urban areas using ensemble quantitative precipitation forecasts (the Taiwan Cooperative Precipitation Ensemble Forecast Experiment, TAPEX). The proposed model uses different sources of information, such as (i) the designed capacity of storm sewer systems, (ii) a flood inundation potential database, and (iii) historical flooding observations, to evaluate the potential for flash flooding situations to occur. Using 24-, 48- and 72-h ahead precipitation forecasts from the TAPEX, the proposed model can assess the flooding potential with two levels of risk and at the township scale with a 3-day lead time. The proposed model is applied to Pingtung County, which includes 33 townships and is located in southern Taiwan. A dataset of typhoon storms from 2010 to 2014 was used to evaluate the model performance. The accuracy and threat score for testing events are 0.68 and 0.30, respectively, with a lead time of 24 h. The accuracy and threat score for training events are 0.82 and 0.31, respectively, with a lead time of 24 h. The model performance decreases when the lead time is extended. However, the model demonstrates its potential as a valuable reference to improve emergency responses to alleviate the loss of lives and property due to flooding.

  2. Study of Beijiang catchment flash-flood forecasting model

    NASA Astrophysics Data System (ADS)

    Chen, Y.; Li, J.; Huang, S.; Dong, Y.

    2015-05-01

    Beijiang catchment is a small catchment in southern China locating in the centre of the storm areas of the Pearl River Basin. Flash flooding in Beijiang catchment is a frequently observed disaster that caused direct damages to human beings and their properties. Flood forecasting is the most effective method for mitigating flash floods, the goal of this paper is to develop the flash flood forecasting model for Beijiang catchment. The catchment property data, including DEM, land cover types and soil types, which will be used for model construction and parameter determination, are downloaded from the website freely. Based on the Liuxihe Model, a physically based distributed hydrological model, a model for flash flood forecasting of Beijiang catchment is set up. The model derives the model parameters from the terrain properties, and further optimized with the observed flooding process, which improves the model performance. The model is validated with a few observed floods occurred in recent years, and the results show that the model is reliable and is promising for flash flood forecasting.

  3. Proper estimation of hydrological parameters from flood forecasting aspects

    NASA Astrophysics Data System (ADS)

    Miyamoto, Mamoru; Matsumoto, Kazuhiro; Tsuda, Morimasa; Yamakage, Yuzuru; Iwami, Yoichi; Yanami, Hitoshi; Anai, Hirokazu

    2016-04-01

    The hydrological parameters of a flood forecasting model are normally calibrated based on an entire hydrograph of past flood events by means of an error assessment function such as mean square error and relative error. However, the specific parts of a hydrograph, i.e., maximum discharge and rising parts, are particularly important for practical flood forecasting in the sense that underestimation may lead to a more dangerous situation due to delay in flood prevention and evacuation activities. We conducted numerical experiments to find the most proper parameter set for practical flood forecasting without underestimation in order to develop an error assessment method for calibration appropriate for flood forecasting. A distributed hydrological model developed in Public Works Research Institute (PWRI) in Japan was applied to fifteen past floods in the Gokase River basin of 1,820km2 in Japan. The model with gridded two-layer tanks for the entire target river basin included hydrological parameters, such as hydraulic conductivity, surface roughness and runoff coefficient, which were set according to land-use and soil-type distributions. Global data sets, e.g., Global Map and Digital Soil Map of the World (DSMW), were employed as input data for elevation, land use and soil type. The values of fourteen types of parameters were evenly sampled with 10,001 patterns of parameter sets determined by the Latin Hypercube Sampling within the search range of each parameter. Although the best reproduced case showed a high Nash-Sutcliffe Efficiency of 0.9 for all flood events, the maximum discharge was underestimated in many flood cases. Therefore, two conditions, which were non-underestimation in the maximum discharge and rising parts of a hydrograph, were added in calibration as the flood forecasting aptitudes. The cases with non-underestimation in the maximum discharge and rising parts of the hydrograph also showed a high Nash-Sutcliffe Efficiency of 0.9 except two flood cases

  4. The policy and science supporting flash flood forecasting in Scotland

    NASA Astrophysics Data System (ADS)

    Cranston, Michael; Maxey, Richard; Speight, Linda; Tavendale, Amy; Cole, Steven; Robson, Alice; Moore, Robert

    2013-04-01

    In 2012, the Scottish Environment Protection Agency (SEPA) published its Flood Warning Strategy. The strategy aims to ensure that emerging science is at the heart of supporting its strategic aim of reducing the impact of river flooding through the provision of reliable and timely flood warnings and allowing Scotland's flood warning authority to develop forecasting approaches in areas not previously considered. One specific area of agreed commitment is in the development of methods for forecasting in rapid response or flashy catchments. Previous policies have stated that flood warning provision would not be possible without adequate hydrological response time (greater than three hours). The particular challenge with meeting this new aim is on the reliance of increasingly uncertain flooding predictions at the shorter timescale against a more cautious and traditional approach to flood warning which relies on hydrological observations and real time verification of forecasts. This therefore places increasing demands on developing hydrometeorological forecasting capabilities. This paper will present on some scientific developments supporting the latest policy. In particular on Grid-2-Grid, a distributed hydrological model, which has been in operation across Scotland for over a year (Cranston, et al., 2012) and on a specific assessment of its capabilities using high resolution and ensemble rainfall forecasts. The paper will focus on Comrie, a community in Scotland that has been devastated twice during 2012 by flash flooding and considers the various challenges in meeting this strategic aim. References Cranston, M., Maxey, R., Tavendale, A., Buchanan, P., Motion, A., Moore, R. M., Cole, S., Robson, A. and Minett, A. (2012) Countrywide flood forecasting in Scotland: challenges for hydrometeorological uncertainty and prediction. Weather Radar and Hydrology (Proceedings of a symposium held in Exeter, UK, April 2011), IAHS Publ. 351, 2012)

  5. NOAA Graphical Flood Severity Inundation Mapping: Enhancing River Forecasts with Geographic Information Systems (GIS)

    NASA Astrophysics Data System (ADS)

    Marcy, D.; Donaldson, T.

    2006-12-01

    The National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS) provides flood forecast information in a variety of formats, including graphical hydrographs and text products. Beginning in 2002, the NOAA Coastal Services Center (CSC) and NWS have worked in partnership to develop geographic information systems (GIS) based graphical flood severity inundation products. GIS techniques are used along with the best available topographic data and flood surface profiles generated from hydraulic models to develop inundation maps of the areal extent of NWS flood categories (minor, moderate, major), along with a range of water surface elevations at selected vertical intervals. The resulting inundation map products are called NWS flood severity inundation map libraries and will become a part of the suite of new products being disseminated via the Advanced Hydrologic Prediction Service (AHPS) program. In 2006, the CSC through the contractor, Watershed Concepts, developed a methodologies and standards document and map template for new graphical flood severity products. This report, titled "Methods and Standards for National Weather Service Flood Severity Inundation Maps" will serve as the basis and guide for creating new flood severity inundation map libraries at specific NWS river forecast points. This paper will describe 1.) the history and components of these inundation maps products, 2.) the process for developing flood severity inundation maps using these methods and standards, 3.) the connection of these products to the FEMA map modernization program, 4.) and delivery of these products via the web.

  6. Perturbation of convection-permitting NWP forecasts for flash-flood ensemble forecasting

    NASA Astrophysics Data System (ADS)

    Vincendon, B.; Ducrocq, V.; Nuissier, O.; Vié, B.

    2011-05-01

    Mediterranean intense weather events often lead to devastating flash-floods. Extending the forecasting lead times further than the watershed response times, implies the use of numerical weather prediction (NWP) to drive hydrological models. However, the nature of the precipitating events and the temporal and spatial scales of the watershed response make them difficult to forecast, even using a high-resolution convection-permitting NWP deterministic forecasting. This study proposes a new method to sample the uncertainties of high-resolution NWP precipitation forecasts in order to quantify the predictability of the streamflow forecasts. We have developed a perturbation method based on convection-permitting NWP-model error statistics. It produces short-term precipitation ensemble forecasts from single-value meteorological forecasts. These rainfall ensemble forecasts are then fed into a hydrological model dedicated to flash-flood forecasting to produce ensemble streamflow forecasts. The verification on two flash-flood events shows that this forecasting ensemble performs better than the deterministic forecast. The performance of the precipitation perturbation method has also been found to be broadly as good as that obtained using a state-of-the-art research convection-permitting NWP ensemble, while requiring less computing time.

  7. Hydrological model calibration for enhancing global flood forecast skill

    NASA Astrophysics Data System (ADS)

    Hirpa, Feyera A.; Beck, Hylke E.; Salamon, Peter; Thielen-del Pozo, Jutta

    2016-04-01

    Early warning systems play a key role in flood risk reduction, and their effectiveness is directly linked to streamflow forecast skill. The skill of a streamflow forecast is affected by several factors; among them are (i) model errors due to incomplete representation of physical processes and inaccurate parameterization, (ii) uncertainty in the model initial conditions, and (iii) errors in the meteorological forcing. In macro scale (continental or global) modeling, it is a common practice to use a priori parameter estimates over large river basins or wider regions, resulting in suboptimal streamflow estimations. The aim of this work is to improve flood forecast skill of the Global Flood Awareness System (GloFAS; www.globalfloods.eu), a grid-based forecasting system that produces flood forecast unto 30 days lead, through calibration of the distributed hydrological model parameters. We use a combination of in-situ and satellite-based streamflow data for automatic calibration using a multi-objective genetic algorithm. We will present the calibrated global parameter maps and report the forecast skill improvements achieved. Furthermore, we discuss current challenges and future opportunities with regard to global-scale early flood warning systems.

  8. Short-term Ensemble Flood Forecasting Experiments in Brazil

    NASA Astrophysics Data System (ADS)

    Collischonn, Walter; Meller, Adalberto; Fan, Fernando; Moreira, Demerval; Dias, Pedro; Buarque, Diogo; Bravo, Juan

    2013-04-01

    Flood Forecasting and issuing early warnings to communities under risk can help reduce the impacts of those events. However, to be effective, warnings should be given several hours in advance. The best solution to extend the lead time is possibly the use of rainfall-runoff models with input given by rainfall and streamflow observations and by forecasts of future precipitation derived from numerical weather prediction (NWP) models. Recent studies showed that probabilistic or ensemble flood forecasts produced using ensemble precipitation forecasts as input data outperform deterministic flood forecasts in several cases in Europe and the United States, and ensemble flood forecasting systems are increasingly becoming operational in these regions. In Brazil, on the other hand, operational flood warning systems are rare, and often based on simplified river routing or linear transfer function models. However, a large number of global and regional meteorological models is operationally run covering most of the country, and forecasts of those models are available for recent years. We used this available data to conduct experiments of short term ensemble flood forecasting in the Paraopeba River basin (12 thousand km2), located in Southeastern Brazil. Streamflow forecasts were produced using the MGB-IPH hydrological model, using a simple empirical state updating method and using an ensemble of precipitation forecasts generated by several models, with different initial conditions and parameterizations, from several weather forecasting centers. A single deterministic streamflow forecast, based on a quantitative precipitation forecast derived from the optimal combination of several outputs of NWP models was used as a reference to assess the performance of the ensemble streamflow forecasts. Flood forecasts experiments were performed for three rainy seasons (austral summer) between 2008-2011. The results for predictions of dichotomous events, which mean exceeding or not flood

  9. High resolution distributed hydrological modeling for river flood forecasting

    NASA Astrophysics Data System (ADS)

    Chen, Y.

    2014-12-01

    High resolution distributed hydrological model can finely describe the river basin hydrological processes, thus having the potential to improve the flood forecasting capabilities, and is regarded as the next generation flood forecast model. But there are great challenges in deploying it in real-time river flood forecasting, such as the awesome computation resources requirement, parameter determination, high resolution precipitation assimilation and uncertainty controls. Liuxihe Model is a physically-based distributed hydrological model proposed mainly for catchment flood forecasting, which is a process-based hydrological model. In this study, based on Liuxihe Model, a parallel computation algorithm for Liuxihe model flood forecasting is proposed, and a cloudy computation system is developed on a high performance computer, this largely improves the applicability of Liuxihe Model in large river. Without the parallel computation, the Liuxihe Model is computationally incapable in application to rivers with drainage area bigger than 10,000km2 at the grid size of 100m. With the parallel computation, the Liuxihe Model is used in a river with a drainage area of 60,000km2, and could be expended indefinitely. Based on this achievement, a model parameter calibration method by using Particle Swale Optimization is proposed and tested in several rivers in southern China with drainage areas ranging from several hundreds to tens thousands km2, and with the model parameter optimization, the model performance has been approved largely. The modeling approach is also tested for coupling radar-based precipitation estimation/prediction for small catchment flash forecasting and for coupling quantitative precipitation estimation/prediction from meteorological model for large river flood forecasting.

  10. Integrated Flood Forecast and Virtual Dam Operation System for Water Resources and Flood Risk Management

    NASA Astrophysics Data System (ADS)

    Shibuo, Yoshihiro; Ikoma, Eiji; Lawford, Peter; Oyanagi, Misa; Kanauchi, Shizu; Koudelova, Petra; Kitsuregawa, Masaru; Koike, Toshio

    2014-05-01

    While availability of hydrological- and hydrometeorological data shows growing tendency and advanced modeling techniques are emerging, such newly available data and advanced models may not always be applied in the field of decision-making. In this study we present an integrated system of ensemble streamflow forecast (ESP) and virtual dam simulator, which is designed to support river and dam manager's decision making. The system consists of three main functions: real time hydrological model, ESP model, and dam simulator model. In the real time model, the system simulates current condition of river basins, such as soil moisture and river discharges, using LSM coupled distributed hydrological model. The ESP model takes initial condition from the real time model's output and generates ESP, based on numerical weather prediction. The dam simulator model provides virtual dam operation and users can experience impact of dam control on remaining reservoir volume and downstream flood under the anticipated flood forecast. Thus the river and dam managers shall be able to evaluate benefit of priori dam release and flood risk reduction at the same time, on real time basis. Furthermore the system has been developed under the concept of data and models integration, and it is coupled with Data Integration and Analysis System (DIAS) - a Japanese national project for integrating and analyzing massive amount of observational and model data. Therefore it has advantage in direct use of miscellaneous data from point/radar-derived observation, numerical weather prediction output, to satellite imagery stored in data archive. Output of the system is accessible over the web interface, making information available with relative ease, e.g. from ordinary PC to mobile devices. We have been applying the system to the Upper Tone region, located northwest from Tokyo metropolitan area, and we show application example of the system in recent flood events caused by typhoons.

  11. Public perception of flood risks, flood forecasting and mitigation

    NASA Astrophysics Data System (ADS)

    Brilly, M.; Polic, M.

    2005-04-01

    A multidisciplinary and integrated approach to the flood mitigation decision making process should provide the best response of society in a flood hazard situation including preparation works and post hazard mitigation. In Slovenia, there is a great lack of data on social aspects and public response to flood mitigation measures and information management. In this paper, two studies of flood perception in the Slovenian town Celje are represented. During its history, Celje was often exposed to floods, the most recent serious floods being in 1990 and in 1998, with a hundred and fifty return period and more than ten year return period, respectively. Two surveys were conducted in 1997 and 2003, with 157 participants from different areas of the town in the first, and 208 in the second study, aiming at finding the general attitude toward the floods. The surveys revealed that floods present a serious threat in the eyes of the inhabitants, and that the perception of threat depends, to a certain degree, on the place of residence. The surveys also highlighted, among the other measures, solidarity and the importance of insurance against floods.

  12. Probabilistic flood warning using grand ensemble weather forecasts

    NASA Astrophysics Data System (ADS)

    He, Y.; Wetterhall, F.; Cloke, H.; Pappenberger, F.; Wilson, M.; Freer, J.; McGregor, G.

    2009-04-01

    As the severity of floods increases, possibly due to climate and landuse change, there is urgent need for more effective and reliable warning systems. The incorporation of numerical weather predictions (NWP) into a flood warning system can increase forecast lead times from a few hours to a few days. A single NWP forecast from a single forecast centre, however, is insufficient as it involves considerable non-predictable uncertainties and can lead to a high number of false or missed warnings. An ensemble of weather forecasts from one Ensemble Prediction System (EPS), when used on catchment hydrology, can provide improved early flood warning as some of the uncertainties can be quantified. EPS forecasts from a single weather centre only account for part of the uncertainties originating from initial conditions and stochastic physics. Other sources of uncertainties, including numerical implementations and/or data assimilation, can only be assessed if a grand ensemble of EPSs from different weather centres is used. When various models that produce EPS from different weather centres are aggregated, the probabilistic nature of the ensemble precipitation forecasts can be better retained and accounted for. The availability of twelve global EPSs through the 'THORPEX Interactive Grand Global Ensemble' (TIGGE) offers a new opportunity for the design of an improved probabilistic flood forecasting framework. This work presents a case study using the TIGGE database for flood warning on a meso-scale catchment. The upper reach of the River Severn catchment located in the Midlands Region of England is selected due to its abundant data for investigation and its relatively small size (4062 km2) (compared to the resolution of the NWPs). This choice was deliberate as we hypothesize that the uncertainty in the forcing of smaller catchments cannot be represented by a single EPS with a very limited number of ensemble members, but only through the variance given by a large number ensembles

  13. SOM-based Hybrid Neural Network Model for Flood Inundation Extent Forecasting

    NASA Astrophysics Data System (ADS)

    Chang, Li-Chiu; Shen, Hung-Yu; Chang, Fi-John

    2014-05-01

    In recent years, the increasing frequency and severity of floods caused by climate change and/or land overuse has been reported both nationally and globally. Therefore, estimation of flood depths and extents may provide disaster information for alleviating risk and loss of life and property. The conventional inundation models commonly need a huge amount of computational time to carry out a high resolution spatial inundation map. Moreover, for implementing appropriate mitigation strategies of various flood conditions, different flood scenarios and the corresponding mitigation alternatives are required. Consequently, it is difficult to reach real-time forecast of the inundation extent by conventional inundation models. This study proposed a SOM-RNARX model, for on-line forecasting regional flood inundation depths and extents. The SOM-RNARX model is composed of SOM (Self-Organizing Map) and RNARX (recurrent configuration of nonlinear autoregressive with exogenous inputs). The SOM network categorizes various flood inundation maps of the study area to produce a meaningful regional flood topological map. The RNARX model is built to forecast the total flooded volume of the study area. To find the neuron with the closest total inundated volume to the forecasted total inundated volumes, the forecasted value is used to adjust the weights (inundated depths) of the closest neuron and obtain a regional flood inundation map. The proposed methodology was trained and tested based on a large number of inundation data generated by a well validated two-dimensional simulation model in Yilan County, Taiwan. For comparison, the CHIM (clustering-based hybrid inundation model) model which was issued by Chang et al. (2010) was performed. The major difference between these two models is that CHIM classify flooding characteristics, and SOM-RNARX extracts the relationship between rainfall pattern and flooding spatial distribution. The results show that (1)two models can adequately provide on

  14. Real time flood forecasting in the Upper Danube basin

    NASA Astrophysics Data System (ADS)

    Nester, Thomas; Komma, Jürgen; Blöschl, Günter

    2016-04-01

    In this contribution, we report on experiences with developing the flood forecasting model for the Upper Danube basin and its operational use since 2006. The model system consists of a hydrological model for the catchments and a hydrodynamic model for the Danube and uses meteorological forecasts for the next 48 hours. The parameters of the hydrological model were estimated based on the Dominant Processes Concept. Runoff data are assimilated in real time to update modelled soil moisture. An analysis of the performance of the hydrological model indicates 88% of the snow cover in the basin to be modelled correctly on more than 80% of the days. Runoff forecasting errors decrease with catchment area and increase with forecast lead time. The forecast ensemble spread is shown to be a meaningful indicator of the forecast uncertainty. We also show forecasts from the 2013 flood in the Upper Danube basin. There was a tendency for the precipitation forecasts to underestimate event precipitation and for the runoff model to overestimate runoff generation which resulted in, overall, rather accurate runoff forecasts.

  15. Dynamic Critical Rainfall-Based Flash Flood Early Warning and Forecasting for Medium-Small Rivers

    NASA Astrophysics Data System (ADS)

    Liu, Z.; Yang, D.; Hu, J.

    2012-04-01

    China is extremely frequent food disasters hit countries, annual flood season flash floods triggered by rainfall, mudslides, landslides have caused heavy casualties and property losses, not only serious threaten the lives of the masses, but the majority of seriously restricting the mountain hill areas of economic and social development and the people become rich, of building a moderately prosperous society goals. In the next few years, China will focus on prevention and control area in the flash flood disasters initially built "for the surveillance, communications, forecasting, early warning and other non-engineering measure based, non-engineering measures and the combinations of engineering measures," the mitigation system. The latest progresses on global torrential flood early warning and forecasting techniques are reviewed in this paper, and then an early warning and forecasting approach is proposed on the basis of a distributed hydrological model according to dynamic critical rainfall index. This approach has been applied in Suichuanjiang River basin in Jiangxi province, which is expected to provide valuable reference for building a national flash flood early warning and forecasting system as well as control of such flooding.

  16. Operational Short-Term Flood Forecasting for Bangladesh: Application of ECMWF Ensemble Precipitation Forecasts

    NASA Astrophysics Data System (ADS)

    Hopson, T. M.; Webster, P. J.

    2004-12-01

    The country of Bangladesh frequently experiences severe catchment-scale flooding from the combined discharges of the Ganges and Brahmaputra rivers. Beginning in 2003, we have been disseminating upper-catchment discharge forecasts for this country to provide advanced warning for evacuation and relief measures. These forecasts are being generated using the European Centre for Medium-Range Weather Forecasting (ECMWF) shortterm ensemble weather forecasts and a combination of distributed and data-based modeling techniques. The forecasts from each of these models are combined using the multi-ensemble technique commonly employed in numerical weather prediction. This leads to a reduction in the overall forecast error and capitalizes on the strengths of each model during different periods of the monsoon season. In addition, the models are combined such that the probabilistic nature of the ensemble precipitation forecasts is retained while being combined with the discharge modeling error to produce true probabilistic forecasts of discharge that are being employed operationally.

  17. Discriminant Flash-Flood Forecasting in an Urban Environment

    NASA Astrophysics Data System (ADS)

    Yates, D.; Sharif, H.; Rindahl, B.

    2003-12-01

    This study demonstrates the application of high-resolution weather radar data, quantitative precipitation nowcasting, combined with simple hydrologic modeling to forecast flood potential for multiple, discriminate urban watersheds. The approach defines meta-data models based on the Extensive Markup Language (XML) to disseminate severe storm attributes (their size, orientation, history, and forecast position) and 5-minute, 2-hour rainfall accumulations for the watersheds to an Automated Location Evaluation in Real Time (ALERT) urban flood warning system- the Urban Drainage and Flood Control District (UDFCD), in Denver Colorado, USA. In addition, a simple graphical display system based on the World Wide Web Consortium's (W3C) Scalable Vector Graphics (SVG) format, requires only the simple exchange of small XML data files from the Nowcasting server to the UDFCD client for monitoring storm position and streamflow by the UDFCD in realtime. Example of severe storms that produce local flooding in the UDFCD domain will be shown.

  18. Evaluation of flash-flood discharge forecasts in complex terrain using precipitation

    USGS Publications Warehouse

    Yates, D.; Warner, T.T.; Brandes, E.A.; Leavesley, G.H.; Sun, Jielun; Mueller, C.K.

    2001-01-01

    Operational prediction of flash floods produced by thunderstorm (convective) precipitation in mountainous areas requires accurate estimates or predictions of the precipitation distribution in space and time. The details of the spatial distribution are especially critical in complex terrain because the watersheds are generally small in size, and small position errors in the forecast or observed placement of the precipitation can distribute the rain over the wrong watershed. In addition to the need for good precipitation estimates and predictions, accurate flood prediction requires a surface-hydrologic model that is capable of predicting stream or river discharge based on the precipitation-rate input data. Different techniques for the estimation and prediction of convective precipitation will be applied to the Buffalo Creek, Colorado flash flood of July 1996, where over 75 mm of rain from a thunderstorm fell on the watershed in less than 1 h. The hydrologic impact of the precipitation was exacerbated by the fact that a significant fraction of the watershed experienced a wildfire approximately two months prior to the rain event. Precipitation estimates from the National Weather Service's operational Weather Surveillance Radar-Doppler 1988 and the National Center for Atmospheric Research S-band, research, dual-polarization radar, colocated to the east of Denver, are compared. In addition, very short range forecasts from a convection-resolving dynamic model, which is initialized variationally using the radar reflectivity and Doppler winds, are compared with forecasts from an automated-algorithmic forecast system that also employs the radar data. The radar estimates of rain rate, and the two forecasting systems that employ the radar data, have degraded accuracy by virtue of the fact that they are applied in complex terrain. Nevertheless, the radar data and forecasts from the dynamic model and the automated algorithm could be operationally useful for input to surface

  19. Flood monitoring for ungauged rivers: the power of combining space-based monitoring and global forecasting models

    NASA Astrophysics Data System (ADS)

    Revilla-Romero, Beatriz; Netgeka, Victor; Raynaud, Damien; Thielen, Jutta

    2013-04-01

    Flood warning systems typically rely on forecasts from national meteorological services and in-situ observations from hydrological gauging stations. This capacity is not equally developed in flood-prone developing countries. Low-cost satellite monitoring systems and global flood forecasting systems can be an alternative source of information for national flood authorities. The Global Flood Awareness System (GloFAS) has been develop jointly with the European Centre for Medium-Range Weather Forecast (ECMWF) and the Joint Research Centre, and it is running quasi operational now since June 2011. The system couples state-of-the art weather forecasts with a hydrological model driven at a continental scale. The system provides downstream countries with information on upstream river conditions as well as continental and global overviews. In its test phase, this global forecast system provides probabilities for large transnational river flooding at the global scale up to 30 days in advance. It has shown its real-life potential for the first time during the flood in Southeast Asia in 2011, and more recently during the floods in Australia in March 2012, India (Assam, September-October 2012) and Chad Floods (August-October 2012).The Joint Research Centre is working on further research and development, rigorous testing and adaptations of the system to create an operational tool for decision makers, including national and regional water authorities, water resource managers, hydropower companies, civil protection and first line responders, and international humanitarian aid organizations. Currently efforts are being made to link GloFAS to the Global Flood Detection System (GFDS). GFDS is a Space-based river gauging and flood monitoring system using passive microwave remote sensing which was developed by a collaboration between the JRC and Dartmouth Flood Observatory. GFDS provides flood alerts based on daily water surface change measurements from space. Alerts are shown on a

  20. Flood forecasting using medium-range probabilistic weather prediction

    NASA Astrophysics Data System (ADS)

    Gouweleeuw, B. T.; Thielen, J.; Franchello, G.; de de Roo, A. P. J.; Buizza, R.

    2005-10-01

    Following the developments in short- and medium-range weather forecasting over the last decade, operational flood forecasting also appears to show a shift from a so-called single solution or 'best guess' deterministic approach towards a probabilistic approach based on ensemble techniques. While this probabilistic approach is now more or less common practice and well established in the meteorological community, operational flood forecasters have only started to look for ways to interpret and mitigate for end-users the prediction products obtained by combining so-called Ensemble Prediction Systems (EPS) of Numerical Weather Prediction (NWP) models with rainfall-runoff models. This paper presents initial results obtained by combining deterministic and EPS hindcasts of the global NWP model of the European Centre for Medium-Range Weather Forecasts (ECMWF) with the large-scale hydrological model LISFLOOD for two historic flood events: the river Meuse flood in January 1995 and the river Odra flood in July 1997. In addition, a possible way to interpret the obtained ensemble based stream flow prediction is proposed.

  1. Looking at the big scale - Global Flood Forecasting

    NASA Astrophysics Data System (ADS)

    Burek, P.; Alfieri, L.; Thielen-del Pozo, J.; Muraro, D.; Pappenberger, F.; Krzeminsk, B.

    2012-04-01

    Reacting to the increasing need for better preparedness to worldwide hydrological extremes, the Joint Research Centre has joined forces with the European Centre for Medium-Range Weather Forecast (ECMWF), to couple state-of-the art weather forecasts with a hydrological model on global scale. On a pre-operationally basis a fully hydro-meteorological flood forecasting model is running since July 2011 and producing daily probabilistic discharge forecast with worldwide coverage and forecast horizon of about 1 month. An important aspect of this global system is that it is set-up on continental scale and therefore independent of administrative and political boundaries - providing downstream countries with information on upstream river conditions as well as continental and global overviews. The prototype of a Global Flood Alert System consists of HTESSEL land surface scheme coupled with LISFLOOD hydrodynamic model for the flow routing in the river network. Both hydrological models are set up on global coverage with horizontal grid resolution of 0.1° and daily time step for input and output data. To estimate corresponding discharge warning thresholds for selected return periods, the coupled HTESSEL-LISFLOOD hydrological model is driven with ERA-Interim input meteorological data for a 21 year period from 1989 onward. For daily forecasts the ensemble stream flow predictions are run by feeding Variable Resolution Ensemble Prediction System (VarEPS) weather forecasts into the coupled model. VarEPS consist of 51-member ensemble global forecasts for 15 days. The hydrological simulations are computed for a 45-day time horizon, to account the routing of flood waves through large river basins with time of concentration of the order of one month. Both results, the discharge thresholds from the long term run and the multiple hydrographs of the daily ensemble stream flow prediction are joined together to produce probabilistic information of critical threshold exceedance. Probabilistic

  2. A first large-scale flood inundation forecasting model

    NASA Astrophysics Data System (ADS)

    Schumann, G. J.-P.; Neal, J. C.; Voisin, N.; Andreadis, K. M.; Pappenberger, F.; Phanthuwongpakdee, N.; Hall, A. C.; Bates, P. D.

    2013-10-01

    At present continental to global scale flood forecasting predicts at a point discharge, with little attention to detail and accuracy of local scale inundation predictions. Yet, inundation variables are of interest and all flood impacts are inherently local in nature. This paper proposes a large-scale flood inundation ensemble forecasting model that uses best available data and modeling approaches in data scarce areas. The model was built for the Lower Zambezi River to demonstrate current flood inundation forecasting capabilities in large data-scarce regions. ECMWF ensemble forecast (ENS) data were used to force the VIC (Variable Infiltration Capacity) hydrologic model, which simulated and routed daily flows to the input boundary locations of a 2-D hydrodynamic model. Efficient hydrodynamic modeling over large areas still requires model grid resolutions that are typically larger than the width of channels that play a key role in flood wave propagation. We therefore employed a novel subgrid channel scheme to describe the river network in detail while representing the floodplain at an appropriate scale. The modeling system was calibrated using channel water levels from satellite laser altimetry and then applied to predict the February 2007 Mozambique floods. Model evaluation showed that simulated flood edge cells were within a distance of between one and two model resolutions compared to an observed flood edge and inundation area agreement was on average 86%. Our study highlights that physically plausible parameter values and satisfactory performance can be achieved at spatial scales ranging from tens to several hundreds of thousands of km2 and at model grid resolutions up to several km2.

  3. Flood Forecasting via Time Lag Forward Network; Kelantan, Malaysia

    NASA Astrophysics Data System (ADS)

    Jajarmizadeh, Milad; Mohd Sidek, Lariyah; Bte Basri, Hidayah; Shakira Jaffar, Aminah

    2016-03-01

    Forecasting water level is one of the critical issues in Malaysia for Kelantan region. Based on the flood events in 2014, this study investigates the hourly-forecasting of water level in one station namely Kg Jenob in Kelantan. For this issue, Time Lag Forward Network (TLFN) is evaluated for forecasting the water level as dynamic model. Heuristic method in stepwise forward methodology is performed. Rainfall and water level are the input and output of the modelling respectively. For selected flood period 15/12/2014 to 30/12/2014, 8 scenarios are developed to obtain a minimum error in water level forecasting. By monitoring the error, it will show that the optimum configuration of network has 2 processors in hidden layer and 7 lags have enough contribution on the result of hourly forecasting. Transfer functions in hidden and output layers are is Tangent hyperbolic and bias. Observed and simulated data are compared with usual error criteria called Mean Square Error (MSE) and Root Mean Square Error (RMSE) which obtained 0.005 and 0.07 respectively. In conclusion, this study will be as a baseline for Kelantan to show that TLFN has promising result to forecast the flood events.

  4. A data based mechanistic approach to nonlinear flood routing and adaptive flood level forecasting

    NASA Astrophysics Data System (ADS)

    Romanowicz, Renata J.; Young, Peter C.; Beven, Keith J.; Pappenberger, Florian

    2008-08-01

    Operational flood forecasting requires accurate forecasts with a suitable lead time, in order to be able to issue appropriate warnings and take appropriate emergency actions. Recent improvements in both flood plain characterization and computational capabilities have made the use of distributed flood inundation models more common. However, problems remain with the application of such models. There are still uncertainties associated with the identifiability of parameters; with the computational burden of calculating distributed estimates of predictive uncertainty; and with the adaptive use of such models for operational, real-time flood inundation forecasting. Moreover, the application of distributed models is complex, costly and requires high degrees of skill. This paper presents an alternative to distributed inundation models for real-time flood forecasting that provides fast and accurate, medium to short-term forecasts. The Data Based Mechanistic (DBM) methodology exploits a State Dependent Parameter (SDP) modelling approach to derive a nonlinear dependence between the water levels measured at gauging stations along the river. The transformation of water levels depends on the relative geometry of the channel cross-sections, without the need to apply rating curve transformations to the discharge. The relationship obtained is used to transform water levels as an input to a linear, on-line, real-time and adaptive stochastic DBM model. The approach provides an estimate of the prediction uncertainties, including allowing for heterescadasticity of the multi-step-ahead forecasting errors. The approach is illustrated using an 80 km reach of the River Severn, in the UK.

  5. An Operational Flood Forecast System for the Indus Valley

    NASA Astrophysics Data System (ADS)

    Shrestha, K.; Webster, P. J.

    2012-12-01

    The Indus River is central to agriculture, hydroelectric power, and the potable water supply in Pakistan. The ever-present risk of drought - leading to poor soil conditions, conservative dam practices, and higher flood risk - amplifies the consequences of abnormally large precipitation events during the monsoon season. Preparation for the 2010 and 2011 floods could have been improved by coupling quantitative precipitation forecasts to a distributed hydrological model. The nature of slow-rise discharge on the Indus and overtopping of riverbanks in this basin indicate that medium-range (1-10 day) probabilistic weather forecasts can be used to assess flood risk at critical points in the basin. We describe a process for transforming these probabilities into an alert system for supporting flood mitigation and response decisions on a daily basis. We present a fully automated two-dimensional flood forecast methodology based on meteorological variables from the European Centre for Medium-Range Weather Forecasts (ECMWF) Variable Ensemble Prediction System (VarEPS). Energy and water fluxes are calculated in 25km grid cells using macroscale hydrologic parameterizations from the UW Variable Infiltration Capacity (VIC) model. A linear routing model transports grid cell surface runoff and baseflow within each grid cell to the outlet and into the stream network. The overflow points are estimated using flow directions, flow velocities, and maximum discharge thresholds from each grid cell. Flood waves are then deconvolved from the in-channel discharge time series and propagated into adjacent cells until a storage criterion based on average grid cell elevation is met. Floodwaters are drained back into channels as a continuous process, thus simulating spatial extent, depth, and persistence on the plains as the ensemble forecast evolves with time.

  6. Hydrologic Ensemble Forecasts for Flash Flood Warnings at Ungauged Locations

    NASA Astrophysics Data System (ADS)

    Demargne, Julie; Javelle, Pierre; Organde, Didier; Ramos, Maria-Helena

    2013-04-01

    Development of operational flash flood warning systems is one of the challenges in operational hydrology: flash floods are devastating but difficult to monitor and predict due to their nature. To provide flash flood warnings for ungauged basins, Météo-France and Irstea (formally Cemagref) have developed a discharge-threshold flood warning system called AIGA, which combines radar-gauge rainfall grids with a simplified distributed rainfall-runoff model run every 15 minutes at a 1-km² resolution. Operational since 2005 in the Southern part of France, the AIGA system produces, every 15 minutes, a map of the river network with a color chart indicating the range of the estimated return period of the ongoing flood event. To increase forecast lead time and quantify the forcing input uncertainty, the rainfall-runoff distributed model ingests the 11 precipitation ensemble members from the PEARP ensemble prediction system of Météo-France. Performance of the experimental probabilistic precipitation and flow forecasts is evaluated from a variety of ensemble verification metrics (e.g., Continuous Ranked Probability Skill Score, Relative Operating Characteristic score) for different French basins. We also discuss planned enhancements and challenges to assess other sources of hydrologic uncertainty and effectively communicate the uncertainty information to forecasters for better risk-based decision making.

  7. Medium Range Ensembles Flood Forecasts for Community Level Applications

    NASA Astrophysics Data System (ADS)

    Fakhruddin, S.; Kawasaki, A.; Babel, M. S.; AIT

    2013-05-01

    Early warning is a key element for disaster risk reduction. In recent decades, there has been a major advancement in medium range and seasonal forecasting. These could provide a great opportunity to improve early warning systems and advisories for early action for strategic and long term planning. This could result in increasing emphasis on proactive rather than reactive management of adverse consequences of flood events. This can be also very helpful for the agricultural sector by providing a diversity of options to farmers (e.g. changing cropping pattern, planting timing, etc.). An experimental medium range (1-10 days) flood forecasting model has been developed for Bangladesh which provides 51 set of discharge ensembles forecasts of one to ten days with significant persistence and high certainty. This could help communities (i.e. farmer) for gain/lost estimation as well as crop savings. This paper describe the application of ensembles probabilistic flood forecast at the community level for differential decision making focused on agriculture. The framework allows users to interactively specify the objectives and criteria that are germane to a particular situation, and obtain the management options that are possible, and the exogenous influences that should be taken into account before planning and decision making. risk and vulnerability assessment was conducted through community consultation. The forecast lead time requirement, users' needs, impact and management options for crops, livestock and fisheries sectors were identified through focus group discussions, informal interviews and questionnaire survey.

  8. Integrating Fluvial and Oceanic Drivers in Operational Flooding Forecasts for San Francisco Bay

    NASA Astrophysics Data System (ADS)

    Herdman, Liv; Erikson, Li; Barnard, Patrick; Kim, Jungho; Cifelli, Rob; Johnson, Lynn

    2016-04-01

    The nine counties that make up the San Francisco Bay area are home to 7.5 million people and these communties are susceptible to flooding along the bay shoreline and inland creeks that drain to the bay. A forecast model that integrates fluvial and oceanic drivers is necessary for predicting flooding in this complex urban environment. The U.S. Geological Survey ( USGS) and National Weather Service (NWS) are developing a state-of-the-art flooding forecast model for the San Francisco Bay area that will predict watershed and ocean-based flooding up to 72 hours in advance of an approaching storm. The model framework for flood forecasts is based on the USGS-developed Coastal Storm Modeling System (CoSMoS) that was applied to San Francisco Bay under the Our Coast Our Future project. For this application, we utilize Delft3D-FM, a hydrodynamic model based on a flexible mesh grid, to calculate water levels that account for tidal forcing, seasonal water level anomalies, surge and in-Bay generated wind waves from the wind and pressure fields of a NWS forecast model, and tributary discharges from the Research Distributed Hydrologic Model (RDHM), developed by the NWS Office of Hydrologic Development. The flooding extent is determined by overlaying the resulting water levels onto a recently completed 2-m digital elevation model of the study area which best resolves the extensive levee and tidal marsh systems in the region. Here we present initial pilot results of hindcast winter storms in January 2010 and December 2012, where the flooding is driven by oceanic and fluvial factors respectively. We also demonstrate the feasibility of predicting flooding on an operational time scale that incorporates both atmospheric and hydrologic forcings.

  9. Flood Monitoring and Forecasting in the Upper-Tisza River Basin

    NASA Astrophysics Data System (ADS)

    Balint, Z.; Gauzer, B.; Konecsny, K.

    2003-04-01

    The Upper-Tisza river basin is shared by four nations: Ukraine, Romania, Slovakia and Hungary. The river itself is the frontier along several kilometres between Ukraine and Romania and between Ukraine and Hungary. All benefits and all problems a river can cause are also shared by the four nations. The river basin experienced catastrophic floods four times in 28 months between November 1998 and March 2001. Each flood surpassed the previous one in magnitude, reaching heights and causing damages bigger than ever before. At the beginning of March 2001 the highest ever flood occurred in the Transcarpathian region in Ukraine. Flood stages exceeded all previous maximums. Flood protection levees were breached at many sites both in Ukraine and in Hungary, causing enormous economic loss and even demanding human lives. The European Union started flood monitoring projects under the PHARE CBC program in Romania and initial steps were taken under TACIS in Ukraine. The Danish Government together with the Slovakian Government is busy with similar purposes on the northern tributaries. NATO responded by setting up a project with the aim of preparing a comprehensive assessment report on flood problems and proposed measures to improve the efficiency of flood management in Ukraine. The first results of a modular flood forecasting system are reported.

  10. Fuzzy exemplar-based inference system for flood forecasting

    NASA Astrophysics Data System (ADS)

    Chang, Li-Chiu; Chang, Fi-John; Tsai, Ya-Hsin

    2005-02-01

    Fuzzy inference systems have been successfully applied in numerous fields since they can effectively model human knowledge and adaptively make decision processes. In this paper we present an innovative fuzzy exemplar-based inference system (FEIS) for flood forecasting. The FEIS is based on a fuzzy inference system, with its clustering ability enhanced through the Exemplar-Aided Constructor of Hyper-rectangles algorithm, which can effectively simulate human intelligence by learning from experience. The FEIS exhibits three important properties: knowledge extraction from numerical data, knowledge (rule) modeling, and fuzzy reasoning processes. The proposed model is employed to predict streamflow 1 hour ahead during flood events in the Lan-Yang River, Taiwan. For the purpose of comparison the back propagation neural network (BPNN) is also performed. The results show that the FEIS model performs better than the BPNN. The FEIS provides a great learning ability, robustness, and high predictive accuracy for flood forecasting.

  11. A pan-African medium-range ensemble flood forecast system

    NASA Astrophysics Data System (ADS)

    Thiemig, Vera; Bisselink, Bernard; Pappenberger, Florian; Thielen, Jutta

    2015-04-01

    AFFS showed limitations for small-scale and short duration flood events. Also the forecasts showed on average a good reliability, and the CRPSS helped identifying regions to focus on for future improvements. The case study for the flood event in March 2003 in the Sabi Basin (Zimbabwe and Mozambique) 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 good prospective as an operational system, as it has demonstrated its significant potential to contribute to the reduction of flood-related losses in Africa by providing national and international aid organizations timely with medium-range flood forecast information. However, issues related to the practical implication will still need to be investigated.

  12. Enhancing the quality of hydrologic model calibrations and their transfer to operational flood forecasters

    NASA Astrophysics Data System (ADS)

    Aggett, Graeme; Spies, Ryan; Szfranski, Bill; Hahn, Claudia; Weil, Page

    2016-04-01

    An adequate forecasting model may not perform well if it is inadequately calibrated. Model calibration is often constrained by the lack of adequate calibration data, especially for small river basins with high spatial rainfall variability. Rainfall/snow station networks may not be dense enough to accurately estimate the catchment rainfall/SWE. High discharges during flood events are subject to significant error due to flow gauging difficulty. Dynamic changes in catchment conditions (e.g., urbanization; losses in karstic systems) invariably introduce non-homogeneity in the water level and flow data. This presentation will highlight some of the challenges in reliable calibration of National Weather Service (i.e. US) operational flood forecast models, emphasizing the various challenges in different physiographic/climatic domains. It will also highlight the benefit of using various data visualization techniques to transfer information about model calibration to operational forecasters so they may understand the influence of the calibration on model performance under various conditions.

  13. California climate change, hydrologic response, and flood forecasting

    SciTech Connect

    Miller, Norman L.

    2003-11-11

    There is strong evidence that the lower atmosphere has been warming at an unprecedented rate during the last 50 years, and it is expected to further increase at least for the next 100 years. Warmer air mass implies a higher capacity to hold water vapor and an increased likelihood of an acceleration of the global water cycle. This acceleration is not validated and considerable new research has gone into understanding aspects of the water cycle (e.g. Miller et al. 2003). Several significant findings on the hydrologic response to climate change can be reported. It is well understood that the observed and expected warming is related to sea level rise. In a recent seminar at Lawrence Berkeley National Laboratory, James Hansen (Director of the Institute for Space Studies, National Aeronautics and Space Administration) stressed that a 1.25 Wm{sup -2} increase in radiative forcing will lead to an increase in the near surface air temperature by 1 C. This small increase in temperature from 2000 levels is enough to cause very significant impacts to coasts. Maury Roos (Chief Hydrologist, California Department of Water Resources) has shown that a 0.3 m rise in sea level shifts the San Francisco Bay 100-year storm surge flood event to a 10-year event. Related coastal protection costs for California based on sea level rise are shown. In addition to rising sea level, snowmelt-related streamflow represents a particular problem in California. Model studies have indicated that there will be approximately a 50% decrease in snow pack by 2100. This potential deficit must be fully recognized and plans need to be put in place well in advance. In addition, the warmer atmosphere can hold more water vapor and result in more intense warm winter-time precipitation events that result in flooding. During anticipated high flow, reservoirs need to release water to maintain their structural integrity. California is at risk of water shortages, floods, and related ecosystem stresses. More research

  14. Model Combination and Weighting Methods in Operational Flood Forecasting

    NASA Astrophysics Data System (ADS)

    Bogner, Konrad; Pappenberger, Florian; Cloke, Hannah L.

    2013-04-01

    In order to get maximum benefits from operational forecast systems based on different model approaches, it is necessary to find an optimal way to combine the forecasts in real-time and to derive the predictive probability distribution by assigning different weights to the different actual forecasts according to the forecast performance of the previous days. In the European Flood Alert System (EFAS) a Bayesian Forecast System has been implemented in order to derive the overall predictive probability distribution. The EFAS is driven by different numerical weather prediction systems like the deterministic forecasts from the German Weather Service and from the ECMWF, as well as Ensemble Prediction Systems from the ECMWS and COSMO-LEPS. In this study the effect of combining these different forecast systems in respect of the total predictive uncertainty are investigated by applying different weighting methods like the Non-homogenous Gaussian Regression (NGR) model, the Bayesian Model Averaging (BMA) and an empirical method. Besides that different methods of bias removal are applied, namely additive and regression based ones, and the applicability in operational forecast is tested. One of the problems identified is the difficulty in optimizing the weight parameters for each lead-time separately resulting in highly inconsistent forecasts, especially for regression based bias removal methods. Therefore in operational use methods with only sub-optimal skill score results, could be preferable showing more realistic shapes of uncertainty bands for the predicted future stream-flow values. Another possible approach could be the optimization of the weighting parameters not for each lead-time separately, but to look at different levels of aggregations over expanding windows of time ranges. First results indicate the importance of the proper choice of the model combination method in view of reliability and sharpness of the forecast system.

  15. A first large-scale flood inundation forecasting model

    SciTech Connect

    Schumann, Guy J-P; Neal, Jeffrey C.; Voisin, Nathalie; Andreadis, Konstantinos M.; Pappenberger, Florian; Phanthuwongpakdee, Kay; Hall, Amanda C.; Bates, Paul D.

    2013-11-04

    At present continental to global scale flood forecasting focusses on predicting at a point discharge, with little attention to the detail and accuracy of local scale inundation predictions. Yet, inundation is actually the variable of interest and all flood impacts are inherently local in nature. This paper proposes a first large scale flood inundation ensemble forecasting model that uses best available data and modeling approaches in data scarce areas and at continental scales. The model was built for the Lower Zambezi River in southeast Africa to demonstrate current flood inundation forecasting capabilities in large data-scarce regions. The inundation model domain has a surface area of approximately 170k km2. ECMWF meteorological data were used to force the VIC (Variable Infiltration Capacity) macro-scale hydrological model which simulated and routed daily flows to the input boundary locations of the 2-D hydrodynamic model. Efficient hydrodynamic modeling over large areas still requires model grid resolutions that are typically larger than the width of many river channels that play a key a role in flood wave propagation. We therefore employed a novel sub-grid channel scheme to describe the river network in detail whilst at the same time representing the floodplain at an appropriate and efficient scale. The modeling system was first calibrated using water levels on the main channel from the ICESat (Ice, Cloud, and land Elevation Satellite) laser altimeter and then applied to predict the February 2007 Mozambique floods. Model evaluation showed that simulated flood edge cells were within a distance of about 1 km (one model resolution) compared to an observed flood edge of the event. Our study highlights that physically plausible parameter values and satisfactory performance can be achieved at spatial scales ranging from tens to several hundreds of thousands of km2 and at model grid resolutions up to several km2. However, initial model test runs in forecast mode

  16. An extended real-time flood impact forecasting system for the Chapare watershed in Bolivia

    NASA Astrophysics Data System (ADS)

    Rossi, Lauro; Gabellani, Simone; Masoero, Alessandro; Dolia, Daniele; Rudari, Roberto

    2016-04-01

    All over the world a lot of cities are located in flood-prone areas and million of people are exposed to inundation risk. To cope with that the social safety demands efficient civil protection structures able to reduce flood risk by issuing warnings. This task requires civil protection organisms to adopt systems able to support their activities in predicting floods and rainfall impacts. For this reason flood early warning systems, based on rainfall observations and predictions, has become very useful because they are able to provide in advance a quantitative evaluation of possible effects in term of discharge and peak flow. Traditionally those forecasting systems use hydrologic models coupled with meteorological models to forecast discharge in relevant river sections and are called hydro-meteorological chains. In order to have a better representation of the flood dynamics, these hydro-meteorological chains can be expanded to include bi-dimensional hydraulic models where the level exposure is high or flow singularities (e.g. junctions, deltas, etc.) require more accurate investigation. That information allows the generation of real-time inundation scenarios that can be used by civil protection and authorities to estimate impact on population and take counter-measures. The new real-time flood impact forecasting chain consists of a suite of hydrometeorological tools that combines meteorological models, a disaggregation tool and a fully distributed hydrological model and a bidimensional hydraulic model that produces inundation scenarios in the most exposed river segments of the flood plain and a scenario tool that allows the assessment of assets involved. The complete modelling chain has been implemented in the Chapare watershed in Bolivia and it is managed by the Dewetra platform, which since 2013 is used by the Civil Defense and National Meteorological service as the main national Early Warning supporting tool.

  17. Flash flood warnings for ungauged basins based on high-resolution precipitation forecasts

    NASA Astrophysics Data System (ADS)

    Demargne, Julie; Javelle, Pierre; Organde, Didier; de Saint Aubin, Céline; Janet, Bruno

    2016-04-01

    Early detection of flash floods, which are typically triggered by severe rainfall events, is still challenging due to large meteorological and hydrologic uncertainties at the spatial and temporal scales of interest. Also the rapid rising of waters necessarily limits the lead time of warnings to alert communities and activate effective emergency procedures. To better anticipate such events and mitigate their impacts, the French national service in charge of flood forecasting (SCHAPI) is implementing a national flash flood warning system for small-to-medium (up to 1000 km²) ungauged basins based on a discharge-threshold flood warning method called AIGA (Javelle et al. 2014). The current deterministic AIGA system has been run in real-time in the South of France since 2005 and has been tested in the RHYTMME project (rhytmme.irstea.fr/). It ingests the operational radar-gauge QPE grids from Météo-France to run a simplified hourly distributed hydrologic model at a 1-km² resolution every 15 minutes. This produces real-time peak discharge estimates along the river network, which are subsequently compared to regionalized flood frequency estimates to provide warnings according to the AIGA-estimated return period of the ongoing event. The calibration and regionalization of the hydrologic model has been recently enhanced for implementing the national flash flood warning system for the entire French territory by 2016. To further extend the effective warning lead time, the flash flood warning system is being enhanced to ingest Météo-France's AROME-NWC high-resolution precipitation nowcasts. The AROME-NWC system combines the most recent available observations with forecasts from the nowcasting version of the AROME convection-permitting model (Auger et al. 2015). AROME-NWC pre-operational deterministic precipitation forecasts, produced every hour at a 2.5-km resolution for a 6-hr forecast horizon, were provided for 3 significant rain events in September and November 2014 and

  18. The National Flood Interoperability Experiment: Bridging Resesarch and Operations

    NASA Astrophysics Data System (ADS)

    Salas, F. R.

    2015-12-01

    The National Weather Service's new National Water Center, located on the University of Alabama campus in Tuscaloosa, will become the nation's hub for comprehensive water resources forecasting. In conjunction with its federal partners the US Geological Survey, Army Corps of Engineers and Federal Emergency Management Agency, the National Weather Service will operationally support both short term flood prediction and long term seasonal forecasting of water resource conditions. By summer 2016, the National Water Center will begin evaluating four streamflow data products at the scale of the NHDPlus river reaches (approximately 2.67 million). In preparation for the release of these products, from September 2014 to August 2015, the National Weather Service partnered with the Consortium of Universities for the Advancement of Hydrologic Science, Inc. to support the National Flood Interoperability Experiment which included a seven week in-residence Summer Institute in Tuscaloosa for university students interested in learning about operational hydrology and flood forecasting. As part of the experiment, 15 hour forecasts from the operational High Resolution Rapid Refresh atmospheric model were used to drive a three kilometer Noah-MP land surface model loosely coupled to a RAPID river routing model operating on the NHDPlus dataset. This workflow was run every three hours during the Summer Institute and the results were made available to those engaged to pursue a range of research topics focused on flood forecasting (e.g. reservoir operations, ensemble forecasting, probabilistic flood inundation mapping, rainfall product evaluation etc.) Although the National Flood Interoperability Experiment was finite in length, it provided a platform through which the academic community could engage federal agencies and vice versa to narrow the gap between research and operations and demonstrate how state of the art research infrastructure, models, services, datasets etc. could be utilized

  19. Evaluation of NWP Precipitation Forecasts for Global Flood Warning

    NASA Astrophysics Data System (ADS)

    Tian, Y.; Adler, R. F.; Peters-Lidard, C. D.

    2008-12-01

    Precipitation forecasts from numerical weather prediction (NWP) models can potentially improve our ability for global flood and landslide warning. In this study, the skills and errors of three NWP precipitation forecast products were analyzed. These forecast products include GEOS5, GDAS and ECMWF, with lead time ranging from 12 hours to 5 days. They were evaluated against the satellite-based, gauge-corrected precipitation estimates, TMPA 3B42, over the land surface as well as the globe. To gain a better perspective, we also evaluated several other satellite-based precipitation products, including GPCP, TMPA 3B42RT, CMORPH and PERSIANN, against TMPA 3B42. Our analysis shows the three NWP forecasts tend to systematically over-estimate global precipitation by approximately 50%. This positive bias does not change much with lead time. In contrast, the satellite-based estimates (GPCP, TMPA, 3B42RT, CMORPH and PERSIANN) have biases mostly less than 20%. In addition, the RMS errors increase with the lead time in NWP forecasts, and in particular for GEOS5, the most increase in RMS errors takes place when the lead time goes from 1 day to 2 days. The RMS errors in the NWP products are also about twice as much as those of the satellite-based products. Further analysis indicates false alarms dominate the errors in the NWP forecasts. Among the NWP products, GEOS5 has slightly better performance than the other two. The implication of these error characteristics on global flood and landslide warning will be discussed.

  20. Probabilistic flood forecast: Exact and approximate predictive distributions

    NASA Astrophysics Data System (ADS)

    Krzysztofowicz, Roman

    2014-09-01

    For quantification of predictive uncertainty at the forecast time t0, the future hydrograph is viewed as a discrete-time continuous-state stochastic process {Hn: n=1,…,N}, where Hn is the river stage at time instance tn>t0. The probabilistic flood forecast (PFF) should specify a sequence of exceedance functions {F‾n: n=1,…,N} such that F‾n(h)=P(Zn>h), where P stands for probability, and Zn is the maximum river stage within time interval (t0,tn], practically Zn=max{H1,…,Hn}. This article presents a method for deriving the exact PFF from a probabilistic stage transition forecast (PSTF) produced by the Bayesian forecasting system (BFS). It then recalls (i) the bounds on F‾n, which can be derived cheaply from a probabilistic river stage forecast (PRSF) produced by a simpler version of the BFS, and (ii) an approximation to F‾n, which can be constructed from the bounds via a recursive linear interpolator (RLI) without information about the stochastic dependence in the process {H1,…,Hn}, as this information is not provided by the PRSF. The RLI is substantiated by comparing the approximate PFF against the exact PFF. Being reasonably accurate and very simple, the RLI may be attractive for real-time flood forecasting in systems of lesser complexity. All methods are illustrated with a case study for a 1430 km headwater basin wherein the PFF is produced for a 72-h interval discretized into 6-h steps.

  1. River Ice and Flood Detection Products Derived from Suomi NPP VIIRS Satellite Data to Support Hydrologic Forecast Operations in Alaska

    NASA Astrophysics Data System (ADS)

    van Breukelen, C. M.; Plumb, E. W.; Li, S.; Holloway, E.; Stevens, E.

    2015-12-01

    A lack of river ice data during spring break-up in Alaska creates many forecast challenges for National Weather Service (NWS) forecasters. Limited and infrequent ice conditions and flood observations are provided by river observers, community officials, and pilots. Although these observations are invaluable, there are extensive spatial and temporal data gaps across Alaska during spring break-up. The Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) satellite imagery has proved to be an extremely beneficial situational awareness and decision support tool for NWS forecast operations. In particular, the VIIRS satellite imagery became highly effective in identifying extensive flooding of many Alaskan rivers due to ice jams during the 2013 spring breakup season. A devastating ice jam flood in the Yukon River community of Galena prompted the development of river ice and flood detection products derived from the VIIRS satellite imagery with the support of the Joint Polar Satellite System/Proving Ground and Risk Reduction (JPSS/PGRR) Program. The two new products from S-NPP/VIIRS imagery provided critical decision making information to NWS forecasters responsible for issuing flood warnings for the region. Since 2013, the NWS continues to evaluate the use of these products in an operational forecast setting, and has expanded the evaluation period to include summertime flooding. There are limitations of these products due to cloud cover, sun zenith angles, product validation, and other issues unique to Alaska. The NWS will continue to provide feedback to the JPSS/PGRR Program in order to further refine and improve the algorithms used to create the river ice and flood detection products. This presentation will demonstrate how these products have been integrated into the NWS forecast process for several types of flood events in Alaska.

  2. A soil moisture sensorweb for use in flood forecasting applications

    NASA Astrophysics Data System (ADS)

    Teillet, Philippe M.; Gauthier, Robert P.; Pultz, Terry J.; Deschamps, A.; Fedosejevs, Gunar; Maloley, Matthew; Ainsley, Gino; Chichagov, Alexander

    2004-02-01

    This paper describes work towards building an integrated Earth sensing capability and focuses on the demonstration of a prototype in-situ sensorweb in remote operation in support of flood forecasting. A five-node sensorweb was deployed in the Roseau River Sub-Basin of the Red River Watershed in Manitoba, Canada in September 2002 and remained there throughout the flood season until the end of June 2003. The sensorweb operated autonomously, with soil moisture measurements and standard meteorological parameters accessed remotely via land line and/or satellite from the Integrated Earth Sensing Workstation (IESW) at the Canada Centre for Remote Sensing (CCRS) in Ottawa. Independent soil moisture data were acquired from actual grab samples and field-portable sensors on the days of RADARSAT and Envisat Synthetic Aperture Radar (SAR) data acquisitions. The in-situ data were used to help generate spatial soil moisture estimates from the remotely sensed SAR data for use in a hydrological model for flood forecasting.

  3. PAI-OFF: A new proposal for online flood forecasting in flash flood prone catchments

    NASA Astrophysics Data System (ADS)

    Schmitz, G. H.; Cullmann, J.

    2008-10-01

    SummaryThe Process Modelling and Artificial Intelligence for Online Flood Forecasting (PAI-OFF) methodology combines the reliability of physically based, hydrologic/hydraulic modelling with the operational advantages of artificial intelligence. These operational advantages are extremely low computation times and straightforward operation. The basic principle of the methodology is to portray process models by means of ANN. We propose to train ANN flood forecasting models with synthetic data that reflects the possible range of storm events. To this end, establishing PAI-OFF requires first setting up a physically based hydrologic model of the considered catchment and - optionally, if backwater effects have a significant impact on the flow regime - a hydrodynamic flood routing model of the river reach in question. Both models are subsequently used for simulating all meaningful and flood relevant storm scenarios which are obtained from a catchment specific meteorological data analysis. This provides a database of corresponding input/output vectors which is then completed by generally available hydrological and meteorological data for characterizing the catchment state prior to each storm event. This database subsequently serves for training both a polynomial neural network (PoNN) - portraying the rainfall-runoff process - and a multilayer neural network (MLFN), which mirrors the hydrodynamic flood wave propagation in the river. These two ANN models replace the hydrological and hydrodynamic model in the operational mode. After presenting the theory, we apply PAI-OFF - essentially consisting of the coupled "hydrologic" PoNN and "hydrodynamic" MLFN - to the Freiberger Mulde catchment in the Erzgebirge (Ore-mountains) in East Germany (3000 km 2). Both the demonstrated computational efficiency and the prediction reliability underline the potential of the new PAI-OFF methodology for online flood forecasting.

  4. An Improved Global Flood Forecasting System Using Satellite Rainfall Information and a Hydrological Model (Invited)

    NASA Astrophysics Data System (ADS)

    Adler, R. F.; Wu, H.; Tian, Y.

    2013-12-01

    A real-time experimental system to estimate and forecast floods over the globe, the Global Flood Monitoring System (GFMS), has been significantly improved to provide flood detection, streamflow and inundation mapping information at higher resolution (as fine as 1 km) and nowcasts and forecasts (out to five days). Images and output data are available for use by the community with updates available every three hours (http://flood.umd.edu). The system uses satellite-based rainfall information, currently the TRMM Multi-satellite Precipitation Analysis [TMPA]), other satellite and conventional information and a newly-developed hydrological and routing combination model. The improved combined model, the Dominant river Routing Integrated with VIC Environment (DRIVE) system, is based on the VIC (Variable Infiltration Capacity) land surface model (U. of Washington) and the Dominant River Tracing Routing (DRTR) method. Within the DRIVE system the surface hydrological calculations are carried out at 0.125° latitude-longitude resolution with routing, streamflow and other calculations done at that resolution and at 1km resolution. Flood detection and intensity estimates are based on water depth and streamflow thresholds calculated from a 15-year retrospective run using the satellite rainfall and model. This period is also used for testing and evaluation with results indicating improved streamflow estimation and flood detection statistics. The satellite rainfall data are integrated with global model NASA GEOS-5 Numerical Weather Prediction (NWP) rainfall predictions (adjusted to the satellite data) to extend the flood calculations out to five days. Examples of results for recent flood events are presented along with validation statistics and comparison with other flood observations (e.g., inundation calculations vs. MODIS and/or SAR flood maps). The outlook for further development in this area in terms of increased utility for national and international disaster management

  5. FEWS Vecht, a crossing boundaries flood forecasting system

    NASA Astrophysics Data System (ADS)

    van Heeringen, Klaas-Jan; Filius, Pieter; Tromp, Gerben; Renner, Tobias

    2013-04-01

    The river Vecht is a cross boundary river, starting in Germany and flowing to the Netherlands. The river is completely dependant on rainfall in the catchment. Being one of the smaller big rivers in the Netherlands, there was still no operational forecasting system avaible because of the hugh number of involved organisations (2 in Germany, 5 in the Netherlands) and many other stake holders. In 2011 a first operational forecasting system has been build by using the Delft-FEWS software. It collects the real time fluvial and meteorological observations from all the organisations, in that sense being a portal where all the collected information is available and can be consistantly interpreted as a whole. In 2012 an HBV rainfall runoff model and a Sobek 1D hydraulic model has been build. These models have been integrated into the FEWS system and are operationally running since the 2012 autumn. The system forecasts 5 days ahead using a 5 days ECMWF rainfall ensemble forecast. It enables making scenarios, especially useful for the operation of storage reservoirs. During the 2012 Christmas days a (relatively small) T=2 flood occurred (Q=175-200 m3/s) and proved the system to run succesfully. Dissemination of the forecasts is performed by using the FEWS system in all organisations, connected to the central system through internet. There is also a (password protected) website available that provides the current forecast to all stake holders in the catchment. The challenge of the project was not to make the models and to build the fews, but to connect all data and all operators together into one system, even cross boundary. Also in that sense the FEWS Vecht system has proved to be very succesful.

