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Sample records for real-time flood forecasting

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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

  6. 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

    Flood forecasting schemes may have the most diverse structure depending on catchment size, response or concentration time and the availability of real time input data. The centre of weight of the hydrological forecasting system is often shifted from hydrological tools to the meteorological observation and forecasting systems. At lowland river sections simple flood routing techniques prevail where accuracy of discharge estimation might depend mostly on the accuracy of upstream discharge estimation. In large river basin systems both elements are present. Attempts are made enabling the use of ensemble of short and medium term meteorological forecast results for real-time flood forecasting by coupling meteorological and hydrological modelling tools. The system is designed in three parts covering the upper and central Danube. The large number of nodes (41) makes the system in fact semi distributed in basin scale. All of the nodes are prepared for forecast purposes. Real time mode runs are carried out in 6 hourly time steps. The available meteorological analysis and forecasting tools are linked to the flood forecasting system. Meteorological forecasts include 6 days and 12 days out of the ECMWF 10-14-day ahead EPS and VarEPS. The hydrological side of the system includes the data ingestion part producing semi distributed catchment wise input from gridded fields and rainfall-runoff, flood routing modules. Operational application of the of the ensemble system has been studied by the comparison of real time deterministic forecast and the experimental real time ensemble forecast results since the summer of 2008 on the river Danube. The period of June-October 2008 included mostly low water period interrupted by smaller floods. The real time ensemble hydrological forecasting experiment proved that the use of meteorological ensembles to produce sets of hydrological predictions increased the capability to issue forecasts with describing current uncertainties. As the result of the

  7. 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

  8. 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.

  9. 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

  10. 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.

  11. 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.

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  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. 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.

  20. 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.

  1. 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.

  2. 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

  3. 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.

  4. 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

  5. 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

  6. 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

  7. 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...

  8. 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

  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. 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.

  11. 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

  12. 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.

  13. 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

  14. A global, real-time flood monitoring model

    NASA Astrophysics Data System (ADS)

    Balcerak, Ernie

    2014-07-01

    Floods kill thousands of people and cause billions of dollars in damage each year, and many floods occur in areas of the world that lack resources for flood monitoring and forecasting systems. Wu et al. report on an experimental real-time global flood monitoring system that employs a widely used land surface model coupled with a hierarchical dominant river tracing-based runoff routing model and satellite-based precipitation data to provide streamflow and flood detection/estimation information over most of the globe every 3 hours.

  15. Development of a Distributed Hydrologic Model Using Triangulated Irregular Networks for Continuous, Real-Time Flood Forecasting

    NASA Astrophysics Data System (ADS)

    Ivanov, V. Y.; Vivoni, E. R.; Bras, R. L.; Entekhabi, D.

    2001-05-01

    The Triangulated Irregular Networks (TINs) are widespread in many finite-element modeling applications stressing high spatial non-uniformity while describing the domain of interest in an optimized fashion that results in superior computational efficiency. TINs, being adaptive to the complexity of any terrain, are capable of maintaining topological relations between critical surface features and therefore afford higher flexibility in data manipulation. The TIN-based Real-time Integrated Basin Simulator (tRIBS) is a distributed hydrologic model that utilizes the mesh architecture and the software environment developed for the CHILD landscape evolution model and employs the hydrologic routines of its raster-oriented version, RIBS. As a totally independent software unit, the tRIBS consolidates the strengths of the distributed approach and efficient computational data platform. The current version couples the unsaturated and the saturated zones and accounts for the interaction of moving infiltration fronts with a variable groundwater surface, allowing the model to handle both storm and interstorm periods in a continuous fashion. Recent model enhancements have included the development of interstorm hydrologic fluxes through an evapotranspiration scheme as well as incorporation of a rainfall interception module. Overall, the tRIBS model has proven to properly mimic successive phases of the distributed catchment response by reproducing various runoff production mechanisms and handling their meteorological constraints. Important improvements in modeling options, robustness to data availability and overall design flexibility have also been accomplished. The current efforts are focused on further model developments as well as the application of the tRIBS to various watersheds.

  16. Real-time forecasts of dengue epidemics

    NASA Astrophysics Data System (ADS)

    Yamana, T. K.; Shaman, J. L.

    2015-12-01

    Dengue is a mosquito-borne viral disease prevalent in the tropics and subtropics, with an estimated 2.5 billion people at risk of transmission. In many areas with endemic dengue, disease transmission is seasonal but prone to high inter-annual variability with occasional severe epidemics. Predicting and preparing for periods of higher than average transmission is a significant public health challenge. Here we present a model of dengue transmission and a framework for optimizing model simulations with real-time observational data of dengue cases and environmental variables in order to generate ensemble-based forecasts of the timing and severity of disease outbreaks. The model-inference system is validated using synthetic data and dengue outbreak records. Retrospective forecasts are generated for a number of locations and the accuracy of these forecasts is quantified.

  17. 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

  18. Streamflow forecast uncertainty evolution and its effect on real-time reservoir operation

    NASA Astrophysics Data System (ADS)

    Chen, Lu; Singh, Vijay P.; Lu, Weiwei; Zhang, Junhong; Zhou, Jianzhong; Guo, Shenglian

    2016-09-01

    When employing streamflow forecasting in practical applications, such as reservoir operation, one important issue is to deal with the uncertainty involved in forecasting. Traditional studies dealing with the uncertainty in streamflow forecasting have been limited in describing the evolution of forecast uncertainty. This paper proposes a copula-based uncertainty evolution (CUE) model to describe the evolution of streamflow forecast uncertainty. The generated forecast uncertainty series fits the observed series well in terms of observed mean, standard deviation and skewness. Daily flow with forecast uncertainty are simulated and used to determine the effect of forecast uncertainty on real-time reservoir operation of the Three Gorges Reservoir (TGR), China. Results show that using the forecast inflow coupled with the pre-release module for reservoir operation of TGR in flood season cannot increase the flood risk.

  19. 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

  20. Spatio-temporal modeling for real-time ozone forecasting

    PubMed Central

    Paci, Lucia; Gelfand, Alan E.; Holland, David M.

    2013-01-01

    The accurate assessment of exposure to ambient ozone concentrations is important for informing the public and pollution monitoring agencies about ozone levels that may lead to adverse health effects. High-resolution air quality information can offer significant health benefits by leading to improved environmental decisions. A practical challenge facing the U.S. Environmental Protection Agency (USEPA) is to provide real-time forecasting of current 8-hour average ozone exposure over the entire conterminous United States. Such real-time forecasting is now provided as spatial forecast maps of current 8-hour average ozone defined as the average of the previous four hours, current hour, and predictions for the next three hours. Current 8-hour average patterns are updated hourly throughout the day on the EPA-AIRNow web site. The contribution here is to show how we can substantially improve upon current real-time forecasting systems. To enable such forecasting, we introduce a downscaler fusion model based on first differences of real-time monitoring data and numerical model output. The model has a flexible coefficient structure and uses an efficient computational strategy to fit model parameters. Our hybrid computational strategy blends continuous background updated model fitting with real-time predictions. Model validation analyses show that we are achieving very accurate and precise ozone forecasts. PMID:24010052

  1. Incorporate Hydrologic Forecast for Real-Time Reservoir Operations

    NASA Astrophysics Data System (ADS)

    Zhao, T.; Cai, X.; Zhao, J.