  6. Enhancing flood forecasting with the help of processed based calibration

    NASA Astrophysics Data System (ADS)

    Cullmann, Johannes; Krauße, Thomas; Philipp, Andy

    Due to the fact that the required input data are not always completely available and model structures are only a crude description of the underlying natural processes, model parameters need to be calibrated. Calibrated model parameters only reflect a small domain of the natural processes well. This imposes an obstacle on the accuracy of modelling a wide range of flood events, which, in turn is crucial for flood forecasting systems. Together with the rigid model structures of currently available rainfall-runoff models this presents a serious constraint to portraying the highly non-linear transformation of precipitation into runoff. Different model concepts (interflow, direct runoff), or rather the represented processes, such as infiltration, soil water movement etc. are more or less dominating different sections of the runoff spectrum. Most models do not account for such transient characteristics inherent to the hydrograph. In this paper we try to show a way out of the dilemma of limited model parameter validity. Exemplarily, we investigate on the model performance of WaSiM-ETH, focusing on the parameterisation strategy in the context of flood forecasting. In order to compensate for the non-transient parameters of the WaSiM model we propose a process based parameterisation strategy. This starts from a detailed analysis of the considered catchments rainfall-runoff characteristics. Based on a classification of events, WaSiM-ETH is calibrated and validated to describe all the event classes separately. These specific WaSiM-ETH event class models are then merged to improve the model performance in predicting peak flows. This improved catchment modelling can be used to train an artificial intelligence based black box forecasting tool as described in [Schmitz, G.H., Cullmann, J., Görner, W., Lennartz, F., Dröge, W., 2005. PAI-OFF: Eine neue Strategie zur Hochwasservorhersage in schnellreagierenden Einzugsgebieten. Hydrologie und Wasserbewirtschaftung 49, 226

  7. An Analytical Framework for Flood Water Conservation Considering Forecast Uncertainty and Acceptable Risk

    NASA Astrophysics Data System (ADS)

    Ding, W.; Zhang, C.

    2015-12-01

    Reservoir water levels are usually not allowed to exceed the flood limited water level (FLWL) during flood season, which neglects the meteorological and real-time forecast information and leads to the great waste of water resources. With the development of weather forecasting, hydrologic modeling, and hydro-climatic teleconnection, the streamflow forecast precision have improved a lot, which provides the technical support for the flood water utilization. This paper addresses how much flood water can be conserved for use after the flood season through the operation of reservoir based on uncertain forecast information by taking into account the residual flood control capacity (the difference between flood conveyance capacity and the expected inflow in a lead time). A two-stage model for dynamic control of the flood limited water level (the maximum allowed water level during the flood season, DC-FLWL) is established considering forecast uncertainty and acceptable flood risk. It is found that DC-FLWL is applicable when the reservoir inflow ranges from small to medium levels of the historical records, while both forecast uncertainty and acceptable risk in the downstream affect the feasible space of DC-FLWL. As forecast uncertainty increases (under a given risk level) or as acceptable risk level decreases (under a given forecast uncertainty level), the minimum required safety margin for flood control increases, and the chance for DC-FLWL decreases. The derived hedging rules from the modeling framework illustrate either the dominant role of water conservation or flood control or the tradeoff between the two objectives under different levels of forecast uncertainty and acceptable risk. These rules may provide useful guidelines for conserving water from flood, especially in the area with heavy water stress.

  8. Application of hydrological models for flood forecasting and flood control in India and Bangladesh

    NASA Astrophysics Data System (ADS)

    Refsgaard, J. C.; Havnø, K.; Ammentorp, H. C.; Verwey, A.

    A general mathematical modelling system for real-time flood forecasting and flood control planning is described. The system comprises a lumped conceptual rainfall-runoff model, a hydrodynamic model for river routing, reservoir and flood plain simulation, an updating procedure for real-time operation and a comprehensive data management system. The system is presently applied for real-time forecasting of the two 20 000 km 2 (Yamuna and Damodar) catchments in India as well as for flood control modelling at the same two catchments in India. In another project the system is being established for the entire Bangladesh with a coarse discretization and for the South East Region of Bangladesh with a fine model discretization. The objectives of the modelling application in Bangladesh are to enable predictions of the effects of alternative river regulation structures in terms of changes in water levels, inundations, siltration and salinity. The modelling system has been transferred to the Central Water Commission of India and the Master Plan Organization of Bangladesh in connection with comprehensive training programmes. The models are presently being operated by Indian and Bangladeshi engineers in the two countries.

  9. Forecasting of Storm Surge Floods Using ADCIRC and Optimized DEMs

    NASA Technical Reports Server (NTRS)

    Valenti, Elizabeth; Fitzpatrick, Patrick

    2005-01-01

    Increasing the accuracy of storm surge flood forecasts is essential for improving preparedness for hurricanes and other severe storms and, in particular, for optimizing evacuation scenarios. An interactive database, developed by WorldWinds, Inc., contains atlases of storm surge flood levels for the Louisiana/Mississippi gulf coast region. These atlases were developed to improve forecasting of flooding along the coastline and estuaries and in adjacent inland areas. Storm surge heights depend on a complex interaction of several factors, including: storm size, central minimum pressure, forward speed of motion, bottom topography near the point of landfall, astronomical tides, and most importantly, maximum wind speed. The information in the atlases was generated in over 100 computational simulations, partly by use of a parallel-processing version of the ADvanced CIRCulation (ADCIRC) model. ADCIRC is a nonlinear computational model of hydrodynamics, developed by the U.S. Army Corps of Engineers and the US Navy, as a family of two- and three-dimensional finite element based codes. It affords a capability for simulating tidal circulation and storm surge propagation over very large computational domains, while simultaneously providing high-resolution output in areas of complex shoreline and bathymetry. The ADCIRC finite-element grid for this project covered the Gulf of Mexico and contiguous basins, extending into the deep Atlantic Ocean with progressively higher resolution approaching the study area. The advantage of using ADCIRC over other storm surge models, such as SLOSH, is that input conditions can include all or part of wind stress, tides, wave stress, and river discharge, which serve to make the model output more accurate.

  10. An automatic system for on-line flash flood forecasting

    NASA Astrophysics Data System (ADS)

    Makin, I.; Rumyantsev, D.; Shemanayev, K.; Shkarbanov, R.

    2012-04-01

    The research group at Russian State Hydrometeorological University continues developing hydrologic software, called SLS+, which might be useful for background flash flood forecasting in poorly gauged regions. Now the SLS+ software has a user-friendly web interface for on-line background flash flood forecasting in training and operational (real time or near real time) modes, and allows issuing stream flow forecasts based on precipitation and evaporation data obtained either from archives, or from field sensors, respectively. The system currently includes two hydrological models, the Sacramento Soil Moisture Accounting model (USA) and Multi-Layer Conceptual Model (Russia). These models can be calibrated either manually, or automatically based on four calibration algorithms: Shuffled Complex Evolution algorithm (SCE), which is quite useful if (1) a number of calibrated parameters does not exceed 6-7 and boundaries of the parameter space are well defined and (2) the parameter space is not too wide; Basic Stepwise Line Search (SLS) algorithm, which is efficient and computationally "inexpensive", if an initial point for pattern optimization is well defined; SLS-2L algorithm (where 2L is an abbreviation for "two loops" or "two cycles"), which is used in regions with scarce soil data and allows first to predetermine the soil hydraulic parameters, and then use these parameters for the refined model parameterization; SLS-E algorithm (where E stands for "Ensemble generation"), which implies the generation of ensembles of one or several forcing processes (for instance, effective precipitation and evaporation) and model calibration for each of those ensembles. This method is primarily designed for models with undistracted parameters at a relatively low density of ground-based meteorological observation network. Currently the trial version of the system is available for testing upon request.

  11. Flooding and Flood Management

    USGS Publications Warehouse

    Brooks, K.N.; Fallon, J.D.; Lorenz, D.L.; Stark, J.R.; Menard, Jason

    2011-01-01

    Floods result in great human disasters globally and nationally, causing an average of $4 billion of damages each year in the United States. Minnesota has its share of floods and flood damages, and the state has awarded nearly $278 million to local units of government for flood mitigation projects through its Flood Hazard Mitigation Grant Program. Since 1995, flood mitigation in the Red River Valley has exceeded $146 million. Considerable local and state funding has been provided to manage and mitigate problems of excess stormwater in urban areas, flooding of farmlands, and flood damages at road crossings. The cumulative costs involved with floods and flood mitigation in Minnesota are not known precisely, but it is safe to conclude that flood mitigation is a costly business. This chapter begins with a description of floods in Minneosta to provide examples and contrasts across the state. Background material is presented to provide a basic understanding of floods and flood processes, predication, and management and mitigation. Methods of analyzing and characterizing floods are presented because they affect how we respond to flooding and can influence relevant practices. The understanding and perceptions of floods and flooding commonly differ among those who work in flood forecasting, flood protection, or water resource mamnagement and citizens and businesses affected by floods. These differences can become magnified following a major flood, pointing to the need for better understanding of flooding as well as common language to describe flood risks and the uncertainty associated with determining such risks. Expectations of accurate and timely flood forecasts and our ability to control floods do not always match reality. Striving for clarity is important in formulating policies that can help avoid recurring flood damages and costs.

  12. Drought Monitoring and Forecasting Using the Princeton/U Washington National Hydrologic Forecasting System

    NASA Astrophysics Data System (ADS)

    Wood, E. F.; Yuan, X.; Roundy, J. K.; Lettenmaier, D. P.; Mo, K. C.; Xia, Y.; Ek, M. B.

    2011-12-01

    Extreme hydrologic events in the form of droughts or floods are a significant source of social and economic damage in many parts of the world. Having sufficient warning of extreme events allows managers to prepare for and reduce the severity of their impacts. A hydrologic forecast system can give seasonal predictions that can be used by mangers to make better decisions; however there is still much uncertainty associated with such a system. Therefore it is important to understand the forecast skill of the system before transitioning to operational usage. Seasonal reforecasts (1982 - 2010) from the NCEP Climate Forecast System (both version 1 (CFS) and version 2 (CFSv2), Climate Prediction Center (CPC) outlooks and the European Seasonal Interannual Prediction (EUROSIP) system, are assessed for forecasting skill in drought prediction across the U.S., both singularly and as a multi-model system The Princeton/U Washington national hydrologic monitoring and forecast system is being implemented at NCEP/EMC via their Climate Test Bed as the experimental hydrological forecast system to support U.S. operational drought prediction. Using our system, the seasonal forecasts are biased corrected, downscaled and used to drive the Variable Infiltration Capacity (VIC) land surface model to give seasonal forecasts of hydrologic variables with lead times of up to six months. Results are presented for a number of events, with particular focus on the Apalachicola-Chattahoochee-Flint (ACF) River Basin in the South Eastern United States, which has experienced a number of severe droughts in recent years and is a pilot study basin for the National Integrated Drought Information System (NIDIS). The performance of the VIC land surface model is evaluated using observational forcing when compared to observed streamflow. The effectiveness of the forecast system to predict streamflow and soil moisture is evaluated when compared with observed streamflow and modeled soil moisture driven by

  13. A pan-African medium-range ensemble flood forecast system

    NASA Astrophysics Data System (ADS)

    Thiemig, V.; Bisselink, B.; Pappenberger, F.; Thielen, J.

    2015-08-01

    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 by the ECMWF (European Centre for Medium-Ranged Weather Forecasts) and critical hydrological thresholds. In this paper, the predictive capability is investigated in a hindcast mode, by reproducing hydrological predictions for the year 2003 when important floods were observed. Results were verified by ground measurements of 36 sub-catchments as well as by 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 the flood event in March 2003 in the Sabi Basin (Zimbabwe) 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.

  14. An analytical framework for flood water conservation considering forecast uncertainty and acceptable risk

    NASA Astrophysics Data System (ADS)

    Ding, Wei; Zhang, Chi; Peng, Yong; Zeng, Ruijie; Zhou, Huicheng; Cai, Ximing

    2015-06-01

    This paper addresses how much flood water can be conserved for use after the flood season through the operation of reservoir by taking into account the residual flood control capacity (the difference between flood conveyance capacity and the expected inflow in a lead time). A two-stage model for dynamic control of the flood-limited water level (the maximum allowed water level during the flood season, DC-FLWL) is established considering forecast uncertainty and acceptable flood risk. It is found that DC-FLWL is applicable when the reservoir inflow ranges from small to medium levels of the historical records, while both forecast uncertainty and acceptable risk in the downstream affect the feasible space of DC-FLWL. As forecast uncertainty increases (under a given risk level) or as acceptable risk level decreases (under a given forecast uncertainty level), the minimum required safety margin for flood control increases, and the chance for DC-FLWL decreases. The derived hedging rules from the modeling framework illustrate either the dominant role of water conservation or flood control or the trade-off between the two objectives under different levels of forecast uncertainty and acceptable risk. These rules may provide useful guidelines for conserving water from flood, especially in the area with heavy water stress. The analysis is illustrated via a case study with a real-world reservoir in northeastern China.

  15. Urban flood early warning systems: approaches to hydrometeorological forecasting and communicating risk

    NASA Astrophysics Data System (ADS)

    Cranston, Michael; Speight, Linda; Maxey, Richard; Tavendale, Amy; Buchanan, Peter

    2015-04-01

    One of the main challenges for the flood forecasting community remains the provision of reliable early warnings of surface (or pluvial) flooding. The Scottish Flood Forecasting Service has been developing approaches for forecasting the risk of surface water flooding including capitalising on the latest developments in quantitative precipitation forecasting from the Met Office. A probabilistic Heavy Rainfall Alert decision support tool helps operational forecasters assess the likelihood of surface water flooding against regional rainfall depth-duration estimates from MOGREPS-UK linked to historical short-duration flooding in Scotland. The surface water flood risk is communicated through the daily Flood Guidance Statement to emergency responders. A more recent development is an innovative risk-based hydrometeorological approach that links 24-hour ensemble rainfall forecasts through a hydrological model (Grid-to-Grid) to a library of impact assessments (Speight et al., 2015). The early warning tool - FEWS Glasgow - presents the risk of flooding to people, property and transport across a 1km grid over the city of Glasgow with a lead time of 24 hours. Communication of the risk was presented in a bespoke surface water flood forecast product designed based on emergency responder requirements and trialled during the 2014 Commonwealth Games in Glasgow. The development of new approaches to surface water flood forecasting are leading to improved methods of communicating the risk and better performance in early warning with a reduction in false alarm rates with summer flood guidance in 2014 (67%) compared to 2013 (81%) - although verification of instances of surface water flooding remains difficult. However the introduction of more demanding hydrometeorological capabilities with associated greater levels of uncertainty does lead to an increased demand on operational flood forecasting skills and resources. Speight, L., Cole, S.J., Moore, R.J., Pierce, C., Wright, B., Golding, B

  16. Forecasting surface water flooding hazard and impact in real-time

    NASA Astrophysics Data System (ADS)

    Cole, Steven J.; Moore, Robert J.; Wells, Steven C.

    2016-04-01

    Across the world, there is increasing demand for more robust and timely forecast and alert information on Surface Water Flooding (SWF). Within a UK context, the government Pitt Review into the Summer 2007 floods provided recommendations and impetus to improve the understanding of SWF risk for both off-line design and real-time forecasting and warning. Ongoing development and trial of an end-to-end real-time SWF system is being progressed through the recently formed Natural Hazards Partnership (NHP) with delivery to the Flood Forecasting Centre (FFC) providing coverage over England & Wales. The NHP is a unique forum that aims to deliver coordinated assessments, research and advice on natural hazards for governments and resilience communities across the UK. Within the NHP, a real-time Hazard Impact Model (HIM) framework has been developed that includes SWF as one of three hazards chosen for initial trialling. The trial SWF HIM system uses dynamic gridded surface-runoff estimates from the Grid-to-Grid (G2G) hydrological model to estimate the SWF hazard. National datasets on population, infrastructure, property and transport are available to assess impact severity for a given rarity of SWF hazard. Whilst the SWF hazard footprint is calculated in real-time using 1, 3 and 6 hour accumulations of G2G surface runoff on a 1 km grid, it has been possible to associate these with the effective rainfall design profiles (at 250m resolution) used as input to a detailed flood inundation model (JFlow+) run offline to produce hazard information resolved to 2m resolution. This information is contained in the updated Flood Map for Surface Water (uFMfSW) held by the Environment Agency. The national impact datasets can then be used with the uFMfSW SWF hazard dataset to assess impacts at this scale and severity levels of potential impact assigned at 1km and for aggregated county areas in real-time. The impact component is being led by the Health and Safety Laboratory (HSL) within the NHP

  17. Sensitivity analysis of surface runoff generation in urban flood forecasting.

    PubMed

    Simões, N E; Leitão, J P; Maksimović, C; Sá Marques, A; Pina, R

    2010-01-01

    Reliable flood forecasting requires hydraulic models capable to estimate pluvial flooding fast enough in order to enable successful operational responses. Increased computational speed can be achieved by using a 1D/1D model, since 2D models are too computationally demanding. Further changes can be made by simplifying 1D network models, removing and by changing some secondary elements. The Urban Water Research Group (UWRG) of Imperial College London developed a tool that automatically analyses, quantifies and generates 1D overland flow network. The overland flow network features (ponds and flow pathways) generated by this methodology are dependent on the number of sewer network manholes and sewer inlets, as some of the overland flow pathways start at manholes (or sewer inlets) locations. Thus, if a simplified version of the sewer network has less manholes (or sewer inlets) than the original one, the overland flow network will be consequently different. This paper compares different overland flow networks generated with different levels of sewer network skeletonisation. Sensitivity analysis is carried out in one catchment area in Coimbra, Portugal, in order to evaluate overland flow network characteristics. PMID:20453333

  18. Semi-distributed flood forecasting system for the Middle Vistula reach

    NASA Astrophysics Data System (ADS)

    Romanowicz, Renata; Karamuz, Emilia; Osuch, Marzena

    2014-05-01

    project "Stochastic flood forecasting system (The River Vistula reach from Zawichost to Warsaw)" carried by the Institute of Geophysics, Polish Academy of Sciences on the order of the National Science Centre (contract No. 2011/01/B/ST10/06866). The water level data were provided by the Institute of Meteorology and Water Management (IMGW), Poland.

  19. Challenges in communicating and using ensemble forecasts in operational flood risk management

    NASA Astrophysics Data System (ADS)

    Nobert, Sébastien; Demeritt, David; Cloke, Hannah

    2010-05-01

    Following trends in operational weather forecasting, where ensemble prediction systems (EPS) are now increasingly the norm, a number of hydrological and flood forecasting centres internationally have begun to experiment with using similar ensemble methods. Most of the research to date has focused on the substantial technical challenges of developing coupled rainfall-runoff systems to represent the full cascade of uncertainties involved in predicting future flooding. As a consequence much less attention has been given to the communication and eventual use of EPS flood forecasts. Thus, this talk addresses the general understanding and communicative challenges in using EPS in operational flood forecasting. Drawing on a set of 48 semi-structured interviews conducted with flood forecasters, meteorologists and civil protection authorities (CPAs) dispersed across 17 European countries, this presentation pulls out some of the tensions between the scientific development of EPS and their application in flood risk management. The scientific uncertainties about whether or not a flood will occur comprise only part of the wider ‘decision' uncertainties faced by those charged with flood protection, who must also consider questions about how warnings they issue will subsequently be interpreted. By making those first order scientific uncertainties more explicit, ensemble forecasts can sometimes complicate, rather than clarify, the second order decision uncertainties they are supposed to inform.

  20. Model initialisation, data assimilation and probabilistic flood forecasting for distributed hydrological models

    NASA Astrophysics Data System (ADS)

    Cole, S. J.; Robson, A. J.; Bell, V. A.; Moore, R. J.

    2009-04-01

    The hydrological forecasting component of the Natural Environment Research Council's FREE (Flood Risk from Extreme Events) project "Exploitation of new data sources, data assimilation and ensemble techniques for storm and flood forecasting" addresses the initialisation, data assimilation and uncertainty of hydrological flood models utilising advances in rainfall estimation and forecasting. Progress will be reported on the development and assessment of simple model-initialisation and state-correction methods for a distributed grid-based hydrological model, the G2G Model. The potential of the G2G Model for area-wide flood forecasting is demonstrated through a nationwide application across England and Wales. Probabilistic flood forecasting in spatial form is illustrated through the use of high-resolution NWP rainfalls, and pseudo-ensemble forms of these, as input to the G2G Model. The G2G Model is configured over a large area of South West England and the Boscastle storm of 16 August 2004 is used as a convective case study. Visualisation of probabilistic flood forecasts is achieved through risk maps of flood threshold exceedence that indicate the space-time evolution of flood risk during the event.

  1. Coupling ensemble weather predictions based on TIGGE database with Grid-Xinanjiang model for flood forecast

    NASA Astrophysics Data System (ADS)

    Bao, H.-J.; Zhao, L.-N.; He, Y.; Li, Z.-J.; Wetterhall, F.; Cloke, H. L.; Pappenberger, F.; Manful, D.

    2011-02-01

    The incorporation of numerical weather predictions (NWP) into a flood forecasting system can increase forecast lead times from a few hours to a few days. A single NWP forecast from a single forecast centre, however, is insufficient as it involves considerable non-predictable uncertainties and lead to a high number of false alarms. The availability of global ensemble numerical weather prediction systems through the THORPEX Interactive Grand Global Ensemble' (TIGGE) offers a new opportunity for flood forecast. The Grid-Xinanjiang distributed hydrological model, which is based on the Xinanjiang model theory and the topographical information of each grid cell extracted from the Digital Elevation Model (DEM), is coupled with ensemble weather predictions based on the TIGGE database (CMC, CMA, ECWMF, UKMO, NCEP) for flood forecast. This paper presents a case study using the coupled flood forecasting model on the Xixian catchment (a drainage area of 8826 km2) located in Henan province, China. A probabilistic discharge is provided as the end product of flood forecast. Results show that the association of the Grid-Xinanjiang model and the TIGGE database gives a promising tool for an early warning of flood events several days ahead.

  2. Fews-Risk: A step towards risk-based flood forecasting

    NASA Astrophysics Data System (ADS)

    Bachmann, Daniel; Eilander, Dirk; de Leeuw, Annemargreet; Diermanse, Ferdinand; Weerts, Albrecht; de Bruijn, Karin; Beckers, Joost; Boelee, Leonore; Brown, Emma; Hazlewood, Caroline

    2015-04-01

    Operational flood prediction and the assessment of flood risk are important components of flood management. Currently, the model-based prediction of discharge and/or water level in a river is common practice for operational flood forecasting. Based on the prediction of these values decisions about specific emergency measures are made within operational flood management. However, the information provided for decision support is restricted to pure hydrological or hydraulic aspects of a flood. Information about weak sections within the flood defences, flood prone areas and assets at risk in the protected areas are rarely used in a model-based flood forecasting system. This information is often available for strategic planning, but is not in an appropriate format for operational purposes. The idea of FEWS-Risk is the extension of existing flood forecasting systems with elements of strategic flood risk analysis, such as probabilistic failure analysis, two dimensional flood spreading simulation and the analysis of flood impacts and consequences. Thus, additional information is provided to the decision makers, such as: • Location, timing and probability of failure of defined sections of the flood defence line; • Flood spreading, extent and hydraulic values in the hinterland caused by an overflow or a breach flow • Impacts and consequences in case of flooding in the protected areas, such as injuries or casualties and/or damages to critical infrastructure or economy. In contrast with purely hydraulic-based operational information, these additional data focus upon decision support for answering crucial questions within an operational flood forecasting framework, such as: • Where should I reinforce my flood defence system? • What type of action can I take to mend a weak spot in my flood defences? • What are the consequences of a breach? • Which areas should I evacuate first? This presentation outlines the additional required workflows towards risk-based flood

  3. A Distributed Hydrologic Model, HL-RDHM, for Flash Flood Forecasting in Hawaiian Watersheds

    NASA Astrophysics Data System (ADS)

    Fares, A.; Awal, R.; Michaud, J.; Chu, P.; Fares, S.; Kevin, K.; Rosener, M.

    2012-12-01

    Hawai'i's watersheds are flash flood prone due to their small contributing areas, and frequent intense spatially variable precipitation. Accurate simulation of the hydrology of these watersheds should incorporate spatial variability of at least the major input data, e.g., precipitation. The goal of this study is to evaluate the performance of the U.S. National Weather Service Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) in flash flood forecasting at Hanalei watershed, Kauai, Hawai'i. Some of the major limitations of using HL-RDHM in Hawaii are: i) Hawaii lies outside the Hydrologic Rainfall Analysis Project (HRAP) coordinate system of the continental US (CONUS), unavailability of a priori SAC-SMA parameter grids, and absence of hourly multi-sensor NEXRAD based precipitation grids. The specific objectives of this study were to i) run HL-RDHM outside CONUS domain, and ii) evaluate the performance of HL-RDHM for flash flood forecasting in the flood prone Hanalei watershed, Kauai, Hawai'i. We i) modified HRAP coordinate system; ii) generated input data of precipitation grids at different resolutions using data from 20 precipitation gauges five of which were within Hanalei watershed; iii) and generated SAC-SMA and routing parameter grids for the modified HRAP coordinate system. The one HRAP resolution grid (4 km x 4 km) was not accurate; thus, the basin averaged annual hourly precipitation of 1 HRAP grid is comparatively lower than that of ½ and ¼ HRAP grids. The performance of HL-RDHM using basin averaged a priori grids and distributed a priori grids was reasonable even using non-optimized a priori parameter values for 2008 data. HL-RDHM reasonably matched the observed streamflow magnitudes of peaks and time to peak during the calibration and validation periods. Overall, HL-RDHM performance is "good" to "very good" if we use input data of finer resolution grids (½ HRAP or ¼ HRAP) and precipitation grids interpolated from sufficient data of

  4. First steps in incorporating data-driven modelling to flood early warning in Norway's Flood Forecasting Service

    NASA Astrophysics Data System (ADS)

    Borsányi, Péter; Hamududu, Byman; Wong Kwok, Wai; Magnusson, Jan; Shi, Min

    2016-04-01

    The national Flood Early Warning Services (FEWS) in Norway use time-series of precipitation and temperature data as input to conceptual physically based rainfall-runoff models for forecasts. Runoff is forecasted in selected catchments and the warnings are based on regionalization of these. This concept proved useful in many catchments, however there are some exceptions, where forecasts are of worse quality. To improve this, data-driven modelling (DDM) techniques are sought applied. The first objective of the study is to identify those DDM methods, which are feasible for application and can easily be fit in the present, well-developed procedures of the operational FEWS. Therefore an experiment is conducted, where about thirty years of daily accumulated precipitation and daily mean temperature as input and observed runoff as output data are used. This was repeated from five, regionally and physically different catchments. In each case different DDMs were developed and their simulation results compared to those generated by the operational (conceptual based) models and to the observations. The methods of Artificial Neural Networks, Genetic Programming, Evolutionary Polynomial Regression and Support Vector Machines were used in the experiment. Various combinations of the last, the last two and the last three timesteps (in this case: days) of the data was tested as possible inputs. Forecast quality was described by Absolute Accumulated Error, Root Mean Square Error, Nash-Sutcliff Efficiency, the Ideal Point Error (combination of the previous) as well as by Taylor-diagrams. The first comparisons show promising results, which need to be further examined. The follow-up study will first focus on standardizing and automating the tests on forecast quality to be able to perform the studies on a larger number of datasets, as well as for other forecast periods. We expect the DDM to perform better in cases where conceptual models don't perform well. In these cases the quality

  5. National Weather Service Forecast Reference Evapotranspiration

    NASA Astrophysics Data System (ADS)

    Osborne, H. D.; Palmer, C. K.; Krone-Davis, P.; Melton, F. S.; Hobbins, M.

    2013-12-01

    The National Weather Service (NWS), Weather Forecasting Offices (WFOs) are producing daily reference evapotranspiration (ETrc) forecasts or FRET across the Western Region and in other selected locations since 2009, using the Penman - Monteith Reference Evapotranspiration equation for a short canopy (12 cm grasses), adopted by the Environmental Water Resources Institute of the American Society of Civil Engineers (ASCE-EWRI, 2004). The sensitivity of these daily calculations to fluctuations in temperatures, humidity, winds, and sky cover allows forecasters with knowledge of local terrain and weather patterns to better forecast in the ETrc inputs. The daily FRET product then evolved into a suite of products, including a weekly ETrc forecast for better water planning and a tabular point forecast for easy ingest into local water management-models. The ETrc forecast product suite allows water managers, the agricultural community, and the public to make more informed water-use decisions. These products permit operational planning, especially with the impending drought across much of the West. For example, the California Department of Water Resources not only ingests the FRET into their soil moisture models, but uses the FRET calculations when determining the reservoir releases in the Sacramento and American Rivers. We will also focus on the expansion of FRET verification, which compares the daily FRET to the observations of ETo from the California Irrigation Management Information System (CIMIS) across California's Central Valley for the 2012 water year.

  6. Status and Future of Global Flood and Landslide Nowcasts and Forecasts Using Satellite Precipitation Observations (Invited)

    NASA Astrophysics Data System (ADS)

    Adler, R. F.; Wu, H.; Kirschbaum, D. B.; Policelli, F.; Hong, Y.; Tian, Y.; Pierce, H.

    2010-12-01

    The advent of quasi-global, real-time precipitation analyses has lead to the reality of running global hydrological models and algorithms for the estimation of the occurrence of floods and rain-induced landslides. These calculations provide information useful to national and international agencies in understanding the intensity, timeline and impact on populations of these significant hazard events. The quality of such applied hydrological estimations should improve with time due to continuation and improvement of multi-satellite precipitation observations through the Global Precipitation Measurement (GPM) program and the further development of the models and algorithms. This talk will summarize the results from the NASA-based, real-time flood and landslide nowcasts and forecasts and describe directions for improving results going into the GPM era. Global flood and landslide estimation systems have been running in real-time at 0.25° latitude/longitude resolution using multi-satellite rainfall analyses for several years, with results available through the TRMM website (trmm.gsfc.nasa.gov). Published evaluations of the current system indicate useful skill in comparison with global event inventories. The evaluations indicate higher skill for larger rainfall systems (e.g., tropical cyclone landfall vs. flash flood). This result is reasonable considering the resolution of the rainfall information (0.25° and 3-hr) and the resolution of the current models/algorithms (0.25°). Improvements over the next few years will include 1) better precipitation analyses utilizing space-time interpolations that maintain accurate intensity distributions, 2) improved rain estimation for shallow, orographic rainfall systems and some types of monsoon rainfall, 3) higher resolution landslide algorithms with combined physical/empirical approaches, 4) higher resolution flood models with accurate routing and regional calibration, and 5) use of satellite soil moisture for more accurate pre

  7. Mapping Coastal Flood Zones for the National Flood Insurance Program

    NASA Astrophysics Data System (ADS)

    Carlton, D.; Cook, C. L.; Weber, J.

    2004-12-01

    The National Flood Insurance Program (NFIP) was created by Congress in 1968, and significantly amended in 1973 to reduce loss of life and property caused by flooding, reduce disaster relief costs caused by flooding and make Federally backed flood insurance available to property owners. These goals were to be achieved by requiring building to be built to resist flood damages, guide construction away from flood hazards, and transferring the cost of flood losses from taxpayers to policyholders. Areas subject to flood hazards were defined as those areas that have a probability greater than 1 percent of being inundated in any given year. Currently over 19,000 communities participate in the NFIP, many of them coastal communities subject to flooding from tides, storm surge, waves, or tsunamis. The mapping of coastal hazard areas began in the early 1970's and has been evolving ever since. At first only high tides and storm surge were considered in determining the hazardous areas. Then, after significant wave caused storm damage to structures outside of the mapped hazard areas wave hazards were also considered. For many years FEMA has had Guidelines and Specifications for mapping coastal hazards for the East Coast and the Gulf Coast. In September of 2003 a study was begun to develop similar Guidelines and Specifications for the Pacific Coast. Draft Guidelines and Specifications will be delivered to FEMA by September 30, 2004. During the study tsunamis were identified as a potential source of a 1 percent flood event on the West Coast. To better understand the analytical results, and develop adequate techniques to estimate the magnitude of a tsunami with a 1 percent probability of being equaled or exceeded in any year, a pilot study has begun at Seaside Oregon. Both the onshore velocity and the resulting wave runup are critical functions for FEMA to understand and potentially map. The pilot study is a cooperative venture between NOAA and USGS that is partially funded by both

  8. Integral assessment of floodplains as a basis for spatially-explicit flood loss forecasts

    NASA Astrophysics Data System (ADS)

    Zischg, Andreas Paul; Mosimann, Markus; Weingartner, Rolf

    2016-04-01

    A key aspect of disaster prevention is flood discharge forecasting which is used for early warning and therefore as a decision support for intervention forces. Hereby, the phase between the issued forecast and the time when the expected flood occurs is crucial for an optimal planning of the intervention. Typically, river discharge forecasts cover the regional level only, i.e. larger catchments. However, it is important to note that these forecasts are not useable directly for specific target groups on local level because these forecasts say nothing about the consequences of the predicted flood in terms of affected areas, number of exposed residents and houses. For this, on one hand simulations of the flooding processes and on the other hand data of vulnerable objects are needed. Furthermore, flood modelling in a high spatial and temporal resolution is required for robust flood loss estimation. This is a resource-intensive task from a computing time point of view. Therefore, in real-time applications flood modelling in 2D is not suited. Thus, forecasting flood losses in the short-term (6h-24h in advance) requires a different approach. Here, we propose a method to downscale the river discharge forecast to a spatially-explicit flood loss forecast. The principal procedure is to generate as many flood scenarios as needed in advance to represent the flooded areas for all possible flood hydrographs, e.g. very high peak discharges of short duration vs. high peak discharges with high volumes. For this, synthetic flood hydrographs were derived from the hydrologic time series. Then, the flooded areas of each scenario were modelled with a 2D flood simulation model. All scenarios were intersected with the dataset of vulnerable objects, in our case residential, agricultural and industrial buildings with information about the number of residents, the object-specific vulnerability, and the monetary value of the objects. This dataset was prepared by a data-mining approach. For each

  9. Application of Medium and Seasonal Flood Forecasts for Agriculture Damage Assessment

    NASA Astrophysics Data System (ADS)

    Fakhruddin, Shamsul; Ballio, Francesco; Menoni, Scira

    2015-04-01

    Early warning is a key element for disaster risk reduction. In recent decades, major advancements have been made in medium range and seasonal flood forecasting. This progress provides a great opportunity to reduce agriculture damage and improve advisories for early action and planning for flood hazards. This approach can facilitate proactive rather than reactive management of the adverse consequences of floods. In the agricultural sector, for instance, farmers can take a diversity of options such as changing cropping patterns, applying fertilizer, irrigating and changing planting timing. An experimental medium range (1-10 day) and seasonal (20-25 days) flood forecasting model has been developed for Thailand and Bangladesh. It provides 51 sets of discharge ensemble forecasts of 1-10 days with significant persistence and high certainty and qualitative outlooks for 20-25 days. This type of forecast could assist farmers and other stakeholders for differential preparedness activities. These ensembles probabilistic flood forecasts have been customized based on user-needs for community-level application focused on agriculture system. The vulnerabilities of agriculture system were calculated based on exposure, sensitivity and adaptive capacity. Indicators for risk and vulnerability assessment were conducted through community consultations. The forecast lead time requirement, user-needs, impacts and management options for crops were identified through focus group discussions, informal interviews and community surveys. This paper illustrates potential applications of such ensembles for probabilistic medium range and seasonal flood forecasts in a way that is not commonly practiced globally today.

  10. Spatially distributed flood forecasting in flash flood prone areas: Application to road network supervision in Southern France

    NASA Astrophysics Data System (ADS)

    Naulin, J.-P.; Payrastre, O.; Gaume, E.

    2013-04-01

    SummaryAccurate flood forecasts are critical to an efficient flood event management strategy. Until now, hydro-meteorological forecasts have mainly been used to establish early-warnings in France (meteorological and flood vigilance maps) or over the world (flash-flood guidances). These forecasts are typically limited either to the main streams covered by the flood forecasting services or to watersheds with specific assets like check dams, which in most cases are well gauged river sections, thus leaving aside large parts of the territory. This paper presents a distributed hydro-meteorological forecasting approach, which makes use of the high spatial and temporal resolution rainfall estimates that are now available, to provide information at ungauged sites. The proposed system intended to detect road inundation risks had initially been developed and tested in areas of limited size. This paper presents the extension of such a system to an entire region (i.e. the Gard region in Southern France), including over 2000 crossing points between rivers and roads and its validation with respect to a large data set of actual reported road inundations observed during recent flash flood events. These initial validation results appear to be most promising. The eventual proposed tool would provide the necessary information for flood event management services to identify the areas at risk and adopt appropriate safety and rescue measures: i.e. pre-positioning of rescue equipment, interruption of the traffic on the exposed roads and determination of safe access or evacuation routes. Moreover, beyond the specific application to the supervision of a road network, the research undertaken herein also provides results for the performance of hydro-meteorological forecasts on ungauged headwaters.

  11. Web-based hydrological modeling system for flood forecasting and risk mapping

    NASA Astrophysics Data System (ADS)

    Wang, Lei; Cheng, Qiuming

    2008-10-01

    Mechanism of flood forecasting is a complex system, which involves precipitation, drainage characterizes, land use/cover types, ground water and runoff discharge. The application of flood forecasting model require the efficient management of large spatial and temporal datasets, which involves data acquisition, storage, pre-processing and manipulation, analysis and display of model results. The extensive datasets usually involve multiple organizations, but no single organization can collect and maintain all the multidisciplinary data. The possible usage of the available datasets remains limited primarily because of the difficulty associated with combining data from diverse and distributed data sources. Difficulty in linking data, analysis tools and model is one of the barriers to be overcome in developing real-time flood forecasting and risk prediction system. The current revolution in technology and online availability of spatial data, particularly, with the construction of Canadian Geospatial Data Infrastructure (CGDI), a lot of spatial data and information can be accessed in real-time from distributed sources over the Internet to facilitate Canadians' need for information sharing in support of decision-making. This has resulted in research studies demonstrating the suitability of the web as a medium for implementation of flood forecasting and flood risk prediction. Web-based hydrological modeling system can provide the framework within which spatially distributed real-time data accessed remotely to prepare model input files, model calculation and evaluate model results for flood forecasting and flood risk prediction. This paper will develop a prototype web-base hydrological modeling system for on-line flood forecasting and risk mapping in the Oak Ridges Moraine (ORM) area, southern Ontario, Canada, integrating information retrieval, analysis and model analysis for near real time river runoff prediction, flood frequency prediction, flood risk and flood inundation

  12. Real-time flood forecasting with high-resolution NWP rainfall and dual data assimilation

    NASA Astrophysics Data System (ADS)

    Liu, Jia; Bray, Michaela; Han, Dawei

    2014-05-01

    Mesoscale Numerical Weather Prediction (NWP) models are nowadays gaining more and more attention in providing high-resolution rainfall forecasts for real-time flood forecasting. In this study, the newest generation NWP model, Weather Research & Forecasting (WRF) model, is integrated with the rainfall-runoff model in real-time to generate accurate flow forecasts at the catchment scale. The rainfall-runoff model is chosen as the Probability Distribution Model (PDM), which has widely been used for flood forecasting. Dual data assimilation is carried out for real-time updating of the flood forecasting system. The 3-Dimensional Variational (3DVar) data assimilation scheme is incorporated with WRF to assimilate meteorological observations and weather radar reflectivity data in order to improve the WRF rainfall forecasts; meanwhile real-time flow observations are assimilated by the Auto-Regressive Moving Average (ARMA) model to update the forecasted flow transformed by PDM. The Brue catchment located in Southwest England with a drainage area of 135.2 km2 is chosen to be the study area. A dense rain gauge network was set up during a project named HYREX (Hydrological radar experiment), which contains 49 rain gauges and a C-band weather radar, providing with sufficient hydrological and radar data for WRF model verification and data assimilation. Besides the radar reflectivity data, two types of NCAR archived data (SYNOP and SOUND, http://dss.ucar.edu) are also assimilated by 3DVar, which provide real-time surface and upper-level observations of pressure, temperature, humidity and wind from fixed and mobile stations. Four 24 hour storm events are selected from the HYREX project with different characteristics regarding storm formation and rainfall-runoff responses. Real-time flood forecasting is then carried out by the constructed forecasting system for the four storm events with a forecast lead time of 12 hours. The forecasting accuracy of the whole system is found to be

  13. An Open-Book Modular Watershed Modeling Framework for Rapid Prototyping of GPM- based Flood Forecasting in International River Basins

    NASA Astrophysics Data System (ADS)

    Katiyar, N.; Hossain, F.

    2006-05-01

    Floods have always been disastrous for human life. It accounts for about 15 % of the total death related to natural disasters. There are around 263 transboundary river basins listed by UNESCO, wherein at least 30 countries have more than 95% of their territory locked in one or more such transboundary basins. For flood forecasting in the lower riparian nations of these International River Basins (IRBs), real-time rainfall data from upstream nations is naturally the most critical factor governing the forecasting effectiveness. However, many upstream nations fail to provide data to the lower riparian nations due to a lack of in-situ rainfall measurement infrastructure or a lack of a treaty for real-time sharing of rainfall data. A potential solution is therefore to use satellites that inherently measure rainfall across political boundaries. NASA's proposed Global Precipitation Measurement (GPM) mission appears very promising in providing this vital rainfall information under the data- limited scenario that will continue to prevail in most IRBs. However, satellite rainfall is associated with uncertainty and hence, proper characterization of the satellite rainfall error propagation in hydrologic models for flood forecasting is a critical priority that should be resolved in the coming years in anticipation of GPM. In this study, we assess an open book modular watershed modeling approach for estimating the expected error in flood forecasting related to GPM rainfall data. Our motivation stems from the critical challenge in identifying the specific IRBs that would benefit from a pre-programmed satellite-based forecasting system in anticipation of GPM. As the number of flood-prone IRBs is large, conventional data-intensive implementation of existing physically-based distributed hydrologic models on case-by-case IRBs is considered time-consuming for completing such a global assessment. A more parsimonious approach is justified at the expense of a tolerable loss of detail and

  14. Implementing the national AIGA flash flood warning system in France

    NASA Astrophysics Data System (ADS)

    Organde, Didier; Javelle, Pierre; Demargne, Julie; Arnaud, Patrick; Caseri, Angelica; Fine, Jean-Alain; de Saint Aubin, Céline

    2015-04-01

    The French national hydro-meteorological and flood forecasting centre (SCHAPI) aims to implement a national flash flood warning system to improve flood alerts for small-to-medium (up to 1000 km2) ungauged basins. This system is based on the AIGA method, co-developed by IRSTEA these last 10 years. The method, initially set up for the Mediterranean area, is based on a simple event-based hourly hydrologic distributed model run every 15 minutes (Javelle et al. 2014). The hydrologic model ingests operational radar-gauge rainfall grids from Météo-France at a 1-km² resolution to produce discharges for successive outlets along the river network. Discharges are then compared to regionalized flood quantiles of given return periods and warnings (expressed as the range of the return period estimated in real-time) are provided on a river network map. The main interest of the method is to provide forecasters and emergency services with a synthetic view in real time of the ongoing flood situation, information that is especially critical in ungauged flood prone areas. In its enhanced national version, the hourly event-based distributed model is coupled to a continuous daily rainfall-runoff model which provides baseflow and a soil moisture index (for each 1-km² pixel) at the beginning of the hourly simulation. The rainfall-runoff models were calibrated on a selection of 700 French hydrometric stations with Météo-France radar-gauge reanalysis dataset for the 2002-2006 period. To estimate model parameters for ungauged basins, the 2 hydrologic models were regionalised by testing both regressions (using different catchment attributes, such as catchment area, soil type, and climate characteristic) and spatial proximity techniques (transposing parameters from neighbouring donor catchments), as well as different homogeneous hydrological areas. The most valuable regionalisation method was determined for each model through jack-knife cross-validation. The system performance was then

  15. Using High Resolution Numerical Weather Prediction Models to Reduce and Estimate Uncertainty in Flood Forecasting

    NASA Astrophysics Data System (ADS)

    Cole, S. J.; Moore, R. J.; Roberts, N.

    2007-12-01

    Forecast rainfall from Numerical Weather Prediction (NWP) and/or nowcasting systems is a major source of uncertainty for short-term flood forecasting. One approach for reducing and estimating this uncertainty is to use high resolution NWP models that should provide better rainfall predictions. The potential benefit of running the Met Office Unified Model (UM) with a grid spacing of 4 and 1 km compared to the current operational resolution of 12 km is assessed using the January 2005 Carlisle flood in northwest England. These NWP rainfall forecasts, and forecasts from the Nimrod nowcasting system, were fed into the lumped Probability Distributed Model (PDM) and the distributed Grid-to-Grid model to predict river flow at the outlets of two catchments important for flood warning. The results show the benefit of increased resolution in the UM, the benefit of coupling the high- resolution rainfall forecasts to hydrological models and the improvement in timeliness of flood warning that might have been possible. Ongoing work aims to employ these NWP rainfall forecasts in ensemble form as part of a procedure for estimating the uncertainty of flood forecasts.

  16. Precipitation and floodiness: forecasts of flood hazard at the regional scale

    NASA Astrophysics Data System (ADS)

    Stephens, Liz; Day, Jonny; Pappenberger, Florian; Cloke, Hannah

    2016-04-01

    In 2008, a seasonal forecast of an increased likelihood of above-normal rainfall in West Africa led the Red Cross to take early humanitarian action (such as prepositioning of relief items) on the basis that this forecast implied heightened flood risk. However, there are a number of factors that lead to non-linearity between precipitation anomalies and flood hazard, so in this presentation we use a recently developed global-scale hydrological model driven by the ERA-Interim/Land precipitation reanalysis (1980-2010) to quantify this non-linearity. Using these data, we introduce the concept of floodiness to measure the incidence of floods over a large area, and quantify the link between monthly precipitation, river discharge and floodiness anomalies. Our analysis shows that floodiness is not well correlated with precipitation, demonstrating the problem of using seasonal precipitation forecasts as a proxy for forecasting flood hazard. This analysis demonstrates the value of developing hydrometeorological forecasts of floodiness for decision-makers. As a result, we are now working with the European Centre for Medium-Range Weather Forecasts and the Joint Research Centre, as partners of the operational Global Flood Awareness System (GloFAS), to implement floodiness forecasts in real-time.

  17. Accounting for Uncertainties in Generating Reliable Probabilistic Flood Forecasts for Bangladesh

    NASA Astrophysics Data System (ADS)

    Hopson, T. M.; Webster, P. J.

    2007-12-01

    The country of Bangladesh experiences life-threatening floods in the basins of the Ganges and Brahmaputra rivers flowing through the country with tragic regularity. These floods result in loss of life on a scale that often greatly eclipses the deaths due to natural disasters in developed countries. Flooding in these basins can occur on weekly time scales (as occurred during the severe Brahmaputra floods of 2004 and of this year) to seasonal time scales (as occurred during the disastrous floods of 1998). Beginning in 2003, the Climate Forecasting Applications for Bangladesh (CFAB) project began issuing operational probabilistic flood forecasts to the country of Bangladesh over a wide-range of time scales to provide advanced warning of severe flood-stage discharges in the catchments of the Ganges and Brahmaputra basins. In this paper we discuss the uncertainty estimator module to our 1- to 10-day in-advance automated real-time operational multi-model flood forecast scheme for the upper basins of the Ganges and Brahmaputra rivers. These forecasts are based on an application of the European Centre for Medium-Range Weather Forecasts (ECMWF) 51-member ensemble weather forecasts, near-real-time GPCP and CMORPH satellite and NOAA CPC rain gauge precipitation estimates, and near-real- time discharge estimates from the Bangladesh Flood Forecasting and Warning Centre. The uncertainty estimator module estimates multi-model hydrologic error utilizing daily-updated hindcasts, which are separate from the forecasted weather variable uncertainty. Such a separation of error sources is done to maximize the sharpness of the final forecast probability distribution function (PDF), as well as to enhance the utility of the ensemble spread as an indicator of ensemble skill; for this latter feature of ensemble forecasts, we also present a new measure to test the spread-skill utility. In the final step of the uncertainty module, we merge these two sources of uncertainty together while at the

  18. Flood Risk, Flood Mitigation, and Location Choice: Evaluating the National Flood Insurance Program's Community Rating System.

    PubMed

    Fan, Qin; Davlasheridze, Meri

    2016-06-01

    Climate change is expected to worsen the negative effects of natural disasters like floods. The negative impacts, however, can be mitigated by individuals' adjustments through migration and relocation behaviors. Previous literature has identified flood risk as one significant driver in relocation decisions, but no prior study examines the effect of the National Flood Insurance Program's voluntary program-the Community Rating System (CRS)-on residential location choice. This article fills this gap and tests the hypothesis that flood risk and the CRS-creditable flood control activities affect residential location choices. We employ a two-stage sorting model to empirically estimate the effects. In the first stage, individuals' risk perception and preference heterogeneity for the CRS activities are considered, while mean effects of flood risk and the CRS activities are estimated in the second stage. We then estimate heterogeneous marginal willingness to pay (WTP) for the CRS activities by category. Results show that age, ethnicity and race, educational attainment, and prior exposure to risk explain risk perception. We find significant values for the CRS-creditable mitigation activities, which provides empirical evidence for the benefits associated with the program. The marginal WTP for an additional credit point earned for public information activities, including hazard disclosure, is found to be the highest. Results also suggest that water amenities dominate flood risk. Thus, high amenity values may increase exposure to flood risk, and flood mitigation projects should be strategized in coastal regions accordingly. PMID:26552993

  19. Understanding uncertainty in distributed flash flood forecasting for semiarid regions 1909

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Semi-arid flash floods pose a significant danger for life and property in the US. One effective way to mitigate flood risk is by implementing a rainfall-runoff model in a real-time forecast and warning system. This study used a physically based, distributed semi-arid rainfall-runoff model driven by ...

  20. How can we deal with ANN in flood forecasting? As a simulation model or updating kernel!

    NASA Astrophysics Data System (ADS)

    Hassan Saddagh, Mohammad; Javad Abedini, Mohammad

    2010-05-01

    Flood forecasting and early warning, as a non-structural measure for flood control, is often considered to be the most effective and suitable alternative to mitigate the damage and human loss caused by flood. Forecast results which are output of hydrologic, hydraulic and/or black box models should secure accuracy of flood values and timing, especially for long lead time. The application of the artificial neural network (ANN) in flood forecasting has received extensive attentions in recent years due to its capability to capture the dynamics inherent in complex processes including flood. However, results obtained from executing plain ANN as simulation model demonstrate dramatic reduction in performance indices as lead time increases. This paper is intended to monitor the performance indices as it relates to flood forecasting and early warning using two different methodologies. While the first method employs a multilayer neural network trained using back-propagation scheme to forecast output hydrograph of a hypothetical river for various forecast lead time up to 6.0 hr, the second method uses 1D hydrodynamic MIKE11 model as forecasting model and multilayer neural network as updating kernel to monitor and assess the performance indices compared to ANN alone in light of increase in lead time. Results presented in both graphical and tabular format indicate superiority of MIKE11 coupled with ANN as updating kernel compared to ANN as simulation model alone. While plain ANN produces more accurate results for short lead time, the errors increase expeditiously for longer lead time. The second methodology provides more accurate and reliable results for longer forecast lead time.

  1. Using subseasonal-to-seasonal (S2S) extreme rainfall forecasts for extended-range flood prediction in Australia

    NASA Astrophysics Data System (ADS)

    White, C. J.; Franks, S. W.; McEvoy, D.

    2015-06-01

    Meteorological and hydrological centres around the world are looking at ways to improve their capacity to be able to produce and deliver skilful and reliable forecasts of high-impact extreme rainfall and flooding events on a range of prediction timescales (e.g. sub-daily, daily, multi-week, seasonal). Making improvements to extended-range rainfall and flood forecast models, assessing forecast skill and uncertainty, and exploring how to apply flood forecasts and communicate their benefits to decision-makers are significant challenges facing the forecasting and water resources management communities. This paper presents some of the latest science and initiatives from Australia on the development, application and communication of extreme rainfall and flood forecasts on the extended-range "subseasonal-to-seasonal" (S2S) forecasting timescale, with a focus on risk-based decision-making, increasing flood risk awareness and preparedness, capturing uncertainty, understanding human responses to flood forecasts and warnings, and the growing adoption of "climate services". The paper also demonstrates how forecasts of flood events across a range of prediction timescales could be beneficial to a range of sectors and society, most notably for disaster risk reduction (DRR) activities, emergency management and response, and strengthening community resilience. Extended-range S2S extreme flood forecasts, if presented as easily accessible, timely and relevant information are a valuable resource to help society better prepare for, and subsequently cope with, extreme flood events.

  2. Evaluation of radar-based precipitation estimates for flash flood forecasting in the Three Gorges Region

    NASA Astrophysics Data System (ADS)

    Li, Z.; Yang, D.; Hong, Y.; Qi, Y.; Cao, Q.

    2015-05-01

    Spatial rainfall pattern plays a critical role in determining hydrological responses in mountainous areas, especially for natural disasters such as flash floods. In this study, to improve the skills of flood forecasting in the mountainous Three Gorges Region (TGR) of the Yangtze River, we developed a first version of a high-resolution (1 km) radar-based quantitative precipitation estimation (QPE) consideration of many critical procedures, such as beam blockage analysis, ground-clutter filter, rain type identification and adaptive Z-R relations. A physically-based distributed hydrological model (GBHM) was established and further applied to evaluate the performance of radar-based QPE for regional flood forecasting, relative to the gauge-driven simulations. With two sets of input data (gauge and radar) collected during summer 2010, the applicability of the current radar-based QPE to rainstorm monitoring and flash flood forecasting in the TGR is quantitatively analysed and discussed.

  3. Research on classified real-time flood forecasting framework based on K-means cluster and rough set.

    PubMed

    Xu, Wei; Peng, Yong

    2015-01-01

    This research presents a new classified real-time flood forecasting framework. In this framework, historical floods are classified by a K-means cluster according to the spatial and temporal distribution of precipitation, the time variance of precipitation intensity and other hydrological factors. Based on the classified results, a rough set is used to extract the identification rules for real-time flood forecasting. Then, the parameters of different categories within the conceptual hydrological model are calibrated using a genetic algorithm. In real-time forecasting, the corresponding category of parameters is selected for flood forecasting according to the obtained flood information. This research tests the new classified framework on Guanyinge Reservoir and compares the framework with the traditional flood forecasting method. It finds that the performance of the new classified framework is significantly better in terms of accuracy. Furthermore, the framework can be considered in a catchment with fewer historical floods. PMID:26442493

  4. Spatial Analytic Hierarchy Process Model for Flood Forecasting: An Integrated Approach

    NASA Astrophysics Data System (ADS)

    Nasir Matori, Abd; Umar Lawal, Dano; Yusof, Khamaruzaman Wan; Hashim, Mustafa Ahmad; Balogun, Abdul-Lateef

    2014-06-01

    Various flood influencing factors such as rainfall, geology, slope gradient, land use, soil type, drainage density, temperature etc. are generally considered for flood hazard assessment. However, lack of appropriate handling/integration of data from different sources is a challenge that can make any spatial forecasting difficult and inaccurate. Availability of accurate flood maps and thorough understanding of the subsurface conditions can adequately enhance flood disasters management. This study presents an approach that attempts to provide a solution to this drawback by combining Geographic Information System (GIS)-based Analytic Hierarchy Process (AHP) model as spatial forecasting tools. In achieving the set objectives, spatial forecasting of flood susceptible zones in the study area was made. A total number of five set of criteria/factors believed to be influencing flood generation in the study area were selected. Priority weights were assigned to each criterion/factor based on Saaty's nine point scale of preference and weights were further normalized through the AHP. The model was integrated into a GIS system in order to produce a flood forecasting map.

  5. Forecasting skills of the ensemble hydro-meteorological system for the Po river floods

    NASA Astrophysics Data System (ADS)

    Ricciardi, Giuseppe; Montani, Andrea; Paccagnella, Tiziana; Pecora, Silvano; Tonelli, Fabrizio

    2013-04-01

    The Po basin is the largest and most economically important river-basin in Italy. Extreme hydrological events, including floods, flash floods and droughts, are expected to become more severe in the next future due to climate change, and related ground effects are linked both with environmental and social resilience. A Warning Operational Center (WOC) for hydrological event management was created in Emilia Romagna region. In the last years, the WOC faced challenges in legislation, organization, technology and economics, achieving improvements in forecasting skill and information dissemination. Since 2005, an operational forecasting and modelling system for flood modelling and forecasting has been implemented, aimed at supporting and coordinating flood control and emergency management on the whole Po basin. This system, referred to as FEWSPo, has also taken care of environmental aspects of flood forecast. The FEWSPo system has reached a very high level of complexity, due to the combination of three different hydrological-hydraulic chains (HEC-HMS/RAS - MIKE11 NAM/HD, Topkapi/Sobek), with several meteorological inputs (forecasted - COSMOI2, COSMOI7, COSMO-LEPS among others - and observed). In this hydrological and meteorological ensemble the management of the relative predictive uncertainties, which have to be established and communicated to decision makers, is a debated scientific and social challenge. Real time activities face professional, modelling and technological aspects but are also strongly interrelated with organization and human aspects. The authors will report a case study using the operational flood forecast hydro-meteorological ensemble, provided by the MIKE11 chain fed by COSMO_LEPS EQPF. The basic aim of the proposed approach is to analyse limits and opportunities of the long term forecast (with a lead time ranging from 3 to 5 days), for the implementation of low cost actions, also looking for a well informed decision making and the improvement of

  6. An operational real-time flood forecasting system in Southern Italy

    NASA Astrophysics Data System (ADS)

    Ortiz, Enrique; Coccia, Gabriele; Todini, Ezio

    2015-04-01

    A real-time flood forecasting system has been operating since year 2012 as a non-structural measure for mitigating the flood risk in Campania Region (Southern Italy), within the Sele river basin (3.240 km2). The Sele Flood Forecasting System (SFFS) has been built within the FEWS (Flood Early Warning System) platform developed by Deltares and it assimilates the numerical weather predictions of the COSMO LAM family: the deterministic COSMO-LAMI I2, the deterministic COSMO-LAMI I7 and the ensemble numerical weather predictions COSMO-LEPS (16 members). Sele FFS is composed by a cascade of three main models. The first model is a fully continuous physically based distributed hydrological model, named TOPKAPI-eXtended (Idrologia&Ambiente s.r.l., Naples, Italy), simulating the dominant processes controlling the soil water dynamics, runoff generation and discharge with a spatial resolution of 250 m. The second module is a set of Neural-Networks (ANN) built for forecasting the river stages at a set of monitored cross-sections. The third component is a Model Conditional Processor (MCP), which provides the predictive uncertainty (i.e., the probability of occurrence of a future flood event) within the framework of a multi-temporal forecast, according to the most recent advancements on this topic (Coccia and Todini, HESS, 2011). The MCP provides information about the probability of exceedance of a maximum river stage within the forecast lead time, by means of a discrete time function representing the variation of cumulative probability of exceeding a river stage during the forecast lead time and the distribution of the time occurrence of the flood peak, starting from one or more model forecasts. This work shows the Sele FFS performance after two years of operation, evidencing the added-values that can provide to a flood early warning and emergency management system.

  7. Improving our understanding of flood forecasting using earlier hydro-meteorological intelligence

    NASA Astrophysics Data System (ADS)

    Shih, Dong-Sin; Chen, Cheng-Hsin; Yeh, Gour-Tsyh

    2014-05-01

    In recent decades, Taiwan has suffered from severe bouts of torrential rain, and typhoon induced floods have become the major natural threat to Taiwan. In order to warn the public of potential risks, authorities are considering establishing an early warning system derived from an integrated hydro-meteorological estimation process. This study aims at the development and accuracy of such a warning system. So it is first necessary to understand the distinctive features of flood forecasting in integrated rainfall-runoff simulations. Additionally the adequacies of a warning system that is based on extracting useful intelligence from earlier, possibly faulty numerical simulation results are discussed. In order to precisely model flooding, hydrological simulations based upon spot measured rainfall data have been utilized in prior studies to calibrate model parameters. Here, precipitation inputs from an ensemble of almost 20 different realizations of rainfall fields have been used to derive flood forecasts. The flood warning system therefore integrates rainfall-runoff calculations, field observations and data assimilations. Simulation results indicate that the ensemble precipitation estimates generated by a Weather Research Forecasting (WRF) mesoscale model produce divergent estimates. Considerable flooding is often shown in the simulated hydrographs, but the results as to the peak time and peak stage are not always in agreement with the observations. In brief, such forecasts can be good for warning against potential damaging floods in the near future, but the meteorological inputs are not good enough to forecast the time and magnitude of the peaks. The key for such warning system is not to expect highly accurate rainfall predictions, but to improve our understanding from individual ensemble flood forecasts.

  8. The Design and Implementation of a Real-Time Flood Forecasting System in Durban, South Africa

    NASA Astrophysics Data System (ADS)

    Sinclair, Scott; Pegram, Geoff

    2003-04-01

    In South Africa, five flood events during the period 1994-1996 resulted in the loss of 173 lives, more than 7000 people requiring evacuation and/or emergency shelter and damages to the value of R680 million (White paper on Disaster Management 1998). The South African Disaster management bill provides for "...preventing or reducing the risk of disasters, mitigating the severity of disasters ...". To this end a pilot study funded by the Water Research Commission aims at providing flood forecasts for the Mgeni and Mlazi catchments near the city of Durban in South Africa. The importance and usefulness of flood forecasting is particularly evident in an urban context where the density of population and infrastructure provide great potential for disaster. A reliable flood warning or forecasting system cannot prevent the occurrence of floods, but provides a key tool that can allow decision makers to be proactive rather than reactive in their response to a flooding event. Taking preventative measures before the fact can significantly reduce the social and economic impacts associated with a disaster. The flood forecasting system described here makes use of a "best estimate" spatial rainfield (obtained by combining radar and telemetered rain gauge rainfall estimates) as input to a linear catchment model. The catchment model parameters are dynamically updated in response to measured streamflows using Kalman filtering techniques, allowing improved forecasts of streamflow as the catchment conditions change. Precomputed flood lines and a graphical representation of the spatial rainfield are dynamically displayed on a GIS in the Durban disaster management control center enabling Disaster Managers to be proactive in times of impending floods.

  9. Development of A Real Time Physically-based Flood Forecasting System In The Piemonte Region, Italy

    NASA Astrophysics Data System (ADS)

    Barbero, S. P.; Rabuffetti, D.; Buffo, M.; Graziadei, M.

    The development and implementation of the Piemonte RegionSs real-time Flood Fore- casting System is described. The area of interest is the Upper Po River basin (North- west Italy) of approximately 37000 km2 and its river network of about 3000 Km and 3 big lakes. FloodWatch, a GIS-based decision support system for real-time flood fore- casting has been developed and operationally used since June 2000 at the Piemonte RegionSs Room for the Situation of Natural Hazards in Torino, Italy. FloodWatch is based on MIKE 11 modules which provide a continuos lumped hydrological model- ing of 187 tree-structured subcatchments connected by a 1D distributed hydrodynamic model. It is directly linked to the existing telemetric system, which provides measured data from more than 270 meteorological stations (rainfall and temperature) and about 80 water level gauging stations. In addition, FloodWatch uses quantitative precipita- tion and temperature forecasts daily issued by the Regional Meteorological Service on the 11 zones in which the study area is subdivided. At present, FloodWatch auto- matically supplies operational forecasts of water-level and discharge at 73 locations for up to 48 hours. The development of a fast and reliable flow forecasting system for this large and heterogeneous river basin required careful balance between the need for rapid and accurate forecasts and of a correct representation of run-off generation, flood propagation, baseflows, snow accumulation and melting. Strengths and limits of the system are focused addressing the need for future development. Some results are presented with particular regard to the October 2000 flood event, when the northwest of Italy experienced one of the largest floods on record. Heavy and prolonged rainfall fell across the entire Po river basin. The flood inundated vast areas causing widespread damage and thousands of people were warned and alerted to evacuate.

  10. Evaluation of Mekong River commission operational flood forecasts, 2000-2012

    NASA Astrophysics Data System (ADS)

    Pagano, T. C.

    2014-07-01

    This study created a 13-year historical archive of operational flood forecasts issued by the Regional Flood Management and Mitigation Center (RFMMC) of the Mekong River Commission. The RFMMC issues 1- to 5-day daily deterministic river height forecasts for 22 locations throughout the wet season (June-October). When these forecasts reach near flood level, government agencies and the public are encouraged to take protective action against damages. When measured by standard skill scores, the forecasts perform exceptionally well (e.g., 1 day-ahead Nash-Sutcliffe > 0.99) although much of this apparent skill is due to the strong seasonal cycle and the narrow natural range of variability at certain locations. Five-day forecasts upstream of Phnom Penh typically have 0.8 m error standard deviation, whereas below Phnom Penh the error is typically 0.3 m. The coefficients of persistence for 1-day forecasts are typically 0.4-0.8 and 5-day forecasts are typically 0.1-0.7. RFMMC uses a series of benchmarks to define a metric of percentage satisfactory forecasts. As the benchmarks were derived based on the average error, certain locations and lead times consistently appear less satisfactory than others. Instead, different benchmarks were proposed and derived based on the 70th percentile of absolute error over the 13-year period. There are no obvious trends in the percentage of satisfactory forecasts from 2002 to 2012, regardless of the benchmark chosen. Finally, when evaluated from a categorical "crossing above/not-crossing above flood level" perspective, the forecasts have a moderate probability of detection (48% at 1 day ahead, 31% at 5 days ahead) and false alarm rate (13% at 1 day ahead, 74% at 5 days ahead).

  11. Evaluation of Mekong River Commission operational flood forecasts, 2000-2012

    NASA Astrophysics Data System (ADS)

    Pagano, T. C.

    2013-11-01

    This study created a 13 yr historical archive of operational flood forecasts issued by the Regional Flood Management and Mitigation Center (RFMMC) of the Mekong River Commission. The RFMMC issues 1 to 5 day-ahead daily deterministic river height forecasts for 22 locations throughout the wet season (June-October). When these forecasts reach near Flood Level, government agencies and the public are encouraged to take protective action against damages. When measured by standard skill scores, the forecasts perform exceptionally well (e.g. 1 day-ahead Nash-Sutcliffe > 0.99) although much of this apparent skill is due to the strong seasonal cycle and the narrow natural range of variability at certain locations. 5 day-ahead forecasts upstream of Phnom Penh typically have 0.8 m error standard deviation, whereas below Phnom Penh the error is typically 0.3 m. The Coefficients of Persistence for 1 day-ahead forecasts are typically 0.4-0.8 and 5 day-ahead forecasts are typically 0.1-0.7. RFMMC uses a series of benchmarks to define a metric of Percentage Satisfactory forecasts. As the benchmarks were derived based on the average error, certain locations and lead-times consistently appear less satisfactory than others. Instead, different benchmarks were proposed and derived based on the 70th percentile of absolute error over the 13 yr period. There are no obvious trends in the Percentage of Satisfactory forecasts from 2002-2012, regardless of the benchmark chosen. Finally, when evaluated from a categorical "crossing above/not-crossing above flood level" perspective, the forecasts have a moderate probability of detection (48% at 1 day-ahead, 31% at 5 day-ahead) and false alarm rate (13% at 1 day-ahead, 74% at 5 days-ahead).

  12. Improving flood forecasting capability of physically based distributed hydrological models by parameter optimization

    NASA Astrophysics Data System (ADS)

    Chen, Y.; Li, J.; Xu, H.

    2016-01-01

    Physically based distributed hydrological models (hereafter referred to as PBDHMs) divide the terrain of the whole catchment into a number of grid cells at fine resolution and assimilate different terrain data and precipitation to different cells. They are regarded to have the potential to improve the catchment hydrological process simulation and prediction capability. In the early stage, physically based distributed hydrological models are assumed to derive model parameters from the terrain properties directly, so there is no need to calibrate model parameters. However, unfortunately the uncertainties associated with this model derivation are very high, which impacted their application in flood forecasting, so parameter optimization may also be necessary. There are two main purposes for this study: the first is to propose a parameter optimization method for physically based distributed hydrological models in catchment flood forecasting by using particle swarm optimization (PSO) algorithm and to test its competence and to improve its performances; the second is to explore the possibility of improving physically based distributed hydrological model capability in catchment flood forecasting by parameter optimization. In this paper, based on the scalar concept, a general framework for parameter optimization of the PBDHMs for catchment flood forecasting is first proposed that could be used for all PBDHMs. Then, with the Liuxihe model as the study model, which is a physically based distributed hydrological model proposed for catchment flood forecasting, the improved PSO algorithm is developed for the parameter optimization of the Liuxihe model in catchment flood forecasting. The improvements include adoption of the linearly decreasing inertia weight strategy to change the inertia weight and the arccosine function strategy to adjust the acceleration coefficients. This method has been tested in two catchments in southern China with different sizes, and the results show

  13. Forecast-based Integrated Flood Detection System for Emergency Response and Disaster Risk Reduction (Flood-FINDER)

    NASA Astrophysics Data System (ADS)

    Arcorace, Mauro; Silvestro, Francesco; Rudari, Roberto; Boni, Giorgio; Dell'Oro, Luca; Bjorgo, Einar

    2016-04-01

    Most flood prone areas in the globe are mainly located in developing countries where making communities more flood resilient is a priority. Despite different flood forecasting initiatives are now available from academia and research centers, what is often missing is the connection between the timely hazard detection and the community response to warnings. In order to bridge the gap between science and decision makers, UN agencies play a key role on the dissemination of information in the field and on capacity-building to local governments. In this context, having a reliable global early warning system in the UN would concretely improve existing in house capacities for Humanitarian Response and the Disaster Risk Reduction. For those reasons, UNITAR-UNOSAT has developed together with USGS and CIMA Foundation a Global Flood EWS called "Flood-FINDER". The Flood-FINDER system is a modelling chain which includes meteorological, hydrological and hydraulic models that are accurately linked to enable the production of warnings and forecast inundation scenarios up to three weeks in advance. The system is forced with global satellite derived precipitation products and Numerical Weather Prediction outputs. The modelling chain is based on the "Continuum" hydrological model and risk assessments produced for GAR2015. In combination with existing hydraulically reconditioned SRTM data and 1D hydraulic models, flood scenarios are derived at multiple scales and resolutions. Climate and flood data are shared through a Web GIS integrated platform. First validation of the modelling chain has been conducted through a flood hindcasting test case, over the Chao Phraya river basin in Thailand, using multi temporal satellite-based analysis derived for the exceptional flood event of 2011. In terms of humanitarian relief operations, the EO-based services of flood mapping in rush mode generally suffer from delays caused by the time required for their activation, programming, acquisitions and

  14. Development of Hydrometeorological Monitoring and Forecasting as AN Essential Component of the Early Flood Warning System:

    NASA Astrophysics Data System (ADS)

    Manukalo, V.

    2012-12-01

    Defining issue The river inundations are the most common and destructive natural hazards in Ukraine. Among non-structural flood management and protection measures a creation of the Early Flood Warning System is extremely important to be able to timely recognize dangerous situations in the flood-prone areas. Hydrometeorological information and forecasts are a core importance in this system. The primary factors affecting reliability and a lead - time of forecasts include: accuracy, speed and reliability with which real - time data are collected. The existing individual conception of monitoring and forecasting resulted in a need in reconsideration of the concept of integrated monitoring and forecasting approach - from "sensors to database and forecasters". Result presentation The Project: "Development of Flood Monitoring and Forecasting in the Ukrainian part of the Dniester River Basin" is presented. The project is developed by the Ukrainian Hydrometeorological Service in a conjunction with the Water Management Agency and the Energy Company "Ukrhydroenergo". The implementation of the Project is funded by the Ukrainian Government and the World Bank. The author is nominated as the responsible person for coordination of activity of organizations involved in the Project. The term of the Project implementation: 2012 - 2014. The principal objectives of the Project are: a) designing integrated automatic hydrometeorological measurement network (including using remote sensing technologies); b) hydrometeorological GIS database construction and coupling with electronic maps for flood risk assessment; c) interface-construction classic numerical database -GIS and with satellite images, and radar data collection; d) providing the real-time data dissemination from observation points to forecasting centers; e) developing hydrometeoroogical forecasting methods; f) providing a flood hazards risk assessment for different temporal and spatial scales; g) providing a dissemination of

  15. The Hurricane-Flood-Landslide Continuum: Forecasting Hurricane Effects at Landfall

    NASA Technical Reports Server (NTRS)

    Negri, A.; Golden, J. H.; Updike, R.

    2004-01-01

    Hurricanes, typhoons, and cyclones strike Central American, Caribbean, Southeast Asian and Pacific Island nations even more frequently than the U.S. The global losses of life and property from the floods, landslides and debris flows caused by cyclonic storms are staggering. One of the keys to reducing these losses, both in the U.S. and internationally, is to have better forecasts of what is about to happen from several hours to days before the event. Particularly in developing nations where science, technology and communication are limited, advance-warning systems can have great impact. In developing countries, warnings of even a few hours or days can mitigate or reduce catastrophic losses of life. With the foregoing needs in mind, we propose an initial project of three years total duration that will aim to develop and transfer a warning system for a prototype region in the Central Caribbean, specifically the islands of Puerto Rico and Hispanola. The Hurricane-Flood-Landslide Continuum will include satellite observations to track and nowcast dangerous levels of precipitation, atmospheric and hydrological models to predict near-future runoff, and streamflow changes in affected regions, and landslide models to warn when and where landslides and debris flows are imminent. Since surface communications are likely to be interrupted during these crises, the project also includes the capability to communicate disaster information via satellite to vital government officials in Puerto Rico, Haiti, and Dominican Republic.

  16. General characteristics of causes of urban flood damage and flood forecasting/warning system in Seoul, Korea Young-Il Moon1, 2, Jong-Suk Kim1, 2 1 Department of Civil Engineering, University of Seoul, Seoul 130-743, South Korea 2 Urban Flood Research Inst

    NASA Astrophysics Data System (ADS)

    Moon, Young-Il; Kim, Jong-Suk

    2015-04-01

    Due to rapid urbanization and climate change, the frequency of concentrated heavy rainfall has increased, causing urban floods that result in casualties and property damage. As a consequence of natural disasters that occur annually, the cost of damage in Korea is estimated to be over two billion US dollars per year. As interest in natural disasters increase, demands for a safe national territory and efficient emergency plans are on the rise. In addition to this, as a part of the measures to cope with the increase of inland flood damage, it is necessary to build a systematic city flood prevention system that uses technology to quantify flood risk as well as flood forecast based on both rivers and inland water bodies. Despite the investment and efforts to prevent landside flood damage, research and studies of landside-river combined hydro-system is at its initial stage in Korea. Therefore, the purpose of this research introduces the causes of flood damage in Seoul and shows a flood forecasting and warning system in urban streams of Seoul. This urban flood forecasting and warning system conducts prediction on flash rain or short-term rainfall by using radar and satellite information and performs prompt and accurate prediction on the inland flooded area and also supports synthetic decision-making for prevention through real-time monitoring. Although we cannot prevent damage from typhoons or localized heavy rain, we can minimize that damage with accurate and timely forecast and a prevention system. To this end, we developed a flood forecasting and warning system, so in case of an emergency there is enough time for evacuation and disaster control. Keywords: urban flooding, flood risk, inland-river system, Korea Acknowledgments This research was supported by a grant (13AWMP-B066744-01) from Advanced Water Management Research Program (AWMP) funded by Ministry of Land, Infrastructure and Transport of Korean government.

  17. Usefulness of satellite water vapour imagery in forecasting strong convection: A flash-flood case study

    NASA Astrophysics Data System (ADS)

    Georgiev, Christo G.; Kozinarova, Gergana

    Using a case study of a severe convective event as an example, a framework for interpreting 6.2 µm channel satellite imagery that enables to indicate upper-level conditioning of the convective environment is presented and discussed. In order to illustrate the approach, all convective cells during the summer of 2007 that produced precipitations over Bulgaria are considered. They are classified regarding the observed moisture pattern in mid-upper levels as well as the low-level conditions of air humidity and convergence of the flow. Water vapour (WV) images are used to study the evolution of the upper-level moist and dry structures. The proposed interpretation is that the role of the upper-level dry boundaries identified in the WV imagery as favoured areas for the initiation of deep moist convection cannot be understood (and hence cannot be forecasted accurately) by considering them in isolation from the dynamic rate at which they are maintained. The paper examines the 23 June 2006 flash flood in Sofia city as a case, in which the operational forecast of the National Institute of Meteorology and Hydrology of Bulgaria based on the mesoscale NWP model ALADIN underestimated the severity of the convective process. A comparison between the satellite water vapour imagery and the corresponding geopotential field of the dynamical tropopause, expressed in terms of potential vorticity (PV), shows an error in the performance of the ARPEGE operational numerical model. There is an obvious mismatch between the PV anomaly structure and the dry zone of the imagery. The forecast field shows underestimation of the tropopause height gradient and displacement of the PV anomaly to the southwest of the real position seen in the satellite image. It is concluded that the observed poor forecast is a result of the ARPEGE failure to treat correctly the interaction between the PV anomaly and the low-level warm anomaly.

  18. Improving flood forecasting capability of physically based distributed hydrological model by parameter optimization

    NASA Astrophysics Data System (ADS)

    Chen, Y.; Li, J.; Xu, H.

    2015-10-01

    Physically based distributed hydrological models discrete the terrain of the whole catchment into a number of grid cells at fine resolution, and assimilate different terrain data and precipitation to different cells, and are regarded to have the potential to improve the catchment hydrological processes simulation and prediction capability. In the early stage, physically based distributed hydrological models are assumed to derive model parameters from the terrain properties directly, so there is no need to calibrate model parameters, but unfortunately, the uncertanties associated with this model parameter deriving is very high, which impacted their application in flood forecasting, so parameter optimization may also be necessary. There are two main purposes for this study, the first is to propose a parameter optimization method for physically based distributed hydrological models in catchment flood forecasting by using PSO algorithm and to test its competence and to improve its performances, the second is to explore the possibility of improving physically based distributed hydrological models capability in cathcment flood forecasting by parameter optimization. In this paper, based on the scalar concept, a general framework for parameter optimization of the PBDHMs for catchment flood forecasting is first proposed that could be used for all PBDHMs. Then, with Liuxihe model as the study model, which is a physically based distributed hydrological model proposed for catchment flood forecasting, the improverd Particle Swarm Optimization (PSO) algorithm is developed for the parameter optimization of Liuxihe model in catchment flood forecasting, the improvements include to adopt the linear decreasing inertia weight strategy to change the inertia weight, and the arccosine function strategy to adjust the acceleration coefficients. This method has been tested in two catchments in southern China with different sizes, and the results show that the improved PSO algorithm could be

  19. Model Integration for Real-Time Flood Forecasting Inundation Mapping for Nashville Tributaries

    NASA Astrophysics Data System (ADS)

    Charley, W.; Moran, B.; LaRosa, J.

    2012-12-01

    In May of 2010, between 14 and 19 inches of rain fell on the Nashville metro area in two days, quickly overwhelming tributaries to the Cumberland River and causing wide-spread, serious flooding. Tractor-trailers and houses were seen floating down Mill Creek, a primary tributary in the south eastern area of Nashville. Twenty-six people died and over 2 billion dollars in damage occurred as a result of the flood. Since that time, several other significant rainfall events have occurred in the area. Emergency responders were unable to deliver aid or preventive measures to areas under threat of flooding (or under water) in time to reduce damages because they could not identify those areas far enough in advance of the floods. Nashville Metro Water, the National Weather Service, the US Geological Survey and the US Army Corps of Engineers established a joint venture to seek ways to better forecast short-term flood events in the region. One component of this effort was a pilot project to compute and display real time inundation maps for Mill Creek, a 108 square-mile basin to the south east of Nashville. HEC-RTS (Real-Time Simulation) was used to assimilate and integrate the hydrologic model HEC-HMS with the hydraulics model HEC-RAS and the inundation mapping program HEC-RAS Mapper. The USGS, along with the other agencies, installed additional precipitation and flow/stage gages in the area. Measurements are recorded every 5-30 minutes and are posted on the USGS NWIS database, which are downloaded by HEC-RTS. Using this data in combination with QPFs (Quantitative Precipitation Forecasts) from the NWS, HEC-RTS applies HEC-HMS and HEC-RAS to estimate current and forecast stage hydrographs. The peak stages are read by HEC-RAS Mapper to compute inundation depths for 6 by 6 foot grid cells. HEC-RTS displays the inundation on a high resolution MrSid aerial photo, along with subbasin boundary, street and various other layers. When a user zooms in and "mouses" over a cell, the

  20. Predictability of horizontal water vapor transport relative to precipitation: Enhancing situational awareness for forecasting western U.S. extreme precipitation and flooding

    USGS Publications Warehouse

    Lavers, David A.; Waliser, Duane E.; Ralph, F. Martin; Dettinger, Michael

    2016-01-01

    The western United States is vulnerable to socioeconomic disruption due to extreme winter precipitation and floods. Traditionally, forecasts of precipitation and river discharge provide the basis for preparations. Herein we show that earlier event awareness may be possible through use of horizontal water vapor transport (integrated vapor transport (IVT)) forecasts. Applying the potential predictability concept to the National Centers for Environmental Prediction global ensemble reforecasts, across 31 winters, IVT is found to be more predictable than precipitation. IVT ensemble forecasts with the smallest spreads (least forecast uncertainty) are associated with initiation states with anomalously high geopotential heights south of Alaska, a setup conducive for anticyclonic conditions and weak IVT into the western United States. IVT ensemble forecasts with the greatest spreads (most forecast uncertainty) have initiation states with anomalously low geopotential heights south of Alaska and correspond to atmospheric rivers. The greater IVT predictability could provide warnings of impending storminess with additional lead times for hydrometeorological applications.

  1. Predictability of horizontal water vapor transport relative to precipitation: Enhancing situational awareness for forecasting western U.S. extreme precipitation and flooding

    NASA Astrophysics Data System (ADS)

    Lavers, David A.; Waliser, Duane E.; Ralph, F. Martin; Dettinger, Michael D.

    2016-03-01

    The western United States is vulnerable to socioeconomic disruption due to extreme winter precipitation and floods. Traditionally, forecasts of precipitation and river discharge provide the basis for preparations. Herein we show that earlier event awareness may be possible through use of horizontal water vapor transport (integrated vapor transport (IVT)) forecasts. Applying the potential predictability concept to the National Centers for Environmental Prediction global ensemble reforecasts, across 31 winters, IVT is found to be more predictable than precipitation. IVT ensemble forecasts with the smallest spreads (least forecast uncertainty) are associated with initiation states with anomalously high geopotential heights south of Alaska, a setup conducive for anticyclonic conditions and weak IVT into the western United States. IVT ensemble forecasts with the greatest spreads (most forecast uncertainty) have initiation states with anomalously low geopotential heights south of Alaska and correspond to atmospheric rivers. The greater IVT predictability could provide warnings of impending storminess with additional lead times for hydrometeorological applications.

  2. A channel dynamics model for real-time flood forecasting

    USGS Publications Warehouse

    Hoos, A.B.; Koussis, A.D.; Beale, G.O.

    1989-01-01

    A new channel dynamics scheme ASPIRE (alternative system predictor in real time), designed specifically for real-time river flow forecasting, is introduced to reduce uncertainty in the forecast. ASPIRE is a storage routing model that limits the influence of catchment model forecast errors to the downstream station closest to the catchment. Comparisons with the Muskingum routing scheme in field tests suggest that the ASPIRE scheme can provide more accurate forecasts, probably because discharge observations are used to a maximum advantage and routing reaches (and model errors in each reach) are uncoupled. Using ASPIRE in conjunction with the Kalman filter did not improve forecast accuracy relative to a deterministic updating procedure. Theoretical analysis suggests that this is due to a large process noise to measurement noise ratio. -Authors

  3. Observed and forecast flood-inundation mapping application-A pilot study of an eleven-mile reach of the White River, Indianapolis, Indiana

    USGS Publications Warehouse

    Kim, Moon H.; Morlock, Scott E.; Arihood, Leslie D.; Kiesler, James L.

    2011-01-01

    Near-real-time and forecast flood-inundation mapping products resulted from a pilot study for an 11-mile reach of the White River in Indianapolis. The study was done by the U.S. Geological Survey (USGS), Indiana Silver Jackets hazard mitigation taskforce members, the National Weather Service (NWS), the Polis Center, and Indiana University, in cooperation with the City of Indianapolis, the Indianapolis Museum of Art, the Indiana Department of Homeland Security, and the Indiana Department of Natural Resources, Division of Water. The pilot project showed that it is technically feasible to create a flood-inundation map library by means of a two-dimensional hydraulic model, use a map from the library to quickly complete a moderately detailed local flood-loss estimate, and automatically run the hydraulic model during a flood event to provide the maps and flood-damage information through a Web graphical user interface. A library of static digital flood-inundation maps was created by means of a calibrated two-dimensional hydraulic model. Estimated water-surface elevations were developed for a range of river stages referenced to a USGS streamgage and NWS flood forecast point colocated within the study reach. These maps were made available through the Internet in several formats, including geographic information system, Keyhole Markup Language, and Portable Document Format. A flood-loss estimate was completed for part of the study reach by using one of the flood-inundation maps from the static library. The Federal Emergency Management Agency natural disaster-loss estimation program HAZUS-MH, in conjunction with local building information, was used to complete a level 2 analysis of flood-loss estimation. A Service-Oriented Architecture-based dynamic flood-inundation application was developed and was designed to start automatically during a flood, obtain near real-time and forecast data (from the colocated USGS streamgage and NWS flood forecast point within the study reach

  4. Initial assessment of a multi-model approach to spring flood forecasting in Sweden

    NASA Astrophysics Data System (ADS)

    Olsson, J.; Uvo, C. B.; Foster, K.; Yang, W.

    2015-06-01

    Hydropower is a major energy source in Sweden and proper reservoir management prior to the spring flood onset is crucial for optimal production. This requires useful forecasts of the accumulated discharge in the spring flood period (i.e. the spring-flood volume, SFV). Today's SFV forecasts are generated using a model-based climatological ensemble approach, where time series of precipitation and temperature from historical years are used to force a calibrated and initialised set-up of the HBV model. In this study, a number of new approaches to spring flood forecasting, that reflect the latest developments with respect to analysis and modelling on seasonal time scales, are presented and evaluated. Three main approaches, represented by specific methods, are evaluated in SFV hindcasts for three main Swedish rivers over a 10-year period with lead times between 0 and 4 months. In the first approach, historically analogue years with respect to the climate in the period preceding the spring flood are identified and used to compose a reduced ensemble. In the second, seasonal meteorological ensemble forecasts are used to drive the HBV model over the spring flood period. In the third approach, statistical relationships between SFV and the large-sale atmospheric circulation are used to build forecast models. None of the new approaches consistently outperform the climatological ensemble approach, but for specific locations and lead times improvements of 20-30 % are found. When combining all forecasts in a weighted multi-model approach, a mean improvement over all locations and lead times of nearly 10 % was indicated. This demonstrates the potential of the approach and further development and optimisation into an operational system is ongoing.

  5. Decision-relevant early-warning thresholds for ensemble flood forecasting systems

    NASA Astrophysics Data System (ADS)

    Stephens, Liz; Pappenberger, Florian; Cloke, Hannah; Alfieri, Lorenzo

    2014-05-01

    Over and under warning of potential future floods is problematic for decision-making, and could ultimately lead to trust being lost in the forecasts. The use of ensemble flood forecasting systems for early warning therefore requires a consideration of how to determine and implement decision-relevant thresholds for flood magnitude and probability. This study uses a year's worth of hindcasts from the Global Flood Awareness System (GloFAS) to explore the sensitivity of the warning system to the choice of threshold. We use a number of different methods for choosing these thresholds, building on current approaches that use model climatologies to determine the critical flow magnitudes, to those that can provide 'first guesses' of potential impacts (through integration with global-scale inundation mapping), as well as methods that could incorporate resource limitations.

  6. Calibrating the FloodMap Model to Improve the Integrated HydroProg-FloodMap Real-Time Multimodel Ensemble System for Forecasting Inundation

    NASA Astrophysics Data System (ADS)

    Świerczyńska, M. G.; Yu, D.; Miziński, B.; Niedzielski, T.; Latocha, A.; Parzóch, K.

    2015-12-01

    HydroProg is a novel system (research project no. 2011/01/D/ST10/04171 of the National Science Centre of Poland) which produces early warnings against peak flows. It works in real time and uses outputs from multiple hydrologic models to compute the multimodel ensemble prediction of riverflow, i.e. the hydrograph. The system has been experimentally implemented for the upper Nysa Kłodzka river basin (SW Poland). We also integrated the system with the well-established hydrodynamic model, known as FloodMap, to forecast flood inundation (HydroProg computes hydrograph prediction and FloodMap maps the hydrograph prognosis into terrain). The HydroProg-FloodMap solution works at five sites. The real-time experimental forecasts are available at http://www.klodzko.hydroprog.uni.wroc.pl/. The FloodMap model is calibrated at each site on a basis of the available Digital Elevation Model (DEM) or Digital Surface Model (DSM) and hydrograph data. However, since the launch of the HydroProg-FloodMap solution no true data on inundation has been available to check the model outputs against observation, and hence to redo the calibration if necessary. If we consider past events, which occurred before the launch of the system, there exists the observed inundation map for the Żelazno site. It was produced by geomorphological mapping of consequences of the flood in June 2009. The aim of the study is therefore to use this specific data set for a single site, calibrate the FloodMap model using inundation data, and identify the physical-geographical characteristics of terrain under which we are allowed to extrapolate the parameters to the other four sites. We conducted a spatial analysis of land use (based on Polish national database of topographical objects) and topography (based on DEM/DSM from the Light Detection and Ranging (LiDAR)) in order to identify similarities of the studied areas and hence to improve the estimates of the Manning's roughness coefficient.

  7. Serbian Torrent Flood Defense Practice - Modeling, observation, forecasting and impact

    NASA Astrophysics Data System (ADS)

    Gavrilovic, Zoran; Stefnovic, Milutin

    2010-05-01

    Many areas in Europe have been affected by an increasing number of severe flood events in the past few years. Because of these floods numerous measures to improve the organization of disaster management have been taken. This includes the preparation of specific alarm plans for flood disaster events. Serbian Torrent Flood Defense methodology, combines observation by radar meteorology, torrential hydrology and new GIS techniques to enable quick determination and assessment of the detected situation in order to provide a sufficient time for the flood defense system to be put in operation. Alarm plans can be seen as one corner stone of disaster management but their practical use can still be optimized. For this end aims to support the risk analysis and risk communication process by improving the availability, reliability and communicability of hazard maps and alarm plans. The main focus will be on levels of population protection and critical infrastructure protection in respect to natural hazards. Paper presents Obtained results in the field of torrent defense in Serbia. Key words: Hydrology, Torrent Flood Analysis, Meteorology, Flood Defense

  8. Use of Precipitation Data Derived from Satellite Data for Hydrologic Modeling: Flood Forecasting and Snowpack Monitoring

    NASA Astrophysics Data System (ADS)

    Artan, G. A.; Shrestha, M.; Tokar, S.; Rowland, J.; Verdin, J. P.; Amer, S.

    2012-12-01

    Floods are the most common and widespread climate-related hazards throughout the globe. Most human losses due to floods occur in the tropical regions of Africa, Asia, and Central America. The use of flood forecasting can reduce the death toll associated with floods. Recent research suggests that the frequency and severity of extreme rainfall events will increase; therefore, there is an urgent need for timely flood forecasting. In those tropical regions, a paucity of the ground-based precipitation data collection networks and the lack of data sharing across international borders for trans-boundary basins have made it impractical to use traditional flood forecasting that relies on station-measured precipitation data. Precipitation estimated from satellite data offers an effective means for calculating areal precipitation estimates in sparsely gauged regions. Because of the apparent uncertainty associated with satellite-based precipitation estimates, the use of such data in hydrologic modeling has been limited in the past. We will present results from our research on the utility of precipitation estimates from satellite data for flood forecasting and snowpack monitoring purposes. We found that remotely sensed precipitation data in combination with distributed hydrologic models can play an important role in early warning and monitoring of floods. For large basins the results of hydrologic models forced with satellite-based precipitation were comparable those the stream flow simulated stream using precipitation measured with ground-based networks. Snowpack simulated with precipitation estimates from satellite data underestimated the snow water content compared with snow water recorded by the SNOTEL network or simulated by SNODAS system; nevertheless, the estimates were found to be useful in mapping the snowpack.

  9. Local flood forecasting using guided model construction, data assimilation and web interfaces

    NASA Astrophysics Data System (ADS)

    Smith, Paul; Beven, Keith

    2013-04-01

    An important aspect of improving resilience to flooding is the provision of timely warnings to flood sensitive locations thus allowing mitigating measures to be implemented. For specific locations such small communities (often in head water catchments) or river side factories the ability of traditional centralised forecasting systems to provide timely & accurate forecasts may be challenged. This is due in part to the finite resources of monitoring agencies which results in courser spatial scales of model and data collection then may be required for the generation of accurate forecasts. One strategy to improve flood resilience at such locations is to install adequate telemetered monitoring equipment; generally a water level sensor and a rain gauge; which allows the construction of a local flood forecast. In this presentation we outline a methodology for providing detailed and location specific forecasts which can be computed either 'on-' or `off-site'. The basis of this is a guided model building process which incorporates both data assimilation and representation of the forecast uncertainty. The process requires the modeller to make only a few choices thus allowing rapid model deployment and revision. To be of use such forecasts require must be made available in real time and updated frequently; maybe every five minutes. Traditional practices in issuing warnings dependent on expert interpretation must therefore be altered so that those at the site of interest become their own `experts'. To aid in this a web interface, showing both the predictions and past performance of the model, designed to encourage realistic interpretation of the forecasts and their uncertainties is presented. This tool and the guided model build are outlined using case studies based in the North West of the UK.

  10. Hourly runoff forecasting for flood risk management: Application of various computational intelligence models

    NASA Astrophysics Data System (ADS)

    Badrzadeh, Honey; Sarukkalige, Ranjan; Jayawardena, A. W.

    2015-10-01

    Reliable river flow forecasts play a key role in flood risk mitigation. Among different approaches of river flow forecasting, data driven approaches have become increasingly popular in recent years due to their minimum information requirements and ability to simulate nonlinear and non-stationary characteristics of hydrological processes. In this study, attempts are made to apply four different types of data driven approaches, namely traditional artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), wavelet neural networks (WNN), and, hybrid ANFIS with multi resolution analysis using wavelets (WNF). Developed models applied for real time flood forecasting at Casino station on Richmond River, Australia which is highly prone to flooding. Hourly rainfall and runoff data were used to drive the models which have been used for forecasting with 1, 6, 12, 24, 36 and 48 h lead-time. The performance of models further improved by adding an upstream river flow data (Wiangaree station), as another effective input. All models perform satisfactorily up to 12 h lead-time. However, the hybrid wavelet-based models significantly outperforming the ANFIS and ANN models in the longer lead-time forecasting. The results confirm the robustness of the proposed structure of the hybrid models for real time runoff forecasting in the study area.

  11. Validating quantitative precipitation forecast for the Flood Meteorological Office, Patna region during 2011-2014

    NASA Astrophysics Data System (ADS)

    Giri, R. K.; Panda, Jagabandhu; Rath, Sudhansu S.; Kumar, Ravindra

    2016-05-01

    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.

  12. Validating quantitative precipitation forecast for the Flood Meteorological Office, Patna region during 2011-2014

    NASA Astrophysics Data System (ADS)

    Giri, R. K.; Panda, Jagabandhu; Rath, Sudhansu S.; Kumar, Ravindra

    2016-06-01

    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.

  13. Calibrating the FloodMap model based on geomorphological fieldwork and terrain analysis to improve the integrated HydroProg-FloodMap system for forecasting inundation

    NASA Astrophysics Data System (ADS)

    Witek, Matylda; Remisz, Joanna; Swierczynska, Malgorzata; Borowicz, Dorota; Parzoch, Krzysztof; Yu, Dapeng

    2016-04-01

    HydroProg is a novel system (research project no. 2011/01/D/ST10/04171 of the National Science Centre of Poland) which produces early warnings against high flows. The system has been experimentally implemented for the upper Nysa Klodzka river basin (SW Poland). HydroProg is also integrated with the well-established hydrodynamic model known as FloodMap. The aim of this integration is to forecast flood inundation (HydroProg is used for computing hydrograph prediction, while FloodMap is utilized for mapping the hydrograph prognosis into spatial domain). The HydroProg-FloodMap solution currently works at four sites (Szalejow Dolny, Zelazno, Gorzuchow and Krosnowice) situated within the Nysa Klodzka river basin in the Southwestern Poland. The FloodMap model has been already calibrated for Zelazno (the Biala Ladecka river), and now we want to obtain model parameters for Gorzuchow (the Scinawka river). We carry out several simulations from the FloodMap model at this site, based on historical and recent flow records, to check where potential inundation may take place. Using the 1-metre LIDAR (Light Detection and Ranging) data we identify old channels of the Scinawka river in this area. In addition, we carried out several field campaigns with the unmanned aerial vehicle (UAV) to produce digital surface model (DSM) which can show morphological changes within an alluvial river valley. This can be perceived as an evidence of past inundations. Both the LIDAR mode and DSM obtained using UAV appeared to be not accurate enough to fully reconstruct the pattern of paleo-fluvial relief. Hence, we additionally performed geodetic survey using a self-reducting theodolite Dhalta 010A. Moreover, to confirm the pattern of the paleochannel of the Scinawka river, paleohydraulic analysis is performed. Finally, calibration of the FloodMap model for the Gorzuchow site becomes possible due to access to newly-acquired data on past inundation episodes.

  14. Status and Future of a Real-time Global Flood Detection and Forecasting System Using Satellite Rainfall Information

    NASA Astrophysics Data System (ADS)

    Adler, R. F.; Wu, H.; Hong, Y.; Policelli, F.; Pierce, H.

    2011-12-01

    Over the last several years a Global Flood Monitoring System (GFMS) has been running in real-time to detect the occurrence of floods (see trmm.gsfc.nasa.gov and click on "Floods and Landslides"). The system uses 3-hr resolution composite rainfall analyses (TRMM Multi-satellite Precipitation Analysis [TMPA]) as input into a hydrological model that calculates water depth at each grid (at 0.25 degree latitude-longitude) over the tropics and mid-latitudes. These calculations can provide information useful to national and international agencies in understanding the location, intensity, timeline and impact on populations of these significant hazard events. The status of these flood calculations will be shown by case study examples and a statistical comparison against a global flood event database. The validation study indicates that results improve with longer duration (> 3 days) floods and that the statistics are impacted by the presence of dams, which are not accounted for in the model calculations. Limitations in the flood calculations that are related to the satellite rainfall estimates include space and time resolution limitations and underestimation of shallow orographic and monsoon system rainfall. The current quality of these flood estimations is at the level of being useful, but there is a potential for significant improvement, mainly through improved and more timely satellite precipitation information and improvement in the hydrological models being used. NASA's Global Precipitation Measurement (GPM) program should lead to better precipitation analyses utilizing space-time interpolations that maintain accurate intensity distributions along with methods to disaggregate the rain information research should lead to improved rain estimation for shallow, orographic rainfall systems and some types of monsoon rainfall, a current problem area for satellite rainfall. Higher resolution flood models with accurate routing and regional calibration, and the use of satellite

  15. Development of roughness updating based on artificial neural network in a river hydraulic model for flash flood forecasting

    NASA Astrophysics Data System (ADS)

    Fu, J. C.; Hsu, M. H.; Duann, Y.

    2016-02-01

    Flood is the worst weather-related hazard in Taiwan because of steep terrain and storm. The tropical storm often results in disastrous flash flood. To provide reliable forecast of water stages in rivers is indispensable for proper actions in the emergency response during flood. The river hydraulic model based on dynamic wave theory using an implicit finite-difference method is developed with river roughness updating for flash flood forecast. The artificial neural network (ANN) is employed to update the roughness of rivers in accordance with the observed river stages at each time-step of the flood routing process. Several typhoon events at Tamsui River are utilized to evaluate the accuracy of flood forecasting. The results present the adaptive n-values of roughness for river hydraulic model that can provide a better flow state for subsequent forecasting at significant locations and longitudinal profiles along rivers.

  16. Improving flash flood forecasting with distributed hydrological model by parameter optimization

    NASA Astrophysics Data System (ADS)

    Chen, Yangbo

    2016-04-01

    In China, flash food is usually regarded as flood occured in small and medium sized watersheds with drainage area less than 200 km2, and is mainly induced by heavy rains, and occurs in where hydrological observation is lacked. Flash flood is widely observed in China, and is the flood causing the most casualties nowadays in China. Due to hydrological data scarcity, lumped hydrological model is difficult to be employed for flash flood forecasting which requires lots of observed hydrological data to calibrate model parameters. Physically based distributed hydrological model discrete the terrain of the whole watershed into a number of grid cells at fine resolution, assimilate different terrain data and precipitation to different cells, and derive model parameteris from the terrain properties, thus having the potential to be used in flash flood forecasting and improving flash flood prediction capability. In this study, the Liuxihe Model, a physically based distributed hydrological model mainly proposed for watershed flood forecasting is employed to simulate flash floods in the Ganzhou area in southeast China, and models have been set up in 5 watersheds. Model parameters have been derived from the terrain properties including the DEM, the soil type and land use type, but the result shows that the flood simulation uncertainty is high, which may be caused by parameter uncertainty, and some kind of uncertainty control is needed before the model could be used in real-time flash flood forecastin. Considering currently many Chinese small and medium sized watersheds has set up hydrological observation network, and a few flood events could be collected, it may be used for model parameter optimization. For this reason, an automatic model parameter optimization algorithm using Particle Swam Optimization(PSO) is developed to optimize the model parameters, and it has been found that model parameters optimized even only with one observed flood events could largely reduce the flood

  17. Improving the accuracy of flood forecasting with transpositions of ensemble NWP rainfall fields considering orographic effects

    NASA Astrophysics Data System (ADS)

    Yu, Wansik; Nakakita, Eiichi; Kim, Sunmin; Yamaguchi, Kosei

    2016-08-01

    The use of meteorological ensembles to produce sets of hydrological predictions increased the capability to issue flood warnings. However, space scale of the hydrological domain is still much finer than meteorological model, and NWP models have challenges with displacement. The main objective of this study to enhance the transposition method proposed in Yu et al. (2014) and to suggest the post-processing ensemble flood forecasting method for the real-time updating and the accuracy improvement of flood forecasts that considers the separation of the orographic rainfall and the correction of misplaced rain distributions using additional ensemble information through the transposition of rain distributions. In the first step of the proposed method, ensemble forecast rainfalls from a numerical weather prediction (NWP) model are separated into orographic and non-orographic rainfall fields using atmospheric variables and the extraction of topographic effect. Then the non-orographic rainfall fields are examined by the transposition scheme to produce additional ensemble information and new ensemble NWP rainfall fields are calculated by recombining the transposition results of non-orographic rain fields with separated orographic rainfall fields for a generation of place-corrected ensemble information. Then, the additional ensemble information is applied into a hydrologic model for post-flood forecasting with a 6-h interval. The newly proposed method has a clear advantage to improve the accuracy of mean value of ensemble flood forecasting. Our study is carried out and verified using the largest flood event by typhoon 'Talas' of 2011 over the two catchments, which are Futatsuno (356.1 km2) and Nanairo (182.1 km2) dam catchments of Shingu river basin (2360 km2), which is located in the Kii peninsula, Japan.

  18. Satellite-supported flood forecasting in river networks: A real case study

    NASA Astrophysics Data System (ADS)

    García-Pintado, Javier; Mason, David C.; Dance, Sarah L.; Cloke, Hannah L.; Neal, Jeff C.; Freer, Jim; Bates, Paul D.

    2015-04-01

    Satellite-based (e.g., Synthetic Aperture Radar [SAR]) water level observations (WLOs) of the floodplain can be sequentially assimilated into a hydrodynamic model to decrease forecast uncertainty. This has the potential to keep the forecast on track, so providing an Earth Observation (EO) based flood forecast system. However, the operational applicability of such a system for floods developed over river networks requires further testing. One of the promising techniques for assimilation in this field is the family of ensemble Kalman (EnKF) filters. These filters use a limited-size ensemble representation of the forecast error covariance matrix. This representation tends to develop spurious correlations as the forecast-assimilation cycle proceeds, which is a further complication for dealing with floods in either urban areas or river junctions in rural environments. Here we evaluate the assimilation of WLOs obtained from a sequence of real SAR overpasses (the X-band COSMO-Skymed constellation) in a case study. We show that a direct application of a global Ensemble Transform Kalman Filter (ETKF) suffers from filter divergence caused by spurious correlations. However, a spatially-based filter localization provides a substantial moderation in the development of the forecast error covariance matrix, directly improving the forecast and also making it possible to further benefit from a simultaneous online inflow error estimation and correction. Additionally, we propose and evaluate a novel along-network metric for filter localization, which is physically-meaningful for the flood over a network problem. Using this metric, we further evaluate the simultaneous estimation of channel friction and spatially-variable channel bathymetry, for which the filter seems able to converge simultaneously to sensible values. Results also indicate that friction is a second order effect in flood inundation models applied to gradually varied flow in large rivers. The study is not conclusive

  19. Ensemble Flood Forecasting in Africa: A Feasibility Study in the Juba-Shabelle River Basin

    NASA Astrophysics Data System (ADS)

    Thiemig, Vera; Pappenberger, Florian; Thielen, Jutta; Gadain, Hussein; de Roo, Ad; Bodis, Katalin; Del Medico, Mauro; Muthusi, Flavian

    2010-05-01

    Over the last years the African continent has increasingly experienced severe transnational floods that caused substantial socio-economic losses and put enormous pressure on countries across the continent. The planning, coordination and realization of flood prevention, protection and mitigation measures require time, which can be provided through an early flood prediction. In this paper, the transferability of the European Flood Alert System (EFAS) to equatorial African basins is assessed. EFAS achieves early flood warnings for large to medium-size river basins with lead times of 10 days. This is based on probabilistic weather forecasts, the exceedance of alert thresholds and persistence indicators. These methodologies, having been tested for different events and time scales in mid-latitude basins in Europe, are being applied in this paper to the Juba-Shabella river basin, shared between Ethopia and Somalia. A variety of different meteorological data sources have been used, including ERA-40 and CHARM for the calculation of climatologies. The unique re-forecasts of the current operational ECMWF model provided hindcasts of historic flood events. The results show that for the selected flood events a detection rate of 85% was achieved, with a high accuracy in terms of timing and magnitude.

  20. The suitability of remotely sensed soil moisture for improving operational flood forecasting

    NASA Astrophysics Data System (ADS)

    Wanders, N.; Karssenberg, D.; de Roo, A.; de Jong, S. M.; Bierkens, M. F. P.

    2014-06-01

    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 (LISFLOOD) for flood predictions with lead times of up to 10 days. For this study, satellite-derived soil moisture from ASCAT (Advanced SCATterometer), AMSR-E (Advanced Microwave Scanning Radiometer - Earth Observing System) and SMOS (Soil Moisture and Ocean Salinity) is assimilated into the LISFLOOD model for the Upper Danube Basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into the hydrological model, an ensemble Kalman filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure increased performance of the EnKF. For the validation, additional discharge observations not used in the EnKF are used as an independent validation data set. 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 35%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of baseflows 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 observational data is assimilated into

  1. Feedbacks of the use of two uncertainty assessment techniques by operational flood forecasters

    NASA Astrophysics Data System (ADS)

    Berthet, Lionel; Bourgin, François; Perrin, Charles; Andréassian, Vazken

    2014-05-01

    In 2013, forecasters working in the French flood forecasting services tested two automatic techniques for forecast uncertainty assessment in their operational context. These techniques were expected to characterize predictive uncertainty, and provide forecasters with confidence intervals (for example, 80% central intervals) associated to their forecasts (forecast intervals) and estimates of the probability of exceeding some warning thresholds. The first technique was the quantile regression method (Weerts et al., 2011), while the second one was a data-based and non-parametric method. These techniques were applied to a forecasting rainfall-runoff model (GRP) and to two hydraulic models (HYDRA and MASCARET). Both techniques are based on the statistical analysis of past forecast errors. In the case of the hydrological model, the past forecast errors were estimated using a 'perfect' rainfall scenario (corresponding to a posteriori observed rainfall). The forecasters pointed out that the approaches are simple enough to be easily understood, which was stressed as a clear advantage over "black-box" tools. The feedbacks showed that many operational forecasters enjoyed the fact that these automatic assessments brought out the qualities and the defaults of the model (e.g., bias) of which they were aware... or not. Therefore these results clearly helped them to better know the limits of their models. The forecast intervals (80%) produced by the methods were often found too large by the forecasters to be very helpful in their decision-making. Moreover, forecasters thought they were able to give narrower intervals (still being reliable) based on their experience. The methods were considered as providing very good starting points by the forecasters, encouraging them to build their own forecast intervals. Forecasters use the probability of exceeeding a threshold as one piece of information (among others) to decide whether to issue a warning or not. It is considered as very

  2. The Ensemble Framework for Flash Flood Forecasting: Global and CONUS Applications

    NASA Astrophysics Data System (ADS)

    Flamig, Z.; Vergara, H. J.; Clark, R. A.; Gourley, J. J.; Kirstetter, P. E.; Hong, Y.

    2015-12-01

    The Ensemble Framework for Flash Flood Forecasting (EF5) is a distributed hydrologic modeling framework combining water balance components such as the Variable Infiltration Curve (VIC) and Sacramento Soil Moisture Accounting (SAC-SMA) with kinematic wave channel routing. The Snow-17 snow pack model is included as an optional component in EF5 for basins where snow impacts are important. EF5 also contains the Differential Evolution Adaptive Metropolis (DREAM) parameter estimation scheme for model calibration. EF5 is made to be user friendly and as such training has been developed into a weeklong course. This course has been tested in modeling workshops held in Namibia and Mexico. EF5 has also been applied to specialized applications including the Flooded Locations and Simulated Hydrographs (FLASH) project. FLASH aims to provide flash flood monitoring and forecasting over the CONUS using Multi-Radar Multi-Sensor precipitation forcing. Using the extensive field measurements database from the 10,000 USGS measurement locations across the CONUS, parameters were developed for the kinematic wave routing in FLASH. This presentation will highlight FLASH performance over the CONUS on basins less than 1,000 km2 and discuss the development of simulated streamflow climatology over the CONUS for data mining applications. A global application of EF5 has also been developed using satellite based precipitation measurements combined with numerical weather prediction forecasts to produce flood and impact forecasts. The performance of this global system will be assessed and future plans detailed.

  3. Integration of Remote Sensing Data In Operational Flood Forecast In Southwest Germany

    NASA Astrophysics Data System (ADS)

    Bach, H.; Appel, F.; Schulz, W.; Merkel, U.; Ludwig, R.; Mauser, W.

    Methods to accurately assess and forecast flood discharge are mandatory to minimise the impact of hydrological hazards. However, existing rainfall-runoff models rarely accurately consider the spatial characteristics of the watershed, which is essential for a suitable and physics-based description of processes relevant for runoff formation. Spatial information with low temporal variability like elevation, slopes and land use can be mapped or extracted from remote sensing data. However, land surface param- eters of high temporal variability, like soil moisture and snow properties are hardly available and used in operational forecasts. Remote sensing methods can improve flood forecast by providing information on the actual water retention capacities in the watershed and facilitate the regionalisation of hydrological models. To prove and demonstrate this, the project 'InFerno' (Integration of remote sensing data in opera- tional water balance and flood forecast modelling) has been set up, funded by DLR (50EE0053). Within InFerno remote sensing data (optical and microwave) are thor- oughly processed to deliver spatially distributed parameters of snow properties and soil moisture. Especially during the onset of a flood this information is essential to estimate the initial conditions of the model. At the flood forecast centres of 'Baden- Württemberg' and 'Rheinland-Pfalz' (Southwest Germany) the remote sensing based maps on soil moisture and snow properties will be integrated in the continuously op- erated water balance and flood forecast model LARSIM. The concept is to transfer the developed methodology from the Neckar to the Mosel basin. The major challenges lie on the one hand in the implementation of algorithms developed for a multisensoral synergy and the creation of robust, operationally applicable remote sensing products. On the other hand, the operational flood forecast must be adapted to make full use of the new data sources. In the operational phase of the

  4. High-resolution simulation and forecasting of Jeddah floods using WRF version 3.5

    NASA Astrophysics Data System (ADS)

    Deng, L.; McCabe, M. F.; Stenchikov, G. L.; Evans, J. P.; Kucera, P. A.

    2013-12-01

    Modeling flash flood events in arid environments is a difficult but important task that has impacts on both water resource related issues and also emergency management and response. The challenge is often related to adequately describing the precursor intense rainfall events that cause these flood responses, as they are generally poorly simulated and forecast. Jeddah, the second largest city in the Kingdom of Saudi Arabia, has suffered from a number of flash floods over the last decade, following short-intense rainfall events. The research presented here focuses on examining four historic Jeddah flash floods (Nov. 25-26 2009, Dec. 29-30 2010, Jan. 14-15 2011 and Jan. 25-26 2011) and investigates the feasibility of using numerical weather prediction models to achieve a more realistic simulation of these flood-producing rainfall events. The Weather Research and Forecasting (WRF) model (version 3.5) is used to simulate precipitation and meteorological conditions via a high-resolution inner domain (1-km) around Jeddah. A range of different convective closure and microphysics parameterization, together with high-resolution (4-km) sea surface temperature data are employed. Through examining comparisons between the WRF model output and in-situ, radar and satellite data, the characteristics and mechanism producing the extreme rainfall events are discussed and the capacity of the WRF model to accurately forecast these rainstorms is evaluated.

  5. Impact of Different Data Assimilation Strategies for SMOS Observations on Flood Forecasting Accuracy

    NASA Astrophysics Data System (ADS)

    Pauwels, V. R. N.; Verhoest, N.; Lievens, H.; Martens, B.; van Den Berg, M. J.; Al-Bitar, A.; Merlin, O.; Kumar Tomer, S.; Cabot, F.; Kerr, Y. H.; Pan, M.; Wood, E. F.; Drusch, M.; Hendricks Franssen, H. J.; Vereecken, H.; De Lannoy, G. J. M.; Dumedah, G.; Walker, J. P.

    2014-12-01

    During the last decade, significant efforts have been directed towards establishing and improving flood forecasting systems for large river basins. Examples include the European Flood Alert System, and the Bureau of Meteorology Flood Warning Systems in Australia. A number of attempts have also been made to increase the accuracy of the forecasted flood volumes from these systems. One attractive way in which this can be achieved is to use remotely sensed surface soil moisture contents to constrain the hydrologic model predictions. Satellite missions such as SMOS can provide very useful information on the wetness conditions of these basins, which in many cases is an important initial condition for discharge generation. Assimilation of these satellite data is thus a logical way to proceed. We will present results from two different assimilation strategies for the Murray-Darling basin in Australia using the Variable Infiltration Capacity (VIC) model. Firstly, the SMOS soil moisture data are assimilated into the hydrologic model at their original spatial resolution. As the spatial resolution of the remote sensing data (25 km) is coarser than the spatial resolution of the model (10 km), a multiscale data assimilation algorithm needs to be implemented. Secondly, the SMOS data are downscaled to the model resolution, prior to their assimilation. In this presentation, the impact of the assimilation of both products on the accuracy of the forecasted flood volumes is assessed.

  6. Verification of National Weather Service spot forecasts using surface observations

    NASA Astrophysics Data System (ADS)

    Lammers, Matthew Robert

    Software has been developed to evaluate National Weather Service spot forecasts issued to support prescribed burns and early-stage wildfires. Fire management officials request spot forecasts from National Weather Service Weather Forecast Offices to provide detailed guidance as to atmospheric conditions in the vicinity of planned prescribed burns as well as wildfires that do not have incident meteorologists on site. This open source software with online display capabilities is used to examine an extensive set of spot forecasts of maximum temperature, minimum relative humidity, and maximum wind speed from April 2009 through November 2013 nationwide. The forecast values are compared to the closest available surface observations at stations installed primarily for fire weather and aviation applications. The accuracy of the spot forecasts is compared to those available from the National Digital Forecast Database (NDFD). Spot forecasts for selected prescribed burns and wildfires are used to illustrate issues associated with the verification procedures. Cumulative statistics for National Weather Service County Warning Areas and for the nation are presented. Basic error and accuracy metrics for all available spot forecasts and the entire nation indicate that the skill of the spot forecasts is higher than that available from the NDFD, with the greatest improvement for maximum temperature and the least improvement for maximum wind speed.

  7. Climate forecasts in disaster management: Red Cross flood operations in West Africa, 2008.

    PubMed

    Braman, Lisette Martine; van Aalst, Maarten Krispijn; Mason, Simon J; Suarez, Pablo; Ait-Chellouche, Youcef; Tall, Arame

    2013-01-01

    In 2008, the International Federation of Red Cross and Red Crescent Societies (IFRC) used a seasonal forecast for West Africa for the first time to implement an Early Warning, Early Action strategy for enhanced flood preparedness and response. Interviews with disaster managers suggest that this approach improved their capacity and response. Relief supplies reached flood victims within days, as opposed to weeks in previous years, thereby preventing further loss of life, illness, and setbacks to livelihoods, as well as augmenting the efficiency of resource use. This case demonstrates the potential benefits to be realised from the use of medium-to-long-range forecasts in disaster management, especially in the context of potential increases in extreme weather and climate-related events due to climate variability and change. However, harnessing the full potential of these forecasts will require continued effort and collaboration among disaster managers, climate service providers, and major humanitarian donors. PMID:23066755

  8. Improvement of operational flood forecasting through the assimilation of satellite observations and multiple river flow data

    NASA Astrophysics Data System (ADS)

    Castelli, Fabio; Ercolani, Giulia

    2016-05-01

    Data assimilation has the potential to improve flood forecasting. However, it is rarely employed in distributed hydrologic models for operational predictions. In this study, we present variational assimilation of river flow data at multiple locations and of land surface temperature (LST) from satellite in a distributed hydrologic model that is part of the operational forecasting chain for the Arno river, in central Italy. LST is used to estimate initial condition of soil moisture through a coupled surface energy/water balance scheme. We present here several hindcast experiments to assess the performances of the assimilation system. The results show that assimilation can significantly improve flood forecasting, although in the limit of data error and model structure.

  9. Natural Uncertainty Measure for Forecasting Floods in Ungauged Basins

    NASA Astrophysics Data System (ADS)

    Mantilla, Ricardo; Krajewski, Witold F.; Gupta, Vijay K.; Ayalew, Tibebu B.

    2015-04-01

    Recent data analysis have shown that peak flows for individual Rainfall-Runoff (RF-RO) events exhibit power law scaling with respect to drainage area, but the scaling slopes and intercepts change from one event to the next. We test this feature in the 32,400 km2 Iowa River basin, and give supporting evidence for our hypothesis that scaling slope and intercept incorporates all the pertinent physical processes that produce floods. These developments serve as the foundations for the key question that is addressed here: How to define uncertainty bounds for flood prediction for each event? We theoretically introduce the concept of Natural Uncertainty Measure for peak discharge (NUMPD) and test it using data from the Iowa River basin. We conjecture that NUMPD puts a limit to predictive uncertainty using measurements and modeling. In other words, the best any amount of data collection combined with any model can do is to come close to predicting NUMPD, but it cannot match or reduce it any further. For the applications of flood predictions, the concepts of Type-I and Type-II uncertainties in flood prediction are explained. We demonstrate Type-I uncertainty using the concept of NUMPD. Our results offer a context for Type-II uncertainty. Our results make a unique contribution to International Association of Hydrologic Sciences (IAHS) decade-long initiative on Predictions in Unaguged Basins (PUB) (2003-2012).

  10. Potentialities of ensemble strategies for flood forecasting over the Milano urban area

    NASA Astrophysics Data System (ADS)

    Ravazzani, Giovanni; Amengual, Arnau; Ceppi, Alessandro; Homar, Víctor; Romero, Romu; Lombardi, Gabriele; Mancini, Marco

    2016-08-01

    Analysis of ensemble forecasting strategies, which can provide a tangible backing for flood early warning procedures and mitigation measures over the Mediterranean region, is one of the fundamental motivations of the international HyMeX programme. Here, we examine two severe hydrometeorological episodes that affected the Milano urban area and for which the complex flood protection system of the city did not completely succeed. Indeed, flood damage have exponentially increased during the last 60 years, due to industrial and urban developments. Thus, the improvement of the Milano flood control system needs a synergism between structural and non-structural approaches. First, we examine how land-use changes due to urban development have altered the hydrological response to intense rainfalls. Second, we test a flood forecasting system which comprises the Flash-flood Event-based Spatially distributed rainfall-runoff Transformation, including Water Balance (FEST-WB) and the Weather Research and Forecasting (WRF) models. Accurate forecasts of deep moist convection and extreme precipitation are difficult to be predicted due to uncertainties arising from the numeric weather prediction (NWP) physical parameterizations and high sensitivity to misrepresentation of the atmospheric state; however, two hydrological ensemble prediction systems (HEPS) have been designed to explicitly cope with uncertainties in the initial and lateral boundary conditions (IC/LBCs) and physical parameterizations of the NWP model. No substantial differences in skill have been found between both ensemble strategies when considering an enhanced diversity of IC/LBCs for the perturbed initial conditions ensemble. Furthermore, no additional benefits have been found by considering more frequent LBCs in a mixed physics ensemble, as ensemble spread seems to be reduced. These findings could help to design the most appropriate ensemble strategies before these hydrometeorological extremes, given the computational

  11. GloFAS - global ensemble streamflow forecasting and flood early warning

    NASA Astrophysics Data System (ADS)

    Alfieri, L.; Burek, P.; Dutra, E.; Krzeminski, B.; Muraro, D.; Thielen, J.; Pappenberger, F.

    2013-03-01

    Anticipation and preparedness for large-scale flood events have a key role in mitigating their impact and optimizing the strategic planning of water resources. Although several developed countries have well-established systems for river monitoring and flood early warning, figures of populations affected every year by floods in developing countries are unsettling. This paper presents the Global Flood Awareness System (GloFAS), which has been set up to provide an overview on upcoming floods in large world river basins. GloFAS is based on distributed hydrological simulation of numerical ensemble weather predictions with global coverage. Streamflow forecasts are compared statistically to climatological simulations to detect probabilistic exceedance of warning thresholds. In this article, the system setup is described, together with an evaluation of its performance over a two-year test period and a qualitative analysis of a case study for the Pakistan flood, in summer 2010. It is shown that hazardous events in large river basins can be skilfully detected with a forecast horizon of up to 1 month. In addition, results suggest that an accurate simulation of initial model conditions and an improved parameterization of the hydrological model are key components to reproduce accurately the streamflow variability in the many different runoff regimes of the earth.

  12. Forecasting future water levels to aid in flash flood risk management

    NASA Astrophysics Data System (ADS)

    Smith, P. J.; Beven, K.; Marchandise, A.; Pappenberger, F.

    2012-04-01

    Flash floods are typically triggered by local intense rainfall in small catchments with short response times. The work presented explores the use of ensemble numerical weather prediction products coupled with a simplified hydrological model to forecast such floods, with up to 2 days lead time, when water level observations can be regularly assimilated. The techniques outlined are presented with reference to a case study, the Gardon d'Anduze basin in France. A Data Based Mechanistic time series model of the rainfall run-off dynamics of the catchment is constructed. The model formulated to address two common sources of observational errors, shifting baselines in the water level observation and incorrect characterisation of the magnitude of the precipitation. It is cast in a state space form shown to be an effective forecaster when driven by observed precipitation data. Substitution of ensemble precipitation forecasts the observed data is used to generate forecasts with with longer lead times. A simple adaptation of the hydrological model is used to represent the uncertainty in the forecasts that may result from incorrect characterisation of the magnitude of the forecast precipitation. Observed water levels are assimilated condition the model forecasts. They can be used both to condition the initial states to the hydrological model prior to being run with the ensemble NWP input but also to condition the hydrological forecasts generated by running the ensemble NWP inputs after they have been generated. The balance between using the hydrological forecasts of most recent NWP ensemble, or those of an older generated by an older ensemble which have undergone more data assimilation is considered.

  13. Interpolation of observed rainfall fields for flood forecasting in data poor areas

    NASA Astrophysics Data System (ADS)

    Rogelis Prada, M. C.; Werner, M. G. F.

    2010-09-01

    Observed rainfall fields constitute a crucial input for operational flood forecasting, providing boundary conditions to hydrological models for prediction of flows and levels in relevant forecast points. Such observed fields are derived through interpolation from available observed data from rain gauges. The reliability of the derived rainfall field depends on the density of the gauge network within the basin, as well as on the variability of the rainfall itself, and the interpolation method. In this paper interpolation methods to estimate rainfall fields under data- poor environments are researched, with the derived rainfall fields being used in operational flood warnings. Methods are applied in a small catchment in Bogotá, Colombia. This catchment has a complex climatology, which is strongly influenced by the inter-tropical convergence zone and orographic enhancement. As is common in such catchments in developing countries, the rainfall gauging network is sparse, while the need for reliable rainfall in flood forecasting is high. The extensive high flood risk zones in the lower areas of the catchment, where urbanization processes are characterized by unplanned occupation of areas close to rivers, is common in developing countries. Results show the sensitivity of interpolated rainfall fields to the interpolation methods chosen, and the importance of the use of indicator variables for improving the spatial distribution of interpolated rainfall. The value of these methods in establishing optimal new gauging sites for augmenting the sparse gauge network is demonstrated.

  14. Operational water management of Rijnland water system and pilot of ensemble forecasting system for flood control

    NASA Astrophysics Data System (ADS)

    van der Zwan, Rene

    2013-04-01

    The Rijnland water system is situated in the western part of the Netherlands, and is a low-lying area of which 90% is below sea-level. The area covers 1,100 square kilometres, where 1.3 million people live, work, travel and enjoy leisure. The District Water Control Board of Rijnland is responsible for flood defence, water quantity and quality management. This includes design and maintenance of flood defence structures, control of regulating structures for an adequate water level management, and waste water treatment. For water quantity management Rijnland uses, besides an online monitoring network for collecting water level and precipitation data, a real time control decision support system. This decision support system consists of deterministic hydro-meteorological forecasts with a 24-hr forecast horizon, coupled with a control module that provides optimal operation schedules for the storage basin pumping stations. The uncertainty of the rainfall forecast is not forwarded in the hydrological prediction. At this moment 65% of the pumping capacity of the storage basin pumping stations can be automatically controlled by the decision control system. Within 5 years, after renovation of two other pumping stations, the total capacity of 200 m3/s will be automatically controlled. In critical conditions there is a need of both a longer forecast horizon and a probabilistic forecast. Therefore ensemble precipitation forecasts of the ECMWF are already consulted off-line during dry-spells, and Rijnland is running a pilot operational system providing 10-day water level ensemble forecasts. The use of EPS during dry-spells and the findings of the pilot will be presented. Challenges and next steps towards on-line implementation of ensemble forecasts for risk-based operational management of the Rijnland water system will be discussed. An important element in that discussion is the question: will policy and decision makers, operator and citizens adapt this Anticipatory Water

  15. Development of an Operational Typhoon Swell Forecasting and Coastal Flooding Early Warning System

    NASA Astrophysics Data System (ADS)

    Fan, Y. M.; Wu, L. C.; Doong, D. J.; Kao, C. C.; Wang, J. H.