    2011-12-01

    Advances in weather forecasting, hydrologic modeling, and hydro-climatic teleconnection relationships have significantly improved streamflow forecast precision and lead-time. The advances provide great opportunities to improve the operation rules of water resources systems, for example, updating reservoir operation curves using long-term forecast, or even replacing operation rules by real-time optimization and simulation models utilizing various streamflow forecast products. However, incorporation of forecast for real-time optimization of reservoir operation needs more understanding of the forecast uncertainty (FU) evolution with forecast horizon (FH, the advance time of a forecast) and the complicating effect of FU and FH. Increasing horizon may provide more information for decision making in a long time framework but with increasing error and less reliable information. This presentation addresses the challenges on the use of hydrologic forecast for real-time reservoir operations through the following two particular studies: 1) Evaluating the effectiveness of the various hydrological forecast products for reservoir operation with an explicit simulation of dynamic evolution of uncertainties involved in those products. A hypothetical example shows that optimal reservoir operation varies with the hydrologic forecast products. The utility of the reservoir operation with ensemble or probabilistic streamflow forecast (with a probabilistic uncertainty distribution) is the highest compared to deterministic streamflow forecast (DSF) with the forecast uncertainty represented in the form of deterministic forecast errors and DSF-based probabilistic streamflow forecast with the forecast uncertainty represented by a conditional distribution of forecast uncertainty for a given DSF. 2) Identifying an effective forecast horizon (EFH) under a limited inflow forecast considering the complicating effect of FH and FU, as well as streamflow variability and reservoir characteristics

  2. A simplified real time method to forecast semi-enclosed basins storm surge

    NASA Astrophysics Data System (ADS)

    Pasquali, D.; Di Risio, M.; De Girolamo, P.

    2015-11-01

    Semi-enclosed basins are often prone to storm surge events. Indeed, their meteorological exposition, the presence of large continental shelf and their shape can lead to strong sea level set-up. A real time system aimed at forecasting storm surge may be of great help to protect human activities (i.e. to forecast flooding due to storm surge events), to manage ports and to safeguard coasts safety. This paper aims at illustrating a simple method able to forecast storm surge events in semi-enclosed basins in real time. The method is based on a mixed approach in which the results obtained by means of a simplified physics based model with low computational costs are corrected by means of statistical techniques. The proposed method is applied to a point of interest located in the Northern part of the Adriatic Sea. The comparison of forecasted levels against observed values shows the satisfactory reliability of the forecasts.

  3. Multireservoir real-time operations for flood control using balanced water level index method.

    PubMed

    Wei, Chih-Chiang; Hsu, Nien-Sheng

    2008-09-01

    This paper presents a real-time simulation-optimization operation procedure for determining the reservoir releases at each time step during a flood. The proposed procedure involves two models, i.e., a hydrological forecasting model and a reservoir operation model. In the reservoir operation model, this paper compares two flood-control operation strategies for a multipurpose multireservoir system. While Strategy 1 is the real-time joint reservoir operations without using the balanced water level index (BWLI) method, Strategy 2 involves real-time joint reservoir operations using the BWLI method. The two strategies presented are formulated as mixed-integer linear programming (MILP) problems. The idea of using the BWLI method is derived from the HEC-5 program developed by the US Army Corps of Engineers. The proposed procedure has been applied to the Tanshui River Basin system in Taiwan using the 6h ahead forecast data of six typhoons. A comparison of the results obtained from the two strategies reveals that Strategy 2 performs much better than Strategy 1 in determining the reservoir real-time releases throughout the system during flood emergencies in order to minimize flooding, while maintaining all reservoirs in the system in balance if possible. Consequently, the proposed model using the BWLI method demonstrates its effectiveness in estimating real-time releases. PMID:17923249

  4. Real-time flood extent maps based on social media

    NASA Astrophysics Data System (ADS)

    Eilander, Dirk; van Loenen, Arnejan; Roskam, Ruud; Wagemaker, Jurjen

    2015-04-01

    During a flood event it is often difficult to get accurate information about the flood extent and the people affected. This information is very important for disaster risk reduction management and crisis relief organizations. In the post flood phase, information about the flood extent is needed for damage estimation and calibrating hydrodynamic models. Currently, flood extent maps are derived from a few sources such as satellite images, areal images and post-flooding flood marks. However, getting accurate real-time or maximum flood extent maps remains difficult. With the rise of social media, we now have a new source of information with large numbers of observations. In the city of Jakarta, Indonesia, the intensity of unique flood related tweets during a flood event, peaked at 8 tweets per second during floods in early 2014. A fair amount of these tweets also contains observations of water depth and location. Our hypothesis is that based on the large numbers of tweets it is possible to generate real-time flood extent maps. In this study we use tweets from the city of Jakarta, Indonesia, to generate these flood extent maps. The data-mining procedure looks for tweets with a mention of 'banjir', the Bahasa Indonesia word for flood. It then removes modified and retweeted messages in order to keep unique tweets only. Since tweets are not always sent directly from the location of observation, the geotag in the tweets is unreliable. We therefore extract location information using mentions of names of neighborhoods and points of interest. Finally, where encountered, a mention of a length measure is extracted as water depth. These tweets containing a location reference and a water level are considered to be flood observations. The strength of this method is that it can easily be extended to other regions and languages. Based on the intensity of tweets in Jakarta during a flood event we can provide a rough estimate of the flood extent. To provide more accurate flood extend

  5. Intelligent Real-Time Reservoir Operation for Flood Control

    NASA Astrophysics Data System (ADS)

    Chang, L.; Hsu, H.

    2008-12-01

    Real-time flood control of a multi-purpose reservoir should consider decreasing the flood peak stage downstream and storing floodwaters for future usage during typhoon seasons. It is a continuous and instant decision-making process based on relevant operating rules, policy and water laws, in addition the immediate rainfall and the hydrology information; however, it is difficult to learn the intelligent experience from the elder operators. The main purpose of this study is to establish the automatic reservoir flood control model to achieve the goal of a reservoir operation during flood periods. In this study, we propose an intelligent reservoir operating methodology for real-time flood control. First, the genetic algorithm is used to search the optimal solutions, which can be considered as extracting the knowledge of reservoir operation strategies. Then, the adaptive network-based fuzzy inference system (ANFIS), which uses a hybrid learning procedure for extracting knowledge in the form of fuzzy if-then rules, is used to learn the input-output patterns and then to estimate the optimal flood operation. The Shihmen reservoir in Northern Taiwan was used as a case study, where its 26 typhoon events are investigated by the proposed method. The results demonstrate that the proposed control model can perform much better than the original reservoir operator in 26 flood events and effectively achieve decreasing peak flood stage downstream and storing floodwaters for future usage.

  6. Real Time Monitoring of Flooding from Microwave Satellite Observations

    NASA Technical Reports Server (NTRS)

    Galantowicz, John F.; Frey, Herb (Technical Monitor)

    2002-01-01

    We have developed a new method for making high-resolution flood extent maps (e.g., at the 30-100 m scale of digital elevation models) in real-time from low-resolution (20-70 km) passive microwave observations. The method builds a "flood-potential" database from elevations and historic flood imagery and uses it to create a flood-extent map consistent with the observed open water fraction. Microwave radiometric measurements are useful for flood monitoring because they sense surface water in clear-or-cloudy conditions and can provide more timely data (e.g., compared to radars) from relatively wide swath widths and an increasing number of available platforms (DMSP, ADEOS-II, Terra, NPOESS, GPM). The chief disadvantages for flood mapping are the radiometers' low resolution and the need for local calibration of the relationship between radiances and open-water fraction. We present our method for transforming microwave sensor-scale open water fraction estimates into high-resolution flood extent maps and describe 30-day flood map sequences generated during a retrospective study of the 1993 Great Midwest Flood. We discuss the method's potential improvement through as yet unimplemented algorithm enhancements and expected advancements in microwave radiometry (e.g., improved resolution and atmospheric correction).

  7. A Methodology for Tsunami Wave Propagation Forecast in Real Time

    NASA Astrophysics Data System (ADS)

    Wang, D.; Walsh, D.; Becker, N. C.; Fryer, G. J.

    2009-12-01

    U.S. Tsunami Warning Centers (TWCs) forecast tsunami wave heights using databases of pre-computed tsunami scenarios such as the Standby Inundation Forecasting of Tsunamis (SIFT) model developed by the Pacific Marine Environmental Laboratory and the database model of the West Coast and Alaska Tsunami Warning Center. These models, however, cannot anticipate all possible earthquake hypocenters and focal mechanisms. We have therefore developed a new wave-height model complimentary to the database approach that uses real-time earthquake parameters to produce a real-time wave propagation forecast, a model we call real-time inundation forecasting of tsunamis (RIFT). Our model employs a mass-conserving second order finite difference method of linear shallow water equations with a leapfrog scheme in time and a staggered grid in space. The model's user may customize its domain by selecting one of twenty predefined ocean basins and marginal seas. The user may also let RIFT choose the computation domain automatically, in which case it will determine its computational domain by calculating tsunami travel time for the earthquake to cover the region specified by the TWCs' warning criteria based on the earthquake's magnitude. For example, the US Tsunami Warning Centers will issue a Tsunami Warning for the region within three hours travel time from the epicenter of a magnitude 7.9 earthquake. We demonstrate that RIFT can produce a tsunami wave-height forecast for this region at 4-arc-minute resolution in less than one minute using a modern 4-CPU Linux workstation, including the time needed to dynamically compute the boundaries of the domain. The user may also use smaller domains to generate a tsunami forecast much more quickly when an earthquake poses only a local tsunami threat, such as a large earthquake in Hawaii. In this case, the model can forecast tsunami wave heights for the entire state of Hawaii in less than five seconds at 1-arc-minute resolution. RIFT needs an earthquake

  8. A Real-Time Web Services Hub to Improve Situation Awareness during Flash Flood Events

    NASA Astrophysics Data System (ADS)

    Salas, F. R.; Liu, F.; Maidment, D. R.; Hodges, B. R.

    2011-12-01

    The central Texas corridor is one of the most flash flood-prone regions in the United States. Over the years, flash floods have resulted in hundreds of flood fatalities and billions of dollars in property damage. In order to mitigate risk to residents and infrastructure during flood events, both citizens and emergency responders need to exhibit proactive behavior instead of reactive. Real-time and forecasted flood information is fairly limited and hard to come by at varying spatial scales. The University of Texas at Austin has collaborated with IBM Research-Austin and ESRI to build a distributed real-time flood information system through a framework that leverages large scale data management and distribution, Open Geospatial Consortium standardized web services, and smart map applications. Within this paradigm, observed precipitation data encoded in WaterML is ingested into HEC-HMS and then delivered to a high performance hydraulic routing software package developed by IBM that utilizes the latest advancements in VLSI design, numerical linear algebra and numerical integration techniques on contemporary multicore architecture to solve fully dynamic Saint Venant equations at both small and large scales. In this paper we present a real-time flood inundation map application that in conjunction with a web services Hub, seamlessly integrates hydrologic information available through both public and private data services, model services and mapping services. As a case study for this project, we demonstrate how this system has been implemented in the City of Austin, Texas.

  9. Real-time Monitoring and Simulating of Urban Flood, a Case Study in Guangzhou

    NASA Astrophysics Data System (ADS)

    Huang, H.; Wang, X.; Zhang, S.; Liu, Y.

    2014-12-01

    In recent years urban flood frequently occurred and seriously impacted city's normal operation, particular on transportation. The increase of urban flood could be attributed to many factors, such as the increase of impervious land surface and extreme precipitation, the decrease of surface storage capacity, poor maintenance of drainage utilities, and so on. In order to provide accurate and leading prediction on urban flooding, this study acquires precise urban topographic data via air-borne Lidar system, collects detailed underground drainage pipes, and installs in-situ monitoring networks on precipitation, water level, video record and traffic speed in the downtown area of Panyu District, Guangzhou, China. Based on the above data acquired, a urban flood model with EPA SWMM5 is established to simulate the flooding and inundation processes in the study area of 20 km2. The model is driven by the real-time precipitation data and calibrated by the water level data, which are converted to flooding volume with precise topographic data. After calibration, the model could be employed to conduct sensitivity analysis for investigating primary factors of urban flooding, and to simulate the flooding processes in different scenarios, which are beneficial to assessment of flooding risk and drainage capacity. This model is expected to provide real-time forecasting in emergency management.

  10. Real Time Flood Alert System (RTFAS) for Puerto Rico

    USGS Publications Warehouse

    Lopez-Trujillo, Dianne

    2010-01-01

    The Real Time Flood Alert System is a web-based computer program, developed as a data integration tool, and designed to increase the ability of emergency managers to rapidly and accurately predict flooding conditions of streams in Puerto Rico. The system includes software and a relational database to determine the spatial and temporal distribution of rainfall, water levels in streams and reservoirs, and associated storms to determine hazardous and potential flood conditions. The computer program was developed as part of a cooperative agreement between the U.S. Geological Survey Caribbean Water Science Center and the Puerto Rico Emergency Management Agency, and integrates information collected and processed by these two agencies and the National Weather Service.

  11. Real-time drought forecasting system for irrigation management

    NASA Astrophysics Data System (ADS)

    Ceppi, A.; Ravazzani, G.; Corbari, C.; Salerno, R.; Meucci, S.; Mancini, M.

    2014-09-01

    In recent years frequent periods of water scarcity have enhanced the need to use water more carefully, even in European areas which traditionally have an abundant supply of water, such as the Po Valley in northern Italy. In dry periods, water shortage problems can be enhanced by conflicting uses of water, such as irrigation, industry and power production (hydroelectric and thermoelectric). Furthermore, in the last decade the social perspective in relation to this issue has been increasing due to the possible impact of climate change and global warming scenarios which emerge from the IPCC Fifth Assessment Report (IPCC, 2013). Hence, the increased frequency of drought periods has stimulated the improvement of irrigation and water management. In this study we show the development and implementation of the PREGI real-time drought forecasting system; PREGI is an Italian acronym that means "hydro-meteorological forecast for irrigation management". The system, planned as a tool for irrigation optimization, is based on meteorological ensemble forecasts (20 members) at medium range (30 days) coupled with hydrological simulations of water balance to forecast the soil water content on a maize field in the Muzza Bassa Lodigiana (MBL) consortium in northern Italy. The hydrological model was validated against measurements of latent heat flux acquired by an eddy-covariance station, and soil moisture measured by TDR (time domain reflectivity) probes; the reliability of this forecasting system and its benefits were assessed in the 2012 growing season. The results obtained show how the proposed drought forecasting system is able to have a high reliability of forecast at least for 7-10 days ahead of time.

  12. 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.

  13. Using Weekly Weather Forecast for Real-Time Irrigation Scheduling

    NASA Astrophysics Data System (ADS)

    Cai, X.; Wang, D.; Hejazi, M.

    2008-12-01

    Irrigation scheduling determines the timing and amount of water applied to an irrigated cropland during the crop growing season. Through a case study with Havana Lowlands region, Illinois, this study addresses two questions: 1) how can we consider the uncertainty of the weekly forecast for real-time irrigation scheduling? 2) What is the economic value of the forecast for irrigation farmers? We propose a coupled simulation and optimization modeling approach. The simulation part adopts an existing soil-water-atmosphere-plant (SWAP) simulation model. The SWAP model simulates soil moisture, the key state variable for irrigation decision, at the current time (day) using observed weather data; meanwhile the model assimilates the daily updated weekly forecast to predict the soil moisture in the future week. The weekly time window moves forward from the beginning to the end of the crop season. The optimization model is formulated as a multiple-stage stochastic optimization model that is based on weather forecast scenarios characterized by probabilities. The multiple-stage decisions are the deterministic water application at the current day ("what to do now") and the application scenarios associated with probabilities in each of the 7 days within one week time horizon ("wait- and-see"). Based on the soil moisture provided by the simulation model and empirical irrigation rules, irrigation water application is determined by the optimization model implemented with a genetic algorithm (GA). The modeling approach is tested with five years (2002-2006), which include two dry years, two normal years and one wet year, with the rainfall in the crop growth season ranging from 230mm to 410mm. The value of forecast is shown to be different across dry, normal, and wet years. In the year of 2002, observed irrigation scheduling is available. Thus the optimized irrigation scheduling is compared to the observed regarding the net crop profit and irrigation water saving.