    2012-04-01

    Coastal floods and typhoon swells are a consistent threat to oceanfront countries, causing major human suffering and substantial economic losses, such as wrecks, ship capsized, and marine construction failure, etc. Climate change is exacerbating the problem. An early warning system is essential to mitigate the loss of life and property from coastal flooding and typhoon swells. The purpose of this study is to develop a typhoon swell forecasting and coastal flooding early warning system by integrating existing sea-state monitoring technology, numerical ocean forecasting models, historical database and experiences, as well as computer science. The proposed system has capability offering data for the past, information for the present, and for the future. The system was developed for Taiwanese coast due to its frequent threat by typhoons. An operational system without any manual work is the basic requirement of the system. Integration of various data source is the system kernel. Numerical ocean models play the important role within the system because they provide data for assessment of possible typhoon swell and flooding. The system includes regional wave model (SWAN) which nested with the large domain wave model (NWW III), is operationally set up for coastal waves forecasting, especially typhoon swell forecasting before typhoon coming, and the storm surge predicted by a POM model. Data assimilation technology is incorporated for enhanced accuracy. A warning signal is presented when the storm water level that accumulated from astronomical tide, storm surge, and wave-induced run-up exceeds the alarm sea level. This warning system has been in practical use for coastal flooding damage mitigation in Taiwan for years. Example of the system operation during Typhoon Haitung struck Taiwan in 2005 is illustrated in this study.

  16. Reduction of the uncertainties in the water level-discharge relation of a 1D hydraulic model in the context of operational flood forecasting

    NASA Astrophysics Data System (ADS)

    Habert, J.; Ricci, S.; Le Pape, E.; Thual, O.; Piacentini, A.; Goutal, N.; Jonville, G.; Rochoux, M.

    2016-01-01

    This paper presents a data-driven hydrodynamic simulator based on the 1-D hydraulic solver dedicated to flood forecasting with lead time of an hour up to 24 h. The goal of the study is to reduce uncertainties in the hydraulic model and thus provide more reliable simulations and forecasts in real time for operational use by the national hydrometeorological flood forecasting center in France. Previous studies have shown that sequential assimilation of water level or discharge data allows to adjust the inflows to the hydraulic network resulting in a significant improvement of the discharge while leaving the water level state imperfect. Two strategies are proposed here to improve the water level-discharge relation in the model. At first, a modeling strategy consists in improving the description of the river bed geometry using topographic and bathymetric measurements. Secondly, an inverse modeling strategy proposes to locally correct friction coefficients in the river bed and the flood plain through the assimilation of in situ water level measurements. This approach is based on an Extended Kalman filter algorithm that sequentially assimilates data to infer the upstream and lateral inflows at first and then the friction coefficients. It provides a time varying correction of the hydrological boundary conditions and hydraulic parameters. The merits of both strategies are demonstrated on the Marne catchment in France for eight validation flood events and the January 2004 flood event is used as an illustrative example throughout the paper. The Nash-Sutcliffe criterion for water level is improved from 0.135 to 0.832 for a 12-h forecast lead time with the data assimilation strategy. These developments have been implemented at the SAMA SPC (local flood forecasting service in the Haute-Marne French department) and used for operational forecast since 2013. They were shown to provide an efficient tool for evaluating flood risk and to improve the flood early warning system

  17. Advanced flood forecasting in Alpine watersheds by coupling meteorological observations and forecasts with a distributed hydrological model

    NASA Astrophysics Data System (ADS)

    Jasper, Karsten; Gurtz, Joachim; Lang, Herbert

    2002-10-01

    Flood forecasting may be improved by coupling atmospheric and hydrological models. To investigate the current potential of such an approach in complex mountain watersheds, the authors carried out a number of combined high-resolution one-way driven model experiments to generate runoff hydrographs for seven extreme flood events which occurred in the Lago Maggiore basin between 1993 and 2000. The Alpine Ticino-Verzasca-Maggia basin (2627 km 2) is located directly to the south of the main Alpine ridge embracing a great part of the drainage area of Lago Maggiore. For this basin, the grid-based hydrological catchment model WaSiM-ETH was employed to determine the continuous runoff hydrographs. In the model experiments, two different sets of meteorological input data were used: (1) surface observation data from station measurements and from weather radar, and (2) forecast data from five different high-resolution numerical weather prediction (NWP) models with grid cell sizes between 2 and 14 km. This paper presents and compares selected results of these flood runoff simulations with particular attention to the experimental design of the model coupling. The configuration and initialization of the hydrological model runs are outlined as well as the down-scale techniques which proved to provide an adequate spatial interpolation of the meteorological variables onto the 500 m×500 m grid of the hydrological model. In order to evaluate the various hydrological model results as generated from the different outputs from the five NWP models, some coupled experiments with 'non-standard' NWP model outputs have been carried out. In particular, the results of these sensitivity studies point to inherent limits of high-resolution flood runoff predictions in complex mountain terrain.

  18. Long-Lead Quantitative Flood Forecasts in Ungauged Basins Using Bayesian Neural Networks

    NASA Astrophysics Data System (ADS)

    Barros, A. P.; Yoo, J.

    2004-05-01

    Previously, Kim and Barros (2001) demonstrated the use of a hierarchy of neural network models to forecast flood peaks in four small and medium size ungauged basins (750 to about 9,000 km-sq) in the Northern Appalachian Mountains in Pennsylvania. Using regional rainfall, radiosonde and mesoscale infrared (IR) satellite imagery, their approach consisted of identifying the presence and type of convective activity from the IR imagery, information which was subsequently used to characterize the dominant synoptic scale weather patters and predict storm path and evolution using rainfall and radiosonde data far away from the forecast location. In this regard, the organizational skeleton of the inputs is built to mimic our understanding of physical processes associated with rainstorms. The approach was very successful with skill scores on the order of 80-90 per cent for 18-hour lead-time forecasts of winter and spring floods in response to heavy rainfall (i.e. not associated with snowmelt alone). One weakness of this work was however the lack of a measure of forecast uncertainty, or alternatively a measure of forecast reliability that could be used in hydrometeorological operations. To address this question, we have modified and adapted the existing neural network models according to the principles of Bayesian statistics. In this context, forecasts are issued along with an error bar and are associated with a known probability distribution. One additional advantage of this methodology is that it provides an objective basis for selecting the best model during learning based on the posterior distribution of the parameters. In this context, forecasts are issued along with an error bar and are associated with a known probability distribution. An intercomparison study against Kim and Barros (2001) shows that the 18- and 24-hour lead time BNN forecasts are statistically more robust than those generated by the standard backward-learning NNs. We submit that given the consistently

  19. Flood Forecasting Based on Multiple EPS -- is it Worth the Effort?

    NASA Astrophysics Data System (ADS)

    Thielen, J.; Pappenberger, F.; Bartholmes, J.; Kalas, M.; Bogner, K.; de Roo, A.

    2008-12-01

    The hydrological community is looking increasingly at the use of ensemble prediction systems instead of single (deterministic) forecasts to increase flood warning times. International initiatives and research projects such as THORPEX, HEPEX, PREVIEW, or MAP-DPHASE foster successfully the interdisciplinary dialogue between the meteorological and hydrological communities. The European Flood Alert System (EFAS) is a pre-operational example of an early flood warning system based on multiple EPS and poor-man's ensembles weather inputs. EFAS research focuses on the exploration of the EPS stream flow information, their visualisation for different end user communities and their application in risk-based decision-making. EFAS further provides a platform for further research on flash floods, droughts and climate change. Here results from a 2 year statistical skill score analysis from EFAS are presented and complemented with a case study on flooding that took place in Romania in 2007. The research focuses on the benefit of using multiple EPS. Data from eight global medium-range EPS have been extracted from the Thorpex Interactive Global Grand Ensemble (TIGGE) archive to compare the performance of single EPS versus multiple EPS. The case study showed clearly the potential benefit of using multiple EPS instead of relying on single EPS that might entirely miss an event. The study is completed by running additional EPS with different resolutions and leadtimes. Monthly forecasts are explored as first indicators for potential floods while the limited area model EPS (COSMO-LEPS) with much higher spatial resolution but only 5 days lead time are explored for better quantitative forecasts. As first assessment an outlook contrasting the computational demands with the apparent benefit is given together with a few thoughts on developments that should be addressed in the near future.

  20. Using Passive Microwaves for Open Water Monitoring and Flood Forecasting

    NASA Astrophysics Data System (ADS)

    Parinussa, R.; Johnson, F.; Sharma, A.; Lakshmi, V.

    2015-12-01

    One of the biggest and severest natural disasters that society faces is floods. An important component that can help in reducing the impact of floods is satellite remote sensing as it allows for consistent monitoring and obtaining catchment information in absence of physical contact. Nowadays, passive microwave remote sensing observations are available in near real time (NRT) with a couple of hours delay from the actual sensing. The Advanced Microwave Scanning Radiometer 2 (AMSR2) is a multi-frequency passive microwave sensor onboard the Global Change Observation Mission 1 - Water that was launched in May 2012. Several of these frequencies have a high sensitivity to the land surface and they also have the capacity to penetrate clouds. These advantages come at the cost of the relatively coarse spatial resolution (footprints range from ~5 to ~50 km) which in turn allows for global monitoring. A relatively simple methodology to monitor the fraction of open water from AMSR2 observations is presented here. Low frequency passive microwave observations have sensitivity to the land surface but are modulated by overlying signals from physical temperature and vegetation cover. We developed a completely microwave based artificial neural network supported by physically based components to monitor the fraction of open water. Three different areas, located in China, Southeast Asia and Australia, were selected for testing purposes and several different characteristics were examined. First, the overall performance of the methodology was evaluated against the NASA NRT Global Flood Mapping system. Second, the skills of the various different AMSR2 frequencies were tested and revealed that artificial contamination is a factor to consider. The different skills of the tested frequencies are of interest to apply the methodology to alternative passive microwave sensors. This will be of benefit in using the numerous multi-frequency passive microwaves sensors currently observing our Earth

  1. Assimilation of Satellite Based Soil Moisture Data in the National Weather Service's Flash Flood Guidance System

    NASA Astrophysics Data System (ADS)

    Seo, D.; Lakhankar, T.; Cosgrove, B.; Khanbilvardi, R.

    2012-12-01

    Climate change and variability increases the probability of frequency, timing, intensity, and duration of flood events. After rainfall, soil moisture is the most important factor dictating flash flooding, since rainfall infiltration and runoff are based on the saturation of the soil. It is difficult to conduct ground-based measurements of soil moisture consistently and regionally. As such, soil moisture is often derived from models and agencies such as the National Oceanic and Atmospheric Administration's National Weather Service (NOAA/NWS) use proxy estimates of soil moisture at the surface in order support operational flood forecasting. In particular, a daily national map of Flash Flood Guidance (FFG) is produced that is based on surface soil moisture deficit and threshold runoff estimates. Flash flood warnings are issued by Weather Forecast Offices (WFOs) and are underpinned by information from the Flash Flood Guidance (FFG) system operated by the River Forecast Centers (RFCs). This study analyzes the accuracy and limitations of the FFG system using reported flash flood cases in 2010 and 2011. The flash flood reports were obtained from the NWS Storm Event database for the Arkansas-Red Basin RFC (ABRFC). The current FFG system at the ABRFC provides gridded flash flood guidance (GFFG) System using the NWS Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) to translate the upper zone soil moisture to estimates of Soil Conservation Service Curve Numbers. Comparison of the GFFG and real-time Multi-sensor Precipitation Estimator derived Quantitative Precipitation Estimate (QPE) for the same duration and location were used to analyze the success of the system. Improved flash flood forecasting requires accurate and high resolution soil surface information. The remote sensing observations of soil moisture can improve the flood forecasting accuracy. The Soil Moisture Active and Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) satellites are two

  2. A Rolling Flood Forecast Method for River Basins with Newly-Built Meteorological and Hydrological Station Network

    NASA Astrophysics Data System (ADS)

    Shi, H.

    2014-12-01

    Destructive flash floods occurred more frequently in the small and medium river basins in China recently. However, meteorological and hydrological station networks in such river basins were usually poor. Some of them were newly-built only several years ago so that long-series observations are unavailable; and therefore, it is impossible to gain the most suitable parameters for flood forecast from the historical data directly. This paper developed a rolling flood forecast method for such regions, taking the Leli River basin in Guangxi Province, China, as the study area. The Digital Yellow River Integrated Model (DYRIM) was adopted to simulate the streamflows of the Tianlin hydrological station for each flood during the study period, and the model parameters were rolling optimized in real time as follows. First, the parameters were calibrated with the observed rainfall and streamflow data of the first flood, and they were used to forecast the flood caused by the next rain. Second, when the rain came true, the parameters were modified with the newly-observed rainfall and streamflow data if the simulation result obtained with the parameters of the last flood was not satisfied; and the new parameters would be used to forecast the next flood. Through repeating the above two steps for each flood, the parameters may be optimized constantly; and finally, the value ranges of the parameters could be obtained. From a sample demonstration, it can be concluded that this flood forecast method was feasible; it would be valuable for the flood forecast of river basins with newly-built meteorological and hydrological station network.

  3. The potential of satellite radar altimetry in flood forecasting: concept and implementation for the Niger-Benue river basin

    NASA Astrophysics Data System (ADS)

    Pandey, R.; Amarnath, G.

    2015-06-01

    Flood forecasting in the downstream part of any hydrological basin is extremely difficult due to the lack of basin-wide hydrological information in near real-time and the absence of a data-sharing treaty among the transboundary nations. The accuracy of forecasts emerging from a hydrological model could be compromised without prior knowledge of the day-to-day flow regulation at different locations upstream of the Niger and Benue rivers. Only satellite altimeter monitoring allows us to identify the actual river levels upstream that reflect the human intervention at that location. This is critical for making accurate downstream forecasts. This present study aims to demonstrate the capability of altimeter-based flood forecasting along the Niger-Benue River in Nigeria. The study includes the comparison of decadal (at every 10 days from Jason-2) or monthly (at every 35 days from Envisat/AltiKa) observations from 2002 to 2014, with historical in situ measurements from 1990 to 2012. The water level obtained from these sources shows a good correlation (0.7-0.9). After validation of hydrological parameters obtained from two sources, a quantitative relation (rating curve) of upstream water level and downstream discharge is derived. This relation is then adopted for calculation of discharge at observation points, which is used to propagate the flow downstream at a desired location using a hydraulic river model. Results from this study from Jason-2 shows a promising correlation (R2 ≥ 90% with a Nash-Sutcliffe coefficient of more than 0.70) with 5~days ahead of downstream flow prediction over the Benue stream.

  4. Development of Flood Forecasting Using Statistical Method in Four River Basins in Terengganu, Malaysia

    NASA Astrophysics Data System (ADS)

    Noor, M. S. F. M.; Sidek, L. M.; Basri, H.; Husni, M. M. M.; Jaafar, A. S.; Kamaluddin, M. H.; Majid, W. H. A. W. A.; Mohammad, A. H.; Osman, S.

    2016-03-01

    One of the critical regions in Malaysia is Terengganu which is located at east coast of Peninsular Malaysia. In Terengganu, flood is experienced regularly because of attributed topography and climate including northeast monsoon. Moreover, rainfall is with high intensity during the November to February in Terengganu as forcing factor to produce of flood. In this study, main objectives are water stage forecasting and deriving the related equations based on least squared method. For this study, it is used two methods which called inclusion of residual (Method A) and non-inclusion residual (Method B) respectively. Result depicts that Method B outperformed to forecast the water stage at selected case studies (Besut, Dungun, Kemaman, Terengganu).

  5. The estimating of Curve Number from River Level for real-time flood forecasting system

    NASA Astrophysics Data System (ADS)

    Han, M.; Yoon, Kanghoon

    2009-04-01

    In the South Korea, the NRCS runoff curve number method is used to estimate the effective rainfall and the CN has much effect on the peak discharge and time for the real-time forecasting system. According to the experience and existing research about flooding forecasting system, the new method to estimate CN would be necessary, since it is very difficult to operate the flood forecasting system using the method which uses the AMC from 5-day antecedent rainfall developed by NRCS. It could be assumed that the maximum potential retention(S) will be related to the groundwater or groundwater levels; therefore, the relationship between water stage in river and maximum potential retention(S) would be investigated. In order to derive the relationship, the flooding data of 1980 through 2007 in Sulmachun and Pyungchang River is used, since this data is delicately constructed. Here, the CN is calculated using the total rainfall discharge and the total depth of runoff discharge at the flooding period and then water stage in river and maximum potential retention(S) would be determined. The relationship between water level in river and maximum potential retention(S) or CN has a higher correlation under the specific water stage of about 0.1m^3/sec/km^2; however, it shows relatively lower correlation above the specific water level. This result shows that NRCS method represents the relationship very well in the lower water stage as infiltration is actively occurred with relatively higher maximum potential retention(S). Keyword : CN, rela-time forecasting system, water stage

  6. Potential application of wavelet neural network ensemble to forecast streamflow for flood management

    NASA Astrophysics Data System (ADS)

    Kasiviswanathan, K. S.; He, Jianxun; Sudheer, K. P.; Tay, Joo-Hwa

    2016-05-01

    Streamflow forecasting, especially the long lead-time forecasting, is still a very challenging task in hydrologic modeling. This could be due to the fact that the forecast accuracy measured in terms of both the amplitude and phase or temporal errors and the forecast precision/reliability quantified in terms of the uncertainty significantly deteriorate with the increase of the lead-time. In the model performance evaluation, the conventional error metrics, which primarily quantify the amplitude error and do not explicitly account for the phase error, have been commonly adopted. For the long lead-time forecasting, the wavelet based neural network (WNN) among a variety of advanced soft computing methods has been shown to be promising in the literature. This paper presented and compared WNN and artificial neural network (ANN), both of which were combined with the ensemble method using block bootstrap sampling (BB), in terms of the forecast accuracy and precision at various lead-times on the Bow River, Alberta, Canada. Apart from conventional model performance metrics, a new index, called percent volumetric error, was proposed, especially for quantifying the phase error. The uncertainty metrics including percentage of coverage and average width were used to evaluate the precision of the modeling approaches. The results obtained demonstrate that the WNN-BB consistently outperforms the ANN-BB in both the categories of the forecast accuracy and precision, especially in the long lead-time forecasting. The findings strongly suggest that the WNN-BB is a robust modeling approach for streamflow forecasting and thus would aid in flood management.

  7. Use of weather radar for flood forecasting in the Sieve River Basin: A sensitivity analysis

    SciTech Connect

    Pessoa, M.L.; Bras, R.L.; Williams, E.R. )

    1993-03-01

    Weather radar, in combination with a distributed rainfall-runoff model, promises to significantly improve real-time flood forecasting. This paper investigates the value of radar-derived precipitation in forecasting streamflow in the Sieve River basin, near Florence, Italy. The basin is modeled with a distributed rainfall-runoff model that exploits topographic information available from digital elevation maps. The sensitivity of the flood forecast to various properties of the radar-derived rainfall is studied. It is found that use of the proper radar reflectivity-rainfall intensity (Z-R) relationship is the most crucial factor in obtaining correct flood hydrographs. Errors resulting from spatially averaging radar rainfall are acceptable, but the use of discrete point information (i.e. raingage) can lead to serious problems. Reducing the resolution of the 5-min radar signal by temporally averaging over 15 and 30 min does not lead to major errors. Using 3-bit radar data (rather than the usual 8-bit data) to represent intensities results in significant operational savings without serious problems in hydrograph accuracy. 24 refs., 28 figs., 2 tabs.

  8. Probabilistic flood forecasting for Rapid Response Catchments using a countrywide distributed hydrological model: experience from the UK

    NASA Astrophysics Data System (ADS)

    Cole, Steven J.; Moore, Robert J.; Robson, Alice J.; Mattingley, Paul S.

    2014-05-01

    Across Britain, floods in rapidly responding catchments are a major concern and regularly cause significant damage (e.g. Boscastle 2004, Morpeth 2008, Cornwall 2010 and Comrie 2012). Typically these catchments have a small area and are characterised by steep slopes and/or significant suburban/urban land-cover. The meteorological drivers can be of convective origin or frontal with locally intense features (e.g. embedded convection or orographic enhancement); saturated catchments can amplify the flood response. Both rainfall and flood forecasting for Rapid Response Catchments (RRCs)are very challenging due to the often small-scale nature of the intense rainfall which is of most concern, the small catchment areas, and the short catchment response times. Over the last 3 to 4 years, new countrywide Flood Forecasting Systems based on the Grid-to-Grid (G2G) distributed hydrological (rainfall-runoff and routing) model have been implemented across Britain for use by the Flood Forecasting Centre and Scottish Flood Forecasting Service. This has achieved a step-change in operational capability with forecasts of flooding several days ahead "everywhere" on a 1 km grid now possible. The modelling and forecasting approach underpins countrywide Flood Guidance Statements out to 5 days which are used by emergency response organisations for planning and preparedness. The initial focus of these systems has been to provide a countrywide overview of flood risk. However, recent research has explored the potential of the G2G approach to support more frequent and detailed alerts relevant to flood warning in RRCs. Integral to this activity is the use of emerging high-resolution (~1.5km) rainfall forecast products, in deterministic and ensemble form. High spatial resolutions are required to capture some of the small-scale processes and intense rainfall features such as orographic enhancement and convective storm evolution. Even though a deterministic high-resolution numerical weather

  9. Simulating and Forecasting Flooding Events in the City of Jeddah, Saudi Arabia

    NASA Astrophysics Data System (ADS)

    Ghostine, Rabih; Viswanadhapalli, Yesubabu; Hoteit, Ibrahim

    2014-05-01

    Metropolitan cities in the Kingdom of Saudi Arabia, as Jeddah and Riyadh, are more frequently experiencing flooding events caused by strong convective storms that produce intense precipitation over a short span of time. The flooding in the city of Jeddah in November 2009 was described by civil defense officials as the worst in 27 years. As of January 2010, 150 people were reported killed and more than 350 were missing. Another flooding event, less damaging but comparably spectacular, occurred one year later (Jan 2011) in Jeddah. Anticipating floods before they occur could minimize human and economic losses through the implementation of appropriate protection, provision and rescue plans. We have developed a coupled hydro-meteorological model for simulating and predicting flooding events in the city of Jeddah. We use the Weather Research Forecasting (WRF) model assimilating all available data in the Jeddah region for simulating the storm events in Jeddah. The resulting rain is then used on 10 minutes intervals to feed up an advanced numerical shallow water model that has been discretized on an unstructured grid using different numerical schemes based on the finite elements or finite volume techniques. The model was integrated on a high-resolution grid size varying between 0.5m within the streets of Jeddah and 500m outside the city. This contribution will present the flooding simulation system and the simulation results, focusing on the comparison of the different numerical schemes on the system performances in terms of accuracy and computational efficiency.

  10. Development of web-based services for an ensemble flood forecasting and risk assessment system

    NASA Astrophysics Data System (ADS)

    Yaw Manful, Desmond; He, Yi; Cloke, Hannah; Pappenberger, Florian; Li, Zhijia; Wetterhall, Fredrik; Huang, Yingchun; Hu, Yuzhong

    2010-05-01

    Flooding is a wide spread and devastating natural disaster worldwide. Floods that took place in the last decade in China were ranked the worst amongst recorded floods worldwide in terms of the number of human fatalities and economic losses (Munich Re-Insurance). Rapid economic development and population expansion into low lying flood plains has worsened the situation. Current conventional flood prediction systems in China are neither suited to the perceptible climate variability nor the rapid pace of urbanization sweeping the country. Flood prediction, from short-term (a few hours) to medium-term (a few days), needs to be revisited and adapted to changing socio-economic and hydro-climatic realities. The latest technology requires implementation of multiple numerical weather prediction systems. The availability of twelve global ensemble weather prediction systems through the ‘THORPEX Interactive Grand Global Ensemble' (TIGGE) offers a good opportunity for an effective state-of-the-art early forecasting system. A prototype of a Novel Flood Early Warning System (NEWS) using the TIGGE database is tested in the Huai River basin in east-central China. It is the first early flood warning system in China that uses the massive TIGGE database cascaded with river catchment models, the Xinanjiang hydrologic model and a 1-D hydraulic model, to predict river discharge and flood inundation. The NEWS algorithm is also designed to provide web-based services to a broad spectrum of end-users. The latter presents challenges as both databases and proprietary codes reside in different locations and converge at dissimilar times. NEWS will thus make use of a ready-to-run grid system that makes distributed computing and data resources available in a seamless and secure way. An ability to run or function on different operating systems and provide an interface or front that is accessible to broad spectrum of end-users is additional requirement. The aim is to achieve robust interoperability

  11. Development of web-based services for a novel ensemble flood forecasting and risk assessment system

    NASA Astrophysics Data System (ADS)

    He, Y.; Manful, D. Y.; Cloke, H. L.; Wetterhall, F.; Li, Z.; Bao, H.; Pappenberger, F.; Wesner, S.; Schubert, L.; Yang, L.; Hu, Y.

    2009-12-01

    Flooding is a wide spread and devastating natural disaster worldwide. Floods that took place in the last decade in China were ranked the worst amongst recorded floods worldwide in terms of the number of human fatalities and economic losses (Munich Re-Insurance). Rapid economic development and population expansion into low lying flood plains has worsened the situation. Current conventional flood prediction systems in China are neither suited to the perceptible climate variability nor the rapid pace of urbanization sweeping the country. Flood prediction, from short-term (a few hours) to medium-term (a few days), needs to be revisited and adapted to changing socio-economic and hydro-climatic realities. The latest technology requires implementation of multiple numerical weather prediction systems. The availability of twelve global ensemble weather prediction systems through the ‘THORPEX Interactive Grand Global Ensemble’ (TIGGE) offers a good opportunity for an effective state-of-the-art early forecasting system. A prototype of a Novel Flood Early Warning System (NEWS) using the TIGGE database is tested in the Huai River basin in east-central China. It is the first early flood warning system in China that uses the massive TIGGE database cascaded with river catchment models, the Xinanjiang hydrologic model and a 1-D hydraulic model, to predict river discharge and flood inundation. The NEWS algorithm is also designed to provide web-based services to a broad spectrum of end-users. The latter presents challenges as both databases and proprietary codes reside in different locations and converge at dissimilar times. NEWS will thus make use of a ready-to-run grid system that makes distributed computing and data resources available in a seamless and secure way. An ability to run or function on different operating systems and provide an interface or front that is accessible to broad spectrum of end-users is additional requirement. The aim is to achieve robust

  12. Assimilation of multiple river flow data for enhanced operational flood forecasts

    NASA Astrophysics Data System (ADS)

    Ercolani, Giulia; Castelli, Fabio

    2016-04-01

    Data assimilation (DA) is widely recognized as a powerful tool to improve flood forecasts, and the need for an effective transition of research advances into operational forecasting systems has been increasingly claimed in recent years. Nevertheless, the majority of studies investigates DA capabilities through synthetic experiments, while applications conducted from an operational perspective are rare. In this work we present variational assimilation of discharge data at multiple locations in a distributed hydrologic model (Mobidic) that is part of the operational forecasting chain for the Arno river, in central Italy.The variational approach needs the derivation of an adjoint model, that is challenging for hydrologic models, but it requires less restrictive hypothesis than Kalman and Monte Carlo filters and smoothers. The developed assimilation system adjusts on a distributed basis initial condition of discharge, initial condition of soil moisture and a parameter representing the frequency of no-rainfall in a time step. The correction evaluated at discharge measurement stations spreads upstream thanks to the coupling between equations of flow channel routing, that results into the coupling between equations of the adjoint model. Sequential assimilations are realized on windows of 6 hours. We extensively examine the performances of the DA system through several hindcast experiments that mimic operational conditions. The case studies include both flood events and false alarms that occurred in the period 2009-2010 in the Arno river basin (about 8230 km2).The hydrologic model is run with the spatial and temporal resolutions that are employed operationally, i.e. 500 m and 15 minutes.The enhancement in discharge forecasts is evaluated through classical performance indexes as error on peak flow and Nash-Sutcliffe efficiency, with strong emphasis on the dependence on lead time. In addition, uncertainty of the estimations is assessed using the Hessian of the cost function

  13. Real-Time Flood Forecasting System Using Channel Flow Routing Model with Updating by Particle Filter

    NASA Astrophysics Data System (ADS)

    Kudo, R.; Chikamori, H.; Nagai, A.

    2008-12-01

    A real-time flood forecasting system using channel flow routing model was developed for runoff forecasting at water gauged and ungaged points along river channels. The system is based on a flood runoff model composed of upstream part models, tributary part models and downstream part models. The upstream part models and tributary part models are lumped rainfall-runoff models, and the downstream part models consist of a lumped rainfall-runoff model for hillslopes adjacent to a river channel and a kinematic flow routing model for a river channel. The flow forecast of this model is updated by Particle filtering of the downstream part model as well as by the extended Kalman filtering of the upstream part model and the tributary part models. The Particle filtering is a simple and powerful updating algorithm for non-linear and non-gaussian system, so that it can be easily applied to the downstream part model without complicated linearization. The presented flood runoff model has an advantage in simlecity of updating procedure to the grid-based distributed models, which is because of less number of state variables. This system was applied to the Gono-kawa River Basin in Japan, and flood forecasting accuracy of the system with both Particle filtering and extended Kalman filtering and that of the system with only extended Kalman filtering were compared. In this study, water gauging stations in the objective basin were divided into two types of stations, that is, reference stations and verification stations. Reference stations ware regarded as ordinary water gauging stations and observed data at these stations are used for calibration and updating of the model. Verification stations ware considered as ungaged or arbitrary points and observed data at these stations are used not for calibration nor updating but for only evaluation of forecasting accuracy. The result confirms that Particle filtering of the downstream part model improves forecasting accuracy of runoff at

  14. The POLIMI forecasting chain for real time flood and drought predictions

    NASA Astrophysics Data System (ADS)

    Ceppi, Alessandro; Ravazzani, Giovanni; Corbari, Chiara; Mancini, Marco

    2016-04-01

    Nowadays coupling meteorological and hydrological models is recognized by scientific community as a necessary way to forecast extreme hydrological phenomena, in order to activate useful mitigation measurements and alert systems in advance. The development and implementation of a real-time forecasting chain with a hydro-meteorological operational alert procedure for flood and drought events is presented in this study. Different weather models are used to build the POLIMI operative chain: the probabilistic COSMO-LEPS model with 16 ensembles developed by ARPA-Emilia Romagna, the deterministic Bolam and Moloch models, developed by the Italian ISAC-CNR, and nine further simulations obtained by different runs of the WRF-ARW (3), WRF-NMM (2), ETA2012 (1) and the GFS (3), provided by the private Epson Meteo Center and Terraria companies. All the meteorological runs are then implemented with the rainfall-runoff physically-based distributed FEST-WB model, developed at Politecnico di Milano to obtain a multi-model approach system with hydrological ensemble forecasts in different areas of study over the Italian country. As far as concerning drought predictions, three test-beds are monitored: two in maize fields, one in the Puglia region (South of Italy), and another in the Po Valley area, (northern Italy), and one in a golf course in Milan city. The hydrological model was here calibrated and validated against measurements of latent heat flux and soil moisture acquired by an eddy-covariance station, TDR probes and remote sensing images. Regarding flood forecasts, two test-sites are chosen: the first one is the urban area northern Milan where three catchments (the Seveso, Olona, and Lambro River basins) are used to show how early warning systems are an effective complement to structural measures for flood control in Milan city which flooded frequently in the last 25 years, while the second test-site is the Idro Lake, located between the Lombardy and Trentino region where the

  15. Towards spatially distributed flood forecasts in flash flood prone areas: application to the supervision of a road network in the South of France

    NASA Astrophysics Data System (ADS)

    Naulin, Jean-Philippe; Payrastre, Olivier; Gaume, Eric; Delrieu, Guy

    2013-04-01

    Accurate flood forecasts are crucial for an efficient flood event management. Until now, hydro-meteorological forecasts have been mainly used for early-warnings in France (Meteorological and flood vigilance maps) or over the world (Flash-flood guidances). These forecasts are generally limited to the main streams covered by the flood forecasting services or to specific watersheds with particular assets like check dams which are in most cases well gauged river sections, leaving aside large parts of the territory. A distributed hydro-meteorological forecasting approach will be presented, able to take advantage of the high spatial and temporal resolution rainfall estimates that are now available to provide information at ungauged sites. The proposed system aiming at detecting road inundation risks had been initially developed and tested in areas of limited size. Its extension to a whole region (the Gard region in the South of France) will be presented, including over 2000 crossing points between rivers and roads and its validation against a large data set of actually reported road inundations observed during recent flash-flood events. These first validation results appear promising. Such a tool would provide the necessary information for flood event management services to identify the areas at risk and to take the appropriate safety and rescue measures: pre-positioning of rescue means, stopping of the traffic on exposed roads, determination of safe accesses or evacuation routes. Moreover, beyond the specific application to the supervision of a road network, this work provides also results concerning the performances of hydro-meteorological forecasts for ungauged headwaters.

  16. A search for model parsimony in a real time flood forecasting system

    NASA Astrophysics Data System (ADS)

    Grossi, G.; Balistrocchi, M.

    2009-04-01

    As regards the hydrological simulation of flood events, a physically based distributed approach is the most appealing one, especially in those areas where the spatial variability of the soil hydraulic properties as well as of the meteorological forcing cannot be left apart, such as in mountainous regions. On the other hand, dealing with real time flood forecasting systems, less detailed models requiring a minor number of parameters may be more convenient, reducing both the computational costs and the calibration uncertainty. In fact in this case a precise quantification of the entire hydrograph pattern is not necessary, while the expected output of a real time flood forecasting system is just an estimate of the peak discharge, the time to peak and in some cases the flood volume. In this perspective a parsimonious model has to be found in order to increase the efficiency of the system. A suitable case study was identified in the northern Apennines: the Taro river is a right tributary to the Po river and drains about 2000 km2 of mountains, hills and floodplain, equally distributed . The hydrometeorological monitoring of this medium sized watershed is managed by ARPA Emilia Romagna through a dense network of uptodate gauges (about 30 rain gauges and 10 hydrometers). Detailed maps of the surface elevation, land use and soil texture characteristics are also available. Five flood events were recorded by the new monitoring network in the years 2003-2007: during these events the peak discharge was higher than 1000 m3/s, which is actually quite a high value when compared to the mean discharge rate of about 30 m3/s. The rainfall spatial patterns of such storms were analyzed in previous works by means of geostatistical tools and a typical semivariogram was defined, with the aim of establishing a typical storm structure leading to flood events in the Taro river. The available information was implemented into a distributed flood event model with a spatial resolution of 90m

  17. The total probabilities from high-resolution ensemble forecasting of floods

    NASA Astrophysics Data System (ADS)

    Olav Skøien, Jon; Bogner, Konrad; Salamon, Peter; Smith, Paul; Pappenberger, Florian

    2015-04-01

    Ensemble forecasting has for a long time been used in meteorological modelling, to give an indication of the uncertainty of the forecasts. As meteorological ensemble forecasts often show some bias and dispersion errors, there is a need for calibration and post-processing of the ensembles. Typical methods for this are Bayesian Model Averaging (Raftery et al., 2005) and Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). There are also methods for regionalizing these methods (Berrocal et al., 2007) and for incorporating the correlation between lead times (Hemri et al., 2013). To make optimal predictions of floods along the stream network in hydrology, we can easily use the ensemble members as input to the hydrological models. However, some of the post-processing methods will need modifications when regionalizing the forecasts outside the calibration locations, as done by Hemri et al. (2013). We present a method for spatial regionalization of the post-processed forecasts based on EMOS and top-kriging (Skøien et al., 2006). We will also look into different methods for handling the non-normality of runoff and the effect on forecasts skills in general and for floods in particular. Berrocal, V. J., Raftery, A. E. and Gneiting, T.: Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecasts, Mon. Weather Rev., 135(4), 1386-1402, doi:10.1175/MWR3341.1, 2007. Gneiting, T., Raftery, A. E., Westveld, A. H. and Goldman, T.: Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation, Mon. Weather Rev., 133(5), 1098-1118, doi:10.1175/MWR2904.1, 2005. Hemri, S., Fundel, F. and Zappa, M.: Simultaneous calibration of ensemble river flow predictions over an entire range of lead times, Water Resour. Res., 49(10), 6744-6755, doi:10.1002/wrcr.20542, 2013. Raftery, A. E., Gneiting, T., Balabdaoui, F. and Polakowski, M.: Using Bayesian Model Averaging to Calibrate Forecast Ensembles, Mon. Weather Rev

  18. Willingness-to-pay for a probabilistic flood forecast: a risk-based decision-making game

    NASA Astrophysics Data System (ADS)

    Arnal, Louise; Ramos, Maria-Helena; Coughlan de Perez, Erin; Cloke, Hannah Louise; Stephens, Elisabeth; Wetterhall, Fredrik; van Andel, Schalk Jan; Pappenberger, Florian

    2016-08-01

    Probabilistic hydro-meteorological forecasts have over the last decades been used more frequently to communicate forecast uncertainty. This uncertainty is twofold, as it constitutes both an added value and a challenge for the forecaster and the user of the forecasts. Many authors have demonstrated the added (economic) value of probabilistic over deterministic forecasts across the water sector (e.g. flood protection, hydroelectric power management and navigation). However, the richness of the information is also a source of challenges for operational uses, due partially to the difficulty in transforming the probability of occurrence of an event into a binary decision. This paper presents the results of a risk-based decision-making game on the topic of flood protection mitigation, called "How much are you prepared to pay for a forecast?". The game was played at several workshops in 2015, which were attended by operational forecasters and academics working in the field of hydro-meteorology. The aim of this game was to better understand the role of probabilistic forecasts in decision-making processes and their perceived value by decision-makers. Based on the participants' willingness-to-pay for a forecast, the results of the game show that the value (or the usefulness) of a forecast depends on several factors, including the way users perceive the quality of their forecasts and link it to the perception of their own performances as decision-makers.

  19. Non-parametric data-based approach for the quantification and communication of uncertainties in river flood forecasts

    NASA Astrophysics Data System (ADS)

    Van Steenbergen, N.; Willems, P.

    2012-04-01

    Reliable flood forecasts are the most important non-structural measures to reduce the impact of floods. However flood forecasting systems are subject to uncertainty originating from the input data, model structure and model parameters of the different hydraulic and hydrological submodels. To quantify this uncertainty a non-parametric data-based approach has been developed. This approach analyses the historical forecast residuals (differences between the predictions and the observations at river gauging stations) without using a predefined statistical error distribution. Because the residuals are correlated with the value of the forecasted water level and the lead time, the residuals are split up into discrete classes of simulated water levels and lead times. For each class, percentile values are calculated of the model residuals and stored in a 'three dimensional error' matrix. By 3D interpolation in this error matrix, the uncertainty in new forecasted water levels can be quantified. In addition to the quantification of the uncertainty, the communication of this uncertainty is equally important. The communication has to be done in a consistent way, reducing the chance of misinterpretation. Also, the communication needs to be adapted to the audience; the majority of the larger public is not interested in in-depth information on the uncertainty on the predicted water levels, but only is interested in information on the likelihood of exceedance of certain alarm levels. Water managers need more information, e.g. time dependent uncertainty information, because they rely on this information to undertake the appropriate flood mitigation action. There are various ways in presenting uncertainty information (numerical, linguistic, graphical, time (in)dependent, etc.) each with their advantages and disadvantages for a specific audience. A useful method to communicate uncertainty of flood forecasts is by probabilistic flood mapping. These maps give a representation of the

  20. Visualising probabilistic flood forecast information: expert preferences and perceptions of best practice in uncertainty communication

    NASA Astrophysics Data System (ADS)

    Pappenberger, F.; Stephens, E. M.; Thielen, J.; Salomon, P.; Demeritt, D.; van Andel, S.; Wetterhall, F.; Alfieri, L.

    2011-12-01

    The aim of this paper is to understand and to contribute to improved communication of the probabilistic flood forecasts generated by Hydrological Ensemble Prediction Systems (HEPS) with particular focus on the inter expert communication. Different users are likely to require different kinds of information from HEPS and thus different visualizations. The perceptions of this expert group are important both because they are the designers and primary users of existing HEPS. Nevertheless, they have sometimes resisted the release of uncertainty information to the general public because of doubts about whether it can be successfully communicated in ways that would be readily understood to non-experts. In this paper we explore the strengths and weaknesses of existing HEPS visualization methods and thereby formulate some wider recommendations about best practice for HEPS visualization and communication. We suggest that specific training on probabilistic forecasting would foster use of probabilistic forecasts with a wider range of applications. The result of a case study exercise showed that there is no overarching agreement between experts on how to display probabilistic forecasts and what they consider essential information that should accompany plots and diagrams. In this paper we propose a list of minimum properties that, if consistently displayed with probabilistic forecasts, would make the products more easily understandable.

  1. Rapid setup of the high resolution interactive hydrological / hydraulic model for flood forecasting at global scale

    NASA Astrophysics Data System (ADS)

    Donchyts, G.; Haag, A.; Winsemius, H.; Baart, F.; Hut, R.; Drost, N.; Van De Giesen, N.

    2014-12-01

    Rapid predictions of flood using high resolution process-based numerical models applied at global scale is a useful tool for flood forecasting. Usually it requires days or even months to create such a model for a specific area and in most cases the process assumes a lot of manual work. Our goal is to significantly decrease the time required for this process by means of software integration of data processing tools, numerical models and global data sets. The methodology is based on the use of the global hydrological model PCR-GLOBWB to identify the potential flood areas. An automated set of tools will be applied to generate a coupled hydrological / hydraulic model using a high resolution input data based on free global data sets such as SRTM, HydroSHEDS, CORINE, and OpenStreetMaps. This information should be sufficient to generate high resolution input for distributed rainfall-runoff and shallow water flow models. For the detection of potential flood areas, and generation of the unstructured model grid required by the D-Flow FM hydrodynamic model, we will use Height Above the Nearest Drainage (HAND) dataset derived from SRTM. For coupling the distributed hydrological and shallow water models we will use the Basic Model Interface (BMI). BMI is a lightweight API that enables communication with numerical models at runtime. We will validate benefits of the algorithm by applying it to the San Francisco bay area. The models and data processing tools will be integrated into an interactive user interface that will enable data exploration and will allow generation of new models based on user request or automatic rules. Using our approach we expect to make significant steps towards realizing our goal of global availability of flood forecasting models.

  2. Development of Hydrological Model of Klang River Valley for flood forecasting

    NASA Astrophysics Data System (ADS)

    Mohammad, M.; Andras, B.

    2012-12-01

    This study is to review the impact of climate change and land used on flooding through the Klang River and to compare the changes in the existing river system in Klang River Basin with the Storm water Management and Road Tunnel (SMART) which is now already operating in the city centre of Kuala Lumpur. Klang River Basin is the most urbanized region in Malaysia. More than half of the basin has been urbanized on the land that is prone to flooding. Numerous flood mitigation projects and studies have been carried out to enhance the existing flood forecasting and mitigation project. The objective of this study is to develop a hydrological model for flood forecasting in Klang Basin Malaysia. Hydrological modelling generally requires large set of input data and this is more often a challenge for a developing country. Due to this limitation, the Tropical Rainfall Measuring Mission (TRMM) rainfall measurement, initiated by the US space agency NASA and Japanese space agency JAXA was used in this study. TRMM data was transformed and corrected by quantile to quantile transformation. However, transforming the data based on ground measurement doesn't make any significant improvement and the statistical comparison shows only 10% difference. The conceptual HYMOD model was used in this study and calibrated using ROPE algorithm. But, using the whole time series of the observation period in this area resulted in insufficient performance. The depth function which used in ROPE algorithm are then used to identified and calibrated using only unusual event to observed the improvement and efficiency of the model.

  3. Pre- and post-processing of hydro-meteorological ensembles for the Norwegian flood forecasting system in 145 basins.

    NASA Astrophysics Data System (ADS)

    Jahr Hegdahl, Trine; Steinsland, Ingelin; Merete Tallaksen, Lena; Engeland, Kolbjørn

    2016-04-01

    Probabilistic flood forecasting has an added value for decision making. The Norwegian flood forecasting service is based on a flood forecasting model that run for 145 basins. Covering all of Norway the basins differ in both size and hydrological regime. Currently the flood forecasting is based on deterministic meteorological forecasts, and an auto-regressive procedure is used to achieve probabilistic forecasts. An alternative approach is to use meteorological and hydrological ensemble forecasts to quantify the uncertainty in forecasted streamflow. The hydrological ensembles are based on forcing a hydrological model with meteorological ensemble forecasts of precipitation and temperature. However, the ensembles of precipitation are often biased and the spread is too small, especially for the shortest lead times, i.e. they are not calibrated. These properties will, to some extent, propagate to hydrological ensembles, that most likely will be uncalibrated as well. Pre- and post-processing methods are commonly used to obtain calibrated meteorological and hydrological ensembles respectively. Quantitative studies showing the effect of the combined processing of the meteorological (pre-processing) and the hydrological (post-processing) ensembles are however few. The aim of this study is to evaluate the influence of pre- and post-processing on the skill of streamflow predictions, and we will especially investigate if the forecasting skill depends on lead-time, basin size and hydrological regime. This aim is achieved by applying the 51 medium-range ensemble forecast of precipitation and temperature provided by the European Center of Medium-Range Weather Forecast (ECMWF). These ensembles are used as input to the operational Norwegian flood forecasting model, both raw and pre-processed. Precipitation ensembles are calibrated using a zero-adjusted gamma distribution. Temperature ensembles are calibrated using a Gaussian distribution and altitude corrected by a constant gradient

  4. Application of satellite-based rainfall and medium range meteorological forecast in real-time flood forecasting in the Mahanadi River basin

    NASA Astrophysics Data System (ADS)

    Nanda, Trushnamayee; Beria, Harsh; Sahoo, Bhabagrahi; Chatterjee, Chandranath

    2016-04-01

    Increasing frequency of hydrologic extremes in a warming climate call for the development of reliable flood forecasting systems. The unavailability of meteorological parameters in real-time, especially in the developing parts of the world, makes it a challenging task to accurately predict flood, even at short lead times. The satellite-based Tropical Rainfall Measuring Mission (TRMM) provides an alternative to the real-time precipitation data scarcity. Moreover, rainfall forecasts by the numerical weather prediction models such as the medium term forecasts issued by the European Center for Medium range Weather Forecasts (ECMWF) are promising for multistep-ahead flow forecasts. We systematically evaluate these rainfall products over a large catchment in Eastern India (Mahanadi River basin). We found spatially coherent trends, with both the real-time TRMM rainfall and ECMWF rainfall forecast products overestimating low rainfall events and underestimating high rainfall events. However, no significant bias was found for the medium rainfall events. Another key finding was that these rainfall products captured the phase of the storms pretty well, but suffered from consistent under-prediction. The utility of the real-time TRMM and ECMWF forecast products are evaluated by rainfall-runoff modeling using different artificial neural network (ANN)-based models up to 3-days ahead. Keywords: TRMM; ECMWF; forecast; ANN; rainfall-runoff modeling

  5. Development Of An Open System For Integration Of Heterogeneous Models For Flood Forecasting And Hazard Mitigation

    NASA Astrophysics Data System (ADS)

    Chang, W.; Tsai, W.; Lin, F.; Lin, S.; Lien, H.; Chung, T.; Huang, L.; Lee, K.; Chang, C.

    2008-12-01

    During a typhoon or a heavy storm event, using various forecasting models to predict rainfall intensity, and water level variation in rivers and flood situation in the urban area is able to reveal its capability technically. However, in practice, the following two causes tend to restrain the further application of these models as a decision support system (DSS) for the hazard mitigation. The first one is due to the difficulty of integration of heterogeneous models. One has to take into consideration the different using format of models, such as input files, output files, computational requirements, and so on. The second one is that the development of DSS requires, due to the heterogeneity of models and systems, a friendly user interface or platform to hide the complexity of various tools from users. It is expected that users can be governmental officials rather than professional experts, therefore the complicated interface of DSS is not acceptable. Based on the above considerations, in the present study, we develop an open system for integration of several simulation models for flood forecasting by adopting the FEWS (Flood Early Warning System) platform developed by WL | Delft Hydraulics. It allows us to link heterogeneous models effectively and provides suitable display modules. In addition, FEWS also has been adopted by Water Resource Agency (WRA), Taiwan as the standard operational system for river flooding management. That means this work can be much easily integrated with the use of practical cases. In the present study, based on FEWS platform, the basin rainfall-runoff model, SOBEK channel-routing model, and estuary tide forecasting model are linked and integrated through the physical connection of model initial and boundary definitions. The work flow of the integrated processes of models is shown in Fig. 1. This differs from the typical single model linking used in FEWS, which only aims at data exchange but without much physical consideration. So it really

  6. Comparing One-way and Two-way Coupled Hydrometeorological Forecasting Systems for Flood Forecasting in the Mediterranean Region

    NASA Astrophysics Data System (ADS)

    Givati, Amir; Gochis, David; Rummler, Thomas; Kunstmann, Harald; Yu, Wei

    2016-04-01

    A pair of hydro-meteorological modeling systems were calibrated and evaluated for the Ayalon basin in central Israel to assess the advantages and limitations of one-way versus two-way coupled modeling systems for flood prediction. The models used included the Hydrological Engineering Center-Hydrological Modeling System (HEC-HMS) model and the Weather Research and Forecasting (WRF) Hydro modeling system. The models were forced by observed, interpolated precipitation from rain-gauges within the basin, and with modeled precipitation from the WRF atmospheric model. Detailed calibration and evaluation was carried out for two major winter storms in January and December 2013. Then both modeling systems were executed and evaluated in an operational mode for the full 2014/2015 rainy season. Outputs from these simulations were compared to observed measurements from hydrometric stations at the Ayalon basin outlet. Various statistical metrics were employed to quantify and analyze the results: correlation, Root Mean Square Error (RMSE) and the Nash-Sutcliffe (NS) efficiency coefficient. Foremost, the results presented in this study highlight the sensitivity of hydrological responses to different sources of precipitation data, and less so, to hydrologic model formulation. With observed precipitation data both calibrated models closely simulated the observed hydrographs. The two-way coupled WRF/WRF-Hydro modeling system produced improved both the precipitation and hydrological simulations as compared to the one-way WRF simulations. Findings from this study suggest that the use of two-way atmospheric-hydrological coupling has the potential to improve precipitation and, therefore, hydrological forecasts for early flood warning applications. However more research needed in order to better understand the land-atmosphere coupling mechanisms driving hydrometeorological processes on a wider variety precipitation and terrestrial hydrologic systems.

  7. Flood and Fire Monitoring and Forecasting Within the Chornobyl Exclusion Zone

    NASA Astrophysics Data System (ADS)

    Los, Victor

    2001-03-01

    Taking into consideration that radioactivity from the contaminating elements of the Chernobyl Exclusion Zone (CEZ) amounts to a huge number, one of the most urgent tasks, at present, is the resolution of problems related to secondary radioactive contamination caused by floods and fires. These factors may lead to critical consequences. For instance, if radioactive contaminants migrate into the water system, namely into the Dnipro River, a threat arises to more than 20 million inhabitants of Ukraine. Additionally, fires in the CEZ potentially could cause contaminants to be dispersed into the air and to migrate in the atmosphere for long distances. The elements of information support system for administrative decision-making to respond to the appearances and consequences of forest fires and floods in contaminated areas of the CEZ have been developed. The system proposes: using Earth Remote Sensing (R/S) data for timely detection of forest fires; integration by Geographic Information System (GIS) of mathematical models for radionuclide migration by air in order to forecast radiological consequences of forest fires; forecasting and assessing flood consequences by means of spatial analysis of GIS and R/S; and development of a system for dissemination of information. This project was performed within the framework of USAID Cooperative Agreement #121-A-00-98-00615-00, dedicated to the establishment of the Ukrainian Land and Resource Management Center.

  8. 24 CFR 570.605 - National Flood Insurance Program.

    Code of Federal Regulations, 2011 CFR

    2011-04-01

    ... accordance with 24 CFR part 91), section 202(a) of the Flood Disaster Protection Act of 1973 (42 U.S.C. 4106) and the regulations in 44 CFR parts 59 through 79 apply to funds provided under this part 570. ... 24 Housing and Urban Development 3 2011-04-01 2010-04-01 true National Flood Insurance...

  9. 24 CFR 570.605 - National Flood Insurance Program.

    Code of Federal Regulations, 2014 CFR

    2014-04-01

    ... accordance with 24 CFR part 91), section 202(a) of the Flood Disaster Protection Act of 1973 (42 U.S.C. 4106) and the regulations in 44 CFR parts 59 through 79 apply to funds provided under this part 570. ... 24 Housing and Urban Development 3 2014-04-01 2013-04-01 true National Flood Insurance...

  10. 24 CFR 570.605 - National Flood Insurance Program.

    Code of Federal Regulations, 2013 CFR

    2013-04-01

    ... accordance with 24 CFR part 91), section 202(a) of the Flood Disaster Protection Act of 1973 (42 U.S.C. 4106) and the regulations in 44 CFR parts 59 through 79 apply to funds provided under this part 570. ... 24 Housing and Urban Development 3 2013-04-01 2013-04-01 false National Flood Insurance...

  11. 24 CFR 570.605 - National Flood Insurance Program.

    Code of Federal Regulations, 2012 CFR

    2012-04-01

    ... accordance with 24 CFR part 91), section 202(a) of the Flood Disaster Protection Act of 1973 (42 U.S.C. 4106) and the regulations in 44 CFR parts 59 through 79 apply to funds provided under this part 570. ... 24 Housing and Urban Development 3 2012-04-01 2012-04-01 false National Flood Insurance...

  12. 24 CFR 570.605 - National Flood Insurance Program.

    Code of Federal Regulations, 2010 CFR

    2010-04-01

    ... accordance with 24 CFR part 91), section 202(a) of the Flood Disaster Protection Act of 1973 (42 U.S.C. 4106) and the regulations in 44 CFR parts 59 through 79 apply to funds provided under this part 570. ... 24 Housing and Urban Development 3 2010-04-01 2010-04-01 false National Flood Insurance...

  13. Advance flood forecasting for flood stricken Bangladesh with a fuzzy reasoning method

    NASA Astrophysics Data System (ADS)

    Liong, Shie-Yui; Lim, Wee-Han; Kojiri, Toshiharu; Hori, Tomoharu

    2000-02-01

    An artificial Neural Network (NN) was successfully applied, in an earlier study, as a prediction tool to forecast water level at Dhaka (Bangladesh), for up to seven lead days in advance, with a high accuracy level. In addition, this high accuracy degree was accompanied with a very short computational time. Both make NN a desirable advance warming forecasting tool. In a later study, a sensitivity analysis was also performed to retain only the most sensitive gauging stations for the Dhaka station. The resulting reduction of gauging stations insignificantly affects the prediction accuracy level.The work concerning the possibility of measurement failure in any of the gauging stations during the critical flow level at Dhaka requires prediction tools which can interpret linguistic assessment of flow levels. A fuzzy logic approach is introduced with two or three membership functions, depending on necessity, for the input stations with five membership functions for the output station. Membership functions for each station are derived from their respective water level frequency distributions, after the Kohonen neural network is used to group the data into clusters. The proposed approach in deriving membership function shows a number of advances over the approach commonly used. When prediction results are compared with measured data, the prediction accuracy level is comparable with that of the data driven neural network approach.

  14. Building Cyberinfrastructure to Support a Real-time National Flood Model

    NASA Astrophysics Data System (ADS)

    Salas, F. R.; Maidment, D. R.; Tolle, K.; Navarro, C.; David, C. H.; Corby, R.

    2014-12-01

    The National Weather Service (NWS) is divided into 13 regional forecast centers across the country where the Sacramento Soil Moisture Accounting (SAC-SMA) model is run on average over a 10 day period, 5 days in the past and 5 days in the future. Model inputs and outputs such as precipitation and surface runoff are spatially aggregated over approximately 6,600 forecast basins with an average area of 1,200 square kilometers. In contrast, the NHDPlus dataset, which represents the geospatial fabric of the country, defines over 3 million catchments with an average area of 3 square kilometers. Downscaling the NWS land surface model outputs to the NHDPlus catchment scale in real-time requires the development of cyberinfrastructure to manage, share, compute and visualize large quantities of hydrologic data; streamflow computations through time for over 3 million river reaches. Between September 2014 and May 2015, the National Flood Interoperability Experiment (NFIE), coordinated through the Integrated Water Resource Science and Services (IWRSS) partners, will focus on building a national flood model for the country. This experiment will work to seamlessly integrate data and model services available on local and cloud servers (e.g. Azure) through disparate data sources operating at various spatial and temporal scales. As such, this paper will present a scalable information model that leverages the Routing Application for Parallel Computation of Discharge (RAPID) model to produce real-time flow estimates for approximately 67,000 NHDPlus river reaches in the NWS West Gulf River Forecast Center region.

  15. Technical Note: Initial assessment of a multi-method approach to spring-flood forecasting in Sweden

    NASA Astrophysics Data System (ADS)

    Olsson, J.; Uvo, C. B.; Foster, K.; Yang, W.

    2016-02-01

    Hydropower is a major energy source in Sweden, and proper reservoir management prior to the spring-flood onset is crucial for optimal production. This requires accurate forecasts of the accumulated discharge in the spring-flood period (i.e. the spring-flood volume, SFV). Today's SFV forecasts are generated using a model-based climatological ensemble approach, where time series of precipitation and temperature from historical years are used to force a calibrated and initialized set-up of the HBV model. In this study, a number of new approaches to spring-flood forecasting that reflect the latest developments with respect to analysis and modelling on seasonal timescales are presented and evaluated. Three main approaches, represented by specific methods, are evaluated in SFV hindcasts for the Swedish river Vindelälven over a 10-year period with lead times between 0 and 4 months. In the first approach, historically analogue years with respect to the climate in the period preceding the spring flood are identified and used to compose a reduced ensemble. In the second, seasonal meteorological ensemble forecasts are used to drive the HBV model over the spring-flood period. In the third approach, statistical relationships between SFV and the large-sale atmospheric circulation are used to build forecast models. None of the new approaches consistently outperform the climatological ensemble approach, but for early forecasts improvements of up to 25 % are found. This potential is reasonably well realized in a multi-method system, which over all forecast dates reduced the error in SFV by ˜ 4 %. This improvement is limited but potentially significant for e.g. energy trading.

  16. Flood forecasting; An alternate response for PMF at the Saluda Dam

    SciTech Connect

    Colon, R.; Wallace, J.R.; Olson, R.W. ); Massey, K.L. )

    1989-01-01

    This paper reports on the Saluda hydroelectric project, a peaking power project located bout 11 miles upstream from Columbia, South Carolina on the Saluda River. The Saluda Dam creates Lake Murray reservoir with a drainage area of about 2,420 square miles and a surface area of approximately 48,000 acres at maximum pool elevation. Storage for this reservoir is about 1.3 million acre-feet between elevations 330 and 360 ft-NGVD. Flood forecasting at the dam is discussed in this paper.

  17. Reinforced recurrent neural networks for multi-step-ahead flood forecasts

    NASA Astrophysics Data System (ADS)

    Chen, Pin-An; Chang, Li-Chiu; Chang, Fi-John

    2013-08-01

    Considering true values cannot be available at every time step in an online learning algorithm for multi-step-ahead (MSA) forecasts, a MSA reinforced real-time recurrent learning algorithm for recurrent neural networks (R-RTRL NN) is proposed. The main merit of the proposed method is to repeatedly adjust model parameters with the current information including the latest observed values and model's outputs to enhance the reliability and the forecast accuracy of the proposed method. The sequential formulation of the R-RTRL NN is derived. To demonstrate its reliability and effectiveness, the proposed R-RTRL NN is implemented to make 2-, 4- and 6-step-ahead forecasts in a famous benchmark chaotic time series and a reservoir flood inflow series in North Taiwan. For comparison purpose, three comparative neural networks (two dynamic and one static neural networks) were performed. Numerical and experimental results indicate that the R-RTRL NN not only achieves superior performance to comparative networks but significantly improves the precision of MSA forecasts for both chaotic time series and reservoir inflow case during typhoon events with effective mitigation in the time-lag problem.

  18. Improving the effectiveness of real-time flood forecasting through Predictive Uncertainty estimation: the multi-temporal approach

    NASA Astrophysics Data System (ADS)

    Barbetta, Silvia; Coccia, Gabriele; Moramarco, Tommaso; Todini, Ezio

    2015-04-01

    The negative effects of severe flood events are usually contrasted through structural measures that, however, do not fully eliminate flood risk. Non-structural measures, such as real-time flood forecasting and warning, are also required. Accurate stage/discharge future predictions with appropriate forecast lead-time are sought by decision-makers for implementing strategies to mitigate the adverse effects of floods. Traditionally, flood forecasting has been approached by using rainfall-runoff and/or flood routing modelling. Indeed, both types of forecasts, cannot be considered perfectly representing future outcomes because of lacking of a complete knowledge of involved processes (Todini, 2004). Nonetheless, although aware that model forecasts are not perfectly representing future outcomes, decision makers are de facto implicitly assuming the forecast of water level/discharge/volume, etc. as "deterministic" and coinciding with what is going to occur. Recently the concept of Predictive Uncertainty (PU) was introduced in hydrology (Krzysztofowicz, 1999), and several uncertainty processors were developed (Todini, 2008). PU is defined as the probability of occurrence of the future realization of a predictand (water level/discharge/volume) conditional on: i) prior observations and knowledge, ii) the available information obtained on the future value, typically provided by one or more forecast models. Unfortunately, PU has been frequently interpreted as a measure of lack of accuracy rather than the appropriate tool allowing to take the most appropriate decisions, given a model or several models' forecasts. With the aim to shed light on the benefits for appropriately using PU, a multi-temporal approach based on the MCP approach (Todini, 2008; Coccia and Todini, 2011) is here applied to stage forecasts at sites along the Upper Tiber River. Specifically, the STAge Forecasting-Rating Curve Model Muskingum-based (STAFOM-RCM) (Barbetta et al., 2014) along with the Rating

  19. Radar-driven High-resolution Hydrometeorological Forecasts of the 26 September 2007 Venice flash flood

    NASA Astrophysics Data System (ADS)

    Massimo Rossa, Andrea; Laudanna Del Guerra, Franco; Borga, Marco; Zanon, Francesco; Settin, Tommaso; Leuenberger, Daniel

    2010-05-01

    Space and time scales of flash floods are such that flash flood forecasting and warning systems depend upon the accurate real-time provision of rainfall information, high-resolution numerical weather prediction (NWP) forecasts and the use of hydrological models. Currently available high-resolution NWP model models can potentially provide warning forecasters information on the future evolution of storms and their internal structure, thereby increasing convective-scale warning lead times. However, it is essential that the model be started with a very accurate representation of on-going convection, which calls for assimilation of high-resolution rainfall data. This study aims to assess the feasibility of using carefully checked radar-derived quantitative precipitation estimates (QPE) for assimilation into NWP and hydrological models. The hydrometeorological modeling chain includes the convection-permitting NWP model COSMO-2 and a hydrologic-hydraulic models built upon the concept of geomorphological transport. Radar rainfall observations are assimilated into the NWP model via the latent heat nudging method. The study is focused on 26 September 2007 extreme flash flood event which impacted the coastal area of north-eastern Italy around Venice. The hydro-meteorological modeling system is implemented over the Dese river, a 90 km2 catchment flowing to the Venice lagoon. The radar rainfall observations are carefully checked for artifacts, including beam attenuation, by means of physics-based correction procedures and comparison with a dense network of raingauges. The impact of the radar QPE in the assimilation cycle of the NWP model is very significant, in that the main individual organized convective systems were successfully introduced into the model state, both in terms of timing and localization. Also, incorrectly localized precipitation in the model reference run without rainfall assimilation was correctly reduced to about the observed levels. On the other hand, the

  20. FUNDAMENTAL STUDY ON REAL-TIME FLOOD FORECASTING METHOD FOR LOCALLY HEAVY RAINFALL IN URBAN DRAINAGE AREAS

    NASA Astrophysics Data System (ADS)

    Kimura, Makoto; Kido, Yoshinobu; Nakakita, Eiichi

    Recently, locally heavy rainfall occurs frequently at highly urbanized area, and causes serious personal accidents, so importance of flood forecasting system is growing in order to reduce damage of inundation. However, flood forecasting that secured lead-time for evacuation is extremely difficult, because the rainfall flows out rapidly. In this study, the numerical simulation model that can finely express inundation mechanism of urban drainage areas was applied with the most recent available data and analysis tool. The influence of the factor (i.e. sewer system, overland and rainfall information) which affected inundation mechanism was evaluated through the sensibility analysis with this model, and evaluation results show some requirements of model condition and information on time and space resolution of real-time flood forecasting.