  14. Real-time drought forecasting system for irrigation managment

    NASA Astrophysics Data System (ADS)

    Ceppi, Alessandro; Ravazzani, Giovanni; Corbari, Chiara; Masseroni, Daniele; Meucci, Stefania; Pala, Francesca; Salerno, Raffaele; Meazza, Giuseppe; Chiesa, Marco; Mancini, Marco

    2013-04-01

    In recent years frequent periods of water scarcity have enhanced the need to use water more carefully, even in in European areas traditionally rich of water such as the Po Valley. In dry periods, the problem of water shortage can be enhanced by conflictual use of water such as irrigation, industrial and power production (hydroelectric and thermoelectric). Further, over the last decade the social perspective on this issue is increasing due to climate change and global warming scenarios which come out from the last IPCC Report. The increased frequency of dry periods has stimulated the improvement of irrigation and water management. In this study we show the development and implementation of the real-time drought forecasting system Pre.G.I., an Italian acronym that stands for "Hydro-Meteorological forecast for irrigation management". The system is based on ensemble prediction at long range (30 days) with hydrological simulation of water balance to forecast the soil water content in every parcel over the Consorzio Muzza basin. The studied area covers 74,000 ha in the middle of the Po Valley, near the city of Lodi. The hydrological ensemble forecasts are based on 20 meteorological members of the non-hydrostatic WRF model with 30 days as lead-time, provided by Epson Meteo Centre, while the hydrological model used to generate the soil moisture and water table simulations is the rainfall-runoff distributed FEST-WB model, developed at Politecnico di Milano. The hydrological model was validated against measurements of latent heat flux and soil moisture acquired by an eddy-covariance station. Reliability of the forecasting system and its benefits was assessed on some cases-study occurred in the recent years.

  15. Real-time forecasts of tomorrow's earthquakes in California

    USGS Publications Warehouse

    Gerstenberger, M.C.; Wiemer, S.; Jones, L.M.; Reasenberg, P.A.

    2005-01-01

    Despite a lack of reliable deterministic earthquake precursors, seismologists have significant predictive information about earthquake activity from an increasingly accurate understanding of the clustering properties of earthquakes. In the past 15 years, time-dependent earthquake probabilities based on a generic short-term clustering model have been made publicly available in near-real time during major earthquake sequences. These forecasts describe the probability and number of events that are, on average, likely to occur following a mainshock of a given magnitude, but are not tailored to the particular sequence at hand and contain no information about the likely locations of the aftershocks. Our model builds upon the basic principles of this generic forecast model in two ways: it recasts the forecast in terms of the probability of strong ground shaking, and it combines an existing time-independent earthquake occurrence model based on fault data and historical earthquakes with increasingly complex models describing the local time-dependent earthquake clustering. The result is a time-dependent map showing the probability of strong shaking anywhere in California within the next 24 hours. The seismic hazard modelling approach we describe provides a better understanding of time-dependent earthquake hazard, and increases its usefulness for the public, emergency planners and the media.

  16. Tracking and forecasting ecosystem interactions in real time.

    PubMed

    Deyle, Ethan R; May, Robert M; Munch, Stephan B; Sugihara, George

    2016-01-13

    Evidence shows that species interactions are not constant but change as the ecosystem shifts to new states. Although controlled experiments and model investigations demonstrate how nonlinear interactions can arise in principle, empirical tools to track and predict them in nature are lacking. Here we present a practical method, using available time-series data, to measure and forecast changing interactions in real systems, and identify the underlying mechanisms. The method is illustrated with model data from a marine mesocosm experiment and limnologic field data from Sparkling Lake, WI, USA. From simple to complex, these examples demonstrate the feasibility of quantifying, predicting and understanding state-dependent, nonlinear interactions as they occur in situ and in real time--a requirement for managing resources in a nonlinear, non-equilibrium world. PMID:26763700

  17. HydroMet: Real-time Forecasting System for Hydrologic Hazards

    NASA Astrophysics Data System (ADS)

    Band, L. E.; Shin, D.; Hwang, T.; Goodall, J.; Reed, M.; Rynge, M.; Stillwell, L.; Galluppi, K.

    2007-12-01

    Recent devastating floods and severe droughts in North Carolina called attention to the need of a reliable nowcasting and forecasting system for these hydrologic hazards. In response to the demand, HydroMet project was launched by RENCI (Renaissance Computing Institute). On a supercomputer in the institute, we integrated (1) WRF (Weather Research and Forecasting) for the mesoscale numerical weather prediction, (2) RHESSys (Regional Hydro-Ecologic Simulation System) for the distributed modeling of runoff generation and soil moisture, and (3) LDAS (Land Data Assimilation Systems) for upgrading the prediction accuracy of soil moisture and energy. By exploiting the powerful parallel computing architecture, the forecasting system was designed to assimilate and produce massive spatio-temporal data in real-time while recalibrating itself automatically. We applied the system for western and central North Carolina as test sites, and forecasted the propagation of flood waves, and the long-term trends of low channel flow and soil moisture at a fine spatial resolution. As we extend the application of the system over the entire North Carolina, it is expected to provide timely and accurate information about floods and droughts in the area, which is prerequisite for more effective prevention and recovery from the hazards.

  18. Multi-phase intelligent decision model for reservoir real-time flood control during typhoons

    NASA Astrophysics Data System (ADS)

    Hsu, Nien-Sheng; Huang, Chien-Lin; Wei, Chih-Chiang

    2015-03-01

    This study applies an Adaptive Network-based Fuzzy Inference System (ANFIS) and a Real-Time Recurrent Learning Neural Network (RTRLNN) with an optimized reservoir release hydrograph using Mixed Integer Linear Programming (MILP) from historical typhoon events to develop a multi-phase intelligent real-time reservoir operation model for flood control. The flood control process is divided into three stages: (1) before flood (Stage I); (2) before peak flow (Stage II); and (3) after peak flow (Stage III). The models are then constructed with either three phase modules (ANFIS-3P and RTRLNN-3P) or two phase (Stage I + II and Stage III) modules (ANFIS-2P and RTRLNN-2P). The multi-phase modules are developed with consideration of the difference in operational decision mechanisms, decision information, release functions, and targets between each flood control stage to solve the problem of time-consuming computation and difficult system integration of MILP. In addition, the model inputs include the coupled short lead time and total reservoir inflow forecast information that are developed using radar- and satellite-based meteorological monitoring techniques, forecasted typhoon tracks, meteorological image similarity analysis, ANFIS and RTRLNN. This study uses the Tseng-Wen Reservoir basin as the study area, and the model results showed that RTRLNN outperformed ANFIS in the simulated outcomes from the optimized hydrographs. This study also applies the models to Typhoons Kalmaegi and Morakot to compare the simulations to historical operations. From the operation results, the RTRLNN-3P model is better than RTRLNN-2P and historical operations. Further, because the RTRLNN-3P model combines the innovative multi-phase module with monitored and forecasted decision information, the operation can simultaneously, effectively and automatically achieve the dual goals of flood detention at peak flow periods and water supply at the end of a typhoon event.

  19. Real-time forecasting urban drainage models: full or simplified networks?

    PubMed

    Leitão, J P; Simões, N E; Maksimović, C; Ferreira, F; Prodanović, D; Matos, J S; Sá Marques, A

    2010-01-01

    Lead time between rainfall prediction results and flood prediction results obtained by hydraulic simulations is one of the crucial factors in the implementation of real-time flood forecasting systems. Therefore, hydraulic simulation times must be as short as possible, with sufficient spatial and temporal flood distribution modelling accuracy. One of the ways to reduce the time required to run hydraulic model simulations is increasing computational speed by simplifying the model networks. This simplification can be conducted by removing and changing some secondary elements using network simplification techniques. The emphasis of this paper is to assess how the level of urban drainage network simplification influences the computational time and overall simulation results' accuracy. The models used in this paper comprise a sewer network and an overland flow drainage system in both 1D/1D and 1D/2D approaches. The 1D/1D model is used as the reference model to generate several models with different levels of simplifications. The results presented in this paper suggest that the 1D/2D models are not yet suitable to be used in real-time flood prediction applications due to long simulation time, while on the other hand, the simplified 1D/1D models show that considerable reductions in simulation time can be achieved without compromising simulation results (flow and water depth) accuracy. PMID:21045338

  20. Real-time Global Flood and Landslide Prediction Using Satellite Observations (Invited)

    NASA Astrophysics Data System (ADS)

    Adler, R. F.; Yilmaz, K. K.; Kirschbaum, D.; Hong, Y.; Pierce, H.; Policelli, F.

    2009-12-01

    A global flood and landslide detection/prediction system is now running in real-time using multi-satellite rainfall analysis in combination with hydrological models and algorithms to estimate flood and landslide locations (http://trmm.gsfc.nasa.gov/publications_dir/potential_flood_hydro.html). The system also uses satellite-based land surface information such as digital elevation information from the NASA SRTM (Shuttle Radar Terrain Mission) and vegetation information from MODIS in the model and algorithm calculations. Progress in using the TRMM Multi-satellite Precipitation Analysis (TMPA) as input to these flood and landslide forecasts is outlined, with case studies as well as validation in terms of flood/landslide events. Examples shown include the August 2009 landslide events in Taiwan and the major flood in Burma in spring of 2008. The flood determination algorithm consists of three major components: 1) multi-satellite precipitation estimation; 2) characterization of land surface including digital elevation information and other surface information, topography-derived hydrologic parameters such as flow direction, flow accumulation, river network and basin boundaries, etc.; 3) a hydrological model to infiltrate rainfall and route overland runoff. Results of calculated water depth over a threshold are then displayed about six hours after real-time. Time-history of inundations are also calculated and displayed. Validation analysis indicates good results for flood detection and evolution, but with limitations in the current routing calculations. Occasional flood events are missed due to limitations in the satellite rain estimations. An improved global hydrological model is being tested and initial improved results will be shown. Global numerical weather prediction rainfall forecasts are also being used experimentally to extend the period of utility of the flood information. In terms of landslides, the satellite rainfall information is combined with a global

  1. 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.

  2. Benchmarking flood models from space in near real-time: accommodating SRTM height measurement errors with low resolution flood imagery

    NASA Astrophysics Data System (ADS)

    Schumann, G.; di Baldassarre, G.; Alsdorf, D.; Bates, P. D.

    2009-04-01

    In February 2000, the Shuttle Radar Topography Mission (SRTM) measured the elevation of most of the Earth's surface with spatially continuous sampling and an absolute vertical accuracy greater than 9 m. The vertical error has been shown to change with topographic complexity, being less important over flat terrain. This allows water surface slopes to be measured and associated discharge volumes to be estimated for open channels in large basins, such as the Amazon. Building on these capabilities, this paper demonstrates that near real-time coarse resolution radar imagery of a recent flood event on a 98 km reach of the River Po (Northern Italy) combined with SRTM terrain height data leads to a water slope remarkably similar to that derived by combining the radar image with highly accurate airborne laser altimetry. Moreover, it is shown that this space-borne flood wave approximation compares well to a hydraulic model and thus allows the performance of the latter, calibrated on a previous event, to be assessed when applied to an event of different magnitude in near real-time. These results are not only of great importance to real-time flood management and flood forecasting but also support the upcoming Surface Water and Ocean Topography (SWOT) mission that will routinely provide water levels and slopes with higher precision around the globe.

  3. Real-Time Application of Multi-Satellite Precipitation Analysis for Floods and Landslides

    NASA Technical Reports Server (NTRS)

    Adler, Robert; Hong, Yang; Huffman, George

    2007-01-01

    Satellite data acquired and processed in real time now have the potential to provide the spacetime information on rainfall needed to monitor flood and landslide events around the world. This can be achieved by integrating the satellite-derived forcing data with hydrological models and landslide algorithms. Progress in using the TRMM Multi-satellite Precipitation Analysis (TMPA) as input to flood and landslide forecasts is outlined, with a focus on understanding limitations of the rainfall data and impacts of those limitations on flood/landslide analyses. Case studies of both successes and failures will be shown, as well as comparison with ground comparison data sets-- both in terms of rainfall and in terms of flood/landslide events. In addition to potential uses in real-time, the nearly ten years of TMPA data allow retrospective running of the models to examine variations in extreme events. The flood determination algorithm consists of four major components: 1) multi-satellite precipitation estimation; 2) characterization of land surface including digital elevation from NASA SRTM (Shuttle Radar Terrain Mission), topography-derived hydrologic parameters such as flow direction, flow accumulation, basin, and river network etc.; 3) a hydrological model to infiltrate rainfall and route overland runoff; and 4) an implementation interface to relay the input data to the models and display the flood inundation results to potential users and decision-makers, In terms of landslides, the satellite rainfall information is combined with a global landslide susceptibility map, derived from a combination of global surface characteristics (digital elevation topography, slope, soil types, soil texture, and land cover classification etc.) using a weighted linear combination approach. In those areas identified as "susceptible" (based on the surface characteristics), landslides are forecast where and when a rainfall intensity/duration threshold is exceeded. Results are described

  4. Best Practice for Rainfall Measurement, Torrential Flood Monitoring and Real Time Alerting System in Serbia

    NASA Astrophysics Data System (ADS)

    Stefanovic, Milutin; Milojevic, Mileta; Zlatanovic, Nikola

    2014-05-01

    Serbia occupies 88.000 km2 and its confined zone menaced with torrent flood occupies 50.000km2. Floods on large rivers and torrents are the most frequent natural disasters in Serbia. This is the result of a geographic position and relief of Serbia. Therefore, defense from these natural disasters has been institutionalized since the 19th century. Through its specialized bodies and public companies, the State organized defense from floods on large rivers and protection of international and other main roads. The Topčiderska River is one of a number of rivers in Serbia that is a threat to both urban and rural environments. In this text, general characteristics of this river will be illustrated, as well as the historical natural hazards that have occurred in the part of Belgrade near Topčiderska River. Belgrade is the capital of Serbia, its political, administrative and financial center, which means that there are significant financial capacities and human resources for investments in all sectors, and specially in the water resources sector. Along the Topčiderska catchment there are many industrial, traffic and residential structures that are in danger of floods and flood protection is more difficult with rapid high flows. The goal is to use monitoring on the Topčiderska River basin to set up a modern system for monitoring in real time and forecast of torrential floods. This paper represents a system of remote detection and monitoring of torrential floods and rain measurements in real time on Topciderka river and ready for a quick response.

  5. Real-time eruption forecasting using the material Failure Forecast Method with a Bayesian approach

    NASA Astrophysics Data System (ADS)

    Boué, A.; Lesage, P.; Cortés, G.; Valette, B.; Reyes-Dávila, G.