  1. Storm surge forecasting for operating the Venice Flood Barrier with minimal impact on port activities

    NASA Astrophysics Data System (ADS)

    Cecconi, Giovanni

    2015-04-01

    The operation of the Venice storm barrier, due to enter into operation by the end of 2017 , is particularly demanding in terms of the required accuracy of the forecast of the max water level for the time lead of 3-6 hours. With present sea level and safeguard level established at 1.1 m a.s.l. of 1895 the barrier is expected to be operated 10 times a year to cope with an average of 5 storms with around 15 redirections of the navigation through the locks. The 5 extra closures and the 10 extra interferences with navigation are needed for compensating the present forecast uncertainty of 10 cm in the maximum storm high for the required time lead of three hours, the time needed to stop navigation before the closures of the lagoon inlets. A decision support system based on these rules have been tested along the last four year with satisfactory results in term of reliability easy of operations. The forecast is presently based on a statistical model associated with a deterministic local model; the main source of uncertainty is related to the prediction of the local wind. Due to delays in the completion of Venice local protection till 1.1 m it is expected that the population will urge a reduction of the safeguard level from 1.1m to 0.9m with an exponential increase in the number of closures with greater impact on navigation. The present acceleration in sea level rise will also contribute to the increase in the number of closures. To reduce the impact on port activity, better forecast accuracy is required together with experimenting new operational closures : e.g. activating only the northern barriers. The paper evaluate the problem and the possible solutions in terms of improving storm surge forecast and developing new schemes for partial operation of the barriers for predicted limited floods not requiring complete closures.

  2. Superposition of three sources of uncertainties in operational flood forecasting chains

    NASA Astrophysics Data System (ADS)

    Zappa, Massimiliano; Jaun, Simon; Germann, Urs; Walser, André; Fundel, Felix

    2011-05-01

    One of the less known aspects of operational flood forecasting systems in complex topographic areas is the way how the uncertainties of its components propagate and superpose when they are fed into a hydrological model. This paper describes an experimental framework for investigating the relative contribution of meteorological forcing uncertainties, initial conditions uncertainties and hydrological model parameter uncertainties in the realization of hydrological ensemble forecasts. Simulations were done for a representative small-scale basin of the Swiss Alps, the Verzasca river basin (186 km 2). For seven events in the time frame from June 2007 to November 2008 it was possible to quantify the uncertainty for a five-day forecast range yielded by inputs of an ensemble numerical weather prediction (NWP) model (COSMO-LEPS, 16 members), the uncertainty in real-time assimilation of weather radar precipitation fields expressed using an ensemble approach (REAL, 25 members), and the equifinal parameter realizations of the hydrological model adopted (PREVAH, 26 members). Combining the three kinds of uncertainty results in a hydrological ensemble of 10,400 members. Analyses of sub-samples from the ensemble provide insight in the contribution of each kind of uncertainty to the total uncertainty. The results confirm our expectations and show that for the operational simulation of peak-runoff events the hydrological model uncertainty is less pronounced than the uncertainty obtained by propagating radar precipitation fields (by a factor larger than 4 in our specific setup) and NWP forecasts through the hydrological model (by a factor larger than 10). The use of precipitation radar ensembles for generating ensembles of initial conditions shows that the uncertainty in initial conditions decays within the first 48 hours of the forecast. We also show that the total spread obtained when superposing two or more sources of uncertainty is larger than the cumulated spread of experiments

  3. Willingness-to-pay for a probabilistic flood forecast: a risk-based decision-making game

    NASA Astrophysics Data System (ADS)

    Arnal, Louise; Ramos, Maria-Helena; Coughlan, Erin; Cloke, Hannah L.; Stephens, Elisabeth; Wetterhall, Fredrik; van Andel, Schalk-Jan; Pappenberger, Florian

    2016-04-01

    Forecast uncertainty is a twofold issue, as it constitutes both an added value and a challenge for the forecaster and the user of the forecasts. Many authors have demonstrated the added (economic) value of probabilistic forecasts over deterministic forecasts for a diversity of activities in the water sector (e.g. flood protection, hydroelectric power management and navigation). However, the richness of the information is also a source of challenges for operational uses, due partially to the difficulty to transform the probability of occurrence of an event into a binary decision. The setup and the results of a risk-based decision-making experiment, designed as a game on the topic of flood protection mitigation, called ``How much are you prepared to pay for a forecast?'', will be presented. The game was played at several workshops in 2015, including during this session at the EGU conference in 2015, and a total of 129 worksheets were collected and analysed. The aim of this experiment was to contribute to the understanding of the role of probabilistic forecasts in decision-making processes and their perceived value by decision-makers. Based on the participants' willingness-to-pay for a forecast, the results of the game showed that the value (or the usefulness) of a forecast depends on several factors, including the way users perceive the quality of their forecasts and link it to the perception of their own performances as decision-makers. Balancing avoided costs and the cost (or the benefit) of having forecasts available for making decisions is not straightforward, even in a simplified game situation, and is a topic that deserves more attention from the hydrological forecasting community in the future.

  4. Time Series Models Adoptable for Forecasting Nile Floods and Ethiopian Rainfalls.

    NASA Astrophysics Data System (ADS)

    El-Fandy, M. G.; Taiel, S. M. M.; Ashour, Z. H.

    1994-01-01

    Long-term rainfall forecasting is used in making economic and agricultural decisions in many countries. It may also be a tool in minimizing the devastation resulting from recurrent droughts. To be able to forecast the total annual rainfall or the levels of seasonal floods, a class of models has first been chosen. The model parameters have then been estimated with an appropriate parameter estimation algorithm. Finally, diagnostic tests have been performed to verify the adequacy of the model. These are the general principles of system identification, which is the most crucial part of the forecasting procedure. In this paper several sets of data have been studied using different statistical procedures. The examined data include a historical 835-year record representing the levels of the seasonal Nile floods in Cairo, Egypt, during the period A.D. 622-1457. These readings were originally carried out by the Arabsto a great degree of accuracy in order to be used in estimating yearly taxes or Zacat (islamic duties). The observations also comprise recent total annual rainfall data over Addis Ababa (Ethiopia) (1907-1984), the total annual discharges of Ethiopian rivers (including the river Sobat discharges at Hillet Doleib, Blue Nile discharge at Roseris, river Dinder, river Rahar, and river Atbara), equatorial lake plateau supply as contributed at Aswan during the period 1912-1982, and the total annual discharges at Aswan during the period 1871-1982. Periodograms have been used to uncover possible peridodicities. Trends of rainfall and discharges of some rivers of east and central Africa have been also estimated.Using the first half of the available record, two autoregressive integrated moving average (ARIMA) time series models have been identified, one for the levels of the seasonal Nile floods in Cairo, the second to model the annual rainfall over Ethiopia. The time series models have been applied in 1-year-ahead forecasting to the other hall of the available record and

  5. Flash Flood Risks and Warning Decisions: A Mental Models Study of Forecasters, Public Officials, and Media Broadcasters in Boulder, Colorado.

    PubMed

    Morss, Rebecca E; Demuth, Julie L; Bostrom, Ann; Lazo, Jeffrey K; Lazrus, Heather

    2015-11-01

    Timely warning communication and decision making are critical for reducing harm from flash flooding. To help understand and improve extreme weather risk communication and management, this study uses a mental models research approach to investigate the flash flood warning system and its risk decision context. Data were collected in the Boulder, Colorado area from mental models interviews with forecasters, public officials, and media broadcasters, who each make important interacting decisions in the warning system, and from a group modeling session with forecasters. Analysis of the data informed development of a decision-focused model of the flash flood warning system that integrates the professionals' perspectives. Comparative analysis of individual and group data with this model characterizes how these professionals conceptualize flash flood risks and associated uncertainty; create and disseminate flash flood warning information; and perceive how warning information is (and should be) used in their own and others' decisions. The analysis indicates that warning system functioning would benefit from professionals developing a clearer, shared understanding of flash flood risks and the warning system, across their areas of expertise and job roles. Given the challenges in risk communication and decision making for complex, rapidly evolving hazards such as flash floods, another priority is development of improved warning content to help members of the public protect themselves when needed. Also important is professional communication with members of the public about allocation of responsibilities for managing flash flood risks, as well as improved system-wide management of uncertainty in decisions. PMID:25988286

  6. Establishing a mountain flash flood forecasting/warning strategy through case studies in different climatic regions in China

    NASA Astrophysics Data System (ADS)

    Miao, Qinghua; Yang, Dawen

    2015-04-01

    Flash flood is one of the most common natural hazards in China, particularly in mountainous areas, causing heavy damages and casualties. However, mountain flash flood forecast remains challenging due to its short response time and the limited monitoring capacity over ungauged regions. This paper aims at assessing the predictability of flash flood in mountainous watersheds in humid, semi-humid and semi-arid regions of China. To access the applicability of flood forecast based on the rain-gauge network, we implement a distributed hydrological model (GBHM) over several mountainous catchments in China with drainage area of 5 to 2882 km2. The response time of flood is first derived using typical rainstorm, and the low limit of catchment area for flash flood forecast based on the rain-gauge network is determined through the intercomparison over different spatial scales. For those catchments smaller than the lowest limit, people can only escape from the flash floods by warning rather than by forecast due to the short response time. Hence the flash flood warning (FFN) method is introduced. Implement of the FFN needs to determine the rainfall threshold that may be different due to the antecedent soil moisture status. Based on the GBHM simulation using the historical rainfall data, we introduced an appropriate method to determine the FFN rainfall threshold in different climatic regions in China. The results show that the rainfall threshold decreases significantly with the antecedent soil moisture in the humid regions, while it keeps constant approximately in different soil wet conditions in the semi-arid regions.

  7. Hydrologic Forecasting at the US National Weather Service in the 21st Century: Transition from the NWS River Forecast System (NWSRFS) to the Community Hydrologic Prediction System (CHPS)

    NASA Astrophysics Data System (ADS)

    Restrepo, Pedro; Roe, Jon; Dietz, Christine; Werner, Micha; Gijsbers, Peter; Hartman, Robert; Opitz, Harold; Olsen, Billy; Halquist, John; Shedd, Robert

    2010-05-01

    The US National Weather Service developed the River Forecast System (NWSRFS) since the 1970s as the platform for performing hydrologic forecasts. The system, originally developed for the computers of that era, was optimized for speed of execution and compact and fast data storage and retrieval. However, with modern computers those features became less of a driver, and, instead, the ability to maintain and transition of new developments in data and modeling research into operations have become the top system priorities for hydrologic forecasting software applications. To address those two new priorities, and to allow the hydrologic research community at large to be able to contribute models and forecasting techniques, the National Weather Service proposed the development of the Community Hydrologic Prediction System (CHPS). CHPS must be sufficiently flexible not only to ensure current operational models and data remain available, but also to integrate readily modeling approaches and data from the wider community of practitioners and scientists involved in hydro-meteorological forecasting. Portability considerations require the computational infrastructure to be programmed in a language such as Java, and data formats conform to open standards such as XML. After examining a number of potential candidates, the NWS settled on the Delft Flood Early Warning System (Delft FEWS) from Deltares as the basis for CHPS, since it shares the basic design characteristics, the underlying community philosophy and was being successfully used in operations in several countries. This paper describes the characteristics of CHPS and the transition path to make it operational and available to the community.

  8. Improving National Air Quality Forecasts with Satellite Aerosol Observations.

    NASA Astrophysics Data System (ADS)

    Al-Saadi, Jassim; Szykman, James; Pierce, R. Bradley; Kittaka, Chieko; Neil, Doreen; Chu, D. Allen; Remer, Lorraine; Gumley, Liam; Prins, Elaine; Weinstock, Lewis; MacDonald, Clinton; Wayland, Richard; Dimmick, Fred; Fishman, Jack

    2005-09-01

    Accurate air quality forecasts can allow for mitigation of the health risks associated with high levels of air pollution. During September 2003, a team of NASA, NOAA, and EPA researchers demonstrated a prototype tool for improving fine particulate matter (PM2.5) air quality forecasts using satellite aerosol observations. Daily forecast products were generated from a near-real-time fusion of multiple input data products, including aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS)/ Earth Observing System (EOS) instrument on the NASA Terra satellite, PM2.5 concentration from over 300 state/local/national surface monitoring stations, meteorological fields from the NOAA/NCEP Eta Model, and fire locations from the NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) Geostationary Operational Environmental Satellite (GOES) Wildfire Automated Biomass Burning Algorithm (WF_ABBA) product. The products were disseminated via a Web interface to a small group of forecasters representing state and local air management agencies and the EPA. The MODIS data improved forecaster knowledge of synoptic-scale air pollution events, particularly over oceans and in regions devoid of surface monitors. Forecast trajectories initialized in regions of high AOD offered guidance for identifying potential episodes of poor air quality. The capability of this approach was illustrated with a case study showing that aerosol resulting from wildfires in the northwestern United States and southwestern Canada is transported across the continent to influence air quality in the Great Lakes region a few days later. The timing of this demonstration was selected to help improve the accuracy of the EPA's AIRNow (www.epa.gov/airnow/) air quality index next-day PM2.5 forecast, which began on 1 October 2003. Based on the positive response from air quality managers and forecasters, this prototype was expanded and transitioned to an operational

  9. Distributed precipitation corrections in Alpine areas for a real-time flood forecasting system

    NASA Astrophysics Data System (ADS)

    Herrnegger, Mathew; Senoner, Tobias; Nachtnebel, Hans-Peter

    2014-05-01

    This contribution presents a method for estimating spatial and temporal distributed precipitation correction factors. The approach is applied for a flood forecasting model in the Upper Enns and Upper Mur catchments in the Central Austrian Alps. Precipitation exhibits a large spatio-temporal variability in Alpine areas. Additionally the density of the monitoring network is low and measurements are subjected to major errors. This can lead to significant deficits in stream flow simulations, e.g. for flood forecasting models. Therefore precipitation correction factors are frequently applied. These correction factors are however mostly applied for whole catchments in a lumped manor, neglecting, that the magnitude of precipitation errors are spatially distributed. For the presented study a multiplicative linear correction model is therefore implemented, which enables a distribution of the correction factors as a function of elevation. The applied rainfall-runoff model COSERO is set up with a spatial resolution of 1x1km2. The correction of the rainfall pattern is thereby applied for every grid cell. To account for the local meteorological conditions, the correction model is derived for two elevation zones: (1) Valley floors to 2000 m a.s.l. and (2) above 2000 m a.s.l. to mountain peaks. Measurement errors also depend on the precipitation type, with higher magnitudes in winter months during snow fall. Therefore additionally separate correction factors for winter and summer months are estimated. The parameters for the correction model are estimated for every catchment based on independent station observations and observed and simulated runoff of the conceptual rainfall-runoff model. As driving input the INCA-precipitation fields of the Austrian Central Institute for Meteorology and Geodynamics (ZAMG) are used. Due to the mentioned errors, these precipitation fields are corrected according to the described method. The results show a significant improvement of the simulated

  10. Petascale Diagnostic Assessment of the Global Portfolio Rainfall Space Missions' Ability to Support Flood Forecasting

    NASA Astrophysics Data System (ADS)

    Reed, P. M.; Chaney, N.; Herman, J. D.; Wood, E. F.; Ferringer, M. P.

    2015-12-01

    This research represents a multi-institutional collaboration between Cornell University, The Aerospace Corporation, and Princeton University that has completed a Petascale diagnostic assessment of the current 10 satellite missions providing rainfall observations. Our diagnostic assessment has required four core tasks: (1) formally linking high-resolution astrodynamics design and coordination of space assets with their global hydrological impacts within a Petascale "many-objective" global optimization framework, (2) developing a baseline diagnostic evaluation of a 1-degree resolution global implementation of the Variable Infiltration Capacity (VIC) model to establish the required satellite observation frequencies and coverage to maintain acceptable global flood forecasts, (3) evaluating the limitations and vulnerabilities of the full suite of current satellite precipitation missions including the recently approved Global Precipitation Measurement (GPM) mission, and (4) conceptualizing the next generation spaced-based platforms for water cycle observation. Our team exploited over 100 Million hours of computing access on the 700,000+ core Blue Waters machine to radically advance our ability to discover and visualize key system tradeoffs and sensitivities. This project represents to our knowledge the first attempt to develop a 10,000 member Monte Carlo global hydrologic simulation at one degree resolution that characterizes the uncertain effects of changing the available frequencies of satellite precipitation on drought and flood forecasts. The simulation—optimization components of the work have set a theoretical baseline for the best possible frequencies and coverages for global precipitation given unlimited investment, broad international coordination in reconfiguring existing assets, and new satellite constellation design objectives informed directly by key global hydrologic forecasting requirements. Our research poses a step towards realizing the integrated

  11. Quantifying Uncertainty in Distributed Flash Flood Forecasting for a Semiarid Region

    NASA Astrophysics Data System (ADS)

    Samadi, S.; Pourreza Bilondi, M.; Ghahraman, B.; Akhoond-Ali, A. M.

    2015-12-01

    Reliability of semiarid flood forecasting is affected by several factors, including rainfall forcing, the system input-state-output behavior, initial soil moisture conditions and model parameters and structure. This study employed Bayesian frameworks to enable the explicit description and assessment of parameter and predictive uncertainty for convective rainfall-runoff modeling of a semiarid watershed system in Iran. We examined the performance and uncertainty analysis of a mixed conceptual and physical based rainfall-runoff model (AFFDEF) linked with three Markov chain Monte Carlo (MCMC) samplers: the DiffeRential Evolution Adaptive Metropolis (DREAM), the Shuffled Complex Evolution Metropolis (SCEM-UA), and DREAM- ZS, to forecast four potential semiarid convective events with varying rainfall duration (<24 hrs) and amount (>20 mm). Calibration results demonstrated that model predictive uncertainty was heavily dominated by error and bias in the soil water storage capacity which reflect inadequate representation of the upper soil zone processes by hydrological model. Furthermore, parameters associated with infiltration and interception capacity along with contributing area threshold for digital river network were identified the key model parameters and more influential on the modeled flood hydrograph. In addition, parameter inference in the DREAM model showed a consistent behavior with the priori assumption by closely matching the inferred error distribution to the empirical distribution of the model residual, indicating that model parameters are well identified. DREAM result further revealed that the uncertainty associated with rainfall of lower magnitudes was higher than rainfall of higher magnitudes. Uncertainty quantification of semiarid convective events provided significant insights into the mathematical relationship and characteristics of short-term forecast error and may be applicable to other semiarid watershed systems with the similar rainfall

  12. Long lead-time flood forecasting using data-driven modeling approaches

    NASA Astrophysics Data System (ADS)

    Bhatia, N.; He, J.; Srivastav, R. K.

    2014-12-01

    In spite of numerous structure measures being taken for floods, accurate flood forecasting is essential to condense the damages in hazardous areas considerably. The need of producing more accurate flow forecasts motivates the researchers to develop advanced innovative methods. In this study, it is proposed to develop a hybrid neural network model to exploit the strengths of artificial neural networks (ANNs). The proposed model has two components: i.) Dual - ANN model developed using river flows; and ii.) Multiple Linear Regression (MLR) model trained on meteorological data (Rainfall and Snow on ground). Potential model inputs that best represent the process of river basin were selected in stepwise manner by identifying input-output relationship using a linear approach, Partial Correlation Input Selection (PCIS) combined with Akaike Information Criterion (AIC) technique. The presented hybrid model was compared with three conventional methods: i) Feed-forward artificial neural network (FF-ANN) using daily river flows; ii) FF-ANN applied on decomposed river flows (low flow, rising limb and falling limb of hydrograph); and iii) Recursive method for daily river flows with lead-time of 7 days. The applicability of the presented model is illustrated through daily river flow data of Bow River, Canada. Data from 1912 to 1976 were used to train the models while data from 1977 to 2006 were used to validate the models. The results of the study indicate that the proposed model is robust enough to capture the non-linear nature of hydrograph and proves to be highly promising to forecast peak flows (extreme values) well in advance (higher lead time).

  13. Assessment of a fuzzy based flood forecasting system optimized by simulated annealing

    NASA Astrophysics Data System (ADS)

    Reyhani Masouleh, Aida; Pakosch, Sabine; Disse, Markus

    2010-05-01

    Flood forecasting is an important tool to mitigate harmful effects of floods. Among the many different approaches for forecasting, Fuzzy Logic (FL) is one that has been increasingly applied over the last decade. This method is principally based on the linguistic description of Rule Systems (RS). A RS is a specific combination of membership functions of input and output variables. Setting up the RS can be implemented either automatically or manually, the choice of which can strongly influence the resulting rule systems. It is therefore the objective of this study to assess the influence that the parameters of an automated rule generation based on Simulated Annealing (SA) have on the resulting RS. The study area is the upper Main River area, located in the northern part of Bavaria, Germany. The data of Mainleus gauge with area of 1165 km2 was investigated in the whole period of 1984 and 2004. The highest observed discharge of 357 m3/s was recorded in 1995. The input arguments of the FL model were daily precipitation, forecasted precipitation, antecedent precipitation index, temperature and melting rate. The FL model of this study has one output variable, daily discharge and was independently set up for three different forecast lead times, namely one-, two- and three-days ahead. In total, each RS comprised 55 rules and all input and output variables were represented by five sets of trapezoidal and triangular fuzzy numbers. Simulated Annealing, which is a converging optimum solution algorithm, was applied for optimizing the RSs in this study. In order to assess the influence of its parameters (number of iterations, temperature decrease rate, initial value for generating random numbers, initial temperature and two other parameters), they were individually varied while keeping the others fixed. With each of the resulting parameter sets, a full-automatic SA was applied to gain optimized fuzzy rule systems for flood forecasting. Evaluation of the performance of the

  14. A flood routing Muskingum type simulation and forecasting model based on level data alone

    NASA Astrophysics Data System (ADS)

    Franchini, Marco; Lamberti, Paolo

    1994-07-01

    While the use of remote hydrometers for measuring the level in water courses is both economical and widespread, the same cannot be said for cross section and channel profile measurements and, even less, for rating curves at the measuring cross sections, all of which are more often than not incomplete, out of date, and unreliable. The mass of data involved in level measurements alone induces a degree of perplexity in those who try to use them, for example, for flood event simulations or the construction of forecasting models which are not purely statistical. This paper proposes a method which uses recorded level data alone to construct a simulation model and a forecasting model, both of them characterized by an extremely simple structure that can be used on any pocket calculator. These models, referring to a river reach bounded by two measuring sections, furnish the downstream levels, where the upstream levels are known, and the downstream level at time t + Δt*, where the upstream and downstream levels are known at time t, respectively. The numerical applications performed show that while the simulation model is somewhat penalized by the simplifications adopted, giving not consistently satisfactory results on validation, the forecasting model generated good results in all the cases examined and seems reliable.

  15. Assimilation of stream discharge for flood forecasting: Updating a semidistributed model with an integrated data assimilation scheme

    NASA Astrophysics Data System (ADS)

    Li, Yuan; Ryu, Dongryeol; Western, Andrew W.; Wang, Q. J.

    2015-05-01

    Real-time discharge observations can be assimilated into flood models to improve forecast accuracy; however, the presence of time lags in the routing process and a lack of methods to quantitatively represent different sources of uncertainties challenge the implementation of data assimilation techniques for operational flood forecasting. To address these issues, an integrated error parameter estimation and lag-aware data assimilation (IEELA) scheme was recently developed for a lumped model. The scheme combines an ensemble-based maximum a posteriori (MAP) error estimation approach with a lag-aware ensemble Kalman smoother (EnKS). In this study, the IEELA scheme is extended to a semidistributed model to provide for more general application in flood forecasting by including spatial and temporal correlations in model uncertainties between subcatchments. The result reveals that using a semidistributed model leads to more accurate forecasts than a lumped model in an open-loop scenario. The IEELA scheme improves the forecast accuracy significantly in both lumped and semidistributed models, and the superiority of the semidistributed model remains in the data assimilation scenario. However, the improvements resulting from IEELA are confined to the outlet of the catchment where the discharge observations are assimilated. Forecasts at "ungauged" internal locations are not improved, and in some instances, even become less accurate.

  16. Taking into account hydrological modelling uncertainty in Mediterranean flash-floods forecasting

    NASA Astrophysics Data System (ADS)

    Edouard, Simon; Béatrice, Vincendon; Véronique, Ducrocq

    2015-04-01

    Title : Taking into account hydrological modelling uncertainty in Mediterranean flash-floods forecasting Authors : Simon EDOUARD*, Béatrice VINCENDON*, Véronique Ducrocq* * : GAME/CNRM(Météo-France, CNRS)Toulouse,France Mediterranean intense weather events often lead to devastating flash-floods (FF). Increasing the lead time of FF forecasts would permit to better anticipate their catastrophic consequences. These events are one part of Mediterranean hydrological cycle. HyMeX (HYdrological cycle in the Mediterranean EXperiment) aims at a better understanding and quantification of the hydrological cycle and related processes in the Mediterranean. In order to get a lot of data, measurement campaigns were conducted. The first special observing period (SOP1) of these campaigns, served as a test-bed for a real-time hydrological ensemble prediction system (HEPS) dedicated to FF forecasting. It produced an ensemble of quantitative discharge forecasts (QDF) using the ISBA-TOP system. ISBATOP is a coupling between the surface scheme ISBA and a version of TOPMODEL dedicated to Mediterranean fast responding rivers. ISBA-TOP was driven with several quantitative precipitation forecasts (QPF) ensembles based on AROME atmospheric convection-permitting model. This permitted to take into account the uncertainty that affects QPF and that propagates up to the QDF. This uncertainty is major for discharge forecasting especially in the case of Mediterranean flash-floods. But other sources of uncertainty need to be sampled in HEPS systems. One of them is inherent to the hydrological modelling. The ISBA-TOP coupled system has been improved since the initial version, that was used for instance during Hymex SOP1. The initial ISBA-TOP consisted into coupling a TOPMODEL approach with ISBA-3L, which represented the soil stratification with 3 layers. The new version consists into coupling the same TOPMODEL approach with a version of ISBA where more than ten layers describe the soil vertical

  17. Operational flood forecasting: further lessons learned form a recent inundation in Tuscany, Italy

    NASA Astrophysics Data System (ADS)

    Caparrini, F.; Castelli, F.; di Carlo, E.

    2010-09-01

    After a few years of experimental setup, model refinement and parameters calibration, a distributed flood forecasting system for the Tuscany region was promoted to operational use in early 2008. The hydrologic core of the system, MOBIDIC, is a fully distributed soil moisture accounting model, with sequential assimilation of hydrometric data. The model is forced by the real-time dense hydrometeorological network of the Regional Hydrologic Service as well from the QPF products of a number of different limited area meteorological models (LAMI, WRF+ECMWF, WRF+GFS). Given the relatively short response time of the Tuscany basins, the river flow forecasts based on ground measured precipitation are operationally used mainly as a monitoring tool, while the true usable predictions are necessarily based on the QPF input. The first severe flooding event the system had to face occurred in late December 2009, when a failure of the right levee of the Serchio river caused an extensive inundation (on December 25th). In the days following the levee breaking, intensive monitoring and forecast was needed (another flood peak occurred on the night between December 29th and January 1st 2010) as a support for decisions regarding the management of the increased vulnerability of the area and the planning of emergency reparation works at the river banks. The operational use of the system during such a complex event, when both the meteorological and the hydrological components may be said to have performed well form a strict modeling point of view, brought to attention a number of additional issues about the system as a whole. The main of these issues may be phrased in terms of additional system requirements, namely: the ranking of different QPF products in terms of some likelihood measure; the rapid redefinition of alarm thresholds due to sudden changes in the river flow capacity; the supervised prediction for evaluating the consequences of different management scenarios for reservoirs

  18. Forecasting of Storm-Surge Floods Using ADCIRC and Optimized DEMs

    NASA Technical Reports Server (NTRS)

    Valenti, Elizabeth; Fitzpatrick, Patrick

    2006-01-01

    Increasing the accuracy of storm-surge flood forecasts is essential for improving preparedness for hurricanes and other severe storms and, in particular, for optimizing evacuation scenarios. An interactive database, developed by WorldWinds, Inc., contains atlases of storm-surge flood levels for the Louisiana/Mississippi gulf coast region. These atlases were developed to improve forecasting of flooding along the coastline and estuaries and in adjacent inland areas. Storm-surge heights depend on a complex interaction of several factors, including: storm size, central minimum pressure, forward speed of motion, bottom topography near the point of landfall, astronomical tides, and, most importantly, maximum wind speed. The information in the atlases was generated in over 100 computational simulations, partly by use of a parallel-processing version of the ADvanced CIRCulation (ADCIRC) model. ADCIRC is a nonlinear computational model of hydrodynamics, developed by the U.S. Army Corps of Engineers and the US Navy, as a family of two- and three-dimensional finite-element-based codes. It affords a capability for simulating tidal circulation and storm-surge propagation over very large computational domains, while simultaneously providing high-resolution output in areas of complex shoreline and bathymetry. The ADCIRC finite-element grid for this project covered the Gulf of Mexico and contiguous basins, extending into the deep Atlantic Ocean with progressively higher resolution approaching the study area. The advantage of using ADCIRC over other storm-surge models, such as SLOSH, is that input conditions can include all or part of wind stress, tides, wave stress, and river discharge, which serve to make the model output more accurate. To keep the computational load manageable, this work was conducted using only the wind stress, calculated by using historical data from Hurricane Camille, as the input condition for the model. Hurricane storm-surge simulations were performed on an

  19. An integrated error parameter estimation and lag-aware data assimilation scheme for real-time flood forecasting

    NASA Astrophysics Data System (ADS)

    Li, Yuan; Ryu, Dongryeol; Western, Andrew W.; Wang, Q. J.; Robertson, David E.; Crow, Wade T.

    2014-11-01

    For operational flood forecasting, discharge observations may be assimilated into a hydrologic model to improve forecasts. However, the performance of conventional filtering schemes can be degraded by ignoring the time lag between soil moisture and discharge responses. This has led to ongoing development of more appropriate ways to implement sequential data assimilation. In this paper, an ensemble Kalman smoother (EnKS) with fixed time window is implemented for the GR4H hydrologic model (modèle du Génie Rural à 4 paramètres Horaire) to update current and antecedent model states. Model and observation error parameters are estimated through the maximum a posteriori method constrained by prior information drawn from flow gauging data. When evaluated in a hypothetical forecasting mode using observed rainfall, the EnKS is found to be more stable and produce more accurate discharge forecasts than a standard ensemble Kalman filter (EnKF) by reducing the mean of the ensemble root mean squared error (MRMSE) by 13-17%. The latter tends to over-correct current model states and leads to spurious peaks and oscillations in discharge forecasts. When evaluated in a real-time forecasting mode using rainfall forecasts from a numerical weather prediction model, the benefit of the EnKS is reduced as uncertainty in rainfall forecasts becomes dominant, especially at large forecast lead time.

  20. Ensemble hydro-meteorological forecasting for early warning of floods and scheduling of hydropower production

    NASA Astrophysics Data System (ADS)

    Solvang Johansen, Stian; Steinsland, Ingelin; Engeland, Kolbjørn

    2016-04-01

    Running hydrological models with precipitation and temperature ensemble forcing to generate ensembles of streamflow is a commonly used method in operational hydrology. Evaluations of streamflow ensembles have however revealed that the ensembles are biased with respect to both mean and spread. Thus postprocessing of the ensembles is needed in order to improve the forecast skill. The aims of this study is (i) to to evaluate how postprocessing of streamflow ensembles works for Norwegian catchments within different hydrological regimes and to (ii) demonstrate how post processed streamflow ensembles are used operationally by a hydropower producer. These aims were achieved by postprocessing forecasted daily discharge for 10 lead-times for 20 catchments in Norway by using EPS forcing from ECMWF applied the semi-distributed HBV-model dividing each catchment into 10 elevation zones. Statkraft Energi uses forecasts from these catchments for scheduling hydropower production. The catchments represent different hydrological regimes. Some catchments have stable winter condition with winter low flow and a major flood event during spring or early summer caused by snow melting. Others has a more mixed snow-rain regime, often with a secondary flood season during autumn, and in the coastal areas, the stream flow is dominated by rain, and the main flood season is autumn and winter. For post processing, a Bayesian model averaging model (BMA) close to (Kleiber et al 2011) is used. The model creates a predictive PDF that is a weighted average of PDFs centered on the individual bias corrected forecasts. The weights are here equal since all ensemble members come from the same model, and thus have the same probability. For modeling streamflow, the gamma distribution is chosen as a predictive PDF. The bias correction parameters and the PDF parameters are estimated using a 30-day sliding window training period. Preliminary results show that the improvement varies between catchments depending

  1. Ensemble hydro-meteorological forecasting for early warning of floods and scheduling of hydropower production

    NASA Astrophysics Data System (ADS)

    Solvang Johansen, Stian; Steinsland, Ingelin; Engeland, Kolbjørn

    2016-04-01

    Running hydrological models with precipitation and temperature ensemble forcing to generate ensembles of streamflow is a commonly used method in operational hydrology. Evaluations of streamflow ensembles have however revealed that the ensembles are biased with respect to both mean and spread. Thus postprocessing of the ensembles is needed in order to improve the forecast skill. The aims of this study is (i) to to evaluate how postprocessing of streamflow ensembles works for Norwegian catchments within different hydrological regimes and to (ii) demonstrate how post processed streamflow ensembles are used operationally by a hydropower producer. These aims were achieved by postprocessing forecasted daily discharge for 10 lead-times for 20 catchments in Norway by using EPS forcing from ECMWF applied the semi-distributed HBV-model dividing each catchment into 10 elevation zones. Statkraft Energi uses forecasts from these catchments for scheduling hydropower production. The catchments represent different hydrological regimes. Some catchments have stable winter condition with winter low flow and a major flood event during spring or early summer caused by snow melting. Others has a more mixed snow-rain regime, often with a secondary flood season during autumn, and in the coastal areas, the stream flow is dominated by rain, and the main flood season is autumn and winter. For post processing, a Bayesian model averaging model (BMA) close to (Kleiber et al 2011) is used. The model creates a predictive PDF that is a weighted average of PD&Facute;s centered on the individual bias corrected forecasts. The weights are here equal since all ensemble members come from the same model, and thus have the same probability. For modeling streamflow, the gamma distribution is chosen as a predictive PDF. The bias correction parameters and the PDF parameters are estimated using a 30-day sliding window training period. Preliminary results show that the improvement varies between catchments

  2. Short-Term Energy Outlook Model Documentation: Macro Bridge Procedure to Update Regional Macroeconomic Forecasts with National Macroeconomic Forecasts

    EIA Publications

    2010-01-01

    The Regional Short-Term Energy Model (RSTEM) uses macroeconomic variables such as income, employment, industrial production and consumer prices at both the national and regional1 levels as explanatory variables in the generation of the Short-Term Energy Outlook (STEO). This documentation explains how national macroeconomic forecasts are used to update regional macroeconomic forecasts through the RSTEM Macro Bridge procedure.

  3. Efficiency of a real time flood forecasting system in the Alps and in the Apennines: deterministic versus ensemble predictions

    NASA Astrophysics Data System (ADS)

    Grossi, G.

    2009-04-01

    Real time hydrological forecasting is still a challenging task for most of the Italian territory, especially in mountain areas where both the topography and the meteorological forcing are affected by a strong spatial variability. Nevertheless there is an increasing request to provide some clues for the development of efficient real time flood forecasting systems, for warning population as well as for water management purposes. In this perspective the efficiency of a real time forecasting system needs to be investigated, with particular care to the uncertainty of the provided prediction and to how this prediction will be handled by water resources managers and land protection services. To this aim a real time flood forecasting system using both deterministic and ensemble meteorological predictions has been implemented at University of Brescia and applied to an Alpine area (the Toce River - Piemonte Region) and to an Apennine area (the Taro River - Emilia Romagna Region). The Map D- Phase experiment (autumn 2007) was a good test for the implemented system: daily rainfall fields provided by high resolution deterministic limited area meteorological models and esemble rainfall predictions provided by coarser resolution meteorological models could be used to force a hydrological model and produce either a single deterministic or an esemble of flood forecats. Namely only minor flood events occurred in the Alpine area in autumn 2007, while one major flood event affected the Taro river at the end of November 2007. Focusing on this major event the potentials of the forecasting system was tested and evaluated with reference also to the geographical and climatic characteristics of the investigated area.

  4. Sub-Optimal Ensemble Filters and distributed hydrologic modeling: a new challenge in flood forecasting

    NASA Astrophysics Data System (ADS)

    Baroncini, F.; Castelli, F.

    2009-09-01

    Data assimilation techniques based on Ensemble Filtering are widely regarded as the best approach in solving forecast and calibration problems in geophysics models. Often the implementation of statistical optimal techniques, like the Ensemble Kalman Filter, is unfeasible because of the large amount of replicas used in each time step of the model for updating the error covariance matrix. Therefore the sub optimal approach seems to be a more suitable choice. Various sub-optimal techniques were tested in atmospheric and oceanographic models, some of them are based on the detection of a "null space". Distributed Hydrologic Models differ from the other geo-fluid-dynamics models in some fundamental aspects that make complex to understanding the relative efficiency of the different suboptimal techniques. Those aspects include threshold processes , preferential trajectories for convection and diffusion, low observability of the main state variables and high parametric uncertainty. This research study is focused on such topics and explore them through some numerical experiments on an continuous hydrologic model, MOBIDIC. This model include both water mass balance and surface energy balance, so it's able to assimilate a wide variety of datasets like traditional hydrometric "on ground" measurements or land surface temperature retrieval from satellite. The experiments that we present concern to a basin of 700 kmq in center Italy, with hourly dataset on a 8 months period that includes both drought and flood events, in this first set of experiment we worked on a low spatial resolution version of the hydrologic model (3.2 km). A new Kalman Filter based algorithm is presented : this filter try to address the main challenges of hydrological modeling uncertainty. The proposed filter use in Forecast step a COFFEE (Complementary Orthogonal Filter For Efficient Ensembles) approach with a propagation of both deterministic and stochastic ensembles to improve robustness and convergence

  5. Quantitative precipitation forecast of the Soverato flood : The role of orography and surface fluxes

    NASA Astrophysics Data System (ADS)

    Federico, S.; Bellecci, C.; Colacino, M.

    2003-01-01

    During the night between 9 and 10 September 2000 a strong flood occurred in Soverato, a small town of Ionian coast of Calabria, killing 13 people. This was the top of an intense precipitation event occurred over the region during 8th, 9th, 10th September. In this paper the study of this event is performed, both analysing the synoptical aspects and using a numerical meteorological model either to reproduce the precipitation fields or to highlight some mesoscale features that determined the very intense and abundant rainfall. After a short description of the case study and presentation of measured rainfall fields, simulations are discussed. The study is based on three numerical simulations performed using the CSU-RAMS model (Regional mesoscale Modeling System) developed at Colorado State University and daily used at Crati Scrl to produce weather forecasts over Calabria peninsula. The first run is the control case and assesses the model ability to reproduce the flood cumulated rainfall by comparison with rain gauge data collected by the “Istituto Idrografico e Mareografico-Dipartimento di Catanzaro”. Second simulation is made to assess the influence of orographic barriers on the precipitation field, while third simulation evaluates the sensitivity to latent and sensible heat fluxes. Results indicate that the model simulate in satisfactory way the location and amount of rainfall, even if some problems are open and require more investigations.

  6. A hydro-meteorological ensemble prediction system for real-time flood forecasting purposes in the Milano area

    NASA Astrophysics Data System (ADS)

    Ravazzani, Giovanni; Amengual, Arnau; Ceppi, Alessandro; Romero, Romualdo; Homar, Victor; Mancini, Marco

    2015-04-01

    Analysis of forecasting strategies that can provide a tangible basis for flood early warning procedures and mitigation measures over the Western Mediterranean region is one of the fundamental motivations of the European HyMeX programme. Here, we examine a set of hydro-meteorological episodes that affected the Milano urban area for which the complex flood protection system of the city did not completely succeed before the occurred flash-floods. Indeed, flood damages have exponentially increased in the area during the last 60 years, due to industrial and urban developments. Thus, the improvement of the Milano flood control system needs a synergism between structural and non-structural approaches. The flood forecasting system tested in this work comprises the Flash-flood Event-based Spatially distributed rainfall-runoff Transformation, including Water Balance (FEST-WB) and the Weather Research and Forecasting (WRF) models, in order to provide a hydrological ensemble prediction system (HEPS). Deterministic and probabilistic quantitative precipitation forecasts (QPFs) have been provided by WRF model in a set of 48-hours experiments. HEPS has been generated by combining different physical parameterizations (i.e. cloud microphysics, moist convection and boundary-layer schemes) of the WRF model in order to better encompass the atmospheric processes leading to high precipitation amounts. We have been able to test the value of a probabilistic versus a deterministic framework when driving Quantitative Discharge Forecasts (QDFs). Results highlight (i) the benefits of using a high-resolution HEPS in conveying uncertainties for this complex orographic area and (ii) a better simulation of the most of extreme precipitation events, potentially enabling valuable probabilistic QDFs. Hence, the HEPS copes with the significant deficiencies found in the deterministic QPFs. These shortcomings would prevent to correctly forecast the location and timing of high precipitation rates and

  7. A Research on Development of The Multi-mode Flood Forecasting System Version Management

    NASA Astrophysics Data System (ADS)

    Shen, J.-C.; Chang, C. H.; Lien, H. C.; Wu, S. J.; Horng, M. J.

    2009-04-01

    paper proposed the feasible avenues and solutions to smoothly integrate different configurations from different teams. In the current system has been completed by 20 of Taiwan's main rivers in the building of the basic structure of the flood forecasting. And regular updating of the relevant parameters, using the new survey results, in order to have a better flood forecasting results.

  8. Evaluating the one-way coupling of WRF-Hydro for flood forecasting

    NASA Astrophysics Data System (ADS)

    Yucel, Ismail; Onen, Alper; Yilmaz, Koray; Gochis, David

    2016-04-01

    A fully-distributed, multi-physics, multi-scale hydrologic and hydraulic modeling system, WRF-Hydro, is used to assess the potential for skillful flood forecasting based on precipitation inputs derived from the Weather Research and Forecasting (WRF) model and the EUMETSAT Multi-sensor Precipitation Estimates (MPEs). Similar to past studies it was found that WRF model precipitation forecast errors related to model initial conditions are reduced when the three dimensional atmospheric data assimilation (3DVAR) scheme in the WRF model simulations is used. A comparative evaluation of the impact of MPE versus WRF precipitation estimates, both with and without data assimilation, in driving WRF-Hydro simulated streamflow is then made. The ten rainfall-runoff events that occurred in the Black Sea Region were used for testing and evaluation. With the availability of streamflow data across rainfall-runoff events, the cal- ibration is only performed on the Bartin sub-basin using two events and the calibrated parameters are then transferred to other neighboring three ungauged sub-basins in the study area. The rest of the events from all sub-basins are then used to evaluate the performance of the WRF-Hydro system with the cali- brated parameters. Following model calibration, the WRF-Hydro system was capable of skillfully repro- ducing observed flood hydrographs in terms of the volume of the runoff produced and the overall shape of the hydrograph. Streamflow simulation skill was significantly improved for those WRF model simula- tions where storm precipitation was accurately depicted with respect to timing, location and amount. Accurate streamflow simulations were more evident in WRF model simulations where the 3DVAR scheme was used compared to when it was not used. Because of substantial dry bias feature of MPE, as compared with surface rain gauges, streamflow derived using this precipitation product is in general very poor. Overall, root mean squared errors for runoff were

  9. Exploring the Limits of Flood Forecasting in Mountain Basins by using QPE and QPF Products in a Physically-based, Distributed Hydrologic Model during Summer Convection

    NASA Astrophysics Data System (ADS)

    Moreno, H. A.; Vivoni, E. R.; Gochis, D. J.

    2012-12-01

    This research work explores the predictability characteristics of floods and flash floods by coupling high-resolution precipitation products to a distributed hydrologic model. The research hypotheses are tested in multiple watersheds of the Colorado Front Range (CFR) under warm-season precipitation. Rainfall error structures propagate into hydrologic simulations with added uncertainties introduced by model parameters and initial conditions. Specifically, the following science questions are responded: (1) What is the utility of Quantitative Precipitation Estimates (QPE) for high-resolution hydrologic forecasts in mountain watersheds of the CFR?, (2) How does the rainfall-reflectivity relation determine the magnitude of errors when radar observations are used for flood forecasts?, and (3) What are the spatiotemporal limits of flood forecasting in mountain basins when radar nowcasts are used in a distributed hydrological model? Results reveal that radar and multisensor QPEs lead to an improved hydrologic performance compared to simulations driven with rain gauge data only. In addition, hydrologic performances attained by satellite products open new avenues for forecasting in regions with limited access and sparse observations. Hydrologic simulations are shown to be sensitive to the uncertainties introduced by the reflectivity-rainfall (Z-R) relation. This suggests that site-specific Z-R relations, prior to forecasting procedures, are desirable in complex terrain regions. Radar nowcasting experiments show the limits of flood forecasting and its dependence functions on lead time and catchment area. Across the majority of the basins, flood forecasting skill decays with lead time, but the functional relation depends on the interactions between watershed properties and rainfall characteristics. Both precipitation and flood forecasting skills are noticeably reduced for lead times greater than 30 minutes. The scale-dependence of hydrologic forecasting errors demonstrates

  10. A Multitemporal Remote Sensing Approach to Streamflow Prediction and Flood Vulnerability Forecasting

    NASA Astrophysics Data System (ADS)

    Weissling, B. P.; Xie, H.

    2006-12-01

    precipitation, land surface temperature, and select vegetation indices accounted for 78% (R2adj = 0.78) of the variance of gage station observed streamflow for calendar year 2004. Efforts are underway to calibrate and validate this model for other time periods within the data availability window of MODIS imagery products, and for other watersheds of varying size and similar climatic regime within the Guadalupe River and neighboring basins. The success of this remote sensing approach will have implications for developing near real-time flood risk and vulnerability forecasting models for both gaged and ungaged watersheds, as well as water supply management in regions of the world with limited resources to undertake conventional ground-based hydrologic studies.

  11. Towards a better knowledge of flash flood forecasting at the Three Gorges Region: Progress over the past decade and challenges ahead

    NASA Astrophysics Data System (ADS)

    Li, Zhe; Yang, Dawen; Yang, Hanbo; Wu, Tianjiao; Xu, Jijun; Gao, Bing; Xu, Tao

    2015-04-01

    The study area, the Three Gorges Region (TGR), plays a critical role in predicting the floods drained into the Three Gorges Reservoir, as reported local floods often exceed 10000m3/s during rainstorm events and trigger fast as well as significant impacts on the Three Gorges Reservoir's regulation. Meanwhile, it is one of typical mountainous areas in China, which is located in the transition zone between two monsoon systems: the East Asian monsoon and the South Asian (Indian) monsoon. This climatic feature, combined with local irregular terrains, has shaped complicated rainfall-runoff regimes in this focal region. However, due to the lack of high-resolution hydrometeorological data and physically-based hydrologic modeling framework, there was little knowledge about rainfall variability and flood pattern in this historically ungauged region, which posed great uncertainties to flash flood forecasting in the past. The present study summarize latest progresses of regional flash floods monitoring and prediction, including installation of a ground-based Hydrometeorological Observation Network (TGR-HMON), application of a regional geomorphology-based hydrological model (TGR-GBHM), development of an integrated forecasting and modeling system (TGR-INFORMS), and evaluation of quantitative precipitation estimations (QPE) and quantitative precipitation forecasting (QPF) products in TGR flash flood forecasting. With these continuing efforts to improve the forecasting performance of flash floods in TGR, we have addressed several critical issues: (1) Current observation network is still insufficient to capture localized rainstorms, and weather radar provides valuable information to forecast flash floods induced by localized rainstorms, although current radar QPE products can be improved substantially in future; (2) Long-term evaluation shows that the geomorphology-based distributed hydrologic model (GBHM) is able to simulate flash flooding processes reasonably, while model

  12. 44 CFR Appendix B to Part 62 - National Flood Insurance Program

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... 44 Emergency Management and Assistance 1 2011-10-01 2011-10-01 false National Flood Insurance..., DEPARTMENT OF HOMELAND SECURITY INSURANCE AND HAZARD MITIGATION National Flood Insurance Program SALE OF INSURANCE AND ADJUSTMENT OF CLAIMS Pt. 62, App. B Appendix B to Part 62—National Flood Insurance Program...

  13. 44 CFR Appendix B to Part 62 - National Flood Insurance Program

    Code of Federal Regulations, 2013 CFR

    2013-10-01

    ... 44 Emergency Management and Assistance 1 2013-10-01 2013-10-01 false National Flood Insurance..., DEPARTMENT OF HOMELAND SECURITY INSURANCE AND HAZARD MITIGATION National Flood Insurance Program SALE OF INSURANCE AND ADJUSTMENT OF CLAIMS Pt. 62, App. B Appendix B to Part 62—National Flood Insurance Program...

  14. 44 CFR Appendix B to Part 62 - National Flood Insurance Program

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    ... 44 Emergency Management and Assistance 1 2014-10-01 2014-10-01 false National Flood Insurance..., DEPARTMENT OF HOMELAND SECURITY INSURANCE AND HAZARD MITIGATION National Flood Insurance Program SALE OF INSURANCE AND ADJUSTMENT OF CLAIMS Pt. 62, App. B Appendix B to Part 62—National Flood Insurance Program...

  15. 44 CFR Appendix B to Part 62 - National Flood Insurance Program

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... 44 Emergency Management and Assistance 1 2012-10-01 2011-10-01 true National Flood Insurance..., DEPARTMENT OF HOMELAND SECURITY INSURANCE AND HAZARD MITIGATION National Flood Insurance Program SALE OF INSURANCE AND ADJUSTMENT OF CLAIMS Pt. 62, App. B Appendix B to Part 62—National Flood Insurance Program...

  16. 44 CFR Appendix B to Part 62 - National Flood Insurance Program

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 44 Emergency Management and Assistance 1 2010-10-01 2010-10-01 false National Flood Insurance..., DEPARTMENT OF HOMELAND SECURITY INSURANCE AND HAZARD MITIGATION National Flood Insurance Program SALE OF INSURANCE AND ADJUSTMENT OF CLAIMS Pt. 62, App. B Appendix B to Part 62—National Flood Insurance Program...

  17. The HBV spatially distributed flash flood forecasting model - The Slovenia case study

    NASA Astrophysics Data System (ADS)

    Tsanis, I. K.; Grillakis, M. G.; Blöschl, G.; Pogačnik, N.

    2009-04-01

    The HBV distributed flash flood forecasting model which is in operational use in northern Austria is applied to a watershed in northwest Slovenia, a case study for the FP6 project HYDRATE. The selected watershed consists of 6 sub-basins with a total area of 646 Km2. Model setup and calibration was performed in this watershed and three long duration rainfall - runoff periods were simulated in order to examine the efficiency of the model. The selected periods included rainfall events that produced high outflows on the exit of the watershed, such as the September 2007 event that caused a flash flooding and severe damages to the towns of Zali Log and Zelezniki. The model uses 1km grid rainfall and temperature data of fifteen minute time intervals in order to simulate the rainfall - runoff process. Inverse distance weighting interpolation is used in order to generate the spatially distributed rainfall and temperature while the hydrological parameters are defined for each 1km grid cell that correspond to one hydrological response units (HRU - areas with analogous hydrogeological characteristics). The basic calibration of the HBV model is based on hydrological parameters of each HRU, parameters that control the rainfall - runoff process within the basin and non HRU parameters that control the river routing between the basins. The model performance is based on seven efficiency criteria that were selected as appropriate for long simulation periods, e.g. coefficient of determination R2 and Nash Sutcliffe efficiency E. The HBV model produced satisfactory results for the three rainfall periods and could be used as an operational model in Slovenia as well.

  18. The potential of flood forecasting using a variable-resolution global Digital Terrain Model and flood extents from Synthetic Aperture Radar images.

    NASA Astrophysics Data System (ADS)

    Mason, David; Garcia-Pintado, Javier; Cloke, Hannah; Dance, Sarah

    2015-08-01

    A basic data requirement of a river flood inundation model is a Digital Terrain Model (DTM) of the reach being studied. The scale at which modeling is required determines the accuracy required of the DTM. For modeling floods in urban areas, a high resolution DTM such as that produced by airborne LiDAR (Light Detection And Ranging) is most useful, and large parts of many developed countries have now been mapped using LiDAR. In remoter areas, it is possible to model flooding on a larger scale using a lower resolution DTM, and in the near future the DTM of choice is likely to be that derived from the TanDEM-X Digital Elevation Model (DEM). A variable-resolution global DTM obtained by combining existing high and low resolution data sets would be useful for modeling flood water dynamics globally, at high resolution wherever possible and at lower resolution over larger rivers in remote areas. A further important data resource used in flood modeling is the flood extent, commonly derived from Synthetic Aperture Radar (SAR) images. Flood extents become more useful if they are intersected with the DTM, when water level observations (WLOs) at the flood boundary can be estimated at various points along the river reach. To illustrate the utility of such a global DTM, two examples of recent research involving WLOs at opposite ends of the spatial scale are discussed. The first requires high resolution spatial data, and involves the assimilation of WLOs from a real sequence of high resolution SAR images into a flood model to update the model state with observations over time, and to estimate river discharge and model parameters, including river bathymetry and friction. The results indicate the feasibility of such an Earth Observation-based flood forecasting system. The second example is at a larger scale, and uses SAR-derived WLOs to improve the lower-resolution TanDEM-X DEM in the area covered by the flood extents. The resulting reduction in random height error is significant.

  19. Forecasting Austrian national elections: The Grand Coalition model

    PubMed Central

    Aichholzer, Julian; Willmann, Johanna

    2014-01-01

    Forecasting the outcomes of national elections has become established practice in several democracies. In the present paper, we develop an economic voting model for forecasting the future success of the Austrian ‘grand coalition’, i.e., the joint electoral success of the two mainstream parties SPOE and OEVP, at the 2013 Austrian Parliamentary Elections. Our main argument is that the success of both parties is strongly tied to the accomplishments of the Austrian system of corporatism, that is, the Social Partnership (Sozialpartnerschaft), in providing economic prosperity. Using data from Austrian national elections between 1953 and 2008 (n=18), we rely on the following predictors in our forecasting model: (1) unemployment rates, (2) previous incumbency of the two parties, and (3) dealignment over time. We conclude that, in general, the two mainstream parties benefit considerably from low unemployment rates, and are weakened whenever they have previously formed a coalition government. Further, we show that they have gradually been losing a good share of their voter basis over recent decades. PMID:26339109

  20. 77 FR 36566 - National Flood Insurance Program (NFIP); Assistance to Private Sector Property Insurers...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-06-19

    ... SECURITY Federal Emergency Management Agency National Flood Insurance Program (NFIP); Assistance to Private..., 2012) private sector property insurers sell flood insurance policies and adjust flood insurance claims... the remaining premium to the Federal Government. The Federal Government pays flood losses and...

  1. Forecasts, warnings and social response to flash floods: Is temporality a major problem? The case of the September 2005 flash flood in the Gard region (France)

    NASA Astrophysics Data System (ADS)

    Lutoff, C.; Anquetin, S.; Ruin, I.; Chassande, M.

    2009-09-01

    Flash floods are complex phenomena. The atmospheric and hydrological generating mechanisms of the phenomenon are not completely understood, leading to highly uncertain forecasts of and warnings for these events. On the other hand warning and crisis response to such violent and fast events is not a straightforward process. In both the social and physical aspect of the problem, space and time scales involved either in hydrometeorology, human behavior and social organizations sciences are of crucial importance. Forecasters, emergency managers, mayors, school superintendents, school transportation managers, first responders and road users, all have different time and space frameworks that they use to take emergency decision for themselves, their group or community. The integration of space and time scales of both the phenomenon and human activities is therefore a necessity to better deal with questions as forecasting lead-time and warning efficiency. The aim of this oral presentation is to focus on the spatio-temporal aspects of flash floods to improve our understanding of the event dynamic compared to the different scales of the social response. The authors propose a framework of analysis to compare the temporality of: i) the forecasts (from Méteo-France and from EFAS (Thielen et al., 2008)), ii) the meteorological and hydrological parameters, iii) the social response at different scales. The September 2005 event is particularly interesting for such analysis. The rainfall episode lasted nearly a week with two distinct phases separated by low intensity precipitations. Therefore the Méteo-France vigilance bulletin where somehow disconnected from the local flood’s impacts. Our analysis focuses on the timings of different types of local response, including the delicate issue of school transportation, in regard to the forecasts and the actual dynamic of the event.

  2. Discharge forecasting using MODIS and radar altimetry: potential application for transboundary flood risk management in Niger-Benue River basin

    NASA Astrophysics Data System (ADS)

    Tarpanelli, Angelica; Amarnath, Giriraj; Brocca, Luca; Moramarco, Tommaso

    2016-04-01

    Flooding is one of most widespread natural disasters in the world. Its impact is particularly severe and destructive in Asia and Africa, because the living conditions of some settlements are inadequate to cope with this type of natural hazard. In this context, the estimation of discharge is extremely important to address water management and flood risk assessment. However, the inadequate monitoring network hampers any control and prediction activity that could improve these disastrous situations. In the last few years, remote sensing sensors have demonstrated their effectiveness in retrieving river discharge, especially in supporting discharge nowcasting and forecasting activities. Recently, the potential of radar altimetry was apparent when used for estimating water levels in an ungauged river site with good accuracy. It has also become a very useful tool for estimation and prediction of river discharge. However, the low temporal resolution of radar altimeter observations (10 or 35 days, depending on the satellite mission) may be not suitable for day-by-day hydrological forecasting. Differently, MODerate resolution Imaging Spectroradiometer (MODIS), considering its proven potential for quantifying the variations in discharge of the rivers at daily time resolution may be more suited to this end. For these reasons, MODIS and radar altimetry data were used in this study to predicting and forecasting the river discharge along the Niger-Benue River, where severe flooding with extensive damage to property and loss of lives occurred. Therefore, an effective method to forecast flooding can support efforts towards creating an early warning system. In order to estimate river discharge, four MODIS products (daily, 8-day, and from AQUA and TERRA satellites) connected at three sites (two gauged and one ungauged) were used. The capability of remote sensing sensors to forecast discharge a few days in advance at a downstream section using MODIS and ENVISAT radar altimetry data

  3. Calibration and parameterization of a semi-distributed hydrological model to support sub-daily ensemble flood forecasting; a watershed in southeast Brazil

    NASA Astrophysics Data System (ADS)

    de Almeida Bressiani, D.; Srinivasan, R.; Mendiondo, E. M.

    2013-12-01

    The use of distributed or semi-distributed models to represent the processes and dynamics of a watershed in the last few years has increased. These models are important tools to predict and forecast the hydrological responses of the watersheds, and they can subside disaster risk management and planning. However they usually have a lot of parameters, of which, due to the spatial and temporal variability of the processes, are not known, specially in developing countries; therefore a robust and sensible calibration is very important. This study conduced a sub-daily calibration and parameterization of the Soil & Water Assessment Tool (SWAT) for a 12,600 km2 watershed in southeast Brazil, and uses ensemble forecasts to evaluate if the model can be used as a tool for flood forecasting. The Piracicaba Watershed, in São Paulo State, is mainly rural, but has about 4 million of population in highly relevant urban areas, and three cities in the list of critical cities of the National Center for Natural Disasters Monitoring and Alerts. For calibration: the watershed was divided in areas with similar hydrological characteristics, for each of these areas one gauge station was chosen for calibration; this procedure was performed to evaluate the effectiveness of calibrating in fewer places, since areas with the same group of groundwater, soil, land use and slope characteristics should have similar parameters; making calibration a less time-consuming task. The sensibility analysis and calibration were performed on the software SWAT-CUP with the optimization algorithm: Sequential Uncertainly Fitting Version 2 (SUFI-2), which uses Latin hypercube sampling scheme in an iterative process. The performance of the models to evaluate the calibration and validation was done with: Nash-Sutcliffe efficiency coefficient (NSE), determination coefficient (r2), root mean square error (RMSE), and percent bias (PBIAS), with monthly average values of NSE around 0.70, r2 of 0.9, normalized RMSE of 0

  4. Can end-users' flood management decision making be improved by information about forecast uncertainty?

    NASA Astrophysics Data System (ADS)

    Frick, J.; Hegg, C.

    2011-05-01

    In the course of the D-PHASE project, a visualisation platform was created, which provided a large amount of meteorological and hydrological information that was used not only by scientists, but also by scientifically aware laypeople in the field of flood prevention. This paper investigates the benefits of the platform for its end-users' situation analysis and decision making, and in particular, its usefulness in providing an ensemble of models instead of already interpreted forecasts. To evaluate the platform's impact on users in Switzerland, a panel approach was used. Twenty-four semi-standardized questionnaires were completed at the beginning of the demonstration phase and 27 questionnaires were completed five months later. The results suggest that the platform was perceived as adding value to both situation analysis and decision making, and helped users to feel more confident about both. Interestingly, users' preference for receiving complex, primary information and forming their own impressions over receiving interpreted information and recommendations increased during the demonstration phase. However, no actual improvement in the quality of decisions was reported.

  5. Regional hydrological models for distributed flash-floods forecasting: towards an estimation of potential impacts and damages

    NASA Astrophysics Data System (ADS)

    Le Bihan, Guillaume; Payrastre, Olivier; Gaume, Eric; Pons, Frederic; Moncoulon, David

    2016-04-01

    Hydrometeorological forecasting is an essential component of real-time flood management. The information it provides is of great help for crisis managers to anticipate the inundations and the associated risks. In the particular case of flash-floods, which may affect a large amount of small watersheds spread over the territory (up to 300 000 km of waterways considering a drained area of 5 km² minimum in France), appropriate flood forecasting systems are still under development. In France, highly distributed hydrological models have been implemented, enabling a real-time assessment of the potential intensity of flash-floods from the records of weather radars: AIGA-hydro system (Lavabre et al., 2005; Javelle et al., 2014), PreDiFlood project (Naulin et al., 2013). The approach presented here aims to go one step further by offering a direct assessment of the potential impacts of the simulated floods on inhabited areas. This approach is based on an a priori analysis of the study area in order (1) to evaluate with a simplified hydraulic approach (DTM treatment) the potentially flooded areas for different discharge levels, and (2) to identify the associated buildings and/or population at risk from geographic databases. This preliminary analysis enables to build an impact model (discharge-impact curve) on each river reach, which is then used to directly estimate the potentially affected assets based on a distributed rainfall runoff model. The overall principle of this approach was already presented at the 8th Hymex workshop. Therefore, the presentation will be here focused on the first validation results in terms of (1) accuracy of flooded areas simulated from DTM treatments, and (2) relevance of estimated impacts. The inundated areas simulated were compared to the European Directive cartography results (where available), showing an overall good correspondence in a large majority of cases, but also very significant errors for approximatively 10% of the river reaches

  6. 4D Floodplain representation in hydrologic flood forecasting using WRFHydro modeling framework

    NASA Astrophysics Data System (ADS)

    Gangodagamage, C.; Li, Z.; Adams, T.; Ito, T.; Maitaria, K.; Islam, M.; Dhondia, J.