    2015-04-01

    Many attempts for deterministic forecasting of eruptions and landslides have been performed using the material Failure Forecast Method (FFM). This method consists in adjusting an empirical power law on precursory patterns of seismicity or deformation. Until now, most of the studies have presented hindsight forecasts based on complete time series of precursors and do not evaluate the ability of the method for carrying out real-time forecasting with partial precursory sequences. In this study, we present a rigorous approach of the FFM designed for real-time applications on volcano-seismic precursors. We use a Bayesian approach based on the FFM theory and an automatic classification of seismic events. The probability distributions of the data deduced from the performance of this classification are used as input. As output, it provides the probability of the forecast time at each observation time before the eruption. The spread of the a posteriori probability density function of the prediction time and its stability with respect to the observation time are used as criteria to evaluate the reliability of the forecast. We test the method on precursory accelerations of long-period seismicity prior to vulcanian explosions at Volcán de Colima (Mexico). For explosions preceded by a single phase of seismic acceleration, we obtain accurate and reliable forecasts using approximately 80% of the whole precursory sequence. It is, however, more difficult to apply the method to multiple acceleration patterns.

  6. Effect of Streamflow Forecast Uncertainty on Real-Time Reservoir Operation

    NASA Astrophysics Data System (ADS)

    Zhao, T.; Cai, X.; Yang, D.

    2010-12-01

    Various hydrological forecast products have been applied to real-time reservoir operation, including deterministic streamflow forecast (DSF), DSF-based probabilistic streamflow forecast (DPSF), and ensemble streamflow forecast (ESF), which represent forecast uncertainty in the form of deterministic forecast error, deterministic forecast error-based uncertainty distribution, and ensemble forecast errors, respectively. Compared to previous studies that treat these forecast products as ad hoc inputs for reservoir operation models, this paper attempts to model the uncertainties involved in the various forecast products and explores their effect on real-time reservoir operation decisions. In hydrology, there are various indices reflecting the magnitude of streamflow forecast uncertainty; meanwhile, few models illustrate the forecast uncertainty evolution process. This research introduces Martingale Model of Forecast Evolution (MMFE) from supply chain management and justifies its assumptions for quantifying the evolution of uncertainty in streamflow forecast as time progresses. Based on MMFE, this research simulates the evolution of forecast uncertainty in DSF, DPSF, and ESF, and applies the reservoir operation models (dynamic programming, DP; stochastic dynamic programming, SDP; and standard operation policy, SOP) to assess the effect of different forms of forecast uncertainty on real-time reservoir operation. Through a hypothetical single-objective real-time reservoir operation model, the results illustrate that forecast uncertainty exerts significant effects. Reservoir operation efficiency, as measured by a utility function, decreases as the forecast uncertainty increases. Meanwhile, these effects also depend on the type of forecast product being used. In general, the utility of reservoir operation with ESF is nearly as high as the utility obtained with a perfect forecast; the utilities of DSF and DPSF are similar to each other but not as efficient as ESF. Moreover

  7. Real-time extreme weather event attribution with forecast seasonal SSTs

    NASA Astrophysics Data System (ADS)

    Haustein, K.; Otto, F. E. L.; Uhe, P.; Schaller, N.; Allen, M. R.; Hermanson, L.; Christidis, N.; McLean, P.; Cullen, H.

    2016-06-01

    Within the last decade, extreme weather event attribution has emerged as a new field of science and garnered increasing attention from the wider scientific community and the public. Numerous methods have been put forward to determine the contribution of anthropogenic climate change to individual extreme weather events. So far nearly all such analyses were done months after an event has happened. Here we present a new method which can assess the fraction of attributable risk of a severe weather event due to an external driver in real-time. The method builds on a large ensemble of atmosphere-only general circulation model simulations forced by seasonal forecast sea surface temperatures (SSTs). Taking the England 2013/14 winter floods as an example, we demonstrate that the change in risk for heavy rainfall during the England floods due to anthropogenic climate change, is of similar magnitude using either observed or seasonal forecast SSTs. Testing the dynamic response of the model to the anomalous ocean state for January 2014, we find that observed SSTs are required to establish a discernible link between a particular SST pattern and an atmospheric response such as a shift in the jetstream in the model. For extreme events occurring under strongly anomalous SST patterns associated with known low-frequency climate modes, however, forecast SSTs can provide sufficient guidance to determine the dynamic contribution to the event.

  8. 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

  9. Further Evaluation of a Satellite-based Real-time Global Flood Monitoring System

    NASA Astrophysics Data System (ADS)

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

    2011-12-01

    A real-time global flood monitoring system (GFMS) driven by Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) rainfall was further developed with a relatively more physically based hydrological model. The performance in flood detection of this new version of the GFMS was evaluated against available flood event archives (Wu et al, 2011). This new GFMS is quantitatively evaluated in terms of flood event detection during the TRMM era (1998-2010) using a global retrospective simulation (3-hourly and 1/8 degree spatial resolution) with the TMPA 3B42V6 rainfall. Four methods were explored to define flood events from the model results, including three percentile-based statistic methods and a Log Pearson-III flood frequency curve method. The evaluation showed the GFMS detection performance improves with longer flood durations and larger affected areas. The impact of dams was detected in the validation statistics. The presence of dams tends to result in more false alarms and false alarm duration. The GFMS statistics for flood durations > 3 days and for areas without dams vary across the four identification methods, but center around a POD of ~ 0.70 and a FAR of ~ 0.65. When both flood events-based categorical verification metrics and flood duration metrics are considered, a method using the 95th percentile runoff depth plus two parameters related to variability and basin size (method 3) may be more suitable for application to our routine, real-time flood calculations. The evaluation showed the GFMS detection performance improves with longer flood durations and larger affected areas. The new GFMS (operationally available at http://trmm.gsfc.nasa.gov/) improved not only the flood detection performance, but also in the presentation of flood evolution (start, development and recession) in the drainage network. The new GFMS is further evaluated with more quantitative flood properties including flood peak timing, peak stage, peak volumes

  10. Real Time Monitoring of Flooding from Microwave Satellite Observations

    NASA Technical Reports Server (NTRS)

    Galantowicz, John F.; Frey, H. (Technical Monitor)

    2001-01-01

    In this report, we review the progress to date including results from data analyses and present a schedule of milestones for the remainder of the project. We discuss the processing of flood extent data and SSM/I brightness temperature data for the 1993 Midwest Flood. We present preliminary results from the derivation of open water fraction from brightness temperatures.

  11. Real-time precipitation forecasting in the Swiss Alps by means of the analogue method

    NASA Astrophysics Data System (ADS)

    Horton, P.; Obled, C.; Jaboyedoff, M.

    2012-04-01

    Tools for real-time hydrometeorological forecasting are now implemented in the Wallis Canton in the Swiss Alps. This is an outcome of the MINERVE project, which aims at reducing the flood peaks of the Rhône River by means of water retention in dams. Precipitation forecasts come from both a numerical weather prediction model at MeteoSwiss and a statistical weather forecasting developed during the project. This model is called Atmoswing (Analog Technique MOdel for Statistical Weather forecastING) and is based on the analogue downscaling technique, which allows precipitation forecasting on the basis of the synoptic circulation and humidity variables resulting from a global circulation model (GCM). The method searches for analog days in a long archive of past situations and uses their measured precipitation amount to build the empirical conditional distribution considered as the probabilistic forecast for the target day. The analogue method was calibrated for different sub-regions of the Rhône catchment. Various regions are sensitive to specific atmospheric circulations, such as south circulation or strong westerlies, and thus are parameterized according to the main influences. An improvement of the analog method is proposed. The classic technique is to consider the synoptic circulation at a certain hour of the day and to search for analogues only at that same hour. This constraint is a consequence of the precipitation daily timestep. However, the availability of finer timeseries allows us to explore the effect of a moving time window. Such an improvement was expected, as similar atmospheric situations may happen at various hours of the day, but couldn't be quantified until now. We also observed a seasonal effect of the improvement and a positive trend with precipitation amounts. The system is operational since late September 2011 and various interesting - or critical - meteorological situations happened since. The method showed a clear signal of the coming