    2015-12-01

    Floods claim more lives and damage more property than any other category of natural disaster in the Continental U.S. A system that can demarcate local flood boundaries dynamically could help flood prone communities prepare for and even prevent from catastrophic flood events. Lateral distance from the centerline of the river to the right and left floodplains for the water levels coming out of the models at each grid location have not been properly integrated with the national hydrography dataset (NHDPlus). The NHDPlus dataset represents the stream network with feature classes such as rivers, tributaries, canals, lakes, ponds, dams, coastlines, and stream gages. The NHDPlus dataset consists of approximately 2.7 million river reaches defining how surface water drains to the ocean. These river reaches have upstream and downstream nodes and basic parameters such as flow direction, drainage area, reach slope etc. We modified an existing algorithm (Gangodagamage et al., 2007, 2011) to provide lateral distance from the centerline of the river to the right and left floodplains for the flows simulated by models. Previous work produced floodplain boundaries for static river stages (i.e. 3D metric: distance along the main stem, flow depth, lateral distance from river center line). Our new approach introduces the floodplain boundary for variable water levels with the fourth dimension, time. We use modeled flows from WRFHydro and demarcate the right and left lateral boundaries of inundation dynamically. This approach dynamically integrates with high resolution models (e.g., hourly and ~ 1 km spatial resolution) that are developed from recent advancements in high computational power with ground based measurements (e.g., Fluxnet), lateral inundation vectors (direction and spatial extent) derived from multi-temporal remote sensing data (e.g., LiDAR, WorldView 2, Landsat, ASTER, MODIS), and improved representations of the physical processes through multi-parameterizations. Our

  7. Remote Sensing-Derived Water Extent and Level to Constrain Hydraulic Flood Forecasting Models: Opportunities and Challenges

    NASA Astrophysics Data System (ADS)

    Grimaldi, Stefania; Li, Yuan; Pauwels, Valentijn R. N.; Walker, Jeffrey P.

    2016-09-01

    Accurate, precise and timely forecasts of flood wave arrival time, depth and velocity at each point of the floodplain are essential to reduce damage and save lives. Current computational capabilities support hydraulic models of increasing complexity over extended catchments. Yet a number of sources of uncertainty (e.g., input and boundary conditions, implementation data) may hinder the delivery of accurate predictions. Field gauging data of water levels and discharge have traditionally been used for hydraulic model calibration, validation and real-time constraint. However, the discrete spatial distribution of field data impedes the testing of the model skill at the two-dimensional scale. The increasing availability of spatially distributed remote sensing (RS) observations of flood extent and water level offers the opportunity for a comprehensive analysis of the predictive capability of hydraulic models. The adequate use of the large amount of information offered by RS observations triggers a series of challenging questions on the resolution, accuracy and frequency of acquisition of RS observations; on RS data processing algorithms; and on calibration, validation and data assimilation protocols. This paper presents a review of the availability of RS observations of flood extent and levels, and their use for calibration, validation and real-time constraint of hydraulic flood forecasting models. A number of conclusions and recommendations for future research are drawn with the aim of harmonising the pace of technological developments and their applications.

  8. Development of Open-book Watershed Modeling for Satellite Based Flood Forecasting in International River Basin

    NASA Astrophysics Data System (ADS)

    Katiyar, N.; Hossain, F.

    2006-12-01

    A parsimonious way to understand the surface hydrology across the political boundaries of a river basin is to adopt an open book watershed modeling approach, first formulated by Yen and Chow (1961). The proposed NASA mission, Global Precipitation Measurement (GPM) may now usher a new era of application of the open- book modeling framework to understand the worth of anticipated availability of high resolution satellite rainfall data for predicting transboundary river flow. In our study we developed, verified and implemented our open-book watershed model for rapid prototyping of satellite rainfall based flood monitoring systems for International River Basins (IRBs). The model follows conservation of mass and momentum balance that brings minimum requirement of data. We first demonstrate the physical consistency of our model through sensitivity analysis of some geo physical basin parameters pertinent to rainfall-runoff transformation. Next, we simulate the stream- flow hydrograph for a 4 month long period using basin-wide radar (WSR-88D) rainfall data over Oklahoma assuming an open-book configuration. Finally, using the radar-simulated hydrograph as the benchmark, and assuming a two-nation hypothetical IRB over Oklahoma, we explored the impact of assimilating NASA's real- time satellite rainfall data (IR-3B41RT) over the upstream nation on the flow monitoring accuracy of the downstream nation. We developed a relationship defining the improvement in flow monitoring that can be expected from assimilating IR-3B41RT over transboundary regions as a function of the relative area occupied by the downstream nation. The relative improvement in flow monitoring accuracy for the downstream nation can be a maximum of 45% when more than 90% of the basin is transboundary to the nation. However, flow monitoring accuracy does not appear amenable for tangible improvement when 25% or less of the basin area is transboundary to the downstream nation. For IR-3B41RT to have non

  9. How does the 6-hour lead time flood forecast issued by the M5 model tree for the Krka River really look like?

    NASA Astrophysics Data System (ADS)

    Stravs, L.; Brilly, M.

    2009-04-01

    The Krka River is one the main tributaries of the Sava River with a drainage area of approximately 2250 km2. Population areas in the Krka River basin are highly endangered by the possibility of extreme flooding, so knowledge about the river flow's behaviour at the time of extreme events like floods is of the highest importance. By using the M5 machine learning method for generation of regression and model tree models 1-, 2-, 3-, 4-, 5- and 6-hour lead time forecasting models (models which include and forecasting models which don't include up to 6 hours ahead precipitation forecasts) were developed for Podbocje gauging station which is located near the Krka River basin outlet. The data used in the process of model development was hourly flow data for the Podbocje gauging station and hourly rainfall data for two weather stations in the Krka River basin. The data included the period from 1999 to 2004. In the field of hydroinformatics the results of the hydrological modelling with the machine learning methods are usually presented separately (individually) for the models with different forecasting lead times (i.e. separately for 1-hour lead time forecasting model, separately for 3-hour lead time forecasting model, etc.), which is usually pretty inconvenient for the end users of the flood forecasting models. So the aim of our research was to evaluate flood forecasts in the form of 6-hour ahead flood forecast curves produced by the combined application of the 6 model tree models with 1-, 2-, 3-, 4-, 5- and 6-hour lead times at the same time. Analysis included both visual and numerical evaluation of the results obtained by the application of the forecasting models with and without the inclusion of the precipitation forecasts in the models' structure.

  10. Uncertainty propagation for flood forecasting in the Alps: different views and impacts from MAP D-PHASE

    NASA Astrophysics Data System (ADS)

    Rotach, M. W.; Arpagaus, M.; Dorninger, M.; Hegg, C.; Montani, A.; Ranzi, R.

    2012-08-01

    D-PHASE was a Forecast Demonstration Project of the World Weather Research Programme (WWRP) related to the Mesoscale Alpine Programme (MAP). Its goal was to demonstrate the reliability and quality of operational forecasting of orographically influenced (determined) precipitation in the Alps and its consequences on the distribution of run-off characteristics. A special focus was, of course, on heavy-precipitation events. The D-PHASE Operations Period (DOP) ran from June to November~2007, during which an end-to-end forecasting system was operated covering many individual catchments in the Alps, with their water authorities, civil protection organizations or other end users. The forecasting system's core piece was a Visualization Platform where precipitation and flood warnings from some 30 atmospheric and 7 hydrological models (both deterministic and probabilistic) and corresponding model fields were displayed in uniform and comparable formats. Also, meteograms, nowcasting information and end user communication was made available to all the forecasters, users and end users. D-PHASE information was assessed and used by some 50 different groups ranging from atmospheric forecasters to civil protection authorities or water management bodies. In the present contribution, D-PHASE is briefly presented along with its outstanding scientific results and, in particular, the lessons learnt with respect to uncertainty propagation. A focus is thereby on the transfer of ensemble prediction information into the hydrological community and its use with respect to other aspects of societal impact. Objective verification of forecast quality is contrasted to subjective quality assessments during the project (end user workshops, questionnaires) and some general conclusions concerning forecast demonstration projects are drawn.

  11. Comparison between genetic programming and an ensemble Kalman filter as data assimilation techniques for probabilistic flood forecasting

    NASA Astrophysics Data System (ADS)

    Mediero, L.; Garrote, L.; Requena, A.; Chávez, A.

    2012-04-01

    Flood events are among the natural disasters that cause most economic and social damages in Europe. Information and Communication Technology (ICT) developments in last years have enabled hydrometeorological observations available in real-time. High performance computing promises the improvement of real-time flood forecasting systems and makes the use of post processing techniques easier. This is the case of data assimilation techniques, which are used to develop an adaptive forecast model. In this paper, a real-time framework for probabilistic flood forecasting is presented and two data assimilation techniques are compared. The first data assimilation technique uses genetic programming to adapt the model to the observations as new information is available, updating the estimation of the probability distribution of the model parameters. The second data assimilation technique uses an ensemble Kalman filter to quantify errors in both hydrologic model and observations, updating estimates of system states. Both forecast models take the result of the hydrologic model calibration as a starting point and adapts the individuals of this first population to the new observations in each operation time step. Data assimilation techniques have great potential when are used in hydrological distributed models. The distributed RIBS (Real-time Interactive Basin Simulator) rainfall-runoff model was selected to simulate the hydrological process in the basin. The RIBS model is deterministic, but it is run in a probabilistic way through Monte Carlo simulations over the probability distribution functions that best characterise the most relevant model parameters, which were identified by a probabilistic multi-objective calibration developed in a previous work. The Manzanares River basin was selected as a case study. Data assimilation processes are computationally intensive. Therefore, they are well suited to test the applicability of the potential of the Grid technology to

  12. Identification of hydrological model parameters for flood forecasting using data depth measures

    NASA Astrophysics Data System (ADS)

    Krauße, T.; Cullmann, J.

    2011-03-01

    validated for flood forecasting in a small catchment characterised by extreme process dynamics.

  13. Forecast of muddy floods using high-resolution radar precipitation forcasting data and erosion modelling

    NASA Astrophysics Data System (ADS)

    Hänsel, Phoebe; Schindewolf, Marcus; Schmidt, Jürgen

    2016-04-01

    In the federal province of Saxony, Eastern Germany, almost 60 % of the agricultural land is endangered by erosion processes, mainly caused by heavy rainfall events. Beside the primary impact of soil loss and decreasing soil fertility, erosion can cause significant effects if transported sediments are entering downslope settlements, infrastructure or traffic routes. Available radar precipitation data are closing the gap between the conventional rainfall point measurements and enable the nationwide rainfall distribution with high spatial and temporal resolution. By means of the radar precipitation data of the German Weather Service (DWD), high-resolution radar-based rainfall data totals up to 5 minute time steps are possible. The radar data are visualised in a grid-based hourly precipitation map. In particular, the daily and hourly precipitation maps help to identify regions with heavy rainfall and possible erosion events. In case of an erosion event on agricultural land, these areas are mapped with an unmanned airborne vehicle (UAV). The camera-equipped UAV delivers high-resolution images of the erosion event, that allow the generation of high-resolution orthophotos. By the application of the high-resolution radar precipitation data as an input for the process-based soil loss and deposition model EROSION 3D, these images are for validation purposes. Future research is focused on large scale soil erosion modelling with the help of the radar forecasting product and an automatic identification of sediment pass over points. The study will end up with an user friendly muddy flood warning tool, which allows the local authorities to initiate immediate measures in order to prevent severe damages in settlements, infrastructure or traffic routes.

  14. Radar-driven high-resolution hydro-meteorological forecasts of the 26 September 2007 Venice flash flood

    NASA Astrophysics Data System (ADS)

    Rossa, Andrea M.; Laudanna Del Guerra, Franco; Borga, Marco; Zanon, Francesco; Settin, Tommaso; Leuenberger, Daniel

    2010-11-01

    SummaryThis study aims to assess the feasibility of assimilating carefully checked radar rainfall estimates into a numerical weather prediction (NWP) to extend the forecasting lead time for an extreme flash flood. The hydro-meteorological modeling chain includes the convection-permitting NWP model COSMO-2 and a coupled hydrological-hydraulic model. Radar rainfall estimates are assimilated into the NWP model via the latent heat nudging method. The study is focused on 26 September 2007 extreme flash flood which impacted the coastal area of North-eastern Italy around Venice. The hydro-meteorological modeling system is implemented over the 90 km2 Dese river basin draining to the Venice Lagoon. The radar rainfall observations are carefully checked for artifacts, including rain-induced signal attenuation, by means of physics-based correction procedures and comparison with a dense network of raingauges. The impact of the radar rainfall estimates in the assimilation cycle of the NWP model is very significant. The main individual organized convective systems are successfully introduced into the model state, both in terms of timing and localization. Also, high-intensity incorrectly localized precipitation is correctly reduced to about the observed levels. On the other hand, the highest rainfall intensities computed after assimilation underestimate the observed values by 20% and 50% at a scale of 20 km and 5 km, respectively. The positive impact of assimilating radar rainfall estimates is carried over into the free forecast for about 2-5 h, depending on when the forecast was started. The positive impact is larger when the main mesoscale convective system is present in the initial conditions. The improvements in the precipitation forecasts are propagated to the river flow simulations, with an extension of the forecasting lead time up to 3 h.

  15. Coupling Radar estimated precipitation with distributed hydrological model for flood forecast

    NASA Astrophysics Data System (ADS)

    Chen, Y.

    2009-12-01

    CINRAD radar is China’s new generation digital weather radar with which more than 100 systems have been deployed currently in China. CINRAD uses a mixture of C-Band and S-Band radars that can detect heavy rain at an interval of 5-6 minutes at 9 elevation angles in the precipitation detection mode with a maximum radius of 460 km. CINRAD can work reliably under extreme weather conditions and at high spatial resolution (depending on the distance to the radar and not greater than 4 km2 spatial resolution). It is expected that CINRAD will contribute to the observation of extreme storms and estimate precipitation quantitatively over much of populated China at high temporal and spatial resolution. In particular, in Southern China where many extreme floods derive from Topical Cyclones the S-Band Doppler radars are likely to revolutionise the development of real-time flood forecasting system design. Physically based distributed hydrological models, which discrete the terrain of a whole basin into a number of squared grids, and assign different model parameters and rainfall to different grids, thus a better representation of the basin characteristics and the hydrologic process, has the potential to better simulate and predict the flood processes. In this study the method coupling a physically based distributed hydrological model-the Liuxihe Model with CINRAD estimated precipitation is tested in several cases in Southern China, including small catchments, large river basins and urban area. The three CINRAD radars studied include the ones deployed in Guangzhou, Shaoguan and Meizhou Cites with data in the past three years. Before employed to estimate precipitation, the radar data reflectivity data was first quality controlled with several procedures including volume scan data, missing data interpolation, abnormal reflectivity detection and correction and fine day reflectivity removal. The estimated precipitation then is corrected with the observed rain gauge precipitation

  16. Sensitivity of precipitation forecasts to convective parameterization in the October 2007 Flash Flood in the Valencia Region (Eastern Spain)

    NASA Astrophysics Data System (ADS)

    Gómez, I.; Pastor, F.; Estrela, M.

    2010-06-01

    The Valencia region, on the Mediterranean coast of the Iberian Peninsula, is an area prone to torrential rains, especially the north of Alicante province and the south of Valencia province. In October 2007, a torrential rain event with accumulated rainfall values exceeding 400 mm in less than 24 h affected the aforementioned areas, producing flash floods that caused extensive economic losses and human casualties. Several simulations of this rain event have been performed with the Regional Atmospheric Modeling System (RAMS) to test the influence of the different convective parameterization scheme implemented in the model on the precipitation forecast.

  17. 77 FR 31814 - National Flood Insurance Program (NFIP); Insurance Coverage and Rates

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-05-30

    ...The Federal Emergency Management Agency (FEMA) is withdrawing a previously published Notice of Proposed Rulemaking (NPRM) concerning National Flood Insurance Program (NFIP) insurance premium rates for structures that have suffered multiple flood losses. The proposed rule would have required owners of such structures to pay a higher premium for flood insurance if they declined an offer of......

  18. Floods

    MedlinePlus

    Floods are common in the United States. Weather such as heavy rain, thunderstorms, hurricanes, or tsunamis can ... is breached, or when a dam breaks. Flash floods, which can develop quickly, often have a dangerous ...

  19. Improvement of rainfall and flood forecasts by blending ensemble NWP rainfall with radar prediction considering orographic rainfall

    NASA Astrophysics Data System (ADS)

    Yu, Wansik; Nakakita, Eiichi; Kim, Sunmin; Yamaguchi, Kosei

    2015-12-01

    Many basins in Japan are characterized by steep mountainous regions, generating orographic rainfall events. Orographic rainfall may cause localized heavy rainfall to induce flash floods and sediment disasters. However, the accuracy of radar-based rainfall prediction was not enough because of the complex geographical pattern of the mountainous areas. In order to reduce damage due to localized heavy rainfall, characteristics of orographic rainfall must be identified into a short-term rainfall prediction procedure. The accuracy of radar-based rainfall prediction performs best for very short lead time, however the accuracy of radar prediction rapidly decreases with increasing lead times. At longer lead times, higher accuracy QPFs are produced by Numerical Weather Prediction (NWP) models, which solve the dynamics and physics of the atmosphere. This study proposes hybrid blending system of ensemble information from radar-based prediction and numerical weather prediction (NWP) to improve the accuracy of rainfall and flood forecasting. First, an improved radar image extrapolation method, which is comprised of the orographic rainfall identification and the error ensemble scheme, is introduced. Then, ensemble NWP outputs are updated based on mean bias of the error fields considering error structure. Finally, the improved radar-based prediction and updated NWP rainfall considering bias correction are blended dynamically with changing weight functions, which are computed from the expected skill of each radar prediction and updated NWP rainfall. The proposed method is verified temporally and spatially through a target event and is applied to the hybrid flood forecasting for updating with 1 h intervals. The newly proposed method shows sufficient reproducibility in peak discharge value, and could reduce the width of ensemble spread, which is expressed as the uncertainty, in the flood forecasting. Our study is carried out and verified using the largest flood event by typhoon

  20. Cross-institutional Flood Forecasting in Regional Water Systems;Innovative application of Delft-FEWS in The Netherlands

    NASA Astrophysics Data System (ADS)

    van Heeringen, Klaas-Jan; Douben, Klaas-Jan; van de Wouw, Mark; IJpelaar, Ruben; van Loenen, Arnejan

    2015-04-01

    The regional water system in the North-Brabant province in The Netherlands is (operationally) managed by four different Water Authorities: Rijkswaterstaat Southern-Netherlands, and the three Regional Water Authorities (RWA's) Aa & Maas, De Dommel and Brabantse Delta. The water systems basically consist of mid-sized (navigable) canals, semi-natural brook valleys in mildly sloping sandy soils, and man-made watercourses in clayey polder areas. The management areas of the De Dommel and Brabantse Delta RWA's are bordering Belgium over a total length of approx. 185 km, and are prone to transboundary flood flows. The current project 'Dynamic Water Management' intends to improve the mutual cooperation and communication between the RWA's and Rijkswaterstaat during periods of both high and low water stages. The project deals with governance issues such as water agreements and water systems analyses. A powerful product of the project is a DSS for flood forecasting ('DSS Brabant'). One of the main benefits of cooperation between the RWA's and Rijkswaterstaat is to enable assistance during peak flows and flood events and to try to optimise operational water systems management by deploying drainage and storage facilities by using the connecting (navigable) canals. A set of hydraulic structures like pumps, weirs and sluices facilitate the control and routing of the water flows. Especially during peak flow and flood events, these canals allow to deviate excess flow to neighbours who suffer less from flooding. During regular conditions the water systems are fully independent, but during floods connections are made by using the canal system. The heart of DSS Brabant consists of a Delft-FEWS application, containing several RTC (1st) and hydrodynamic Sobek (2nd order) models FEWS is receiving a variety of data on hourly or six-hourly basis, consisting of measured and forecasted meteorological input (radar-precipitation/HIRLAM, evaporation and wind), water levels and discharges at

  1. A Climate Service Prototype for the Hydropower Industry: Using a Multi-Model Approach to Improve Seasonal Forecasts of the Spring Flood Period in Sweden.

    NASA Astrophysics Data System (ADS)

    Foster, K. L.; Uvo, C. B.; Olsson, J.; Bosshard, T.; Berg, P.; Södling, J.

    2015-12-01

    The Ångerman River is Sweden's third largest river with over 50 regulation reservoirs, 42 hydropower stations and accounts for nearly 20% of the country's hydropower production. In seasonally snow covered regions, such as the Ångerman river system, the winter precipitation is often temporarily stored in the snow pack during the colder months and released over a relatively short period of intense flows during in the warmer months. These spring flood events dominate the hydrology in these regions and as such reliable hydrological forecasts of these events are in great demand. However, it has been shown that the spread in the forecast error of operational hydrological forecasts in Sweden have not changed significantly over the last 25 years (Arheimer et al., 2012). Building on previous work by Foster et al. (2011) and Olsson et al. (2015), a multi-model hydrological seasonal forecast prototype has been developed for the Ångerman river system and applied to the spring flood season. The prototype employs different model-chain approaches: (1) a climatological forecast, where historical observations for the same period are selected to run the hydrological model, (2) a reduced historical ensemble, where analogue years from the historical observations dataset are selected to run the hydrological model, (3) using bias corrected meteorological seasonal forecasts from the ECMWF to force the hydrological model, and (4) statistical downscaling large-scale circulation variables from ECMWF seasonal forecasts directly to accumulated discharge. These different chains are combined into a multi-model ensemble forecast. The different approaches and the multi-model prototype are evaluated against the state-of-the-art operational system for the spring flood season for the period 1981-2014 in the Ångerman river system. References Arheimer, B., Lindström, G., and Olsson, J.: A systematic review of sensitivities in the Swedish flood-forecasting system, Atmos. Res., 100, 275-284, 2011

  2. All-season flash flood forecasting system for real-time operations

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Flash floods can cause extensive damage to both life and property, especially because they are difficult to predict. Flash flood prediction requires high-resolution meteorologic observations and predictions, as well as calibrated hydrologic models in addition to extensive data handling. We have de...

  3. Near-real-time simulation and internet-based delivery of forecast-flood inundation maps using two-dimensional hydraulic modeling--A pilot study for the Snoqualmie River, Washington

    USGS Publications Warehouse

    Jones, Joseph L.; Fulford, Janice M.; Voss, Frank D.

    2002-01-01

    A system of numerical hydraulic modeling, geographic information system processing, and Internet map serving, supported by new data sources and application automation, was developed that generates inundation maps for forecast floods in near real time and makes them available through the Internet. Forecasts for flooding are generated by the National Weather Service (NWS) River Forecast Center (RFC); these forecasts are retrieved automatically by the system and prepared for input to a hydraulic model. The model, TrimR2D, is a new, robust, two-dimensional model capable of simulating wide varieties of discharge hydrographs and relatively long stream reaches. TrimR2D was calibrated for a 28-kilometer reach of the Snoqualmie River in Washington State, and is used to estimate flood extent, depth, arrival time, and peak time for the RFC forecast. The results of the model are processed automatically by a Geographic Information System (GIS) into maps of flood extent, depth, and arrival and peak times. These maps subsequently are processed into formats acceptable by an Internet map server (IMS). The IMS application is a user-friendly interface to access the maps over the Internet; it allows users to select what information they wish to see presented and allows the authors to define scale-dependent availability of map layers and their symbology (appearance of map features). For example, the IMS presents a background of a digital USGS 1:100,000-scale quadrangle at smaller scales, and automatically switches to an ortho-rectified aerial photograph (a digital photograph that has camera angle and tilt distortions removed) at larger scales so viewers can see ground features that help them identify their area of interest more effectively. For the user, the option exists to select either background at any scale. Similar options are provided for both the map creator and the viewer for the various flood maps. This combination of a robust model, emerging IMS software, and application

  4. Ensemble Statistical Post-Processing of the National Air Quality Forecast Capability: Enhancing Ozone Forecasts in Baltimore, Maryland

    NASA Technical Reports Server (NTRS)

    Garner, Gregory G.; Thompson, Anne M.

    2013-01-01

    An ensemble statistical post-processor (ESP) is developed for the National Air Quality Forecast Capability (NAQFC) to address the unique challenges of forecasting surface ozone in Baltimore, MD. Air quality and meteorological data were collected from the eight monitors that constitute the Baltimore forecast region. These data were used to build the ESP using a moving-block bootstrap, regression tree models, and extreme-value theory. The ESP was evaluated using a 10-fold cross-validation to avoid evaluation with the same data used in the development process. Results indicate that the ESP is conditionally biased, likely due to slight overfitting while training the regression tree models. When viewed from the perspective of a decision-maker, the ESP provides a wealth of additional information previously not available through the NAQFC alone. The user is provided the freedom to tailor the forecast to the decision at hand by using decision-specific probability thresholds that define a forecast for an ozone exceedance. Taking advantage of the ESP, the user not only receives an increase in value over the NAQFC, but also receives value for An ensemble statistical post-processor (ESP) is developed for the National Air Quality Forecast Capability (NAQFC) to address the unique challenges of forecasting surface ozone in Baltimore, MD. Air quality and meteorological data were collected from the eight monitors that constitute the Baltimore forecast region. These data were used to build the ESP using a moving-block bootstrap, regression tree models, and extreme-value theory. The ESP was evaluated using a 10-fold cross-validation to avoid evaluation with the same data used in the development process. Results indicate that the ESP is conditionally biased, likely due to slight overfitting while training the regression tree models. When viewed from the perspective of a decision-maker, the ESP provides a wealth of additional information previously not available through the NAQFC alone

  5. Performing Multiple Simulations for Multiple Watersheds in Flood Forecasting Using the GSSHA Distributed Hydrologic Models in Large Basins

    NASA Astrophysics Data System (ADS)

    Perez, F.; Jones, N.; Nelson, E. J.; Christensen, S. D.

    2014-12-01

    When massive storm events are imminently expected over a given area, flood forecast centers often need to execute hydrologic model simulations for multiple watersheds some of which may be large in size for distributed models to be able to complete in a relatively short time. And since it is advantageous to subdivide the watersheds for a distributed model like GSSHA to be able to have short run times, the operational solution needs to consider simultaneous simulations runs for the models of the individual sub-basins. The number of simulations in each watershed would increase if there are different scenarios that consider several weather forecasts with storm tracks and direction that produce many rainfall patterns that would differ in the magnitude and spatial distribution of rainfall. The problem is further increased when it is necessary to analyze several possible watershed conditions and several emergency operation alternatives for flood control. Some computer hardware solution is needed to perform the many that would result from the combination of the forecasted scenarios, the operation alternatives, watershed conditions in an already large number of watersheds which might also be subdivided into many sub-basins. The first problem is finding a suitable watershed sub-division that breaks down the size of the areas for the models in a way that saves run-time. This is accomplished in two test-case watersheds using a workflow of basin-model orchestration where arrangements of parallel and cascade simulations are done for the sub-basins of tributary rivers and the main river. The second problem of running all the possible simulations is dealt with using distributed computing options such as HT Condor and Microsoft Azure. The results are compared using operational performance indicators.

  6. National Air Quality Forecast Capability: Status and Research Needs

    NASA Astrophysics Data System (ADS)

    Stajner, I.; McQueen, J.; Lee, P.; Draxler, R. R.; Tong, D.; Pan, L.; Huang, J. P.; Shafran, P.; Dickerson, P.; Upadhayay, S.

    2014-12-01

    Operational air quality predictions for the United States (U. S.) are provided by National Air Quality Forecasting Capability (NAQFC), which is being built by NOAA in partnership with the U.S. EPA. NAQFC provides nationwide operational predictions of ozone, smoke from wildfires, as well as dust from dust storms for the contiguous 48 states. Predictions are produced beyond midnight of the following day at 12 km resolution and 1 hour time intervals and distributed at http://airquality.weather.gov. Ozone predictions and developmental testing of aerosol predictions combine the NOAA National Centers for Environmental Prediction (NCEP) operational North American Mesoscale (NAM) weather predictions with the Community Multiscale Air Quality (CMAQ) model. Predictions of smoke and dust storms use the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Routine verification of ozone and developmental aerosol predictions relies on AIRNow observations, whereas smoke and dust predictions rely on satellite retrievals. Recent updates to operational ozone prediction at NOAA have focused on mobile emissions, which were updated using the projections of mobile sources for 2012. Satellite and ground observations were used to derive NOx trends, which were compared with the emissions data used by NAQFC indicating improved agreement over large metropolitan areas in the US. Updates to the chemical mechanism are being tested for operational implementation. Recent testing of PM2.5 predictions is relying on National Emission Inventory (NEI) inputs augmented by real time sources from wildfires and dust storms. Testing of PM2.5 predictions continues to exhibit seasonal biases - overprediction in the winter and underprediction in the summer. Current efforts are focusing on inclusion of bias correction and development of linkages with global atmospheric composition predictions.

  7. Effects of temperature on flood forecasting: analysis of an operative case study in Alpine basins

    NASA Astrophysics Data System (ADS)

    Ceppi, A.; Ravazzani, G.; Salandin, A.; Rabuffetti, D.; Montani, A.; Borgonovo, E.; Mancini, M.

    2013-04-01

    In recent years the interest in the forecast and prevention of natural hazards related to hydro-meteorological events has increased the challenge for numerical weather modelling, in particular for limited area models, to improve the quantitative precipitation forecasts (QPF) for hydrological purposes. After the encouraging results obtained in the MAP D-PHASE Project, we decided to devote further analyses to show recent improvements in the operational use of hydro-meteorological chains, and above all to better investigate the key role played by temperature during snowy precipitation. In this study we present a reanalysis simulation of one meteorological event, which occurred in November 2008 in the Piedmont Region. The attention is focused on the key role of air temperature, which is a crucial feature in determining the partitioning of precipitation in solid and liquid phase, influencing the quantitative discharge forecast (QDF) into the Alpine region. This is linked to the basin ipsographic curve and therefore by the total contributing area related to the snow line of the event. In order to assess hydrological predictions affected by meteorological forcing, a sensitivity analysis of the model output was carried out to evaluate different simulation scenarios, considering the forecast effects which can radically modify the discharge forecast. Results show how in real-time systems hydrological forecasters have to consider also the temperature uncertainty in forecasts in order to better understand the snow dynamics and its effect on runoff during a meteorological warning with a crucial snow line over the basin. The hydrological ensemble forecasts are based on the 16 members of the meteorological ensemble system COSMO-LEPS (developed by ARPA-SIMC) based on the non-hydrostatic model COSMO, while the hydrological model used to generate the runoff simulations is the rainfall-runoff distributed FEST-WB model, developed at Politecnico di Milano.

  8. 77 FR 23270 - Agency Information Collection Activities: Proposed Collection; Comment Request, National Flood...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-04-18

    ...; Comment Request, National Flood Insurance Program Call Center and Agent Referral Enrollment Form AGENCY... them in obtaining such coverage. Collection of Information Title: National Flood Insurance Program Call Center and Agent Referral Enrollment Form. Type of Information Collection: Revision of a...

  9. Flood warning level forecasting for ungauged catchments by means of a combined API storage concept

    NASA Astrophysics Data System (ADS)

    Lehmann, T.; Holzmann, H.

    2008-11-01

    The knowledge of the expected dimension of the flood peak is of major importance for security and warning services to take preventive measures. In this paper the authors want to introduce the concept of the Antecedent Precipitation Index (API) as a possible variable to estimate runoff warning classes. The aim was (a) to define API warning classes which correspond to runoff warning classes at a certain runoff gauge and (b) apply the method to ungauged basins. To consider time and state dependant rainfall losses a spatially distributed linear storage concept was applied to intercept the actual rainfall. The 3-parameter API function was fitted to several flood events at observed gauges within the district of lower Austria and lead to a set of optimized parameters. Through extreme value statistics the 1, 5 and 30 years API extremes were derived and set into correlation to the corresponding flood events. These API extremes together with the optimized API parameters were spatially interpolated and thus transferred to ungauged basins. The calculated flood events had the tendency to underestimate the smaller flood frequencies whereas the extreme flood classes could be reliably performed.

  10. IMPROVING NATIONAL AIR QUALITY FORECASTS WITH SATELLITE AEROSOL OBSERVATIONS

    EPA Science Inventory

    Air quality forecasts for major US metropolitan areas have been provided to the public through a partnership between the US Environmental Protection Agency and state and local air agencies since 1997. Recent years have witnessed improvement in forecast skill and expansion of fore...

  11. The Hurricane-Flood-Landslide Continuum: An Integrated, End-to-end Forecast and Warning System for Mountainous Islands in the Tropics

    NASA Astrophysics Data System (ADS)

    Golden, J.; Updike, R. G.; Verdin, J. P.; Larsen, M. C.; Negri, A. J.; McGinley, J. A.

    2004-12-01

    In the 10 days of 21-30 September 1998, Hurricane Georges left a trail of destruction in the Caribbean region and U.S. Gulf Coast. Subsequently, in the same year, Hurricane Mitch caused widespread destruction and loss of life in four Central American nations, and in December,1999 a tropical disturbance impacted the north coast of Venezuela causing hundreds of deaths and several million dollars of property loss. More recently, an off-season disturbance in the Central Caribbean dumped nearly 250 mm rainfall over Hispaniola during the 24-hr period on May 23, 2004. Resultant flash floods and debris flows in the Dominican Republic and Haiti killed at least 1400 people. In each instance, the tropical system served as the catalyst for major flooding and landslides at landfall. Our goal is to develop and transfer an end-to-end warning system for a prototype region in the Central Caribbean, specifically the islands of Puerto Rico and Hispaniola, which experience frequent tropical cyclones and other disturbances. The envisioned system would include satellite and surface-based observations to track and nowcast dangerous levels of precipitation, atmospheric and hydrological models to predict short-term runoff and streamflow changes, geological models to warn when and where landslides and debris flows are imminent, and the capability to communicate forecast guidance products via satellite to vital government offices in Puerto Rico, Haiti, and the Dominican Republic. In this paper, we shall present a preliminary proof-of-concept study for the May 21-24, 2004 floods and debris-flows over Hispaniola to show that the envisaged flow of data, models and graphical products can produce the desired warning outputs. The multidisciplinary research and technology transfer effort will require blending the talents of hydrometeorologists, geologists, remote sensing and GIS experts, and social scientists to ensure timely delivery of tailored graphical products to both weather offices and local

  12. A wavelet-based non-linear autoregressive with exogenous inputs (WNARX) dynamic neural network model for real-time flood forecasting using satellite-based rainfall products

    NASA Astrophysics Data System (ADS)

    Nanda, Trushnamayee; Sahoo, Bhabagrahi; Beria, Harsh; Chatterjee, Chandranath

    2016-08-01

    Although flood forecasting and warning system is a very important non-structural measure in flood-prone river basins, poor raingauge network as well as unavailability of rainfall data in real-time could hinder its accuracy at different lead times. Conversely, since the real-time satellite-based rainfall products are now becoming available for the data-scarce regions, their integration with the data-driven models could be effectively used for real-time flood forecasting. To address these issues in operational streamflow forecasting, a new data-driven model, namely, the wavelet-based non-linear autoregressive with exogenous inputs (WNARX) is proposed and evaluated in comparison with four other data-driven models, viz., the linear autoregressive moving average with exogenous inputs (ARMAX), static artificial neural network (ANN), wavelet-based ANN (WANN), and dynamic nonlinear autoregressive with exogenous inputs (NARX) models. First, the quality of input rainfall products of Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA), viz., TRMM and TRMM-real-time (RT) rainfall products is assessed through statistical evaluation. The results reveal that the satellite rainfall products moderately correlate with the observed rainfall, with the gauge-adjusted TRMM product outperforming the real-time TRMM-RT product. The TRMM rainfall product better captures the ground observations up to 95 percentile range (30.11 mm/day), although the hit rate decreases for high rainfall intensity. The effect of antecedent rainfall (AR) and climate forecast system reanalysis (CFSR) temperature product on the catchment response is tested in all the developed models. The results reveal that, during real-time flow simulation, the satellite-based rainfall products generally perform worse than the gauge-based rainfall. Moreover, as compared to the existing models, the flow forecasting by the WNARX model is way better than the other four models studied herein with the

  13. Use of ASCAT derived soil moisture product for real-time flood forecasting in the Upper Tiber River

    NASA Astrophysics Data System (ADS)

    Brocca, Luca; Melone, Florisa; Moramarco, Tommaso; Wagner, Wolfgang; Hasenauer, Stefan; Berni, Nicola

    2010-05-01

    The role and the importance of soil moisture for meteorological, agricultural and hydrological applications is widely acknowledged. In particular, for a given storm event, different values of initial soil moisture conditions can discriminate between minor or catastrophic effects. Therefore, a real time flood forecasting system founded on a rainfall-runoff model strictly requires an accurate estimation of the initial state of the catchment wetness to obtain a reliable estimation of the flood hydrograph. It has to be pointed out that, in flood-prone areas, a Flood Monitoring and Warning System operating in real time represents the main non-structural measure to be actuated to dampen the risk. At the catchment scale, soil moisture monitoring can be addressed by using sensors operating on remote sensing platforms. Among them, the coarse resolution scatterometers have been employed in different studies due to their high temporal resolution suitable for hydrological applications. Specifically, the Advanced Scatterometer (ASCAT) on-board of the Meteorological Operational satellite provides an operative surface soil moisture product available at global scale since March 2007. This sensor is characterized by a spatial resolution of 25/50 km and a nearly daily time step. To get profile soil moisture estimates, an exponential filter is applied to the time series of the ASCAT surface soil moisture obtaining the so-called Soil Wetness Index (SWI). The reliability of the SWI was recently evaluated through the comparison both with in-situ and modelled soil moisture data in the Upper Tiber River basin. In this study, the effects of assimilating satellite-derived soil moisture estimates into a continuous and distributed rainfall-runoff model, named MISDc, were assessed. This topic is relevant not only for scientific purposes but also for operational applications. In fact, the MISDc model is actually operative at the Umbria Region Functional Centre for real time flood forecasting in

  14. Ensemble statistical post-processing of the National Air Quality Forecast Capability: Enhancing ozone forecasts in Baltimore, Maryland

    NASA Astrophysics Data System (ADS)

    Garner, Gregory G.; Thompson, Anne M.

    2013-12-01

    An ensemble statistical post-processor (ESP) is developed for the National Air Quality Forecast Capability (NAQFC) to address the unique challenges of forecasting surface ozone in Baltimore, MD. Air quality and meteorological data were collected from the eight monitors that constitute the Baltimore forecast region. These data were used to build the ESP using a moving-block bootstrap, regression tree models, and extreme-value theory. The ESP was evaluated using a 10-fold cross-validation to avoid evaluation with the same data used in the development process. Results indicate that the ESP is conditionally biased, likely due to slight overfitting while training the regression tree models. When viewed from the perspective of a decision-maker, the ESP provides a wealth of additional information previously not available through the NAQFC alone. The user is provided the freedom to tailor the forecast to the decision at hand by using decision-specific probability thresholds that define a forecast for an ozone exceedance. Taking advantage of the ESP, the user not only receives an increase in value over the NAQFC, but also receives value for costly decisions that the NAQFC couldn't provide alone.

  15. Toward a US National Air Quality Forecast Capability: Current and Planned Capabilities

    EPA Science Inventory

    As mandated by Congress, NOAA is establishing a US national air quality forecast capability. This capability is being built with EPA, to provide air quality forecast information with enough accuracy and lead-time so that people can take actions to limit harmful effects of poor a...

  16. Idaho National Laboratory Materials and Fuels Complex Natural Phenomena Hazards Flood Assessment

    SciTech Connect

    Gerald Sehlke; Paul Wichlacz

    2010-12-01

    This report presents the results of flood hazards analyses performed for the Materials and Fuels Complex (MFC) and the adjacent Transient Reactor Experiment and Test Facility (TREAT) located at Idaho National Laboratory. The requirements of these analyses are provided in the U.S. Department of Energy Order 420.1B and supporting Department of Energy (DOE) Natural Phenomenon Hazard standards. The flood hazards analyses were performed by Battelle Energy Alliance and Pacific Northwest National Laboratory. The analyses addressed the following: • Determination of the design basis flood (DBFL) • Evaluation of the DBFL versus the Critical Flood Elevations (CFEs) for critical existing structures, systems, and components (SSCs).

  17. Uncertainty analysis of neural network based flood forecasting models: An ensemble based approach for constructing prediction interval

    NASA Astrophysics Data System (ADS)

    Kasiviswanathan, K.; Sudheer, K.

    2013-05-01

    Artificial neural network (ANN) based hydrologic models have gained lot of attention among water resources engineers and scientists, owing to their potential for accurate prediction of flood flows as compared to conceptual or physics based hydrologic models. The ANN approximates the non-linear functional relationship between the complex hydrologic variables in arriving at the river flow forecast values. Despite a large number of applications, there is still some criticism that ANN's point prediction lacks in reliability since the uncertainty of predictions are not quantified, and it limits its use in practical applications. A major concern in application of traditional uncertainty analysis techniques on neural network framework is its parallel computing architecture with large degrees of freedom, which makes the uncertainty assessment a challenging task. Very limited studies have considered assessment of predictive uncertainty of ANN based hydrologic models. In this study, a novel method is proposed that help construct the prediction interval of ANN flood forecasting model during calibration itself. The method is designed to have two stages of optimization during calibration: at stage 1, the ANN model is trained with genetic algorithm (GA) to obtain optimal set of weights and biases vector, and during stage 2, the optimal variability of ANN parameters (obtained in stage 1) is identified so as to create an ensemble of predictions. During the 2nd stage, the optimization is performed with multiple objectives, (i) minimum residual variance for the ensemble mean, (ii) maximum measured data points to fall within the estimated prediction interval and (iii) minimum width of prediction interval. The method is illustrated using a real world case study of an Indian basin. The method was able to produce an ensemble that has an average prediction interval width of 23.03 m3/s, with 97.17% of the total validation data points (measured) lying within the interval. The derived

  18. Development of a national Flash flood warning system in France using the AIGA method: first results and main issues

    NASA Astrophysics Data System (ADS)

    Javelle, Pierre; Organde, Didier; Demargne, Julie; de Saint-Aubin, Céline; Garandeau, Léa; Janet, Bruno; Saint-Martin, Clotilde; Fouchier, Catherine

    2016-04-01

    Developing a national flash flood (FF) warning system is an ambitious and difficult task. On one hand it rises huge expectations from exposed populations and authorities since induced damages are considerable (ie 20 casualties in the recent October 2015 flood at the French Riviera). But on the other hand, many practical and scientific issues have to be addressed and limitations should be clearly stated. The FF warning system to be implemented by 2016 in France by the SCHAPI (French national service in charge of flood forecasting) will be based on a discharge-threshold flood warning method called AIGA (Javelle et al. 2014). The AIGA method has been experimented in real time in the south of France in the RHYTMME project (http://rhytmme.irstea.fr). It consists in comparing discharges generated by a simple conceptual hourly hydrologic model run at a 1-km² resolution to reference flood quantiles of different return periods, at any point along the river network. The hydrologic model ingests operational rainfall radar-gauge products from Météo-France. Model calibration was based on ~700 hydrometric stations over the 2002-2015 period and then hourly discharges were computed at ~76 000 catchment outlets, with areas ranging from 10 to 3 500 km², over the last 19 years. This product makes it possible to calculate reference flood quantiles at each outlet. The on-going evaluation of the FF warnings is currently made at two levels: in a 'classical' way, using discharges available at the hydrometric stations, but also in a more 'exploratory' way, by comparing past flood reports and warnings issued by the system over the 76 000 catchment outlets. The interest of the last method is that it better fit the system objectives since it is designed to monitor small ungauged catchments. Javelle, P., Demargne, J., Defrance, D, .Pansu, J, .Arnaud, P. (2014). Evaluating flash-flood warnings at ungauged locations using post-event surveys: a case study with the AIGA warning system

  19. The impact of satellite temperature soundings on the forecasts of a small national meteorological service

    NASA Technical Reports Server (NTRS)

    Wolfson, N.; Thomasell, A.; Alperson, Z.; Brodrick, H.; Chang, J. T.; Gruber, A.; Ohring, G.

    1984-01-01

    The impact of introducing satellite temperature sounding data on a numerical weather prediction model of a national weather service is evaluated. A dry five level, primitive equation model which covers most of the Northern Hemisphere, is used for these experiments. Series of parallel forecast runs out to 48 hours are made with three different sets of initial conditions: (1) NOSAT runs, only conventional surface and upper air observations are used; (2) SAT runs, satellite soundings are added to the conventional data over oceanic regions and North Africa; and (3) ALLSAT runs, the conventional upper air observations are replaced by satellite soundings over the entire model domain. The impact on the forecasts is evaluated by three verification methods: the RMS errors in sea level pressure forecasts, systematic errors in sea level pressure forecasts, and errors in subjective forecasts of significant weather elements for a selected portion of the model domain. For the relatively short range of the present forecasts, the major beneficial impacts on the sea level pressure forecasts are found precisely in those areas where the satellite sounding are inserted and where conventional upper air observations are sparse. The RMS and systematic errors are reduced in these regions. The subjective forecasts of significant weather elements are improved with the use of the satellite data. It is found that the ALLSAT forecasts are of a quality comparable to the SAR forecasts.

  20. Evaluation of the Sacramento Soil Moisture Accounting Model for Flood Forecasting in a Hawaiian Watershed

    NASA Astrophysics Data System (ADS)

    Awal, R.; Fares, A.; Michaud, J.; Chu, P.; Fares, S.; Rosener, M.; Kevin, K.

    2012-12-01

    The focus of this study was to assess the performance of the U.S. National Weather Service Sacramento Soil Moisture Accounting Model (SAC-SMA) on the flash flood prone Hanalei watershed, Kauai, Hawaii, using site specific hydrologic data. The model was calibrated and validated using six-years of observed field hydrological data, e.g., stream flow, and spatially distributed rainfall. The ordinary kriging method was used to calculate mean watershed wide hourly precipitation for the six years using data from twenty rain gauges from north shore Kauai including five rain gauges within the watershed. Ranges of the values of a priori SAC-SMA parameters were also estimated based on the site specific soil hydrological properties; these calculated values were well within those reported in literature for different watersheds SAC-SMA was run for one year runs using the calibration and validation data. The performance of model in predicting streamflow using average watershed wide values of the a priori parameters was very poor. SAC-SMA over predicted streamflow throughout the year as compared to observed streamflow data. The upper limit of the lower layer tension water capacity, LZTWM, parameter was higher than those reported in the literature this might be due to the wetter conditions, higher precipitation, in Hanalei watershed (>6400mm) than the other previously studied watersheds (<1600mm). When the upper bound of LZTWM varied between 2500 and 3000 during calibration, SAC-SMA's performance improved to satisfactory and even to good for almost all years based on PBIAS and Nash-Sutcliffe coefficients of efficiency. When we used optimized parameter of one year to other years for the validation, the performance of optimized parameter of year 2005 was satisfactory for most of the year when upper bound of LZTWM = 2500 and the optimized parameter of year 2004 was satisfactory for most of the year when upper bound of LZTWM = 3000. The annual precipitation of 2004 was the highest

  1. Seasonal Drought Prediction in East Africa: Can National Multi-Model Ensemble Forecasts Help?

    NASA Technical Reports Server (NTRS)

    Shukla, Shraddhanand; Roberts, J. B.; Funk, Christopher; Robertson, F. R.; Hoell, Andrew

    2014-01-01

    The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. As recently as in 2011 part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However seasonal drought prediction in this region faces several challenges. Lack of skillful seasonal rainfall forecasts; the focus of this presentation, is one of those major challenges. In the past few decades, major strides have been taken towards improvement of seasonal scale dynamical climate forecasts. The National Centers for Environmental Prediction's (NCEP) National Multi-model Ensemble (NMME) is one such state-of-the-art dynamical climate forecast system. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ ensemble member forecasts. Recent studies have indicated that in general NMME offers improvement over forecasts from any single model. However thus far the skill of NMME for forecasting rainfall in a vulnerable region like the East Africa has been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons for the region. (i.e. March-April-May, July-August-September and October-November- December). Simultaneously we also describe hybrid approaches; that combine statistical approaches with NMME forecasts; to improve rainfall forecast skill in the region when raw NMME forecasts lack in skill.

  2. Seasonal Drought Prediction in East Africa: Can National Multi-Model Ensemble Forecasts Help?

    NASA Technical Reports Server (NTRS)

    Shukla, Shraddhanand; Roberts, J. B.; Funk, Christopher; Robertson, F. R.; Hoell, Andrew

    2015-01-01

    The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. As recently as in 2011 part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However seasonal drought prediction in this region faces several challenges. Lack of skillful seasonal rainfall forecasts; the focus of this presentation, is one of those major challenges. In the past few decades, major strides have been taken towards improvement of seasonal scale dynamical climate forecasts. The National Centers for Environmental Prediction's (NCEP) National Multi-model Ensemble (NMME) is one such state-of-the-art dynamical climate forecast system. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ ensemble member forecasts. Recent studies have indicated that in general NMME offers improvement over forecasts from any single model. However thus far the skill of NMME for forecasting rainfall in a vulnerable region like the East Africa has been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons for the region. (i.e. March-April-May, July-August-September and October-November- December). Simultaneously we also describe hybrid approaches; that combine statistical approaches with NMME forecasts; to improve rainfall forecast skill in the region when raw NMME forecasts lack in skill.

  3. National Weather Service (NWS) Implementation of the Hydrologic Ensemble Forecast Service

    NASA Astrophysics Data System (ADS)

    Hartman, R. K.; Fresch, M. A.; Wells, E.

    2015-12-01

    Operational hydrologic forecasters as well as the communities that they serve have long recognized the value of including uncertainty in hydrologic projections. While single value (deterministic) forecasts are easy to understand and link to specific mitigation actions, the potential for using modern risk management strategies is very limited. This is particularly evident at lead times beyond a few days when forecast skill may be low but the value (and costs) of mitigation actions may be quite high. Based on nearly ten years of research and development, the NWS's National Water Center (NWC, formerly the Office of Hydrologic Development) implemented and evaluated the Hydrologic Ensemble Forecast Service (HEFS, see Demargne et al. 2014 Brown et al., 2013, Brown et al., 2014a/b/c). The HEFS provides hydrologic forecasts that reflect the total uncertainty, including that contributed by the meteorological forcing and the hydrologic modeling. The HEFS leverages the skill in weather and climate forecasts to produce ensemble forecasts of precipitation, temperature and streamflow at forecast lead times ranging from one hour to one year. The resulting ensembles represent a rich dataset from which a wide variety of risk-based decision support information can be derived. The NWS River Forecast Centers (RFCs) are starting to incorporate the Hydrologic Ensemble Forecast Service (HEFS,) into their routine operations. In 2012, five (of thirteen) RFCs began running and testing HEFS in an experimental mode. In 2015, HEFS was deployed (including training and software support) to the eight remaining RFCs. Currently, all RFCs are running the HEFS every day in real-time for an increasing number of forecast locations. Eventually, forecasts from the HEFS will be integrated into the warning/hazard services at the NWS Weather Forecast Offices (WFOs). This contribution describes the HEFS framework, the development and deployment strategy, and the operational plans for HEFS going forward.

  4. Understanding sources of uncertainty in flash-flood forecasting for semi-arid regions 1913

    Technology Transfer Automated Retrieval System (TEKTRAN)

    About one-third of the earth’s landsurface is located in arid or semi-arid regions, often in areas suffering severely from the negative impacts of desertification and population pressure. Reliable hydrological forecasts across spatial and temporal scales are crucial in order to achieve water securit...

  5. Hydrological modelling for flood forecasting: Calibrating the post-fire initial conditions

    NASA Astrophysics Data System (ADS)

    Papathanasiou, C.; Makropoulos, C.; Mimikou, M.

    2015-10-01

    Floods and forest fires are two of the most devastating natural hazards with severe socioeconomic, environmental as well as aesthetic impacts on the affected areas. Traditionally, these hazards are examined from different perspectives and are thus investigated through different, independent systems, overlooking the fact that they are tightly interrelated phenomena. In fact, the same flood event is more severe, i.e. associated with increased runoff discharge and peak flow and decreased time to peak, if it occurs over a burnt area than that occurring over a land not affected by fire. Mediterranean periurban areas, where forests covered with flammable vegetation coexist with agricultural land and urban zones, are typical areas particularly prone to the combined impact of floods and forest fires. Hence, the accurate assessment and effective management of post-fire flood risk becomes an issue of priority. The research presented in this paper aims to develop a robust methodological framework, using state of art tools and modern technologies to support the estimation of the change in time of five representative hydrological parameters for post-fire conditions. The proposed methodology considers both longer- and short-term initial conditions in order to assess the dynamic evolution of the selected parameters. The research focuses on typical Mediterranean periurban areas that are subjected to both hazards and concludes with a set of equations that associate post-fire and pre-fire conditions for five Fire Severity (FS) classes and three soil moisture states. The methodology has been tested for several flood events on the Rafina catchment, a periurban catchment in Eastern Attica (Greece). In order to validate the methodology, simulated hydrographs were produced and compared against available observed data. Results indicate a close convergence of observed and simulated flows. The proposed methodology is particularly flexible and thus easily adaptable to catchments with similar

  6. Calibration of structure in a distributed forecasting model for a semiarid flash flood: Dynamic surface storage and channel roughness

    NASA Astrophysics Data System (ADS)

    García-Pintado, Javier; Barberá, Gonzalo G.; Erena, Manuel; Castillo, Victor M.

    2009-10-01

    SummaryFlash floods pose a significant danger for life and property. Unfortunately, in arid and semiarid environment the runoff generation shows a complex non-linear behavior with a strong spatial and temporal non-uniformity. As a result, the predictions made by physically-based simulations in semiarid areas are subject to great uncertainty, and a failure in the predictive behavior of existing models is common. Thus better descriptions of physical processes at the watershed scale need to be incorporated into the hydrological model structures. For example, terrain relief has been systematically considered static in flood modelling at the watershed scale. Here, we show that the integrated effect of small distributed relief variations originated through concurrent hydrological processes within a storm event was significant on the watershed scale hydrograph. We model these observations by introducing dynamic formulations of two relief-related parameters at diverse scales: maximum depression storage, and roughness coefficient in channels. In the final ( a posteriori) model structure these parameters are allowed to be both time-constant or time-varying. The case under study is a convective storm in a semiarid Mediterranean watershed with ephemeral channels and high agricultural pressures (the Rambla del Albujón watershed; 556 km), which showed a complex multi-peak response. First, to obtain quasi-sensible simulations in the ( a priori) model with time-constant relief-related parameters, a spatially distributed parameterization was strictly required. Second, a generalized likelihood uncertainty estimation (GLUE) inference applied to the improved model structure, and conditioned to observed nested hydrographs, showed that accounting for dynamic relief-related parameters led to improved simulations. The discussion is finally broadened by considering the use of the calibrated model both to analyze the sensitivity of the watershed to storm motion and to attempt the flood

  7. What is the Safest Way to Cross the Valley of Death: Wisdom gained from Making a Satellite based Flood Forecasting System Operational and Owned by Stakeholders

    NASA Astrophysics Data System (ADS)

    Hossain, F.

    2013-12-01

    More than a decade ago, the National Research Council report popularized the term 'Valley of Death' to describe the region where research on Weather Satellites had struggled to survive before reaching maturity for societal applications. For example, the space vantage of earth observing satellites can solve some of the world's otherwise fundamentally intractable operational problems on water resources. However, recent experiences show that many of the potential beneficiaries, who are not as familiar with water cycle remote sensing missions or anthropogenic climate studies, referred here as the ';non-traditional consumers,' may have a more skeptical view based on their current practices. This talk will focus on one such non-traditional consumer group: the water resources managers/staff in developing nations of South Asia. Using real-world examples on applications and hands-on-training to make a satellite based flood forecasting system operational, the talk will dissect the view that is shared by many water managers of Bangladesh on satellite remote sensing for day to day decision making. The talk will share the experience and wisdom generated in the successful capacity building of emerging satellite technology for water management. It will end with an overview of initiatives for more effective promotion of the value of planned water cycle satellite missions for water resources management community in the developing world.

  8. The elasticity of hydrological forecast skill with respect to initial conditions and meteorological forcing for two major flood events in Germany

    NASA Astrophysics Data System (ADS)

    Thober, Stephan; Wood, Andy; Samaniego, Luis; Clark, Martyn; Kumar, Rohini; Zink, Matthias

    2014-05-01

    Major flood events are causing severe socio-economic damages. In Germany alone, the havoc wreaked by the 2002 and 2013 floods along the Elbe and Danube river amounted to more than 11 bn EUR. Highly skilled hydrological forecasts can help to mitigate such damages. Among several factors, these hydrological forecasts are strongly dependent on the initial conditions of the land surface at the beginning of the forecast period and the forecast skill of the meteorological forcing. Prior research has investigated how uncertainties of the initial conditions and meteorological forcing impact hydrological forecasts. In these studies, uncertainty is investigated by coupling an ensemble of basin initial conditions (e.g., snow, soil moisture) with an ensemble of meteorological forecasts (e.g., precipitation). However, most previous hydrological predictability studies focus on seasonal forecasts (e.g., forecasts of June-July-August flow volume, initialized on April 1st), and neglect the errors in meteorological forecasts at lead times from 1-14 days. In this study, an error growth model is proposed to investigate hydrological predictability at lead times of 1-14 days. This error growth model calculates a time-dependent weighted average between the perfect forecast and a stochastic perturbation of this. The time-dependent weights are derived from a logistic function. This error growth model thus attributes high weights to the perfect forecast for short lead times (e.g., less than five days) and low weights for longer lead times (e.g., more than five days). For longer lead times, more weight is given to the stochastic perturbation of the forecast and, hence, the ensemble spread is larger for these lead times resembling a higher uncertainty. Analogous to the error growth model, the initial conditions are calculated as a weighted average between the perfect condition and a historic condition of the land surface. The proposed framework is tested in Germany for the 2002 and 2013 flood

  9. Real time flood forecasting in the Nan Basin, Thailand, by using a distributed Xin'anjiang Model

    NASA Astrophysics Data System (ADS)

    Chen, Xiaohong; Qiu, Xiaobin

    2015-04-01

    process is adaptively corrected by Attenuation memory least squares method to obtain final forecasting flood process. Verifications of this real-time flood forecasting model show high precision and the model system has been practically used in Thailand.

  10. 76 FR 45281 - National Flood Insurance Program (NFIP); Assistance to Private Sector Property Insurers...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-07-28

    ... SECURITY Federal Emergency Management Agency National Flood Insurance Program (NFIP); Assistance to Private... private insurance companies (Companies) and to make available to the Companies the terms for subscription... July 1, 2011) private sector property insurers issue flood insurance policies and adjust...

  11. Skill Assessment of National Multi-Model Ensemble Forecasts for Seasonal Drought Prediction in East Africa

    NASA Astrophysics Data System (ADS)

    Shukla, S.; Hoell, A.; Roberts, J. B.; Funk, C. C.; Robertson, F. R.

    2014-12-01

    The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. As recently as 2011, part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at a seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However, seasonal drought prediction in this region faces several challenges including lack of skillful seasonal rainfall forecasts. The National Multi-model Ensemble (NMME); a state-of-the-art dynamical climate forecast system is potentially a promising tool for drought prediction in this region. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ forecasts ensemble members. Recent studies have indicated that in general NMME offers improvement over forecasts from any of the individual model. However, thus far the skill of NMME for forecasting rainfall in a vulnerable region like East Africa has largely been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons of the region. (i.e. March-April-May, July-August-September, and October-November-December). Additionally we describe a hybrid approach that combines statistical method with NMME forecasts to improve rainfall forecast skill in the region when raw NMME forecasts skill is lacking. This approach uses constructed analog method to improve NMME's March-April-May rainfall forecast skill in East Africa.

  12. Performance of an Advanced MOS System in the 1996-97 National Collegiate Weather Forecasting Contest.

    NASA Astrophysics Data System (ADS)

    Vislocky, Robert L.; Fritsch, J. Michael

    1997-12-01

    A prototype advanced model output statistics (MOS) forecast system that was entered in the 1996-97 National Collegiate Weather Forecast Contest is described and its performance compared to that of widely available objective guidance and to contest participants. The prototype system uses an optimal blend of aviation (AVN) and nested grid model (NGM) MOS forecasts, explicit output from the NGM and Eta guidance, and the latest surface weather observations from the forecast site. The forecasts are totally objective and can be generated quickly on a personal computer. Other "objective" forms of guidance tracked in the contest are 1) the consensus forecast (i.e., the average of the forecasts from all of the human participants), 2) the combination of NGM raw output (for precipitation forecasts) and NGM MOS guidance (for temperature forecasts), and 3) the combination of Eta Model raw output (for precipitation forecasts) and AVN MOS guidance (for temperature forecasts).Results show that the advanced MOS system finished in 20th place out of 737 original entrants, or better than approximately 97% of the human forecasters who entered the contest. Moreover, the advanced MOS system was slightly better than consensus (23d place). The fact that an objective forecast system finished ahead of consensus is a significant accomplishment since consensus is traditionally a very formidable "opponent" in forecast competitions. Equally significant is that the advanced MOS system was superior to the traditional guidance products available from the National Centers for Environmental Prediction (NCEP). Specifically, the combination of NGM raw output and NGM MOS guidance finished in 175th place, and the combination of Eta Model raw output and AVN MOS guidance finished in 266th place. The latter result is most intriguing since the proposed elimination of all NGM products would likely result in a serious degradation of objective products disseminated by NCEP, unless they are replaced with equal

  13. Meteorological and Hydrogeological Warning Thresholds in the operational bulletins of the Albanian National Centre for Forecast and Monitoring of Natural Risks

    NASA Astrophysics Data System (ADS)

    Marku, M.; Mustaqi, V.; Abazi, E.; Zaimi, K.; Vako, E.; Gjonaj, M.; Hoxhaj, F.; Deda, M.; Fiori, E.; Massabò, M.; Castelli, F.

    2012-04-01

    Most operational meteo-hydrological warning system uses fixed rainfall thresholds on given durations to switch alerting bulletins. This may be a too rough approximation in regions with strong climate gradient like Albanian, especially when this bulletins need to include the evaluation of potential ground effect like floods. In the framework of the International cooperation between the Civil Protection of Italy and Albania, the National Centre for Forecast and Monitoring of Natural Risks has been established at the Institute of Geosciences, Energy, Water and Environment (IGEWE). The Centre is supported by expertise of CIMA Research Foundation - International Centre on Environmental Monitoring. The Centre issues (every morning) on a daily basis a Meteorological Warning Bulletin (the first bulletin was issued quite recently on the 20th of December 2011). It is mostly dedicated to the precipitation forecast, the most important hazard in Albania. It covers 36 hours, starting for the noon of the current day till the end of the next day. It offers a detailed precipitation forecast for each prefecture of Albania (12 in total). The prefectures that have to do with the most problematic river (Drini) are divided in a few warning areas each homogenous with respect to climatologic and hydrologic conditions. The meteo-warning is synthetically evaluated for each prefecture; it contains the assessment of the experts about the severity of the forecasted storm in terms of average precipitations, and maximum and, possible storms (if rainfall intensity exceed 90 mm in 3 hours). Reference meteorological model is COSMO LAMI7 (managed by ARPA Bologna, Italy), its spatial resolution is 7 km and temporal resolution for the outputs is 3 hours. Also ECMWF model is available. After the pure meteorological evaluation, possible adverse ground effects are assessed with a second level of variable rainfall thresholds, whose estimated recurrence interval is compared to soil moisture dependent

  14. Using ensemble NWP wind power forecasts to improve national power system management

    NASA Astrophysics Data System (ADS)

    Cannon, D.; Brayshaw, D.; Methven, J.; Coker, P.; Lenaghan, D.

    2014-12-01

    National power systems are becoming increasingly sensitive to atmospheric variability as generation from wind (and other renewables) increases. As such, the days-ahead predictability of wind power has significant implications for power system management. At this time horizon, power system operators plan transmission line outages for maintenance. In addition, forecast users begin to form backup strategies to account for the uncertainty in wind power predictions. Under-estimating this uncertainty could result in a failure to meet system security standards, or in the worst instance, a shortfall in total electricity supply. On the other hand, overly conservative assumptions about the forecast uncertainty incur costs associated with the unnecessary holding of reserve power. Using the power system of Great Britain (GB) as an example, we construct time series of GB-total wind power output using wind speeds from either reanalyses or global weather forecasts. To validate the accuracy of these data sets, wind power reconstructions using reanalyses and forecast analyses over a recent period are compared to measured GB-total power output. The results are found to be highly correlated on time scales greater than around 6 hours. Results are presented using ensemble wind power forecasts from several national and international forecast centres (obtained through TIGGE). Firstly, the skill with which global ensemble forecasts can represent the uncertainty in the GB-total power output at up to 10 days ahead is quantified. Following this, novel ensemble forecast metrics are developed to improve estimates of forecast uncertainty within the context of power system operations, thus enabling the development of more cost effective strategies. Finally, the predictability of extreme events such as prolonged low wind periods or rapid changes in wind power output are examined in detail. These events, if poorly forecast, induce high stress scenarios that could threaten the security of the power

  15. 77 FR 50706 - National Flood Insurance Program Programmatic Environmental Impact Statement

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-08-22

    ... SECURITY Federal Emergency Management Agency National Flood Insurance Program Programmatic Environmental Impact Statement AGENCY: Federal Emergency Management Agency, DHS. ACTION: Notice. SUMMARY: The Federal... 77 FR 28891, and requested public comments no later than July 16, 2012. Due to the...

  16. 76 FR 70745 - Agency Information Collection Activities: Proposed Collection; Comment Request; National Flood...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-11-15

    ... SECURITY Federal Emergency Management Agency Agency Information Collection Activities: Proposed Collection; Comment Request; National Flood Insurance Program--Mortgage Portfolio Protection Program AGENCY: Federal Emergency Management Agency, DHS. ACTION: Notice. SUMMARY: The Federal Emergency Management Agency, as...

  17. 75 FR 69096 - Public Meetings of National Flood Insurance Program (NFIP) Reform Effort

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-11-10

    ... SECURITY Federal Emergency Management Agency Public Meetings of National Flood Insurance Program (NFIP) Reform Effort AGENCY: Federal Emergency Management Agency, DHS. ACTION: Announcement of public meetings... Effort. In performing its mission, the Federal Emergency Management Agency (FEMA) believes it...

  18. In Brief: Flood impact map

    NASA Astrophysics Data System (ADS)

    Showstack, Randy

    2010-03-01

    The U.S. Federal Emergency Management Agency (FEMA) and the National Oceanic and Atmospheric Administration (NOAA) have created an interactive “flood impact map.” The map, available at http://www.floodsmart.gov/noaa, features localized, searchable data about the scope and severity of flood events in recent years. Other forecasting and warning tools include those available at http://www.weather.gov/water and NOAA Weather Radio (http://www.nws.noaa.gov/nwr/).

  19. Development of flood profiles and flood-inundation maps for the Village of Killbuck, Ohio

    USGS Publications Warehouse

    Ostheimer, Chad J.

    2013-01-01

    Digital flood-inundation maps for a reach of Killbuck Creek near the Village of Killbuck, Ohio, were created by the U.S. Geological Survey (USGS), in cooperation with Holmes County, Ohio. The inundation maps depict estimates of the areal extent of flooding corresponding to water levels (stages) at the USGS streamgage Killbuck Creek near Killbuck (03139000) and were completed as part of an update to Federal Emergency Management Agency Flood-Insurance Study. The maps were provided to the National Weather Service (NWS) for incorporation into a Web-based flood-warning system that can be used in conjunction with NWS flood-forecast data to show areas of predicted flood inundation associated with forecasted flood-peak stages. The digital maps also have been submitted for inclusion in the data libraries of the USGS interactive Flood Inundation Mapper. Data from the streamgage can be used by emergency-management personnel, in conjunction with the flood-inundation maps, to help determine a course of action when flooding is imminent. Flood profiles for selected reaches were prepared by calibrating a steady-state step-backwater model to an established streamgage rating curve. The step-backwater model then was used to determine water-surface-elevation profiles for 10 flood stages at the streamgage with corresponding streamflows ranging from approximately the 50- to 0.2-percent annual exceedance probabilities. The computed flood profiles were used in combination with digital elevation data to delineate flood-inundation areas.

  20. Assimilation of brightness temperature observations into a process-based hydrological model for flood forecasting applications

    NASA Astrophysics Data System (ADS)

    Weerts, A.; Reggiani, P.; Kwadijk, J.; de Jeu, R.

    2006-12-01

    The aim of this project, sponsored by the Dutch agency for aerospace programmes, is to demonstrate that assimilation of soil moisture data obtained from AMSR-E remotely sensed brightness temperature measurements leads to a reduction of the uncertainty in the stream flow predictions. The method is applied to the 60000 km2 Mosel sub-basin of the river Rhine. Data streams are provided from ground-based telemetric observation networks and from AMSR-E remotely sensed brightness temperature measurements. The satellite soil moisture observations are assimilated into the physcially based representative elementary watershed (REW) model of the Mosel by means of an Ensemble Kalman Filter (EnKF) over a historical simulation time window. The model is then driven in forecast mode with deterministic medium-range weather prediction products. The aim is to demonstrate that the predictive uncertainty on streamflow forecasts due to model deficiencies and errors in the observations can be effectively limited through exploitation of additional information about the system. Remotely sensed brightness temperature observations will be compared with the results of a process-based hydrological modeling platform in which spatial distribution of surface soil moisture and its effect on hydrological response signals can be adequately taken into consideration.

  1. A statistical forecast model for Tropical Cyclone Rainfall and flood events for the Hudson River

    NASA Astrophysics Data System (ADS)

    Cioffi, Francesco; Conticello, Federico; Hall, Thimoty; Lall, Upmanu; Orton, Philip

    2014-05-01

    Tropical Cyclones (TCs) lead to potentially severe coastal flooding through wind surge and also through rainfall-runoff processes. There is growing interest in modeling these processes simultaneously. Here, a statistical approach that can facilitate this process is presented with an application to the Hudson River Basin that is associated with the New York City metropolitan area. Three submodels are used in sequence. The first submodel is a stochastic model of the complete life cycle of North Atlantic (NA) tropical cyclones developed by Hall and Yonekura (2011). It uses archived data of TCs throughout the North Atlantic to estimate landfall rates at high geographic resolution as a function of the ENSO state and of sea surface temperature (SST). The second submodel translates the attributes of a tropical cyclone simulated by the first model to rainfall intensity at selected stations within the watershed of Hudson River. Two different approaches are used and compared: artificial neural network (ANN) and k-nearest neighbor (KNN). Finally, the third submodel transforms, once again, by using an ANN approach and KNN, the rainfall intensities, calculated for the ensemble of the stations, to the streamflows at specific points of the tributaries of the Hudson River. These streamflows are to be used as inputs in a hydrodynamic model that includes storm surge surge dynamics for the simulation of coastal flooding along the Hudson River. Calibration and validation of the model is carried out by using, selected tropical cyclone data since 1950, and hourly station rainfall and streamflow recorded for such extreme events. Four stream gauges (Troy dam, Mohawk River at Cohoes, Mohawk River diversion at Crescent Dam, Hudson River above lock one nr Waterford), a gauge from a tributary in the lower Hudson River, and over 20 rain gauges are used. The performance of the proposed model as tool for storm events is then analyzed and discussed.

  2. Towards real-time flood forecasting in hydraulics: merits of in situ discharge and water level data assimilation for the modeling of the Marne catchment in France

    NASA Astrophysics Data System (ADS)

    Ricci, S. M.; Habert, J.; Le Pape, E.; Piacentini, A.; Jonville, G.; Thual, O.; Zaoui, F.

    2011-12-01

    The present study describes the assimilation of river flow and water level observations and the resulting improvement in flood forecasting. The Kalman Filter algorithm was built on top of the one-dimensional hydraulic model, MASCARET, [1] which describes the Saint-Venant equations. The assimilation algorithm folds in two steps: the first one was based on the assumption that the upstream flow can be adjusted using a three-parameter correction; the second one consisted of directly correcting the hydraulic state. This procedure was previously applied on the Adour Maritime Catchment using water level observations [2]. On average, it was shown that the data assimilation procedure enables an improvement of 80% in the simulated water level over the reanalysis period, 60 % in the forecast water level at a one-hour lead time, and 25% at a twelve-hour lead time. The procedure was then applied on the Marne Catchment, which includes karstic tributaries, located East of the Paris basin, characterized by long flooding periods and strong sensitivity to local precipitations. The objective was to geographically extend and improve the existing model used by the flood forecasting service located in Chalons-en-Champagne. A hydrological study first enabled the specification of boundary conditions (upstream flow or lateral inflow), then the hydraulic model was calibrated using in situ discharge data (adjustment of Strickler coefficients or cross sectional geometry). The assimilation of water level data enabled the reduction of the uncertainty in the hydrological boundary conditions and led to significant improvement of the simulated water level in re-analysis and forecast modes. Still, because of errors in the Strickler coefficients or cross section geometry, the improvement of the simulated water level sometimes resulted in a degradation of discharge values. This problem was overcome by controlling the correction of the hydrological boundary conditions by directly assimilating

  3. Forecasting Flooding in the Brahmaputra and Ganges Delta of Bangladesh on Short (1-10 days), Medium (20-30 days) and Seasonal Time Scales (1-6 months)

    NASA Astrophysics Data System (ADS)

    Webster, P. J.; Hoyos, C. D.; Hopson, T. M.; Chang, H.; Jian, J.

    2007-12-01

    Following the devastating flood years of 1998 during which 60% of Bangladesh was under water for a period of 3 months, the Climate Forecast Applications in Bangladesh (CFAB) project was formed with funding by USAID and NSF which eventually resulted in a joint project with the European Centre for Medium Range Weather Forecasting (ECMWF), the Asian Disaster Preparedness Centre (ADPC) and the Bangladesh Flood Forecasting and Warning Centre. The project was organized and developed through the Georgia Institute of Technology. The aim of CFAB was to develop innovative methods of extending the warning of flooding in Bangladesh noting that there was a unique problem: India provided no upstream discharge data to Bangladesh so that before CFAB the maximum lead time of a forecast was that given by measuring river discharge at the India-Bangladesh border: no lead-time at the border and 2 days in the southern parts of the country. Given that the Brahmaputra and Ganges catchment areas had to be regarded as essentially unguaged, it was clear that innovative techniques had to be developed. On of the basic criterion was that the system should provide probabilistic forecasts in order for the Bangladeshis to assess risk. A three-tier system was developed to allow strategic and tactical decisions to be made for agricultural purposes and disaster mitigation: seasonal (1-6 months: strategic), medium range (20-30 days: strategic/tactical) and short range (1-10 days: tactical). The system that has been developed brings together for the first time operational meteorological forecasts (ensemble forecasts from ECMWF), with satellite and discharge data and a suite of hydrological models. In addition, with ADPC and FFWC we have developed an in-country forecast dispersion system that allows a rapid dissemination. The system has proven to be rather successful, especially in the short range. The flooding events of 2004 were forecast with all forecasting tiers at the respective lead time. In

  4. Improvement of hydrological flood forecasting through an event based output correction method

    NASA Astrophysics Data System (ADS)

    Klotz, Daniel; Nachtnebel, Hans Peter

    2014-05-01

    This contribution presents an output correction method for hydrological models. A conceptualisation of the method is presented and tested in an alpine basin in Salzburg, Austria. The aim is to develop a method which is not prone to the drawbacks of autoregressive models. Output correction methods are an attractive option for improving hydrological predictions. They are complementary to the main modelling process and do not interfere with the modelling process itself. In general, output correction models estimate the future error of a prediction and use the estimation to improve the given prediction. Different estimation techniques are available dependent on the utilized information and the estimation procedure itself. Autoregressive error models are widely used for such corrections. Autoregressive models with exogenous inputs (ARX) allow the use of additional information for the error modelling, e.g. measurements from upper basins or predicted input-signals. Autoregressive models do however exhibit deficiencies, since the errors of hydrological models do generally not behave in an autoregressive manner. The decay of the error is usually different from an autoregressive function and furthermore the residuals exhibit different patterns under different circumstances. As for an example, one might consider different error-propagation behaviours under high- and low-flow situations or snow melt driven conditions. This contribution presents a conceptualisation of an event-based correction model and focuses on flood events only. The correction model uses information about the history of the residuals and exogenous variables to give an error-estimation. The structure and parameters of the correction models can be adapted to given event classes. An event-class is a set of flood events that exhibit a similar pattern for the residuals or the hydrological conditions. In total, four different event-classes have been identified in this study. Each of them represents a different

  5. Bulletin of Forest Fire risk in Albania- The experience of the Albania National Centre for forecast and Monitoring of Natural Risks

    NASA Astrophysics Data System (ADS)

    Berdufi, I.; Jaupaj, O.; Marku, M.; Deda, M.; Fiori, E.; D'Andrea, M.; Biondi, G.; Fioruci, P.; Massabò, M.; Zorba, P.; Gjonaj, M.

    2012-04-01

    In the territory of Albania usually every year around 1000 ha are affected by forest fires, from which about 500 ha are completely destroyed. The number of forest fires (nf), with the burning surface (bs) in years has been like this: during the years 1988-1998: nf / bs = 2.19, 1998-2001: nf / bs = 5.66, year 2002 -2005: nf / bs = 8.2, and during the years 2005-2006: nf / bs = 11.9, while economic losses in a year by forest fires is about 2 million of Euro. The increase in years of these figures and the last floods which happened in the last two years in Shkoder, led to an international cooperation, that between the Italian Civil Protection Department and the Albania General Directorate of Civil Emergency. The focus of this cooperation was the building capacity of the Albanian National System of Civil Protection in forecasting, monitoring and prevention forest fires and floods risks. As a result of this collaboration the "National Center for the Forecast and Monitoring of Natural Risks", was set up at the Institute of Geosciences, Energy, Water and Environment. The Center is the first of its kind in Albania. The mission of the Center is the prediction and monitoring of the forest fire and flood risk in the Albanian territory, as a tools for risk reduction and mitigation. The first step to achieve this strategy was the implementation of the forest fires risk forecast model "RISICO". RISICO was adapted for whole Albania territory by CIMA Research Foundation. The Center, in the summer season, issues a daily bulletin. The bulletin reports the potential risk scenarios related with the ignition and propagation of fires in Albania. The bulletin is broadcasted through email or fax within 12.00 AM of each working day. It highlights where and when severe risk conditions may occur within the next 48 hours

  6. A one-way coupled atmospheric-hydrological modeling system with combination of high-resolution and ensemble precipitation forecasting

    NASA Astrophysics Data System (ADS)

    Wu, Zhiyong; Wu, Juan; Lu, Guihua

    2015-11-01

    Coupled hydrological and atmospheric modeling is an effective tool for providing advanced flood forecasting. However, the uncertainties in precipitation forecasts are still considerable. To address uncertainties, a one-way coupled atmospheric-hydrological modeling system, with a combination of high-resolution and ensemble precipitation forecasting, has been developed. It consists of three high-resolution single models and four sets of ensemble forecasts from the THORPEX Interactive Grande Global Ensemble database. The former provides higher forecasting accuracy, while the latter provides the range of forecasts. The combined precipitation forecasting was then implemented to drive the Chinese National Flood Forecasting System in the 2007 and 2008 Huai River flood hindcast analysis. The encouraging results demonstrated that the system can clearly give a set of forecasting hydrographs for a flood event and has a promising relative stability in discharge peaks and timing for warning purposes. It not only gives a deterministic prediction, but also generates probability forecasts. Even though the signal was not persistent until four days before the peak discharge was observed in the 2007 flood event, the visualization based on threshold exceedance provided clear and concise essential warning information at an early stage. Forecasters could better prepare for the possibility of a flood at an early stage, and then issue an actual warning if the signal strengthened. This process may provide decision support for civil protection authorities. In future studies, different weather forecasts will be assigned various weight coefficients to represent the covariance of predictors and the extremes of distributions.

  7. A one-way coupled atmospheric-hydrological modeling system with combination of high-resolution and ensemble precipitation forecasting

    NASA Astrophysics Data System (ADS)

    Wu, Zhiyong; Wu, Juan; Lu, Guihua

    2016-09-01

    Coupled hydrological and atmospheric modeling is an effective tool for providing advanced flood forecasting. However, the uncertainties in precipitation forecasts are still considerable. To address uncertainties, a one-way coupled atmospheric-hydrological modeling system, with a combination of high-resolution and ensemble precipitation forecasting, has been developed. It consists of three high-resolution single models and four sets of ensemble forecasts from the THORPEX Interactive Grande Global Ensemble database. The former provides higher forecasting accuracy, while the latter provides the range of forecasts. The combined precipitation forecasting was then implemented to drive the Chinese National Flood Forecasting System in the 2007 and 2008 Huai River flood hindcast analysis. The encouraging results demonstrated that the system can clearly give a set of forecasting hydrographs for a flood event and has a promising relative stability in discharge peaks and timing for warning purposes. It not only gives a deterministic prediction, but also generates probability forecasts. Even though the signal was not persistent until four days before the peak discharge was observed in the 2007 flood event, the visualization based on threshold exceedance provided clear and concise essential warning information at an early stage. Forecasters could better prepare for the possibility of a flood at an early stage, and then issue an actual warning if the signal strengthened. This process may provide decision support for civil protection authorities. In future studies, different weather forecasts will be assigned various weight coefficients to represent the covariance of predictors and the extremes of distributions.

  8. Mosquito density forecast from flooding: population dynamics model for Aedes caspius (Pallas).

    PubMed

    Balenghien, T; Carron, A; Sinègre, G; Bicout, D J

    2010-06-01

    Insect population dynamics depend strongly on environmental factors. For floodwater mosquitoes, meteorological conditions are crucial in the rhythm of mosquito abundances. Indeed, rainfall triggers the egg hatching after flooding breeding sites, and temperature controls the duration of the aquatic immature development up to adult emergence. According to this, we have developed a simple mechanistic and tractable model that describes the population dynamics of floodwater mosquitoes as a function only of the most accessible meteorological variables, rainfall and temperature. The model involves three parameters: development duration tdev of the immature aquatic stages, the adult emergence rate function f(t) (characterized by the emergence time scale tau and shaping the profile of adult population abundance), and the depletion rate, alpha, of adult disappearance. The developed model was subsequently applied to fit experimental field data of the dynamics of Aedes caspius (Pallas), the main pest mosquito in southern France. First, it was found that the emergence rate function of adult mosquitoes very well reproduce experimental data of the dynamics of immature development for all sampled temperatures. The estimated values of tdev and tau both exhibit Arrhenius behaviour as a function of temperature. Second, using the meteorological records of rainfall and temperature as inputs, the model correctly fit data from a two-site CO2 trapping survey conducted in 2004 and 2005. The estimated depletion rates (summation of the mortality and the emigration rates) were found to be a concave quadratic function of temperature with a maximum of 0.5 per days at about 22 degrees C. PMID:20170592

  9. Error discrimination of an operational hydrological forecasting system at a national scale

    NASA Astrophysics Data System (ADS)

    Jordan, F.; Brauchli, T.

    2010-09-01

    The use of operational hydrological forecasting systems is recommended for hydropower production as well as flood management. However, the forecast uncertainties can be important and lead to bad decisions such as false alarms and inappropriate reservoir management of hydropower plants. In order to improve the forecasting systems, it is important to discriminate the different sources of uncertainties. To achieve this task, reanalysis of past predictions can be realized and provide information about the structure of the global uncertainty. In order to discriminate between uncertainty due to the weather numerical model and uncertainty due to the rainfall-runoff model, simulations assuming perfect weather forecast must be realized. This contribution presents the spatial analysis of the weather uncertainties and their influence on the river discharge prediction of a few different river basins where an operational forecasting system exists. The forecast is based on the RS 3.0 system [1], [2], which is also running the open Internet platform www.swissrivers.ch [3]. The uncertainty related to the hydrological model is compared to the uncertainty related to the weather prediction. A comparison between numerous weather prediction models [4] at different lead times is also presented. The results highlight an important improving potential of both forecasting components: the hydrological rainfall-runoff model and the numerical weather prediction models. The hydrological processes must be accurately represented during the model calibration procedure, while weather prediction models suffer from a systematic spatial bias. REFERENCES [1] Garcia, J., Jordan, F., Dubois, J. & Boillat, J.-L. 2007. "Routing System II, Modélisation d'écoulements dans des systèmes hydrauliques", Communication LCH n° 32, Ed. Prof. A. Schleiss, Lausanne [2] Jordan, F. 2007. Modèle de prévision et de gestion des crues - optimisation des opérations des aménagements hydroélectriques à accumulation

  10. Robust multi-objective calibration strategies - chances for improving flood forecasting

    NASA Astrophysics Data System (ADS)

    Krauße, T.; Cullmann, J.; Saile, P.; Schmitz, G. H.

    2011-04-01

    application of data depth metrics. In a preliminary assessment MO-PSO-GA is compared with other multi-objective optimisation algorithms. In the frame of a real world case study MO-ROPE is applied identifying robust parameter vectors of a distributed hydrological model with focus on flood events in a small, pre-alpine, and fast responding catchment in Switzerland. The method is compared with existing robust parameter estimation methods.

  11. Robust multi-objective calibration strategies - possibilities for improving flood forecasting

    NASA Astrophysics Data System (ADS)

    Krauße, T.; Cullmann, J.; Saile, P.; Schmitz, G. H.

    2012-10-01

    parameter vectors with respect to one single objective. The consideration of multiple objectives is just possible by aggregation. In this paper, we present an approach that combines the principles of multi-objective optimisation and depth-based sampling, entitled Multi-Objective Robust Parameter Estimation (MOROPE). It applies a multi-objective optimisation algorithm in order to identify non-dominated robust model parameter vectors. Subsequently, it samples parameter vectors with high data depth using a further developed sampling algorithm presented in Krauße and Cullmann (2012a). We study the effectivity of the proposed method using synthetical test functions and for the calibration of a distributed hydrologic model with focus on flood events in a small, pre-alpine, and fast responding catchment in Switzerland.

  12. Use of radar rainfall data for high-resolution flash flood forecasting in the Dallas-Fort Worth Metroplex area

    NASA Astrophysics Data System (ADS)

    Seo, D. J.; Habibi, H.; Rafieei Nasab, A.; Norouzi, A.; Nazari, B.; Lee, H. S.; Cosgrove, B.; Cui, Z.

    2015-12-01

    For monitoring and prediction of water-related hazards such as flash flooding in urban areas, high-resolution hydrologic and hydraulic modeling is necessary. Because of large sensitivity and scale dependence of rainfall-runoff models to errors in quantitative precipitation estimates (QPE), it is important that the accuracy of QPE be improved to the greatest extent possible. In this presentation, we describe the ongoing efforts in the Dallas-Fort Worth Metroplex area to provide location- and time-specific flash flooding warnings in real-time. The hydrologic modeling system used is the National Weather Service (NWS) Hydrology Laboratory's Research Distributed Hydrologic Model (HLRDHM) applied at spatiotemporal resolutions ranging from 250 m to 2 km and from 1 min to 1 hour. The high-resolution precipitation input is from the DFW Demonstration Network of the Collaborative Adaptive Sensing of the Atmosphere (CASA) radars, the Next Generation QPE (Q2), and the NWS Multisensor Precipitation Estimator (MPE). Also described are the assessment of sensitivity of streamflow simulation to the spatiotemporal resolution of precipitation input and hydrologic modeling, the needs for high-quality high-resolution precipitation data sets for hydro-meteorological and -climatological applications particularly in large urban areas, and possible approaches to realize them.

  13. A real-time flood forecasting and simulation system based on GIS and DEM: Analysis of sensitivity to scale factors

    NASA Astrophysics Data System (ADS)

    Garcia, Sandra G.

    The hydrometeorological telemetric networks in real time interrelated with weather forecasting and rainfall information obtained from remote sensing, constitute real forecasting and protection instruments in the event of flash flooding, so typical of semiarid environments. In this Thesis, spatial analysis approached with functions embedded in a Geographical Information System (GIS) are proposed. The aims are: (a) To combine efficiently information from different sources (telemetric networks and radar-satellite technology). (b) To develop methodology of application of spatially distributed and hybrid hydrologic models, which are topographically based and event-oriented. (c) To extract automatically from Digital Elevation Models (DEM) the relevant parameters of the hydrologic models used. When extracting the drainage networks from a DEM, various questions arise: what is the most suitable drainage density for the hydrographic network? What degree of affection does the selection of DEM cell size have on the hydrologic results, or are they not sensitive to it? Can any invariable property by defined with the scale which characterizes indexes or parameters based on the drainage network hierarchy? A clear inter-relationship can be seen between the geomorphological and hydrologic parameters and the DEM resolution. The morphometric parameters are also affected by threshold area variation. It is proposed a methodology to identify a priori the range of DEM resolutions and threshold areas for in which the parameters present a certain stability for modelling based on drainage networks topology. When working with spatially distributed models, several questions crop up: Are the distributed parameters derived from DEM and the complete hydrologic results affected by cell size? Is it feasible to identify invariable properties with the scale which characterizes the spatial distributions of the parameters? The terrain slope and the flow path length are affected by the DEM cell

  14. NOAA-EPA's New National Air Quality Forecast Capability: Initial Steps

    NASA Astrophysics Data System (ADS)

    Davidson, P.

    2005-12-01

    In partnership with the US EPA, NOAA has developed, tested and implemented the first two stages of a national air quality forecast capability into the National Weather Service (NWS) operational suite. The initial capability was implemented in September, 2004 and provided ground-level ozone predictions over Northeastern United States. In a program of phased development and testing to expand this capability, the domain has been extended over the entire Eastern United states as of August 31, 2005. Predictions are made with the NOAA-EPA Community Model for Air Quality (CMAQ) driven by NOAA's operational mesoscale weather prediction model (Eta-12). The capability is an end-to-end forecast guidance system providing twice daily predictions of hour-by-hour ground-level ozone concentrations on a 12km grid, disseminated over operational NWS and EPA dataservers. Forecast guidance products are hosted on operational dataservers: fully backed up, with archiving and near-real-time verification in place to monitor forecast accuracy. In order to demonstrate readiness for operational implementation, required accuracy of 90% and reliability of 95% on-time delivery have been demonstrated in the pre-deployment testing. During the Summers of 2004 and 2005, pre-deployment testing of forecast domains over Northeastern US and Eastern US, respectively, have led to operational implementation of the first two stages of the capability. Prior to pre-deployment testing, developmental testing was conducted to demonstrate feasibility of the prototype operational configuration using forecast components for air quality (CMAQ and pollutant emissions pre-processing) adapted from research and assessment simulations. Developmental testing identified priorities for system enhancements needed to improve guidance accuracy; for example: improved model linkage, updated emissions information, improved treatments of solar radiation for photolysis rate estimation, and improved treatments of vertical mixing and

  15. Performance and robustness of probabilistic river forecasts computed with quantile regression based on multiple independent variables

    NASA Astrophysics Data System (ADS)

    Hoss, F.; Fischbeck, P. S.

    2015-09-01

    This study applies quantile regression (QR) to predict exceedance probabilities of various water levels, including flood stages, with combinations of deterministic forecasts, past forecast errors and rates of water level rise as independent variables. A computationally cheap technique to estimate forecast uncertainty is valuable, because many national flood forecasting services, such as the National Weather Service (NWS), only publish deterministic single-valued forecasts. The study uses data from the 82 river gauges, for which the NWS' North Central River Forecast Center issues forecasts daily. Archived forecasts for lead times of up to 6 days from 2001 to 2013 were analyzed. Besides the forecast itself, this study uses the rate of rise of the river stage in the last 24 and 48 h and the forecast error 24 and 48 h ago as predictors in QR configurations. When compared to just using the forecast as an independent variable, adding the latter four predictors significantly improved the forecasts, as measured by the Brier skill score and the continuous ranked probability score. Mainly, the resolution increases, as the forecast-only QR configuration already delivered high reliability. Combining the forecast with the other four predictors results in a much less favorable performance. Lastly, the forecast performance does not strongly depend on the size of the training data set but on the year, the river gauge, lead time and event threshold that are being forecast. We find that each event threshold requires a separate configuration or at least calibration.

  16. THE NOAA - EPA NATIONAL AIR QUALITY FORECASTING SYSTEM

    EPA Science Inventory

    Building upon decades of collaboration in air pollution meteorology research, in 2003 the National Oceanic and Atmospheric Administration (NOAA) and the United States Environmental Protection Agency (EPA) signed formal partnership agreements to develop and implement an operationa...

  17. Evaluating the Performance of Wavelet-based Data-driven Models for Multistep-ahead Flood Forecasting in an Urbanized Watershed

    NASA Astrophysics Data System (ADS)

    Kasaee Roodsari, B.; Chandler, D. G.

    2015-12-01

    A real-time flood forecast system is presented to provide emergency management authorities sufficient lead time to execute plans for evacuation and asset protection in urban watersheds. This study investigates the performance of two hybrid models for real-time flood forecasting at different subcatchments of Ley Creek watershed, a heavily urbanized watershed in the vicinity of Syracuse, New York. Hybrid models include Wavelet-Based Artificial Neural Network (WANN) and Wavelet-Based Adaptive Neuro-Fuzzy Inference System (WANFIS). Both models are developed on the basis of real time stream network sensing. The wavelet approach is applied to decompose the collected water depth timeseries to Approximation and Detail components. The Approximation component is then used as an input to ANN and ANFIS models to forecast water level at lead times of 1 to 10 hours. The performance of WANN and WANFIS models are compared to ANN and ANFIS models for different lead times. Initial results demonstrated greater predictive power of hybrid models.

  18. Analysis of Flood Hazards for the Materials and Fuels Complex at the Idaho National Laboratory Site

    SciTech Connect

    Skaggs, Richard; Breithaupt, Stephen A.; Waichler, Scott R.; Kim, Taeyun; Ward, Duane L.

    2010-11-01

    Researchers at Pacific Northwest National Laboratory conducted a flood hazard analysis for the Materials and Fuels Complex (MFC) site located at the Idaho National Laboratory (INL) site in southeastern Idaho. The general approach for the analysis was to determine the maximum water elevation levels associated with the design-basis flood (DBFL) and compare them to the floor elevations at critical building locations. Two DBFLs for the MFC site were developed using different precipitation inputs: probable maximum precipitation (PMP) and 10,000 year recurrence interval precipitation. Both precipitation inputs were used to drive a watershed runoff model for the surrounding upland basins and the MFC site. Outflows modeled with the Hydrologic Engineering Centers Hydrologic Modeling System were input to the Hydrologic Engineering Centers River Analysis System hydrodynamic flood routing model.

  19. Calibration and Evaluation of a Flood Forecasting System: Utility of Numerical Weather Prediction Model, Data Assimilation and Satellite-based Rainfall

    NASA Astrophysics Data System (ADS)

    Yucel, Ismail; Onen, Alper; Yilmaz, Koray; Gochis, David

    2015-04-01

    A fully-distributed, multi-physics, multi-scale hydrologic and hydraulic modeling system, WRF-Hydro, is used to assess the potential for skillful flood forecasting based on precipitation inputs derived from the Weather Research and Forecasting (WRF) model and the EUMETSAT Multi-sensor Precipitation Estimates (MPEs). Similar to past studies it was found that WRF model precipitation forecast errors related to model initial conditions are reduced when the three dimensional atmospheric data assimilation (3DVAR) scheme in the WRF model simulations is used. The study then undertook a comparative evaluation of the impact of MPE versus WRF precipitation estimates, both with and without data assimilation, in driving WRF-Hydro simulated streamflow. Several flood events that occurred in the Black Sea region were used for testing and evaluation. Following model calibration, the WRF-Hydro system was capable of skillfully reproducing observed flood hydrographs in terms of the volume of the runoff produced and the overall shape of the hydrograph. Streamflow simulation skill was significantly improved for those WRF model simulations where storm precipitation was accurately depicted with respect to timing, location and amount. Accurate streamflow simulations were more evident in WRF model simulations where the 3DVAR scheme was used compared to when it was not used. Because of substantial dry bias feature of MPE, streamflow derived using this precipitation product is in general very poor. Overall, root mean squared errors for runoff were reduced by 22.2% when hydrological model calibration is performed with WRF precipitation. Errors were reduced by 36.9% (above uncalibrated model performance) when both WRF model data assimilation and hydrological model calibration was utilized. Our results also indicated that when assimilated precipitation and model calibration is performed jointly, the calibrated parameters at the gauged sites could be transferred to ungauged neighboring basins

  20. Configuring the HYSPLIT Model for National Weather Service Forecast Office and Spaceflight Meteorology Group Applications

    NASA Technical Reports Server (NTRS)

    Dreher, Joseph; Blottman, Peter F.; Sharp, David W.; Hoeth, Brian; Van Speybroeck, Kurt

    2009-01-01

    The National Weather Service Forecast Office in Melbourne, FL (NWS MLB) is responsible for providing meteorological support to state and county emergency management agencies across East Central Florida in the event of incidents involving the significant release of harmful chemicals, radiation, and smoke from fires and/or toxic plumes into the atmosphere. NWS MLB uses the National Oceanic and Atmospheric Administration Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to provide trajectory, concentration, and deposition guidance during such events. Accurate and timely guidance is critical for decision makers charged with protecting the health and well-being of populations at risk. Information that can describe the geographic extent of areas possibly affected by a hazardous release, as well as to indicate locations of primary concern, offer better opportunity for prompt and decisive action. In addition, forecasters at the NWS Spaceflight Meteorology Group (SMG) have expressed interest in using the HYSPLIT model to assist with Weather Flight Rules during Space Shuttle landing operations. In particular, SMG would provide low and mid-level HYSPLIT trajectory forecasts for cumulus clouds associated with smoke plumes, and high-level trajectory forecasts for thunderstorm anvils. Another potential benefit for both NWS MLB and SMG is using the HYSPLIT model concentration and deposition guidance in fog situations.

  1. Application of quantitative precipitation forecasting and precipitation ensemble prediction for hydrological forecasting

    NASA Astrophysics Data System (ADS)

    Tao, P.; Tie-Yuan, S.; Zhi-Yuan, Y.; Jun-Chao, W.

    2015-05-01

    The precipitation in the forecast period influences flood forecasting precision, due to the uncertainty of the input to the hydrological model. Taking the ZhangHe basin as the example, the research adopts the precipitation forecast and ensemble precipitation forecast product of the AREM model, uses the Xin Anjiang hydrological model, and tests the flood forecasts. The results show that the flood forecast result can be clearly improved when considering precipitation during the forecast period. Hydrological forecast based on Ensemble Precipitation prediction gives better hydrological forecast information, better satisfying the need for risk information for flood prevention and disaster reduction, and has broad development opportunities.

  2. 76 FR 64361 - Agency Information Collection Activities: Proposed Collection; Comment Request; National Flood...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-10-18

    ... nature of their claim appeal, names and titles of persons contacted, dates of contact, contact... revision of the National Flood Insurance Claims Appeals Process. The appeal process establishes a formal mechanism to allow policyholders to appeal the decisions of any insurance agent, adjuster, insurance...

  3. National scale high-resolution quantification of fluvial flood risk in Great Britain

    NASA Astrophysics Data System (ADS)

    Thornton, James; Thomson, Tina; Liu, Ye; Chaney, Sarah; Dunning, Paul; Hutchings, Stephen; Taylor, Peter; Pickering, Cathy

    2014-05-01

    Britain has experienced repeated episodes of widespread river flooding in recent years, with considerable implications for insurance companies. Probabilistic models enable these companies to robustly quantify flood risk. Because flood events are often very localised, the models would ideally incorporate high-resolution flood data, but although such data are increasingly available at a national scale their inclusion has, to date, been a daunting 'big data' challenge. Here, we discuss some of the scientific and technological advancements we have made to develop a detailed probabilistic model which is underpinned by high-resolution flood data. Return period river flows were first estimated at a large number of locations along the national river network using the Flood Estimation Handbook approach. These flows were then routed across a high-resolution Digital Terrain Model using our 2D hydraulic model, JFlow, to produce 5m resolution river flood hazard maps for the entire county. Our probabilistic model integrates these 'design' hazard data, a state-of-the-art stochastic event set containing tens of thousands of synthetic extreme flow events, a 'built environment' database and 'vulnerability functions' (which relate water depth and damage) to determine the probability distribution of annual river flood losses to insured properties. Stochastic events were carefully assigned to 'years' in the simulation period, with each year being a plausible version of 'next year'. The flood footprint associated with each simulated event was defined, and event-by-event total damage and insured loss calculated. Precise property locations could be provided as an input, and all calculations were carried out on an extremely fine grid to minimise uncertainties due to data aggregation. Being comprised of large data tables, models of this nature are computationally demanding; to enable full analyses on reasonable timescales, our model was re-coded to run on IBM's PureData for Analytics

  4. Development of a Flood-Warning System and Flood-Inundation Mapping for the Blanchard River in Findlay, Ohio

    USGS Publications Warehouse

    Whitehead, Matthew T.; Ostheimer, Chad J.

    2009-01-01

    Digital flood-inundation maps of the Blanchard River in Findlay, Ohio, were created by the U.S. Geological Survey (USGS) in cooperation with the City of Findlay, Ohio. The maps, which correspond to water levels at the USGS streamgage at Findlay (04189000), were provided to the National Weather Service (NWS) for incorporation into a Web-based flood-warning system that can be used in conjunction with NWS flood-forecast data to show areas of predicted flood inundation associated with forecasted flood-peak stages. The USGS reestablished one streamgage and added another on the Blanchard River upstream of Findlay. Additionally, the USGS established one streamgage each on Eagle and Lye Creeks, tributaries to the Blanchard River. The stream-gage sites were equipped with rain gages and multiple forms of telemetry. Data from these gages can be used by emergency management personnel to determine a course of action when flooding is imminent. Flood profiles computed by means of a step-backwater model were prepared and calibrated to a recent flood with a return period exceeding 100 years. The hydraulic model was then used to determine water-surface-elevation profiles for 11 flood stages with corresponding streamflows ranging from approximately 2 to 100 years in recurrence interval. The simulated flood profiles were used in combination with digital elevation data to delineate the flood-inundation areas. Maps of Findlay showing flood-inundation areas overlain on digital orthophotographs are presented for the selected floods.

  5. Developments of the European Flood Awareness System (EFAS)

    NASA Astrophysics Data System (ADS)

    Olav Skøien, Jon; Salamon, Peter; Pappenberger, Florian; Wetterhall, Fredrik; Holst, Bo; Asp, Sara-Sofia; Garcia Padilla, Mercedes; Garcia Sanchez, Rafael J.; Schweim, Christoph; Ziese, Markus

    2016-04-01

    EFAS (http://www.efas.eu) is an operational system for flood forecasting and flood warning for Europe which has become fully operational as part of the Copernicus Emergency Management Service in 2012. The aim of EFAS is to gain time for preparedness measures before major flood events strike particularly for trans-national river basins both at country as well as on European level. This is achieved by providing complementary, added value information to the national hydrological services. Using a coherent model for all of Europe forced with a range of deterministic and ensemble weather forecasts, the system can give a probabilistic flood forecast for a medium range lead time (up to 10 days) independent of country borders. The system is under continuous development, and we will present the basic set up, some prominent examples of recent and ongoing developments and the future challenges.

  6. Effects of forcing uncertainties in the improvement skills of assimilating satellite soil moisture retrievals into flood forecasting models

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Floods have negative impacts on society, causing damages in infrastructures and industry, and in the worst cases, causing loss of human lives. Thus early and accurate warning is crucial to significantly reduce the impacts on public safety and economy. Reliable flood warning can be generated using ...

  7. Flood hazard assessment of the Hoh River at Olympic National Park ranger station, Washington

    USGS Publications Warehouse

    Kresch, D.L.; Pierson, T.C.

    1987-01-01

    Federal regulations require buildings and public facilities on Federal land to be located beyond or protected from inundation by a 100-year flood. Flood elevations, velocities and boundaries were determined for the occurrence of a 100-year flood through a reach, approximately 1-mi-long, of the Hoh River at the ranger station complex in Olympic National Park. Flood elevations, estimated by step-backwater analysis of the 100-year flood discharge through 14 channel and flood-plain cross sections of the Hoh River, indicate that the extent of flooding in the vicinity of buildings or public facilities at the ranger station complex is likely to be limited mostly to two historic meander channels that lie partly within loop A of the public campground and that average flood depths of about 2 feet or less would be anticipated in these channels. Mean flow velocities at the cross sections, corresponding to the passage of a 100-year flood, ranged from about 5 to over 11 ft/sec. Flooding in the vicinity of either the visitors center or the residential and maintenance areas is unlikely unless the small earthen dam at the upstream end of Taft Creek were to fail. Debris flows with volumes on the order of 100 to 1,000 cu yards could be expected to occur in the small creeks that drain the steep valley wall north of the ranger station complex. Historic debris flows in these creeks have generally traveled no more than about 100 yards out onto the valley floor. The potential risk that future debris flows in these creeks might reach developed areas within the ranger station complex is considered to be small because most of the developed areas within the complex are situated more than 100 yards from the base of the valley wall. Landslides or rock avalanches originating from the north valley wall with volumes potentially much larger than those for debris flows could have a significant impact on the ranger station complex. The probability that such landslides or avalanches may occur is

  8. Aiming towards improved flood forecasting: Identification of an adequate model structure for a semi-arid and data-scarce region

    NASA Astrophysics Data System (ADS)

    Pilz, Tobias; Francke, Till; Bronstert, Axel

    2015-04-01

    A lot of effort has already been put into the development of forecasting systems to warn people of approaching flood events. Such systems, however, are influenced by various sources of uncertainty which constrain the skill of forecasts. The main goal of this study is the identification, quantification and reduction of uncertainties to provide improved early warnings with adequate lead times in a data-scarce region with strong seasonality of the hydrological regime. This includes the setup of hydrological models and post-processing of simulation results by mathematical means such as data assimilation. The focus area is the Jaguaribe watershed in northeastern Brazil. The region is characterized by a seasonal climate with strong inter-annual variation and recurrent droughts. To ensure a secure water supply also during the dry season several thousand small and some large reservoirs have been constructed. On the other hand, floods caused by heavy rain events are an issue as well. This topic, however, so far has hardly been considered by the scientific community and until today no flood forecasting system exists for that region. To identify the most appropriate model structure for the catchment the process-based hydrological model for semi-arid environments WASA was implemented into the eco-hydrological simulation environment ECHSE. The environment consists of a generic part providing data types and simulation methods, and a problem-specific part where the user can implement different model formulations. This provides the possibility to test various process realisations under consistent input and output data structures. The most appropriate model structure can then be determined by statistical means such as Bayesian model averaging. Subsequently, forecast results may be updated by post-processing and/or data assimilation. Furthermore, methods of data fusion can be used to combine measurements of different quality and resolution, such as in-situ and remotely sensed data

  9. Urban flood simulation based on the SWMM model

    NASA Astrophysics Data System (ADS)

    Jiang, L.; Chen, Y.; Wang, H.

    2015-05-01

    China is the nation with the fastest urbanization in the past decades which has caused serious urban flooding. Flood forecasting is regarded as one of the important flood mitigation methods, and is widely used in catchment flood mitigation, but is not widely used in urban flooding mitigation. This paper, employing the SWMM model, one of the widely used urban flood planning and management models, simulates the urban flooding of Dongguan City in the rapidly urbanized southern China. SWMM is first set up based on the DEM, digital map and underground pipeline network, then parameters are derived based on the properties of the subcatchment and the storm sewer conduits; the parameter sensitivity analysis shows the parameter robustness. The simulated results show that with the 1-year return period precipitation, the studied area will have no flooding, but for the 2-, 5-, 10- and 20-year return period precipitation, the studied area will be inundated. The results show the SWMM model is promising for urban flood forecasting, but as it has no surface runoff routing, the urban flooding could not be forecast precisely.

  10. Comparison of streamflow prediction skills from NOAH-MP/RAPID, VIC/RAPID and SWAT toward an ensemble flood forecasting framework over large scales

    NASA Astrophysics Data System (ADS)

    Rajib, M. A.; Tavakoly, A. A.; Du, L.; Merwade, V.; Lin, P.

    2015-12-01

    Considering the differences in how individual models represent physical processes for runoff generation and streamflow routing, use of ensemble output is desirable in an operational streamflow estimation and flood forecasting framework. To enable the use of ensemble streamflow, comparison of multiple hydrologic models at finer spatial resolution over a large domain is yet to be explored. The objective of this work is to compare streamflow prediction skills from three different land surface/hydrologic modeling frameworks: NOAH-MP/RAPID, VIC/RAPID and SWAT, over the Ohio River Basin with a drainage area of 491,000 km2. For a uniform comparison, all the three modeling frameworks share the same setup with common weather inputs, spatial resolution, and gauge stations being employed in the calibration procedure. The runoff output from NOAH-MP and VIC land surface models is routed through a vector-based river routing model named RAPID, that is set up on the high resolution NHDPlus reaches and catchments. SWAT model is used with its default tightly coupled surface-subsurface hydrology and channel routing components to obtain streamflow for each NHDPlus reach. Model simulations are performed in two modes, including: (i) hindcasting/calibration mode in which the models are calibrated against USGS daily streamflow observations at multiple locations, and (ii) validation mode in which the calibrated models are executed at 3-hourly time interval for historical flood events. In order to have a relative assessment on the model-specific nature of biases during storm events as well as dry periods, time-series of surface runoff and baseflow components at the specific USGS gauging locations are extracted from corresponding observed/simulated streamflow data using a recursive digital filter. The multi-model comparison presented here provides insights toward future model improvements and also serves as the first step in implementing an operational ensemble flood forecasting framework

  11. Somerset County Flood Information System

    USGS Publications Warehouse

    Hoppe, Heidi L.

    2007-01-01

    The timely warning of a flood is crucial to the protection of lives and property. One has only to recall the floods of August 2, 1973, September 16 and 17, 1999, and April 16, 2007, in Somerset County, New Jersey, in which lives were lost and major property damage occurred, to realize how costly, especially in terms of human life, an unexpected flood can be. Accurate forecasts and warnings cannot be made, however, without detailed information about precipitation and streamflow in the drainage basin. Since the mid 1960's, the National Weather Service (NWS) has been able to forecast flooding on larger streams in Somerset County, such as the Raritan and Millstone Rivers. Flooding on smaller streams in urban areas was more difficult to predict. In response to this problem the NWS, in cooperation with the Green Brook Flood Control Commission, installed a precipitation gage in North Plainfield, and two flash-flood alarms, one on Green Brook at Seeley Mills and one on Stony Brook at Watchung, in the early 1970's. In 1978, New Jersey's first countywide flood-warning system was installed by the U.S. Geological Survey (USGS) in Somerset County. This system consisted of a network of eight stage and discharge gages equipped with precipitation gages linked by telephone telemetry and eight auxiliary precipitation gages. The gages were installed throughout the county to collect precipitation and runoff data that could be used to improve flood-monitoring capabilities and flood-frequency estimates. Recognizing the need for more detailed hydrologic information for Somerset County, the USGS, in cooperation with Somerset County, designed and installed the Somerset County Flood Information System (SCFIS) in 1990. This system is part of a statewide network of stream gages, precipitation gages, weather stations, and tide gages that collect data in real time. The data provided by the SCFIS improve the flood forecasting ability of the NWS and aid Somerset County and municipal agencies in

  12. Development of a flood-warning network and flood-inundation mapping for the Blanchard River in Ottawa, Ohio

    USGS Publications Warehouse

    Whitehead, Matthew T.

    2011-01-01

    Digital flood-inundation maps of the Blanchard River in Ottawa, Ohio, were created by the U.S. Geological Survey (USGS) in cooperation with the U.S. Department of Agriculture, Natural Resources Conservation Service and the Village of Ottawa, Ohio. The maps, which correspond to water levels (stages) at the USGS streamgage at Ottawa (USGS streamgage site number 04189260), were provided to the National Weather Service (NWS) for incorporation into a Web-based flood-warning Network that can be used in conjunction with NWS flood-forecast data to show areas of predicted flood inundation associated with forecasted flood-peak stages. Flood profiles were computed by means of a step-backwater model calibrated to recent field measurements of streamflow. The step-backwater model was then used to determine water-surface-elevation profiles for 12 flood stages with corresponding streamflows ranging from less than the 2-year and up to nearly the 500-year recurrence-interval flood. The computed flood profiles were used in combination with digital elevation data to delineate flood-inundation areas. Maps of the Village of Ottawa showing flood-inundation areas overlain on digital orthophotographs are presented for the selected floods. As part of this flood-warning network, the USGS upgraded one streamgage and added two new streamgages, one on the Blanchard River and one on Riley Creek, which is tributary to the Blanchard River. The streamgage sites were equipped with both satellite and telephone telemetry. The telephone telemetry provides dual functionality, allowing village officials and the public to monitor current stage conditions and enabling the streamgage to call village officials with automated warnings regarding flood stage and/or predetermined rates of stage increase. Data from the streamgages serve as a flood warning that emergency management personnel can use in conjunction with the flood-inundation maps by to determine a course of action when flooding is imminent.

  13. Flood characteristics for the New River in the New River Gorge National River, West Virginia

    USGS Publications Warehouse

    Wiley, J.B.; Cunningham, M.K.

    1994-01-01

    The frequency and magnitude of flooding of the New River in the New River Gorge National River was studied. A steady-state, one-dimensional flow model was applied to the study reach. Rating curves, cross sections, and Manning's roughness coefficients that were used are presented in this report. Manning's roughness coefficients were evaluated by comparing computed elevations (from application of the steady-state, one-dimensional flow model) to rated elevations at U.S. Geological Survey (USGS) streamflow-gaging stations and miscellaneous-rating sites. Manning's roughness coefficients ranged from 0.030 to 0.075 and varied with hydraulic depth. The 2-, 25-, and 100-year flood discharges were esti- mated on the basis of information from flood- insurance studies of Summers County, Fayette County, and the city of Hinton, and flood-frequency analysis of discharge records for the USGS streamflow-gaging stations at Hinton and Thurmond. The 100-year discharge ranged from 107,000 cubic feet per second at Hinton to 150,000 cubic feet per second at Fayette.

  14. 78 FR 14315 - Notice of Chargeable Rates Under the National Flood Insurance Program for Non-Primary Residences

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-03-05

    ... SECURITY Federal Emergency Management Agency Notice of Chargeable Rates Under the National Flood Insurance Program for Non-Primary Residences AGENCY: Federal Emergency Management Agency, DHS. ACTION: Notice... Federal Insurance, Federal Emergency Management Agency. BILLING CODE 9110-11-P...

  15. Flood-inundation maps for the Schoharie Creek at Prattsville, New York, 2014

    USGS Publications Warehouse

    Nystrom, Elizabeth A.

    2015-01-01

    These flood-inundation maps, along with near-real-time stage data from USGS streamgages and forecasted stage data from the National Weather Service, can provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures as well as for postflood recovery efforts.

  16. Flood-inundation maps for the White River near Edwardsport, Indiana

    USGS Publications Warehouse

    Fowler, Kathleen K.

    2014-01-01

    The availability of these maps, along with Internet information regarding current stage from the USGS streamgage 03360730 White River near Edwardsport, Ind., and forecasted stream stages from the National Weather Service, provides emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for post-flood recovery efforts.

  17. Configuring the HYSPLIT Model for National Weather Service Forecast Office and Spaceflight Meteorology Group Applications

    NASA Technical Reports Server (NTRS)

    Dreher, Joseph G.

    2009-01-01

    For expedience in delivering dispersion guidance in the diversity of operational situations, National Weather Service Melbourne (MLB) and Spaceflight Meteorology Group (SMG) are becoming increasingly reliant on the PC-based version of the HYSPLIT model run through a graphical user interface (GUI). While the GUI offers unique advantages when compared to traditional methods, it is difficult for forecasters to run and manage in an operational environment. To alleviate the difficulty in providing scheduled real-time trajectory and concentration guidance, the Applied Meteorology Unit (AMU) configured a Linux version of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) (HYSPLIT) model that ingests the National Centers for Environmental Prediction (NCEP) guidance, such as the North American Mesoscale (NAM) and the Rapid Update Cycle (RUC) models. The AMU configured the HYSPLIT system to automatically download the NCEP model products, convert the meteorological grids into HYSPLIT binary format, run the model from several pre-selected latitude/longitude sites, and post-process the data to create output graphics. In addition, the AMU configured several software programs to convert local Weather Research and Forecast (WRF) model output into HYSPLIT format.

  18. Ensemble flood forecasting to support dam water release operation using 10 and 2 km-resolution JMA Nonhydrostatic Model ensemble rainfalls

    NASA Astrophysics Data System (ADS)

    Kobayashi, K.; Otsuka, S.; Apip; Saito, K.

    2015-12-01

    This paper presents a study on short-term ensemble flood forecasting specifically for small dam catchments in Japan. Numerical ensemble simulations of rainfall from the Japan Meteorological Agency Nonhydrostatic Model are used as the input data to a rainfall-runoff model for predicting river discharge into a dam. The ensemble weather simulations use a conventional 10 km and a high-resolution 2 km spatial resolution. A distributed rainfall-runoff model is constructed for the Kasahori dam catchment (approx. 70 km2) and applied with the ensemble rainfalls. The results show that the hourly maximum and cumulative catchment-average rainfalls of the 2 km-resolution JMA-NHM ensemble simulation are more appropriate than the 10 km-resolution rainfalls. All the simulated inflows based on the 2 and 10 km rainfalls become larger than the flood discharge of 140 m3 s-1; a threshold value for flood control. The inflows with the 10 km-resolution ensemble rainfall are all considerably smaller than the observations, while, at least one simulated discharge out of 11 ensemble members with the 2 km-resolution rainfalls reproduces the first peak of the inflow at the Kasahori dam with similar amplitude to observations, although there are spatiotemporal lags between simulation and observation. To take positional lags into account of the ensemble discharge simulation, the rainfall distribution in each ensemble member is shifted so that the catchment-averaged cumulative rainfall of the Kasahori dam maximizes. The runoff simulation with the position-shifted rainfalls show much better results than the original ensemble discharge simulations.

  19. Long-term trends in flood fatalities in the United State

    NASA Astrophysics Data System (ADS)

    Sharif, Hatim; Chaturvedi, Smita

    2015-04-01

    This presentation reviews flood-related fatalities in the United States between 1959 and 2013. Information on flood fatality victims and the flood-causing events was obtained from the National Climatic Data Center. The data collected included the date, time, location, and weather conditions and the gender and age of the flood victims. Long term trends in the numbers of fatalities and fatality rates were analyzed. For most of the states fatalities were largely caused by single catastrophic events. The analysis indicates that the standardized annual flood fatality rates are decreasing significantly for all states. Vehicle related fatalities represent more than 50% of flood fatalities for most of the states and can be as high as 77%. A combination of improved hydrometeorological forecasting, educational programs aimed at enhancing public awareness of flood risk and the seriousness of flood warnings, and timely and appropriate action by local emergency and safety authorities will help further reduce flood fatalities in Texas.

  20. Department of Energy award DE-SC0004164 Climate and National Security: Securing Better Forecasts

    SciTech Connect

    Reno Harnish

    2011-08-16

    The Climate and National Security: Securing Better Forecasts symposium was attended by senior policy makers and distinguished scientists. The juxtaposition of these communities was creative and fruitful. They acknowledged they were speaking past each other. Scientists were urged to tell policy makers about even improbable outcomes while articulating clearly the uncertainties around the outcomes. As one policy maker put it, we are accustomed to making these types of decisions. These points were captured clearly in an article that appeared on the New York Times website and can be found with other conference materials most easily on our website, www.scripps.ucsd.edu/cens/. The symposium, generously supported by the NOAA/JIMO, benefitted the public by promoting scientifically informed decision making and by the transmission of objective information regarding climate change and national security.

  1. The Impact of Corps Flood Control Reservoirs in the June 2008 Upper Mississippi Flood

    NASA Astrophysics Data System (ADS)

    Charley, W. J.; Stiman, J. A.

    2008-12-01

    dissemination. The system uses precipitation and flow data, collected in real-time, along with forecasted flow from the National Weather Service to model and optimize reservoir operations and forecast downstream flows and stages, providing communities accurate and timely information to aid their flood-fighting. This involves integrating several simulation modeling programs, including HEC-HMS to forecast flows, HEC-ResSim to model reservoir operations and HEC-RAS to compute forecasted stage hydrographs. An inundation boundary and depth map of water in the flood plain can be calculated from the HEC-RAS results using ArcInfo. By varying future precipitation and releases, engineers can evaluate different "What if?" scenarios. The effectiveness of this tool and Corps reservoirs are examined.

  2. Calibration and evaluation of a flood forecasting system: Utility of numerical weather prediction model, data assimilation and satellite-based rainfall

    NASA Astrophysics Data System (ADS)

    Yucel, I.; Onen, A.; Yilmaz, K. K.; Gochis, D. J.

    2015-04-01

    A fully-distributed, multi-physics, multi-scale hydrologic and hydraulic modeling system, WRF-Hydro, is used to assess the potential for skillful flood forecasting based on precipitation inputs derived from the Weather Research and Forecasting (WRF) model and the EUMETSAT Multi-sensor Precipitation Estimates (MPEs). Similar to past studies it was found that WRF model precipitation forecast errors related to model initial conditions are reduced when the three dimensional atmospheric data assimilation (3DVAR) scheme in the WRF model simulations is used. A comparative evaluation of the impact of MPE versus WRF precipitation estimates, both with and without data assimilation, in driving WRF-Hydro simulated streamflow is then made. The ten rainfall-runoff events that occurred in the Black Sea Region were used for testing and evaluation. With the availability of streamflow data across rainfall-runoff events, the calibration is only performed on the Bartin sub-basin using two events and the calibrated parameters are then transferred to other neighboring three ungauged sub-basins in the study area. The rest of the events from all sub-basins are then used to evaluate the performance of the WRF-Hydro system with the calibrated parameters. Following model calibration, the WRF-Hydro system was capable of skillfully reproducing observed flood hydrographs in terms of the volume of the runoff produced and the overall shape of the hydrograph. Streamflow simulation skill was significantly improved for those WRF model simulations where storm precipitation was accurately depicted with respect to timing, location and amount. Accurate streamflow simulations were more evident in WRF model simulations where the 3DVAR scheme was used compared to when it was not used. Because of substantial dry bias feature of MPE, as compared with surface rain gauges, streamflow derived using this precipitation product is in general very poor. Overall, root mean squared errors for runoff were reduced by

  3. Towards large scale stochastic rainfall models for flood risk assessment in trans-national basins

    NASA Astrophysics Data System (ADS)

    Serinaldi, F.; Kilsby, C. G.

    2012-04-01

    While extensive research has been devoted to rainfall-runoff modelling for risk assessment in small and medium size watersheds, less attention has been paid, so far, to large scale trans-national basins, where flood events have severe societal and economic impacts with magnitudes quantified in billions of Euros. As an example, in the April 2006 flood events along the Danube basin at least 10 people lost their lives and up to 30 000 people were displaced, with overall damages estimated at more than half a billion Euros. In this context, refined analytical methods are fundamental to improve the risk assessment and, then, the design of structural and non structural measures of protection, such as hydraulic works and insurance/reinsurance policies. Since flood events are mainly driven by exceptional rainfall events, suitable characterization and modelling of space-time properties of rainfall fields is a key issue to perform a reliable flood risk analysis based on alternative precipitation scenarios to be fed in a new generation of large scale rainfall-runoff models. Ultimately, this approach should be extended to a global flood risk model. However, as the need of rainfall models able to account for and simulate spatio-temporal properties of rainfall fields over large areas is rather new, the development of new rainfall simulation frameworks is a challenging task involving that faces with the problem of overcoming the drawbacks of the existing modelling schemes (devised for smaller spatial scales), but keeping the desirable properties. In this study, we critically summarize the most widely used approaches for rainfall simulation. Focusing on stochastic approaches, we stress the importance of introducing suitable climate forcings in these simulation schemes in order to account for the physical coherence of rainfall fields over wide areas. Based on preliminary considerations, we suggest a modelling framework relying on the Generalized Additive Models for Location, Scale

  4. A Novel Hydro-information System for Improving National Weather Service River Forecast System

    NASA Astrophysics Data System (ADS)

    Nan, Z.; Wang, S.; Liang, X.; Adams, T. E.; Teng, W. L.; Liang, Y.

    2009-12-01

    A novel hydro-information system has been developed to improve the forecast accuracy of the NOAA National Weather Service River Forecast System (NWSRFS). An MKF-based (Multiscale Kalman Filter) spatial data assimilation framework, together with the NOAH land surface model, is employed in our system to assimilate satellite surface soil moisture data to yield improved evapotranspiration. The latter are then integrated into the distributed version of the NWSRFS to improve its forecasting skills, especially for droughts, but also for disaster management in general. Our system supports an automated flow into the NWSRFS of daily satellite surface soil moisture data, derived from the TRMM Microwave Imager (TMI) and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), and the forcing information of the North American Land Data Assimilation System (NLDAS). All data are custom processed, archived, and supported by the NASA Goddard Earth Sciences Data Information and Services Center (GES DISC). An optional data fusing component is available in our system, which fuses NEXRAD Stage III precipitation data with the NLDAS precipitation data, using the MKF-based framework, to provide improved precipitation inputs. Our system employs a plug-in, structured framework and has a user-friendly, graphical interface, which can display, in real-time, the spatial distributions of assimilated state variables and other model-simulated information, as well as their behaviors in time series. The interface can also display watershed maps, as a result of the integration of the QGIS library into our system. Extendibility and flexibility of our system are achieved through the plug-in design and by an extensive use of XML-based configuration files. Furthermore, our system can be extended to support multiple land surface models and multiple data assimilation schemes, which would further increase its capabilities. Testing of the integration of the current system into the NWSRFS is

  5. Flood-inundation maps for a 12.5-mile reach of Big Papillion Creek at Omaha, Nebraska

    USGS Publications Warehouse

    Strauch, Kellan R.; Dietsch, Benjamin J.; Anderson, Kayla J.

    2016-01-01

    The availability of these flood-inundation maps, along with Internet information regarding current stage from the USGS streamgage and forecasted high-flow stages from the National Weather Service, will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for postflood recovery efforts.

  6. Probabilistic forecasts for Decision Support at the North Central River Forecast Center

    NASA Astrophysics Data System (ADS)

    Restrepo, Pedro; Buan, Steven; Connelly, Brian; DeWeese, Michael; Diamond, Laura; Ellis, Larry; Goering, Dustin; Holz, Andrea; Husaby, James; Merrigan, Douglas; Palmer, Justin; Pokorny, Daniel; Reckel, Holly; Sites, William; Stockhaus, Scott; Thornburg, Jonathon; Wavrin, Robert.; Ziemer, Mark

    2013-04-01

    The North Central River Forecast Center (NCRFC) of the US National Weather Service has the responsibility for issuing river forecasts at 426 points over an area of nearly 890,000 km2, covering the Upper Mississippi river basin, the US watersheds flowing to lakes Superior, Huron and Michigan, and rivers flowing from the US to the Hudson Bay in Canada. The NCRFC issues probabilistic outlook forecasts at all its forecast points starting on December. While focused primarily on the risks associated with flooding during the spring snow melt down, the RFC frequently issues probabilistic forecasts to deal with water resources operations during drought times. This presentation will focus on probabilistic forecasts issued to assess flooding risk at Red River of the North , to support navigation operations on the Mississippi river during drought conditions, and on support of reservoir operations for hydropower generation and recreation. The presentation will discuss the improvements over the current practice that will be possible to achieve once the NWS Hydrologic Ensemble Forecasting System is put into operations later this year.

  7. Ensemble stream flow predictions, a way towards better hydrological forecasting

    NASA Astrophysics Data System (ADS)

    Edlund, C.