  12. New Techniques for Real-Time Stage Forecasting for Tributaries in the Nashville Area

    NASA Astrophysics Data System (ADS)

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

    2011-12-01

    On Saturday, May 1, 2010, heavy rain began falling in the Cumberland River Valley, Tennessee, and continued through the following day. 13.5 inches was measured at Nashville, an unprecedented amount that doubled the previous 2-day record, and exceeded the May monthly total record of 11 inches. Elsewhere in the valley, amounts of over 19 inches were measured. This intensity of rainfall quickly overwhelmed tributaries to the Cumberland in the Nashville area, causing wide-spread and 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. As a result of the flood, agencies in the Nashville area want better capabilities to forecast stages for the local tributaries. Better stage forecasting will help local agencies close roads, evacuate homes and businesses and similar actions. An interagency group, consisting of Metro Nashville Water Services and Office of Emergency Management, the National Weather Service, the US Geological Survey and the US Army Corps of Engineers, has been established to seek ways to better forecast short-term events in the region. It should be noted that the National Weather Service has the official responsibility of forecasting stages. This paper examines techniques and algorithms that are being developed to meet this need and the practical aspects of integrating them into a usable product that can quickly and accurately forecast stages in the short-time frame of the tributaries. This includes not only the forecasting procedure, but also the procedure to acquire the latest precipitation and stage data to make the forecasts. These procedures are integrated into the program HEC-RTS, the US Army Corps of Engineers Real-Time Simulation program. HEC-RTS is a Java-based integration tool that

  13. Real time probabilistic precipitation forecasts in the Milano urban area: comparison between a physics and pragmatic approach

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

    Precipitation forecasts from mesoscale numerical weather prediction (NWP) models often contain features that are not deterministically predictable. In particular, accurate forecasts of deep moist convection and extreme rainfall are arduous to be predicted in terms of amount, time and target over small hydrological basins due to uncertainties arising from the numerical weather prediction (NWP), physical parameterizations and high sensitivity to misrepresentation of the atmospheric state, therefore they require a probabilistic forecast approach. Here, we examine some hydro-meteorological episodes that affected the Milano urban watersheds using 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. The first approach is based on a hydrological ensemble prediction system (HEPS) designed to explicitly cope with uncertainties in the initial and lateral boundary conditions (IC/LBCs) and physical parameterizations of the NWP model. The second involves a pragmatic post-processing procedure by randomly shifting in space the precipitation field provided by the deterministic WRF model run in order to get a cluster of different simulations. Although the physics-based approach needs a high computational cost, it outperforms the pragmatic set of configurations, which, however, turns out to be an acceptable low-budget alternative for real time flood forecasts over small urban basins when a single deterministic run is available.

  14. Near Real Time Data for Operational Space Weather Forecasting

    NASA Astrophysics Data System (ADS)

    Berger, T. E.

    2014-12-01

    Space weather operations presents unique challenges for data systems and providers. Space weather events evolve more quickly than terrestrial weather events. While terrestrial weather occurs on timescales of minutes to hours, space weather storms evolve on timescales of seconds to minutes. For example, the degradation of the High Frequency Radio communications between the ground and commercial airlines is nearly instantaneous when a solar flare occurs. Thus the customer is observing impacts at the same time that the operational forecast center is seeing the event unfold. The diversity and spatial scale of the space weather system is such that no single observation can capture the salient features. The vast space that encompasses space weather and the scarcity of observations further exacerbates the situation and make each observation even more valuable. The physics of interplanetary space, through which many major storms propagate, is very different from the physics of the ionosphere where most of the impacts are felt. And while some observations can be made from ground-based observatories, many of the most critical data comes from satellites, often in unique orbits far from Earth. In this presentation, I will describe some of the more important sources and types of data that feed into the operational alerts, watches, and warnings of space weather storms. Included will be a discussion of some of the new space weather forecast models and the data challenges that they bring forward.

  15. 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.

  16. [Real-time forecasting model for monitoring pollutant with differential optical absorption spectroscopy].

    PubMed

    Li, Su-Wen; Liu, Wen-Qing; Xie, Pin-Hua; Wang, Feng-Sui; Yang, Yi-Jun

    2009-11-01

    For real-time and on-line monitoring DOAS (differential optical absorption spectroscopy) system, a model based on an improved Elman network for monitoring pollutant concentrations was proposed. In order to reduce the systematical complexity, the forecasting factors have been obtained based on the step-wise regression method. The forecasting factors were current concentrations, temperature and relative humidity, and wind speed and wind direction. The dynamic back propagation (BP) algorithm was used for creating training set. The experiment results show that the predicted value follows the real well. So the modified Elman network can meet the demand of DOAS system's real time forecasting. PMID:20101985

  17. CFlood: A Constrained Flooding Protocol for Real-time Data Delivery in Wireless Sensor Networks

    NASA Astrophysics Data System (ADS)

    Jiang, Bo; Ravindran, Binoy; Cho, Hyeonjoong

    Real-time performance is critical for many time-sensitive applications of wireless sensor networks. We present a constrained flooding protocol, called CFlood, which enhances the deadline satisfaction ratio per unit energy consumption of time-sensitive packets in sensor networks. CFlood improves real-time performance by flooding, but effectively constrains energy consumption by controlling the scale of flooding, i.e., flooding only when necessary. If unicasting meets the distributed sub-deadline of a hop, CFlood aborts further flooding even after flooding has occurred in the current hop. Our simulation-based experimental studies show that CFlood achieves higher deadline satisfaction ratio per unit energy consumption than previous multipath forwarding protocols, especially in sparsely deployed or unreliable sensor network environments.

  18. Development of a real-time geospace simulator for space weather forecast

    NASA Astrophysics Data System (ADS)

    Shinagawa, H.; Shimazu, H.; Fujita, S.; Tanaka, T.; Terada, N.; Nakamura, M.; Obara, T.

    Space weather forecast requires real-time numerical models along with various real-time observational data on the ground and in space Real-time numerical models not only give present information on the space environment but also predict upcoming space weather disturbances Recently a real-time global MHD model of the solar wind interaction with the earth s magnetosphere has been developed by National Institute of Information and Communications Technology NICT in collaboration with Kyushu University The model is now operated at the NICT s space weather forecast center to understand present state of the magnetospheric environment and to predict magnetic disturbances However the present real-time model has not satisfactorily included particle effects on the space environment and realistic ionosphere and thermosphere High-energy particles could damage satellite instruments and human bodies in space while ionospheric storms could affect various communication and broadcasting systems Therefore it is important to develop a numerical model which is able to predict such disturbances We have started to improve our real-time magnetospheric model by including the particle effects as well as the ionosphere-thermosphere system We will describe the current status and future prospect of the real-time geospace model

  19. Earthquake and failure forecasting in real-time: A Forecasting Model Testing Centre

    NASA Astrophysics Data System (ADS)

    Filgueira, Rosa; Atkinson, Malcolm; Bell, Andrew; Main, Ian; Boon, Steven; Meredith, Philip