    2009-04-01

    The hydrological forecasting division at SMHI has been using hydrological EPS and hydrological probabilities forecasts operationally since some years ago. The inputs to the hydrological model HBV are the EPS forecasts from ECMWF. From the ensemble, non-exceedance probabilities are estimated and final correction of the ensemble spread, based on evaluation is done. Ensemble stream flow predictions are done for about 80 indicator basins in Sweden, where there is a real-time discharge gauge. The EPS runs are updated daily against the latest observed discharge. Flood probability maps for exceeding a certain threshold, i.e. a certain warning level, are produced automatically once a day. The flood probabilistic forecasts are based on a HBV- model application, (called HBV-Sv, HBV Sweden) that covers the whole country and consist of 1001 subbasins with an average size between 200 and 700 km2. Probabilities computations for exceeding a certain warning level are made for each one of these 1001 subbasins. Statistical flood levels have been calculated for each river sub-basin. Hydrological probability forecasts should be seen as an early warning product that can give better support in decision making to end-users communities, for instance Civil Protections Offices and County Administrative Boards, within flood risk management. The main limitations with probability forecasts are: on one hand, difficulties to catch small-scale rain (mainly due to resolution of meteorological models); on the other hand, the hydrological model can't be updated against observations in all subbasins. The benefits of working with probabilities consist, first of all, of a new approach when working with flood risk management and scenarios. A probability forecast can give an early indication for Civil Protection that "something is going to happen" and to gain time in preparing aid operations. The ensemble stream flow prediction at SMHI is integrated with the national forecasting system and the products

  8. Forecasts of 21st Century Snowpack and Implications for Snowmobile and Snowcoach Use in Yellowstone National Park

    PubMed Central

    Tercek, Michael; Rodman, Ann

    2016-01-01

    Climate models project a general decline in western US snowpack throughout the 21st century, but long-term, spatially fine-grained, management-relevant projections of snowpack are not available for Yellowstone National Park. We focus on the implications that future snow declines may have for oversnow vehicle (snowmobile and snowcoach) use because oversnow tourism is critical to the local economy and has been a contentious issue in the park for more than 30 years. Using temperature-indexed snow melt and accumulation equations with temperature and precipitation data from downscaled global climate models, we forecast the number of days that will be suitable for oversnow travel on each Yellowstone road segment during the mid- and late-21st century. The west entrance road was forecast to be the least suitable for oversnow use in the future while the south entrance road was forecast to remain at near historical levels of driveability. The greatest snow losses were forecast for the west entrance road where as little as 29% of the December–March oversnow season was forecast to be driveable by late century. The climatic conditions that allow oversnow vehicle use in Yellowstone are forecast by our methods to deteriorate significantly in the future. At some point it may be prudent to consider plowing the roads that experience the greatest snow losses. PMID:27467778

  9. Forecasts of 21st Century Snowpack and Implications for Snowmobile and Snowcoach Use in Yellowstone National Park.

    PubMed

    Tercek, Michael; Rodman, Ann

    2016-01-01

    Climate models project a general decline in western US snowpack throughout the 21st century, but long-term, spatially fine-grained, management-relevant projections of snowpack are not available for Yellowstone National Park. We focus on the implications that future snow declines may have for oversnow vehicle (snowmobile and snowcoach) use because oversnow tourism is critical to the local economy and has been a contentious issue in the park for more than 30 years. Using temperature-indexed snow melt and accumulation equations with temperature and precipitation data from downscaled global climate models, we forecast the number of days that will be suitable for oversnow travel on each Yellowstone road segment during the mid- and late-21st century. The west entrance road was forecast to be the least suitable for oversnow use in the future while the south entrance road was forecast to remain at near historical levels of driveability. The greatest snow losses were forecast for the west entrance road where as little as 29% of the December-March oversnow season was forecast to be driveable by late century. The climatic conditions that allow oversnow vehicle use in Yellowstone are forecast by our methods to deteriorate significantly in the future. At some point it may be prudent to consider plowing the roads that experience the greatest snow losses. PMID:27467778

  10. Development and Implementation of Dynamic Scripts to Support Local Model Verification at National Weather Service Weather Forecast Offices

    NASA Technical Reports Server (NTRS)

    Zavodsky, Bradley; Case, Jonathan L.; Gotway, John H.; White, Kristopher; Medlin, Jeffrey; Wood, Lance; Radell, Dave

    2014-01-01

    Local modeling with a customized configuration is conducted at National Weather Service (NWS) Weather Forecast Offices (WFOs) to produce high-resolution numerical forecasts that can better simulate local weather phenomena and complement larger scale global and regional models. The advent of the Environmental Modeling System (EMS), which provides a pre-compiled version of the Weather Research and Forecasting (WRF) model and wrapper Perl scripts, has enabled forecasters to easily configure and execute the WRF model on local workstations. NWS WFOs often use EMS output to help in forecasting highly localized, mesoscale features such as convective initiation, the timing and inland extent of lake effect snow bands, lake and sea breezes, and topographically-modified winds. However, quantitatively evaluating model performance to determine errors and biases still proves to be one of the challenges in running a local model. Developed at the National Center for Atmospheric Research (NCAR), the Model Evaluation Tools (MET) verification software makes performing these types of quantitative analyses easier, but operational forecasters do not generally have time to familiarize themselves with navigating the sometimes complex configurations associated with the MET tools. To assist forecasters in running a subset of MET programs and capabilities, the Short-term Prediction Research and Transition (SPoRT) Center has developed and transitioned a set of dynamic, easily configurable Perl scripts to collaborating NWS WFOs. The objective of these scripts is to provide SPoRT collaborating partners in the NWS with the ability to evaluate the skill of their local EMS model runs in near real time with little prior knowledge of the MET package. The ultimate goal is to make these verification scripts available to the broader NWS community in a future version of the EMS software. This paper provides an overview of the SPoRT MET scripts, instructions for how the scripts are run, and example use

  11. Flood-inundation maps for the Hoosic River, North Adams and Williamstown, Massachusetts, from the confluence with the North Branch Hoosic River to the Vermont State line

    USGS Publications Warehouse

    Lombard, Pamela J.; Bent, Gardner C.

    2015-01-01

    The availability of the flood-inundation maps, combined with information regarding current (near real-time) stage from USGS streamgage Hoosic River near Williamstown, and forecasted flood stages from the National Weather Service Advanced Hydrologic Prediction Service will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, and post-flood recovery efforts. The flood-inundation maps are nonregulatory, but provide Federal, State, and local agencies and the public with estimates of the potential extent of flooding during selected peak-flow events.

  12. European Flood Awareness System - now operational

    NASA Astrophysics Data System (ADS)

    Alionte Eklund, Cristina.; Hazlinger, Michal; Sprokkereef, Eric; Garcia Padilla, Mercedes; Garcia, Rafael J.; Thielen, Jutta; Salamon, Peter; Pappenberger, Florian

    2013-04-01

    The European Commission's Communication "Towards a Stronger European Union Disaster Response" adopted and endorsed by the Council in 2010, underpins the importance of strengthening concerted actions for natural disasters including floods, which are amongst the costliest natural disasters in the EU. The European Flood Awareness System (EFAS) contributes in the case of major flood events. to better protection of the European Citizen, the environment, property and cultural heritage. The disastrous floods in Elbe and Danube rivers in 2002 confronted the European Commission with non-coherent flood warning information from different sources and of variable quality, complicating planning and organisation of aid. Thus, the Commission initiated the development of a European Flood Awareness System (EFAS) which is now going operational. EFAS has been developed and tested at the Joint Research Centre, the Commission's in house science service, in close collaboration with the National hydrological and meteorological services, European Civil Protection through the Monitoring and Information Centre (MIC) and other research institutes. EFAS provides Pan-European overview maps of flood probabilities up to 10 days in advance as well as detailed forecasts at stations where the National services are providing real time data. More than 30 hydrological services and civil protection services in Europe are part of the EFAS network. Since 2011, EFAS is part of the COPERNICUS Emergency Management Service, (EMS) and is now an operational service since 2012. The Operational EFAS is being executed by several consortia dealing with different operational aspects: • EFAS Hydrological data collection centre —REDIAM and ELIMCO- will be collecting historic and realtime discharge and water levels data in support to EFAS • EFAS Meteorological data collection centre —outsourced but running onsite of JRC Ispra. Will be collecting historic and realtime meteorological data in support to EFAS

  13. Extreme Rainfall and Flood Events for the Hudson River Induced by Tropical Cyclones: a Statistical Forecast Model

    NASA Astrophysics Data System (ADS)

    Conticello, F.; Hall, T. M.; Lall, U.; Orton, P. M.; Cioffi, F.; Georgas, N.

    2014-12-01

    Tropical Cyclones (TCs) lead to potentially severe coastal flooding through wind surge and also through rainfall-runoff processes. There is growing interest in modeling these processes simultaneously. Here, a statistical approach that can facilitate this process is presented with an application to the Hudson River Basin that is associated with the New York City metropolitan area. Three submodels are used in sequence. The first submodel is a stochastic model of the complete life cycle of North Atlantic (NA) tropical cyclones developed by Hall and Yonekura (2011). It uses archived data of TCs throughout the North Atlantic to estimate landfall rates at high geographic resolution as a function of the ENSO state and of sea surface temperature (SST). The second submodel translates the attributes of a tropical cyclone simulated by the first model to the streamflows at specific points of the tributaries of the Hudson River. That points are the closure sections of five different watersheds. Two different approaches are used and compared: 1) an ANN-cyclone/rainfall clustering model which calculates the rainfall intensity at selected stations within the watershed, that are then used as inputs of an ANN rainfall/runoff model; 2) an ANN/ Bayesian multivariate approach that translates the TC attributes ( track, SST, Velocities,..) directly in streamflows in the tributaries. Finally, the streamflows of the tributaries of the Hudson River are to be used as inputs in a hydrodynamic model that includes storm surge dynamics for the simulation of coastal flooding along the Hudson River. Calibration and validation of the model is carried out by using, selected tropical cyclone data since 1950, and hourly and daily station rainfall and streamflow recorded for such extreme events. Seven stream gauges (Croton River, Rondout Creek, Wappinger Creek, Troy dam, Mohawk River at Cohoes, Mohawk River diversion at Crescent Dam, Hudson River above lock one nr Waterford) and over 20 rain gauges are

  14. Potential flood and debris hazards at Cottonwood Cove, Lake Mead National Recreation Area, Clark County, Nevada

    USGS Publications Warehouse

    Moosburner, Otto

    1981-01-01

    At Cottonwood Cove, Nevada, most of the existing dikes at the recreation sites are effective in diverting and routing floodflows, up to and including the 100-year flood, away from people and facilities. The dikes across Ranger Residence Wash and Access Road Wash at the mouth divert floods up to the 50-year recurrence interval away from residential areas. Flow and debris damage in protected areas will be relatively minor minor for floods including the 100-year flood, whereas damage caused by sediment deposition at the mouths of the washes near Lake Mohave could be significant for floods equal to or less than the 100-year flood. The extreme flood, a flood meteorologically and hydrologically possible but so rare as to preclude a frequency estimate, could cause great damage and possible loss of life. The present dikes would be topped or breached by such flooding. (USGS)

  15. Using Forecast Information in Reservoir Operation

    NASA Astrophysics Data System (ADS)

    Faber, B.

    2011-12-01

    Reservoir operation is a series of decisions made over time. We choose whether to release water for various downstream purposes, or store it for later use. We choose whether to detain high flows to protect downstream areas, or pass that flow to retain space to store imminent higher flows. These decisions are driven by current and future inflows to the reservoir, and yet those inflows are uncertain and extremely variable. Conceptually, more information provides opportunity for better decisions, and so information about future inflows can improve reservoir operations. However, uncertain information must be used carefully, with awareness of the uncertainty and the likely consequence of "wrong" decisions (i.e., those with consequences worse than decisions that might otherwise have been made.) The historical streamflow record offers one source of information on the range and timing of streamflows. Streamflow forecasting provides additional valuable information on coming reservoir inflows, both at short and longer time scales. For example, 5-day flow forecasts based on forecasted precipitation can inform rain-flood operations, while seasonal snowmelt forecasts can aid snowmelt-flood operation, reservoir refill, and seasonal allocation of water supply. Forecast information can aid our decision-making greatly, but too much reliance on an incorrect forecast can make operation worse. Informed use of forecasts requires an understanding of the expected range of the actual streamflow (the error distribution). Forecast products must therefore be provided with a description of skill and error distribution understood by the producers and users of that information. Using forecasts wisely, with an understanding of their uncertainty, is an important aspect of the operation of our nation's Federal reservoirs.

  16. Ensemble stream flow predictions using the ECMWF forecasts

    NASA Astrophysics Data System (ADS)

    Kiczko, Adam; Romanowicz, Renata; Osuch, Marzena; Pappenberger, Florian; Karamuz, Emilia

    2015-04-01

    ://www.ecmwf.int/en/research/projects/tigge) to prolong the lead time of the forecasts downstream. Both hydrological models show different performances when forced with raw and de-biased ECMWF ensembles. This work was partly supported by the project "Stochastic flood forecasting system (The River Vistula reach from Zawichost to Warsaw)" carried out by the Institute of Geophysics, Polish Academy of Sciences by order of the National Science Centre (contract No. 2011/01/B/ST10/06866). The rainfall and flow data were provided by the Institute of Meteorology and Water Management (IMGW), Poland.

  17. Population forecasts for South Pacific nations using autoregressive models, 1985-2000.

    PubMed

    Ahlburg, D A

    1987-11-01

    "This paper uses an autoregressive statistical model to forecast population for Fiji, Western Samoa, Tonga, Solomon Islands, and Vanuatu and compares these forecasts with those obtained from other methods. The growth rate of population is predicted to continue to fall in Fiji and Tonga, rise a little for Western Samoa, and rise considerably in Vanuatu and the Solomon Islands. The implications of the forecasts for recent government development plans are also discussed." PMID:12314995

  18. Evaluating National Weather Service Seasonal Forecast Products in Reservoir Operation Case Studies

    NASA Astrophysics Data System (ADS)

    Nielson, A.; Guihan, R.; Polebistki, A.; Palmer, R. N.; Werner, K.; Wood, A. W.

    2014-12-01

    Forecasts of future weather and streamflow can provide valuable information for reservoir operations and water management. A challenge confronting reservoir operators today is how to incorporate both climate and streamflow products into their operations and which of these forecast products are most informative and useful for optimized water management. This study incorporates several reforecast products provided by the Colorado Basin River Forecast Center (CBRFC) which allows a complete retrospective analysis of climate forecasts, resulting in an evaluation of each product's skill in the context of water resources management. The accuracy and value of forecasts generated from the Climate Forecast System version 2 (CFSv2) are compared to the accuracy and value of using an Ensemble Streamflow Predictions (ESP) approach. Using the CFSv2 may offer more insight when responding to climate driven extremes than the ESP approach because the CFSv2 incorporates a fully coupled climate model into its forecasts rather than using all of the historic climate record as being equally probable. The role of forecast updating frequency will also be explored. Decision support systems (DSS) for both Salt Lake City Parley's System and the Snohomish County Public Utility Department's (SnoPUD) Jackson project will be used to illustrate the utility of forecasts. Both DSS include a coupled simulation and optimization model that will incorporate system constraints, operating policies, and environmental flow requirements. To determine the value of the reforecast products, performance metrics meaningful to the managers of each system are to be identified and quantified. Without such metrics and awareness of seasonal operational nuances, it is difficult to identify forecast improvements in meaningful ways. These metrics of system performance are compared using the different forecast products to evaluate the potential benefits of using CFSv2 seasonal forecasts in systems decision making.

  19. Revisions Recommended to Bulletin 17B - US National Flood Frequency Guidelines

    NASA Astrophysics Data System (ADS)

    England, J. F.; Cohn, T. A.; Faber, B. A.; Stedinger, J. R.; Thomas, W. O.; Mason, R. R.

    2013-12-01

    The Hydrologic Frequency Analysis Work Group (HFAWG) has synthesized recent research and completed its own studies to support proposed revisions to the current the US national flood frequency guidelines (Bulletin 17B). Bulletin 17 was originally issued in 1976; Bulletin 17B, the last revision, was published in 1982, over 30 years ago. To reflect advances that have occurred since 1982, the HFAWG has proposed revisions in four main areas: (1) use of historical information; (2) the motivation for low outlier identification and their statistical definition and treatment; (3) procedures for estimating generalized/regional skew; and (4) procedures for estimating confidence intervals for estimated quantiles. We present overviews of the HFAWG process and technical studies that led us to these revisions. The focus is on the use of the Expected Moments Algorithm (EMA) with the log-Pearson Type III distribution. A new Multiple Grubbs-Beck low outlier test and improved EMA confidence intervals are important parts of the revision.

  20. Estimated flood-inundation maps for Cowskin Creek in western Wichita, Kansas

    USGS Publications Warehouse

    Studley, Seth E.

    2003-01-01

    The October 31, 1998, flood on Cowskin Creek in western Wichita, Kansas, caused millions of dollars in damages. Emergency management personnel and flood mitigation teams had difficulty in efficiently identifying areas affected by the flooding, and no warning was given to residents because flood-inundation information was not available. To provide detailed information about future flooding on Cowskin Creek, high-resolution estimated flood-inundation maps were developed using geographic information system technology and advanced hydraulic analysis. Two-foot-interval land-surface elevation data from a 1996 flood insurance study were used to create a three-dimensional topographic representation of the study area for hydraulic analysis. The data computed from the hydraulic analyses were converted into geographic information system format with software from the U.S. Army Corps of Engineers' Hydrologic Engineering Center. The results were overlaid on the three-dimensional topographic representation of the study area to produce maps of estimated flood-inundation areas and estimated depths of water in the inundated areas for 1-foot increments on the basis of stream stage at an index streamflow-gaging station. A Web site (http://ks.water.usgs.gov/Kansas/cowskin.floodwatch) was developed to provide the public with information pertaining to flooding in the study area. The Web site shows graphs of the real-time streamflow data for U.S. Geological Survey gaging stations in the area and monitors the National Weather Service Arkansas-Red Basin River Forecast Center for Cowskin Creek flood-forecast information. When a flood is forecast for the Cowskin Creek Basin, an estimated flood-inundation map is displayed for the stream stage closest to the National Weather Service's forecasted peak stage. Users of the Web site are able to view the estimated flood-inundation maps for selected stages at any time and to access information about this report and about flooding in general. Flood

  1. Use of Air Quality Observations by the National Air Quality Forecast Capability

    NASA Astrophysics Data System (ADS)

    Stajner, I.; McQueen, J.; Lee, P.; Stein, A. F.; Kondragunta, S.; Ruminski, M.; Tong, D.; Pan, L.; Huang, J. P.; Shafran, P.; Huang, H. C.; Dickerson, P.; Upadhayay, S.

    2015-12-01

    The National Air Quality Forecast Capability (NAQFC) operational predictions of ozone and wildfire smoke for the United States (U.S.) and predictions of airborne dust for continental U.S. are available at http://airquality.weather.gov/. NOAA National Centers for Environmental Prediction (NCEP) operational North American Mesoscale (NAM) weather predictions are combined with the Community Multiscale Air Quality (CMAQ) model to produce the ozone predictions and test fine particulate matter (PM2.5) predictions. The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model provides smoke and dust predictions. Air quality observations constrain emissions used by NAQFC predictions. NAQFC NOx emissions from mobile sources were updated using National Emissions Inventory (NEI) projections for year 2012. These updates were evaluated over large U.S. cities by comparing observed changes in OMI NO2 observations and NOx measured by surface monitors. The rate of decrease in NOx emission projections from year 2005 to year 2012 is in good agreement with the observed changes over the same period. Smoke emissions rely on the fire locations detected from satellite observations obtained from NESDIS Hazard Mapping System (HMS). Dust emissions rely on a climatology of areas with a potential for dust emissions based on MODIS Deep Blue aerosol retrievals. Verification of NAQFC predictions uses AIRNow compilation of surface measurements for ozone and PM2.5. Retrievals of smoke from GOES satellites are used for verification of smoke predictions. Retrievals of dust from MODIS are used for verification of dust predictions. In summary, observations are the basis for the emissions inputs for NAQFC, they are critical for evaluation of performance of NAQFC predictions, and furthermore they are used in real-time testing of bias correction of PM2.5 predictions, as we continue to work on improving modeling and emissions important for representation of PM2.5.

  2. A Quantitative Approach to Flash Flood Prediction in Southern Utah

    NASA Astrophysics Data System (ADS)

    Hurwitz, M. M.; Gibson, C. V.; Jackson, M.; McInerney, B.

    2005-05-01

    Flash flood monitoring and prediction is considered to be a critical part of National Weather Service (NWS) severe weather operations in the semi-arid western United States. The complex terrain and steep slopes in this area, combined with impervious rock and soils, can induce flash flooding with relatively light rainfall. This reduces the value of using the more common conceptual flash flood models developed for the central and eastern United States. Thus, forecasters at the NWS Weather Forecast Office in Salt Lake City, Utah, have relied on a locally developed conceptual model to predict the likelihood of flash flooding on a given day. Until this study, common practice was to assume that humid and unstable air combined with low wind speeds in the lower troposphere would yield rainfall conductive to flash flooding. A new approach to flash flood prediction, exploring the connection between atmospheric variables and flash flood reports, will increase situational awareness and provide forecasters with quantitative flash flood guidance. A record of historical flash floods in southern Utah was compiled to determine the frequency of events from 1959 to 2003. A complete data set, consisting of both historical flash flooding days and non-event days, was assembled. A trial of the 2003 three-month flash flood season assessed which variables and which dataset to use in studying the eight flash flood seasons from 1996 to 2003; the trial concluded that the best source of atmospheric data was a set of soundings from Flagstaff, Arizona, a location close to and generally upstream of southern Utah. Neural networks were used to determine the relationship between the atmospheric state and a particular day's flash flood severity. The final neural network used six input variables and a discretized output variable. Precipitable water, low-level relative humidity, convective available potential energy, the 500hPa height change between 12Z and 0Z the following day, and the previous day

  3. Floods and Flash Flooding

    MedlinePlus

    Floods and flash flooding Now is the time to determine your area’s flood risk. If you are not sure whether you ... If you are in a floodplain, consider buying flood insurance. Do not drive around barricades. If your ...

  4. Coastal ocean forecasting systems in Europe

    NASA Astrophysics Data System (ADS)

    Dugan, John

    During my tour as the liaison oceanographer at the Office of Naval Research's European branch, I conducted a focused study of coastal ocean forecasting systems. This study is of direct interest to ONR because of an increased interest in the coastal zone and to the civilian U.S. oceanographic community because of numerous problems in the coastal zone that could be alleviated with an operational forecasting system. The Europeans have a long history of excellent research and developmental work in this area. The Europeans' distinguished history in coastal ocean forecasting is due in part to their strong dependence on the sea. However, the original motivation for these systems was the recognition early in this century that weather conditions were responsible for damaging storm surges around the periphery of the North Sea and that science could predict these catastrophic floods. Forecasting systems called tide-surge prediction systems, which provide warnings of impending flood conditions, were designed and constructed and are operational in the various meteorological centers of the nations surrounding the North Sea. Over time, the services have been extended to provide forecasts of ocean waves, water depth for navigation, and currents for a large customer base. These systems now are being extended further into the three-dimensional domain that is required for management of problems associated with water quality, pollution, and aquaculture and fisheries interests.

  5. A successful forecast of an El Nino winter

    SciTech Connect

    Kerr, R.A.

    1992-01-24

    This year, for the first time, weather forecasters used signs of a warming in the tropical Pacific as the basis for a long-range prediction of winter weather patterns across the United States. Now forecasters are talking about the next step: stretching the lead time for such forecasts by a year or more. That seems feasible because although this Pacific warming was unmistakable by the time forecasters at the National Weather Service's Climate Analysis Center (CAC) in Camp Springs, Maryland, issued their winter forecast, the El Nino itself had been predicted almost 2 years in advance by a computer model. Next time around, the CAC may well be listening to the modelers and predicting El Nino-related patterns of warmth and flooding seasons in advance.

  6. Flooding and Schools

    ERIC Educational Resources Information Center

    National Clearinghouse for Educational Facilities, 2011

    2011-01-01

    According to the Federal Emergency Management Agency, flooding is the nation's most common natural disaster. Some floods develop slowly during an extended period of rain or in a warming trend following a heavy snow. Flash floods can occur quickly, without any visible sign of rain. Catastrophic floods are associated with burst dams and levees,…

  7. Development of flood index by characterisation of flood hydrographs

    NASA Astrophysics Data System (ADS)

    Bhattacharya, Biswa; Suman, Asadusjjaman

    2015-04-01

    In recent years the world has experienced deaths, large-scale displacement of people, billions of Euros of economic damage, mental stress and ecosystem impacts due to flooding. Global changes (climate change, population and economic growth, and urbanisation) are exacerbating the severity of flooding. The 2010 floods in Pakistan and the 2011 floods in Australia and Thailand demonstrate the need for concerted action in the face of global societal and environmental changes to strengthen resilience against flooding. Due to climatological characteristics there are catchments where flood forecasting may have a relatively limited role and flood event management may have to be trusted upon. For example, in flash flood catchments, which often may be tiny and un-gauged, flood event management often depends on approximate prediction tools such as flash flood guidance (FFG). There are catchments fed largely by flood waters coming from upstream catchments, which are un-gauged or due to data sharing issues in transboundary catchments the flow of information from upstream catchment is limited. Hydrological and hydraulic modelling of these downstream catchments will never be sufficient to provide any required forecasting lead time and alternative tools to support flood event management will be required. In FFG, or similar approaches, the primary motif is to provide guidance by synthesising the historical data. We follow a similar approach to characterise past flood hydrographs to determine a flood index (FI), which varies in space and time with flood magnitude and its propagation. By studying the variation of the index the pockets of high flood risk, requiring attention, can be earmarked beforehand. This approach can be very useful in flood risk management of catchments where information about hydro-meteorological variables is inadequate for any forecasting system. This paper presents the development of FI and its application to several catchments including in Kentucky in the USA

  8. Flood Frequency Analysis using different flood descriptors - the Warsaw reach of the river Vistula case study

    NASA Astrophysics Data System (ADS)

    Karamuz, Emilia; Kochanek, Krzysztof; Romanowicz, Renata

    2014-05-01

    Flood frequency analysis (FFA) is customarily performed using annual maximum flows. However, there is a number of different flood descriptors that could be used. Among them are water levels, peaks over the threshold, flood-wave duration, flood volume, etc. In this study we compare different approaches to FFA for their suitability for flood risk assessment. The main goal is to obtain the FFA curve with the smallest possible uncertainty limits, in particular for the distribution tail. The extrapolation of FFA curves is crucial in future flood risk assessment in a changing climate. We compare the FFA curves together with their uncertainty limits obtained using flows, water levels, flood inundation area and volumes for the Warsaw reach of the river Vistula. Moreover, we derive the FFA curves obtained using simulated flows. The results are used to derive the error distribution for the maximum simulated and observed values under different modelling techniques and assess its influence on flood risk predictions for ungauged catchments. MIKE11, HEC-RAS and transfer function model are applied in average and extreme conditions to model flow propagation in the Warsaw Vistula reach. The additional questions we want to answer are what is the range of application of different modelling tools under various flow conditions and how can the uncertainty of flood risk assessment be decreased. This work was partly supported by the projects "Stochastic flood forecasting system (The River Vistula reach from Zawichost to Warsaw)" and "Modern statistical models for analysis of flood frequency and features of flood waves", carried by the Institute of Geophysics, Polish Academy of Sciences on the order of the National Science Centre (contracts Nos. 2011/01/B/ST10/06866 and 2012/05/B/ST10/00482, respectively). The water level and flow data were provided by the Institute of Meteorology and Water Management (IMGW), Poland.

  9. Sensitivity of flood events to global climate change

    NASA Astrophysics Data System (ADS)

    Panagoulia, Dionysia; Dimou, George

    1997-04-01

    The sensitivity of Acheloos river flood events at the outfall of the mountainous Mesochora catchment in Central Greece was analysed under various scenarios of global climate change. The climate change pattern was simulated through a set of hypothetical and monthly GISS (Goddard Institute for Space Studies) scenarios of temperature increase coupled with precipitation changes. The daily outflow of the catchment, which is dominated by spring snowmelt runoff, was simulated by the coupling of snowmelt and soil moisture accounting models of the US National Weather Service River Forecast System. Two threshold levels were used to define a flood day—the double and triple long-term mean daily streamflow—and the flood parameters (occurrences, duration, magnitude, etc.) for these cases were determined. Despite the complicated response of flood events to temperature increase and threshold, both hypothetical and monthly GISS representations of climate change resulted in more and longer flood events for climates with increased precipitation. All climates yielded larger flood volumes and greater mean values of flood peaks with respect to precipitation increase. The lower threshold resulted in more and longer flood occurrences, as well as smaller flood volumes and peaks than those of the upper one. The combination of higher and frequent flood events could lead to greater risks of inudation and possible damage to structures. Furthermore, the winter swelling of the streamflow could increase erosion of the river bed and banks and hence modify the river profile.

  10. Forecasting for natural avalanches during spring opening of Going-to-the-Sun Road, Glacier National Park, Montana, USA

    USGS Publications Warehouse

    Reardon, Blase; Lundy, Chris

    2004-01-01

    The annual spring opening of the Going-to-the-Sun Road in Glacier National Park presents a unique avalanche forecasting challenge. The highway traverses dozens of avalanche paths mid-track in a 23-kilometer section that crosses the Continental Divide. Workers removing seasonal snow and avalanche debris are exposed to paths that can produce avalanches of destructive class 4. The starting zones for most slide paths are within proposed Wilderness, and explosive testing or control are not currently used. Spring weather along the Divide is highly variable; rain-on-snow events are common, storms can bring several feet of new snow as late as June, and temperature swings can be dramatic. Natural avalanches - dry and wet slab, dry and wet loose, and glide avalanches - present a wide range of hazards and forecasting issues. This paper summarizes the forecasting program instituted in 2002 for the annual snow removal operations. It focuses on tools and techniques for forecasting natural wet snow avalanches by incorporating two case studies, including a widespread climax wet slab cycle in 2003. We examine weather and snowpack conditions conducive to wet snow avalanches, indicators for instability, and suggest a conceptual model for wet snow stability in a northern intermountain snow climate.

  11. Verification of precipitation forecasts from two numerical weather prediction models in the Middle Atlantic Region of the USA: A precursory analysis to hydrologic forecasting

    NASA Astrophysics Data System (ADS)

    Siddique, Ridwan; Mejia, Alfonso; Brown, James; Reed, Seann; Ahnert, Peter

    2015-10-01

    Accurate precipitation forecasts are required for accurate flood forecasting. The structures of different precipitation forecasting systems are constantly evolving, with improvements in forecasting techniques, increases in spatial and temporal resolution, improvements in model physics and numerical techniques, and better understanding of, and accounting for, predictive uncertainty. Hence, routine verification is necessary to understand the quality of forecasts as inputs to hydrologic modeling. In this study, we verify precipitation forecasts from the National Centers for Environmental Prediction (NCEP) 11-member Global Ensemble Forecast System Reforecast version 2 (GEFSRv2), as well as the 21-member Short Range Ensemble Forecast (SREF) system. Specifically, basin averaged precipitation forecasts are verified for different basin sizes (spatial scales) in the operating domain of the Middle Atlantic River Forecast Center (MARFC), using multi-sensor precipitation estimates (MPEs) as the observed data. The quality of the ensemble forecasts is evaluated conditionally upon precipitation amounts, forecast lead times, accumulation periods, and seasonality using different verification metrics. Overall, both GEFSRv2 and SREF tend to overforecast light to moderate precipitation and underforecast heavy precipitation. In addition, precipitation forecasts from both systems become increasingly reliable with increasing basin size and decreasing precipitation threshold, and the 24-hourly forecasts show slightly better skill than the 6-hourly forecasts. Both systems show a strong seasonal trend, characterized by better skill during the cool season than the warm season. Ultimately, the verification results lead to guidance on the expected quality of the precipitation forecasts, together with an assessment of their relative quality and unique information content, which is useful and necessary for their application in hydrologic forecasting.

  12. Natural glide slab avalanches, Glacier National Park, USA: A unique hazard and forecasting challenge

    USGS Publications Warehouse

    Reardon, Blase; Fagre, Daniel B.; Dundas, Mark; Lundy, Chris

    2006-01-01

    In a museum of avalanche phenomena, glide cracks and glide avalanches might be housed in the “strange but true” section. These oddities are uncommon in most snow climates and tend to be isolated to specific terrain features such as bedrock slabs. Many glide cracks never result in avalanches, and when they do, the wide range of time between crack formation and slab failure makes them highly unpredictable. Despite their relative rarity, glide cracks and glide avalanches pose a regular threat and complex forecasting challenge during the annual spring opening of the Going-to-the-Sun Road in Glacier National Park, U.S.A. During the 2006 season, a series of unusual glide cracks delayed snow removal operations by over a week and provided a unique opportunity to record detailed observations of glide avalanches and characterize their occurrence and associated weather conditions. Field observations were from snowpits, crown profiles and where possible, measurements of slab thickness, bed surface slope angle, substrate and other physical characteristics. Weather data were recorded at one SNOTEL site and two automated stations located from 0.6-10 km of observed glide slab avalanches. Nearly half (43%) of the 35 glide slab avalanches recorded were Class D2-2.5, with 15% Class D3-D3.5. The time between glide crack opening and failure ranged from 2 days to over six weeks, and the avalanches occurred in cycles associated with loss of snow water equivalent and spikes in temperature and radiation. We conclude with suggest ions for further study.

  13. Necessity of Flood Early Warning Systems in India

    NASA Astrophysics Data System (ADS)

    Kurian, C.; Natesan, U.; Durga Rao, K. H. V.

    2014-12-01

    India is one of the highly flood prone countries in the world. National flood commission has reported that 400,000 km² of geographical area is prone to floods, constituting to twelve percent of the country's geographical area. Despite the reoccurrences of floods, India still does not have a proper flood warning system. Probably this can be attributed to the lack of trained personnel in using advanced techniques. Frequent flood hazards results in damage to livelihood, infrastructure and public utilities. India has a potential to develop an early warning system since it is one of the few countries where satellite based inputs are regularly used for monitoring and mitigating floods. However, modeling of flood extent is difficult due to the complexity of hydraulic and hydrologic processes during flood events. It has been reported that numerical methods of simulations can be effectively used to simulate the processes correctly. Progress in computational resources, data collection and development of several numerical codes has enhanced the use of hydrodynamic modeling approaches to simulate the flood extent in the floodplains. In this study an attempt is made to simulate the flood in one of the sub basins of Godavari River in India using hydrodynamic modeling techniques. The modeling environment includes MIKE software, which simulates the water depth at every grid cell of the study area. The runoff contribution from the catchment was calculated using Nebdor Afstromnings model. With the hydrodynamic modeling approach, accuracy in discharge and water level computations are improved compared to the conventional methods. The results of the study are proming to develop effective flood management plans in the basin. Similar studies could be taken up in other flood prone areas of the country for continuous modernisation of flood forecasting techniques, early warning systems and strengthening decision support systems, which will help the policy makers in developing management

  14. Forecasting and nowcasting process: A case study analysis of severe precipitation event in Athens, Greece

    NASA Astrophysics Data System (ADS)

    Matsangouras, Ioannis; Nastos, Panagiotis; Avgoustoglou, Euripides; Gofa, Flora; Pytharoulis, Ioannis; Kamberakis, Nikolaos

    2016-04-01

    An early warning process is the result of interplay between the forecasting and nowcasting interactions. Therefore, (1) an accurate measurement and prediction of the spatial and temporal distribution of rainfall over an area and (2) the efficient and appropriate description of the catchment properties are important issues in atmospheric hazards (severe precipitation, flood, flash flood, etc.). In this paper, a forecasting and nowcasting analysis is presented, regarding a severe precipitation event that took place on September 21, 2015 in Athens, Greece. The severe precipitation caused a flash flood event at the suburbs of Athens, with significant impacts to the local society. Quantitative precipitation forecasts from European Centre for Medium-Range Weather Forecasts and from the COSMO.GR atmospheric model, including ensemble forecast of precipitation and probabilistic approaches are analyzed as tools in forecasting process. Satellite remote sensing data close and six hours prior to flash flood are presented, accompanied with radar products from Hellenic National Meteorological Service, illustrating the ability to depict the convection process.

  15. The August 2002 flood in Salzburg / Austria experience gained and lessons learned from the ``Flood of the century''?

    NASA Astrophysics Data System (ADS)

    Wiesenegger, H.

    2003-04-01

    On the {12th} of August 2002 a low pressure system moved slowly from northern Italy towards Slovakia. It continuously carried moist air from the Mediterranean towards the northern rim of the Alps with the effect of wide-spread heavy rainfall in Salzburg and other parts of Austria. Daily precipitation amounts of 100 - 160 mm, in some parts even more, as well as rainfall intensities of 5 - 10 mm/h , combined with well saturated soils lead to a rare flood with a return period of 100 years and more. This rare hydrological event not only caused a national catastrophe with damages of several Billion Euro, but also endangered more than 200,000 people, and even killed some. As floods are dangerous, life-threatening, destructive, and certainly amongst the most frequent and costly natural disasters in terms of human hardship as well as economic loss, a great effort, therefore, has to be made to protect people against negative impacts of floods. In order to achieve this objective, various regulations in land use planning (flood maps), constructive measurements (river regulations and technical constructions) as well as flood warning systems, which are not suitable to prevent big floods, but offer in-time-warnings to minimize the loss of human lives, are used in Austria. HYDRIS (Hydrological Information System for flood forecasting in Salzburg), a modular river basin model, developed at Technical University Vienna and operated by the Hydrological Service of Salzburg, was used during the August 2002 flood providing accurate 3 to 4 hour forecasts within 3 % of the real peak discharge of the fast flowing River Salzach. The August {12^th}} flood was in many ways an exceptional, very fast happening event which took many people by surprise. At the gauging station Salzburg / Salzach (catchment area 4425 {km^2}) it took only eighteen hours from mean annual discharge (178 {m3/s}) to the hundred years flood (2300 {m3/s}). The August flood made clear, that there is a strong need for

  16. Flood-inundation maps for the Suncook River in Epsom, Pembroke, Allenstown, and Chichester, New Hampshire

    USGS Publications Warehouse

    Flynn, Robert H.; Johnston, Craig M.; Hays, Laura

    2012-01-01

    Digital flood-inundation maps for a 16.5-mile reach of the Suncook River in Epsom, Pembroke, Allenstown, and Chichester, N.H., from the confluence with the Merrimack River to U.S. Geological Survey (USGS) Suncook River streamgage 01089500 at Depot Road in North Chichester, N.H., were created by the USGS in cooperation with the New Hampshire Department of Homeland Security and Emergency Management. The inundation maps presented in this report depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage at Suncook River at North Chichester, N.H. (station 01089500). The current conditions at the USGS streamgage may be obtained on the Internet (http://waterdata.usgs.gov/nh/nwis/uv/?site_no=01089500&PARAmeter_cd=00065,00060). The National Weather Service forecasts flood hydrographs at many places that are often collocated with USGS streamgages. Forecasted peak-stage information is available on the Internet at the National Weather Service (NWS) Advanced Hydrologic Prediction Service (AHPS) flood-warning system site (http://water.weather.gov/ahps/) and may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation. These maps along with real-time stream stage data from the USGS Suncook River streamgage (station 01089500) and forecasted stream stage from the NWS will provide emergency management personnel and residents with information that is critical for flood-response activities, such as evacuations, road closures, disaster declarations, and post-flood recovery. The maps, along with current stream-stage data from the USGS Suncook River streamgage and forecasted stream-stage data from the NWS, can be accessed at the USGS Flood Inundation Mapping Science Web site http://water.usgs.gov/osw/flood_inundation/.

  17. Forecasting Distributional Responses of Limber Pine to Climate Change at Management-Relevant Scales in Rocky Mountain National Park

    PubMed Central

    Monahan, William B.; Cook, Tammy; Melton, Forrest; Connor, Jeff; Bobowski, Ben

    2013-01-01

    Resource managers at parks and other protected areas are increasingly expected to factor climate change explicitly into their decision making frameworks. However, most protected areas are small relative to the geographic ranges of species being managed, so forecasts need to consider local adaptation and community dynamics that are correlated with climate and affect distributions inside protected area boundaries. Additionally, niche theory suggests that species' physiological capacities to respond to climate change may be underestimated when forecasts fail to consider the full breadth of climates occupied by the species rangewide. Here, using correlative species distribution models that contrast estimates of climatic sensitivity inferred from the two spatial extents, we quantify the response of limber pine (Pinus flexilis) to climate change in Rocky Mountain National Park (Colorado, USA). Models are trained locally within the park where limber pine is the community dominant tree species, a distinct structural-compositional vegetation class of interest to managers, and also rangewide, as suggested by niche theory. Model forecasts through 2100 under two representative concentration pathways (RCP 4.5 and 8.5 W/m2) show that the distribution of limber pine in the park is expected to move upslope in elevation, but changes in total and core patch area remain highly uncertain. Most of this uncertainty is biological, as magnitudes of projected change are considerably more variable between the two spatial extents used in model training than they are between RCPs, and novel future climates only affect local model predictions associated with RCP 8.5 after 2091. Combined, these results illustrate the importance of accounting for unknowns in species' climatic sensitivities when forecasting distributional scenarios that are used to inform management decisions. We discuss how our results for limber pine may be interpreted in the context of climate change vulnerability and used to

  18. Integrated Forecast and Reservoir Management for Northern California

    NASA Astrophysics Data System (ADS)

    Georgakakos, K. P.; Graham, N.; Georgakakos, A. P.; Yao, H.

    2011-12-01

    The INFORM (Integrated Forecast and Reservoir Management) Demonstration Project was created to demonstrate the utility of climate, weather and hydrologic predictions for water resources management in Northern California (includes Trinity River, the Sacramento River, the Feather River, the American River, the San Joaquin River, and the Sacramento-San Joaquin Delta). The INFORM system integrates climate-weather-hydrology forecasting and adaptive reservoir management methods, explicitly accounting for system input and model uncertainties. Operational ensemble forecasts from the Global Forecast System (GFS) and the Climate Forecast System (CFS) of the National Centers of Environmental Prediction (NCEP) are used to drive the WRF model and an Intermediate Complexity Regional Model (ICRM) to produce ensemble precipitation and temperature forecasts with a 10km x 10km resolution and from 6 hours to 30 days. These forecasts feed hydrologic models and provide ensemble inflow forecasts for the major reservoirs of Northern California. The ensemble inflow forecasts are input to a multiobjective and multisite adaptive decision support system designed to support the planning and management processes by deriving real time trade-offs among all relevant water management objectives (i.e., water supply and conservation, hydroelectric power production, flood control, and fisheries and environmental management) at user preferred risk levels. Operational tests over an initial three-year demonstration phase showed good operational performance both for wet and dry years. The presentation focuses on (1) modeling aspects of the current forecast and reservoir components and recent tests and (2) use of recent forecasts for the generation of applicable operational tradeoffs. The test results corroborate the operational value of the integrated forecast-management system.

  19. Wind Energy Forecasting: A Collaboration of the National Center for Atmospheric Research (NCAR) and Xcel Energy

    SciTech Connect

    Parks, K.; Wan, Y. H.; Wiener, G.; Liu, Y.

    2011-10-01

    The focus of this report is the wind forecasting system developed during this contract period with results of performance through the end of 2010. The report is intentionally high-level, with technical details disseminated at various conferences and academic papers. At the end of 2010, Xcel Energy managed the output of 3372 megawatts of installed wind energy. The wind plants span three operating companies1, serving customers in eight states2, and three market structures3. The great majority of the wind energy is contracted through power purchase agreements (PPAs). The remainder is utility owned, Qualifying Facilities (QF), distributed resources (i.e., 'behind the meter'), or merchant entities within Xcel Energy's Balancing Authority footprints. Regardless of the contractual or ownership arrangements, the output of the wind energy is balanced by Xcel Energy's generation resources that include fossil, nuclear, and hydro based facilities that are owned or contracted via PPAs. These facilities are committed and dispatched or bid into day-ahead and real-time markets by Xcel Energy's Commercial Operations department. Wind energy complicates the short and long-term planning goals of least-cost, reliable operations. Due to the uncertainty of wind energy production, inherent suboptimal commitment and dispatch associated with imperfect wind forecasts drives up costs. For example, a gas combined cycle unit may be turned on, or committed, in anticipation of low winds. The reality is winds stayed high, forcing this unit and others to run, or be dispatched, to sub-optimal loading positions. In addition, commitment decisions are frequently irreversible due to minimum up and down time constraints. That is, a dispatcher lives with inefficient decisions made in prior periods. In general, uncertainty contributes to conservative operations - committing more units and keeping them on longer than may have been necessary for purposes of maintaining reliability. The downside is costs are

  20. Sediment capture in flood plains of the Mississippi River: A case study in Cat Island National Wildlife Refuge, Louisiana

    NASA Astrophysics Data System (ADS)

    Smith, M.; Bentley, S. J., Sr.

    2015-03-01

    To plan restoration of the Mississippi River Delta, it is imperative to know how much sediment the Mississippi River currently provides. Recent research has demonstrated that between Tarbert Landing and St Francisville on the Mississippi, as much as 67 million metric tons (Mt) per year is lost from river transport, of which ~16 Mt is muddy suspended sediment. So where does this sediment go? Two pathways for loss have been proposed: riverbed storage, and overbank deposition in regions that lack manmade levées. Cat Island National Wildlife Refuge, on the unleveed Mississippi River east bank near St Francisville, Louisiana, consists of undisturbed bottomland forest that is inundated most years by river flooding. To determine fluvial sediment accumulation rates (SAR) from flooding, pushcores 40-50 cm long were collected then dated by Pb-210 and Cs-137 geochronology. Preliminary data suggests that muddy sediment accumulation is 10-13% of muddy suspended sediment lost from river transport along this river reach.

  1. Hydrological Forecasting Practices in Brazil

    NASA Astrophysics Data System (ADS)

    Fan, Fernando; Paiva, Rodrigo; Collischonn, Walter; Ramos, Maria-Helena

    2016-04-01

    This work brings a review on current hydrological and flood forecasting practices in Brazil, including the main forecasts applications, the different kinds of techniques that are currently being employed and the institutions involved on forecasts generation. A brief overview of Brazil is provided, including aspects related to its geography, climate, hydrology and flood hazards. A general discussion about the Brazilian practices on hydrological short and medium range forecasting is presented. Detailed examples of some hydrological forecasting systems that are operational or in a research/pre-operational phase using the large scale hydrological model MGB-IPH are also presented. Finally, some suggestions are given about how the forecasting practices in Brazil can be understood nowadays, and what are the perspectives for the future.

  2. The FASTER Approach: A New Tool for Calculating Real-Time Tsunami Flood Hazards

    NASA Astrophysics Data System (ADS)

    Wilson, R. I.; Cross, A.; Johnson, L.; Miller, K.; Nicolini, T.; Whitmore, P.

    2014-12-01

    In the aftermath of the 2010 Chile and 2011 Japan tsunamis that struck the California coastline, emergency managers requested that the state tsunami program provide more detailed information about the flood potential of distant-source tsunamis well ahead of their arrival time. The main issue is that existing tsunami evacuation plans call for evacuation of the predetermined "worst-case" tsunami evacuation zone (typically at a 30- to 50-foot elevation) during any "Warning" level event; the alternative is to not call an evacuation at all. A solution to provide more detailed information for secondary evacuation zones has been the development of tsunami evacuation "playbooks" to plan for tsunami scenarios of various sizes and source locations. To determine a recommended level of evacuation during a distant-source tsunami, an analytical tool has been developed called the "FASTER" approach, an acronym for factors that influence the tsunami flood hazard for a community: Forecast Amplitude, Storm, Tides, Error in forecast, and the Run-up potential. Within the first couple hours after a tsunami is generated, the National Tsunami Warning Center provides tsunami forecast amplitudes and arrival times for approximately 60 coastal locations in California. At the same time, the regional NOAA Weather Forecast Offices in the state calculate the forecasted coastal storm and tidal conditions that will influence tsunami flooding. Providing added conservatism in calculating tsunami flood potential, we include an error factor of 30% for the forecast amplitude, which is based on observed forecast errors during recent events, and a site specific run-up factor which is calculated from the existing state tsunami modeling database. The factors are added together into a cumulative FASTER flood potential value for the first five hours of tsunami activity and used to select the appropriate tsunami phase evacuation "playbook" which is provided to each coastal community shortly after the forecast

  3. Development of flood-inundation maps for the West Branch Susquehanna River near the Borough of Jersey Shore, Lycoming County, Pennsylvania

    USGS Publications Warehouse

    Roland, Mark A.; Hoffman, Scott A.

    2011-01-01

    Streamflow data, water-surface-elevation profiles derived from a Hydrologic Engineering Center River Analysis System hydraulic model, and geographical information system digital elevation models were used to develop a set of 18 flood-inundation maps for an approximately 5-mile reach of the West Branch Susquehanna River near the Borough of Jersey Shore, Pa. The inundation maps were created by the U.S. Geological Survey in cooperation with the Susquehanna River Basin Commission and Lycoming County as part of an ongoing effort by the National Oceanic and Atmospheric Administration's National Weather Service to focus on continued improvements to the flood forecasting and warning abilities in the Susquehanna River Basin and to modernize flood-forecasting methodologies. The maps, ranging from 23.0 to 40.0 feet in 1-foot increments, correspond to river stage at the U.S. Geological Survey streamgage 01549760 at Jersey Shore. The electronic files used to develop the maps were provided to the National Weather Service for incorporation into their Advanced Hydrologic Prediction Service website. The maps are displayed on this website, which serves as a web-based floodwarning system, and can be used to identify areas of predicted flood inundation associated with forecasted flood-peak stages. During times of flooding or predicted flooding, these maps can be used by emergency managers and the public to take proactive steps to protect life and reduce property damage caused by floods.

  4. National forecast for geothermal resource exploration and development with techniques for policy analysis and resource assessment

    SciTech Connect

    Cassel, T.A.V.; Shimamoto, G.T.; Amundsen, C.B.; Blair, P.D.; Finan, W.F.; Smith, M.R.; Edeistein, R.H.

    1982-03-31

    The backgrund, structure and use of modern forecasting methods for estimating the future development of geothermal energy in the United States are documented. The forecasting instrument may be divided into two sequential submodels. The first predicts the timing and quality of future geothermal resource discoveries from an underlying resource base. This resource base represents an expansion of the widely-publicized USGS Circular 790. The second submodel forecasts the rate and extent of utilization of geothermal resource discoveries. It is based on the joint investment behavior of resource developers and potential users as statistically determined from extensive industry interviews. It is concluded that geothermal resource development, especially for electric power development, will play an increasingly significant role in meeting US energy demands over the next 2 decades. Depending on the extent of R and D achievements in related areas of geosciences and technology, expected geothermal power development will reach between 7700 and 17300 Mwe by the year 2000. This represents between 8 and 18% of the expected electric energy demand (GWh) in western and northwestern states.

  5. Toward an Ensemble Streamflow Forecast Over the Entire France

    NASA Astrophysics Data System (ADS)

    Rousset, F.; Habets, F.; Noilhan, J.; Morel, S.; Le Moigne, P.

    2004-12-01

    Since the year 2003, the French National Weather Service (Meteo-France) uses an operationnal real-time system that provides a daily monitoring of the water budget, streamflows and aquifer levels over the entire France : the SAFRAN-ISBA-MODCOU (SIM) system. This coupled model is composed of the ISBA surface scheme and of the distributed hydrological model MODCOU. The system is used in a forced mode, with the atmospheric forcing derived from observations through the use of the SAFRAN analysis system. Such a system has been validated over 3 large french basins~: the Rhone, the Adour-Garonne and the Seine basins. It was shown that the system satisfactorily reproduces the water and energy budgets, as well as the observed streamflows, aquifer levels and snow-packs. In particular, the main long-duration floods of the Seine are well simulated. The SIM system is also used for streamflow forecasting. As a first step, experiments of determinist forecasts have been performed over the Rhone basin, using 2- and 3-day quantitive precipitation forecast. The encouraging results showed the potential of SIM for flood forecasting. As a next step, an ensemble streamflow prediction system is now being built. The forecasts from the Ensemble Prediction System of the ECMWF are used to force the system. The initial conditions of soil moisture, aquifer levels, etc. are given by the operationnal run of SIM, and the results are analysed for each forecast day. This system is expected to give 10-day forecasts of the streamflow of the main french rivers with a measure of the associated confidence, which is greatly valuable for flood warning and water management.

  6. Transition of Suomi National Polar-Orbiting Partnership (S-NPP) Data Products for Operational Weather Forecasting Applications

    NASA Astrophysics Data System (ADS)

    Smith, M. R.; Fuell, K.; Molthan, A.; Jedlovec, G.

    2012-12-01

    The launch of the Suomi National Polar-Orbiting Partnership (S-NPP) satellite provides new and exciting opportunities for the application of remotely sensed data products in operational weather forecasting environments. The NASA Short-term Prediction Research and Transition (SPoRT) Center in Huntsville, Alabama is a NASA and NOAA-funded project to assist with the transition of experimental and research products to the operational weather community through partnership with NOAA/National Weather Service Weather Forecast Offices (NWS WFOs) throughout the United States. This presentation will provide the S-NPP community with an update on current and future SPoRT projects related to the dissemination of S-NPP derived data to NWS WFOs and highlight unique applications and value of data from the Visible Infrared Imaging Radiometer Suite (VIIRS), specifically applications of high resolution visible and infrared data, uses of the day-night (or near constant contrast) band, and multispectral composites. Other applications are envisioned through use of selected channels of the Cross-track Infrared Sounder (CrIS), the Advanced Technology Microwave Sounder (ATMS), and the Ozone Mapper Profiler Suite (OMPS). This presentation will also highlight opportunities for future collaboration with SPoRT and activities planned for participation in the NOAA Joint Polar Satellite Program (JPSS) Proving Ground.

  7. Improving urban streamflow forecasting using a high-resolution large scale modeling framework

    NASA Astrophysics Data System (ADS)

    Read, Laura; Hogue, Terri; Gochis, David; Salas, Fernando

    2016-04-01

    Urban flood forecasting is a critical component in effective water management, emergency response, regional planning, and disaster mitigation. As populations across the world continue to move to cities (~1.8% growth per year), and studies indicate that significant flood damages are occurring outside the floodplain in urban areas, the ability to model and forecast flow over the urban landscape becomes critical to maintaining infrastructure and society. In this work, we use the Weather Research and Forecasting- Hydrological (WRF-Hydro) modeling framework as a platform for testing improvements to representation of urban land cover, impervious surfaces, and urban infrastructure. The three improvements we evaluate include: updating the land cover to the latest 30-meter National Land Cover Dataset, routing flow over a high-resolution 30-meter grid, and testing a methodology for integrating an urban drainage network into the routing regime. We evaluate performance of these improvements in the WRF-Hydro model for specific flood events in the Denver-Metro Colorado domain, comparing to historic gaged streamflow for retrospective forecasts. Denver-Metro provides an interesting case study as it is a rapidly growing urban/peri-urban region with an active history of flooding events that have caused significant loss of life and property. Considering that the WRF-Hydro model will soon be implemented nationally in the U.S. to provide flow forecasts on the National Hydrography Dataset Plus river reaches - increasing capability from 3,600 forecast points to 2.7 million, we anticipate that this work will support validation of this service in urban areas for operational forecasting. Broadly, this research aims to provide guidance for integrating complex urban infrastructure with a large-scale, high resolution coupled land-surface and distributed hydrologic model.

  8. A comparison of the causes, effects and aftermaths of the coastal flooding of England in 1953 and France in 2010

    NASA Astrophysics Data System (ADS)

    Lumbroso, D. M.; Vinet, F.

    2011-08-01

    This paper provides a comparison of the causes, effects and aftermaths of the coastal flooding that occurred on the east coast of England in 1953 and the west coast of France in 2010 that resulted in 307 and 47 deaths respectively. The causes of both events are strikingly similar. Both were caused by a combination of high tides, low atmospheric pressure, high winds and the failure of poorly maintained flood defences. In both cases the number of deaths was related to the vulnerability of the buildings and people. Buildings in the flood zones were often single storey bungalows and the people who died were mostly over 60 yr of age. Both tragedies were national disasters. The 1953 flood in England acted as a catalyst for an acceleration in flood risk management policy and practice. It resulted in: the development of a Storm Tide Warning System for the east coast of England; the setting of new design standards for coastal flood defences; increased investment in improving coastal defences; and a substantial new research effort into coastal processes, protection and forecasting. In France there has also been an episodic shift in flood risk management policy with the focus falling on: control of urban developments in areas at risk of flooding; improved coastal forecasting and warning; strengthening of flood defences; and developing a "culture of risk awareness". This paper outlines the lessons that can be learnt from the two events and provides recommendations concerning how future loss of life as a result of coastal flooding can be reduced.

  9. The integrated local flood warning system: A look at the flood response system

    SciTech Connect

    Neal, D.M.; Lee, R.

    1988-01-01

    Local Flood Warning Systems are instituted and maintained at a local level. They consist of two parts: (1) the flood forecast system, and (2) the flood response system. The flood forecast system is primarily built around the technology used to predict flooding. In this paper, we stress two points about local flood warning systems. First, the system must be integrated. Specifically, collecting data, transmitting data, forecasting the flood, informing local officials, warning local residents, and taking protective action (including evacuation of residents) must all occur in an integrated fashion if the whole system is to succeed. Second, we outline some important organizational characteristics that should be improved when developing a local flood response system. Key organizational characteristics include experience, networks, communications, decision making, everyday disaster task overlap. By focusing upon experience (including learning from the past flood or disaster experience or participating in drills and exercises) and by improving preparedness can be inexpensively improved. 6 refs.,

  10. Assessment of NOx and O3 forecasting performances in the U.S. National Air Quality Forecasting Capability before and after the 2012 major emissions updates

    NASA Astrophysics Data System (ADS)

    Pan, Li; Tong, Daniel; Lee, Pius; Kim, H.-C.; Chai, Tianfeng

    2014-10-01

    In this study, we address outdated emissions inventory problems in air quality forecasting systems. The National Emissions Inventory for NOx from area and mobile sources is projected from 2005 to 2012 and NOx from point sources is projected from 2010 to 2012, in which we find that NOx emissions from area, mobile and point sources reduce by 8.1%, 37.8% and 4.1%, respectively. The majority of the NOx emissions reduction occurs in megacities over the CONtiguous U.S. (CONUS), in which the spatial distribution pattern is generally supported by the NO2 column result retrieved from the GOME-2 satellite data. The CMAQ-predicted NOx and O3 concentrations using updated NOx emissions were then compared to Air Quality System (AQS) ground observations in order to evaluate the updated NOx emissions inventory. The comparison showed an improvement in NOx and O3 predictions over the CONUS. The NOx bias, in July 2011, for urban, suburban and rural land-use types was reduced by 2.34 ppb, 2.09 ppb and 0.57 ppb, respectively. Meanwhile, the O3 bias is reduced by 0.92 ppb, 1.26 ppb and 1.87 ppb, respectively. However, problems remain in CMAQ for NOx and O3 simulations despite undertaking this emissions adjustment. For example, the O3 overestimation in CMAQ during the daytime over the CONUS decreases when the NOx underestimation increases, suggesting that in addition to the NOx emissions inventory, further study of VOC emissions, NOx chemical and physical mechanisms as well as meteorology parameters in the NAQFC is necessary.

  11. Forecasting forecast skill

    NASA Technical Reports Server (NTRS)

    Kalnay, Eugenia; Dalcher, Amnon

    1987-01-01

    It is shown that it is possible to predict the skill of numerical weather forecasts - a quantity which is variable from day to day and region to region. This has been accomplished using as predictor the dispersion (measured by the average correlation) between members of an ensemble of forecasts started from five different analyses. The analyses had been previously derived for satellite-data-impact studies and included, in the Northern Hemisphere, moderate perturbations associated with the use of different observing systems. When the Northern Hemisphere was used as a verification region, the prediction of skill was rather poor. This is due to the fact that such a large area usually contains regions with excellent forecasts as well as regions with poor forecasts, and does not allow for discrimination between them. However, when regional verifications were used, the ensemble forecast dispersion provided a very good prediction of the quality of the individual forecasts.

  12. Impact of the use of two different hydrological models on scores of hydrological ensemble forecasts

    NASA Astrophysics Data System (ADS)

    Ramos, M. H.; Thirel, G.; Andréassian, V.; Martin, E.

    2009-04-01

    The aim of this study is two-fold. Firstly, a comparative analysis is conducted to assess the quality of streamflow forecasts issued by two different modelling conceptualizations of catchment response, both driven by the same weather ensemble prediction system. Secondly, the results are jointly investigated with a view to providing guidance on the operational use of ensemble forecast products for flood warning at national hydrologic forecasting services. The study is based on weather forecasts from the ensemble prediction system PEARP of Météo-France, which was originally developed to better predict high impact storms in France. PEARP forecasts are based on the global spectral ARPEGE model zoomed over France. Initial perturbations are generated by the singular vector technique. The model runs 11 perturbed members for a forecast range of 60 hours. In this study, the two hydrological modelling approaches used are: 1) the coupled physically-based hydro-meteorological model SAFRAN-ISBA-MODCOU developed at Météo-France and based on a fully distributed catchment model, and 2) the GRPE forecasting system developed at Cemagref and based on a lumped soil-moisture-accounting type rainfall-runoff model. Both models were set up and tested on about 1000 catchments in France. For this study, a common subset of about 250 gauging stations representative of a wide range of upstream areas and hydro-meteorological conditions was selected. The discharges simulated by both systems are compared over an 18-month period (March 2005-September 2006). Skill scores are then computed for the first two days of forecast range and the performance of both hydrologic ensemble forecasting systems is assessed. The results of this experiment are examined with a focus on the setting up of a fully operational product in real-time hydrological forecasting. The combined use of forecasts issued by different systems is a demand of French operational forecasting service to better guide flood warning

  13. Flood-Inundation Maps for Sugar Creek at Crawfordsville, Indiana

    USGS Publications Warehouse

    Martin, Zachary W.

    2016-01-01

    Digital flood-inundation maps for a 6.5-mile reach of Sugar Creek at Crawfordsville, Indiana, were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Office of Community and Rural Affairs. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage 03339500, Sugar Creek at Crawfordsville, Ind. Near-real-time stages at this streamgage may be obtained on the Internet from the USGS National Water Information System at http://waterdata.usgs.gov/ or the National Weather Service (NWS) Advanced Hydrologic Prediction Service at http://water.weather.gov/ahps/, which also forecasts flood hydrographs at this site (NWS site CRWI3).Flood profiles were computed for the USGS streamgage 03339500, Sugar Creek at Crawfordsville, Ind., reach by means of a one-dimensional step-backwater hydraulic modeling software developed by the U.S. Army Corps of Engineers. The hydraulic model was calibrated using the current stage-discharge rating at the USGS streamgage 03339500, Sugar Creek at Crawfordsville, Ind., and high-water marks from the flood of April 19, 2013, which reached a stage of 15.3 feet. The hydraulic model was then used to compute 13 water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum ranging from 4.0 ft (the NWS “action stage”) to 16.0 ft, which is the highest stage interval of the current USGS stage-discharge rating curve and 2 ft higher than the NWS “major flood stage.” The simulated water-surface profiles were then combined with a Geographic Information System digital elevation model (derived from light detection and ranging [lidar]) data having a 0.49-ft root mean squared error and 4.9-ft horizontal resolution) to delineate the area flooded at each stage.The availability

  14. Using constructed analogs to improve the skill of National Multi-Model Ensemble March–April–May precipitation forecasts in equatorial East Africa

    USGS Publications Warehouse

    Shukla, Shraddhanand; Funk, Christopher C.; Hoell, Andrew

    2014-01-01

    In this study we implement and evaluate a simple 'hybrid' forecast approach that uses constructed analogs (CA) to improve the National Multi-Model Ensemble's (NMME) March–April–May (MAM) precipitation forecasts over equatorial eastern Africa (hereafter referred to as EA, 2°S to 8°N and 36°E to 46°E). Due to recent declines in MAM rainfall, increases in population, land degradation, and limited technological advances, this region has become a recent epicenter of food insecurity. Timely and skillful precipitation forecasts for EA could help decision makers better manage their limited resources, mitigate socio-economic losses, and potentially save human lives. The 'hybrid approach' described in this study uses the CA method to translate dynamical precipitation and sea surface temperature (SST) forecasts over the Indian and Pacific Oceans (specifically 30°S to 30°N and 30°E to 270°E) into terrestrial MAM precipitation forecasts over the EA region. In doing so, this approach benefits from the post-1999 teleconnection that exists between precipitation and SSTs over the Indian and tropical Pacific Oceans (Indo-Pacific) and EA MAM rainfall. The coupled atmosphere-ocean dynamical forecasts used in this study were drawn from the NMME. We demonstrate that while the MAM precipitation forecasts (initialized in February) skill of the NMME models over the EA region itself is negligible, the ranked probability skill score of hybrid CA forecasts based on Indo-Pacific NMME precipitation and SST forecasts reach up to 0.45.