    2013-04-01

    Across Europe there are a large number of rock deformation laboratories, each of which runs many experiments. Similarly there are a large number of theoretical rock physicists who develop constitutive and computational models both for rock deformation and changes in geophysical properties. Here we consider how to open up opportunities for sharing experimental data in a way that is integrated with multiple hypothesis testing. We present a prototype for a new forecasting model testing centre based on e-infrastructures for capturing and sharing data and models to accelerate the Rock Physicist (RP) research. This proposal is triggered by our work on data assimilation in the NERC EFFORT (Earthquake and Failure Forecasting in Real Time) project, using data provided by the NERC CREEP 2 experimental project as a test case. EFFORT is a multi-disciplinary collaboration between Geoscientists, Rock Physicists and Computer Scientist. Brittle failure of the crust is likely to play a key role in controlling the timing of a range of geophysical hazards, such as volcanic eruptions, yet the predictability of brittle failure is unknown. Our aim is to provide a facility for developing and testing models to forecast brittle failure in experimental and natural data. Model testing is performed in real-time, verifiably prospective mode, in order to avoid selection biases that are possible in retrospective analyses. The project will ultimately quantify the predictability of brittle failure, and how this predictability scales from simple, controlled laboratory conditions to the complex, uncontrolled real world. Experimental data are collected from controlled laboratory experiments which includes data from the UCL Laboratory and from Creep2 project which will undertake experiments in a deep-sea laboratory. We illustrate the properties of the prototype testing centre by streaming and analysing realistically noisy synthetic data, as an aid to generating and improving testing methodologies in

  20. Value of Probabilistic Weather Forecasts: Assessment by Real-Time Optimization of Irrigation Scheduling

    SciTech Connect

    Cai, Ximing; Hejazi, Mohamad I.; Wang, Dingbao

    2011-09-29

    This paper presents a modeling framework for real-time decision support for irrigation scheduling using the National Oceanic and Atmospheric Administration's (NOAA's) probabilistic rainfall forecasts. The forecasts and their probability distributions are incorporated into a simulation-optimization modeling framework. In this study, modeling irrigation is determined by a stochastic optimization program based on the simulated soil moisture and crop water-stress status and the forecasted rainfall for the next 1-7 days. The modeling framework is applied to irrigated corn in Mason County, Illinois. It is found that there is ample potential to improve current farmers practices by simply using the proposed simulation-optimization framework, which uses the present soil moisture and crop evapotranspiration information even without any forecasts. It is found that the values of the forecasts vary across dry, normal, and wet years. More significant economic gains are found in normal and wet years than in dry years under the various forecast horizons. To mitigate drought effect on crop yield through irrigation, medium- or long-term climate predictions likely play a more important role than short-term forecasts. NOAA's imperfect 1-week forecast is still valuable in terms of both profit gain and water saving. Compared with the no-rain forecast case, the short-term imperfect forecasts could lead to additional 2.4-8.5% gain in profit and 11.0-26.9% water saving. However, the performance of the imperfect forecast is only slightly better than the ensemble weather forecast based on historical data and slightly inferior to the perfect forecast. It seems that the 1-week forecast horizon is too limited to evaluate the role of the various forecast scenarios for irrigation scheduling, which is actually a seasonal decision issue. For irrigation scheduling, both the forecast quality and the length of forecast time horizon matter. Thus, longer forecasts might be necessary to evaluate the role

  1. Evaluation of a Satellite-based Near Real-time Global Flood Prediction System

    NASA Astrophysics Data System (ADS)

    Yilmaz, K. K.; Adler, R. F.; Hong, Y.; Pierce, H. F.

    2008-12-01

    Satellite-based rainfall and geospatial datasets are potentially useful for cost effective detection and early warning of natural hazards, such as floods, specifically for regions of the world where local data are sparse or non-existent. An initial satellite-based near real-time global flood prediction system is operationally available on our website (http://trmm.gsfc.nasa.gov/publications_dir/potential_flood_hydro.html). The key input to the current system is the near real-time rainfall estimates from the NASA-based Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA; 3 hourly, 0.258 x 0.258 degree). A relatively simple hydrologic model, based on the runoff curve number (CN) and antecedent precipitation index (API) methods, transforms rainfall into runoff. In this study we will present an in-depth testing/evaluation of this current flood prediction system, discuss its strengths and limitations and point toward potential improvements necessary for increasing its near real-time global flood prediction reliability and accuracy. This evaluation study will focus on the severe flooding events and will include comparison of the current product with observed runoff/inundation data at global and watershed scale as well as with other available remotely sensed products (e.g., MODIS-based inundation maps from Dartmouth Flood Observatory).

  2. 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.

  3. Portals for Real-Time Earthquake Data and Forecasting: Challenge and Promise (Invited)

    NASA Astrophysics Data System (ADS)

    Rundle, J. B.; Holliday, J. R.; Graves, W. R.; Feltstykket, R.; Donnellan, A.; Glasscoe, M. T.

    2013-12-01

    Earthquake forecasts have been computed by a variety of countries world-wide for over two decades. For the most part, forecasts have been computed for insurance, reinsurance and underwriters of catastrophe bonds. However, recent events clearly demonstrate that mitigating personal risk is becoming the responsibility of individual members of the public. Open access to a variety of web-based forecasts, tools, utilities and information is therefore required. Portals for data and forecasts present particular challenges, and require the development of both apps and the client/server architecture to deliver the basic information in real time. The basic forecast model we consider is the Natural Time Weibull (NTW) method (JBR et al., Phys. Rev. E, 86, 021106, 2012). This model uses small earthquakes (';seismicity-based models') to forecast the occurrence of large earthquakes, via data-mining algorithms combined with the ANSS earthquake catalog. This method computes large earthquake probabilities using the number of small earthquakes that have occurred in a region since the last large earthquake. Localizing these forecasts in space so that global forecasts can be computed in real time presents special algorithmic challenges, which we describe in this talk. Using 25 years of data from the ANSS California-Nevada catalog of earthquakes, we compute real-time global forecasts at a grid scale of 0.1o. We analyze and monitor the performance of these models using the standard tests, which include the Reliability/Attributes and Receiver Operating Characteristic (ROC) tests. It is clear from much of the analysis that data quality is a major limitation on the accurate computation of earthquake probabilities. We discuss the challenges of serving up these datasets over the web on web-based platforms such as those at www.quakesim.org , www.e-decider.org , and www.openhazards.com.

  4. Real-time forecasting of the April 11, 2012 Sumatra tsunami

    NASA Astrophysics Data System (ADS)

    Wang, Dailin; Becker, Nathan C.; Walsh, David; Fryer, Gerard J.; Weinstein, Stuart A.; McCreery, Charles S.; Sardiña, Victor; Hsu, Vindell; Hirshorn, Barry F.; Hayes, Gavin P.; Duputel, Zacharie; Rivera, Luis; Kanamori, Hiroo; Koyanagi, Kanoa K.; Shiro, Brian

    2012-10-01

    The April 11, 2012, magnitude 8.6 earthquake off the northern coast of Sumatra generated a tsunami that was recorded at sea-level stations as far as 4800 km from the epicenter and at four ocean bottom pressure sensors (DARTs) in the Indian Ocean. The governments of India, Indonesia, Sri Lanka, Thailand, and Maldives issued tsunami warnings for their coastlines. The United States' Pacific Tsunami Warning Center (PTWC) issued an Indian Ocean-wide Tsunami Watch Bulletin in its role as an Interim Service Provider for the region. Using an experimental real-time tsunami forecast model (RIFT), PTWC produced a series of tsunami forecasts during the event that were based on rapidly derived earthquake parameters, including initial location and Mwp magnitude estimates and the W-phase centroid moment tensor solutions (W-phase CMTs) obtained at PTWC and at the U. S. Geological Survey (USGS). We discuss the real-time forecast methodology and how successive, real-time tsunami forecasts using the latest W-phase CMT solutions improved the accuracy of the forecast.

  5. Real-time forecasting of the April 11, 2012 Sumatra tsunami

    USGS Publications Warehouse

    Wang, Dailin; Becker, Nathan C.; Walsh, David; Fryer, Gerard J.; Weinstein, Stuart A.; McCreery, Charles S.; and others

    2012-01-01

    The April 11, 2012, magnitude 8.6 earthquake off the northern coast of Sumatra generated a tsunami that was recorded at sea-level stations as far as 4800 km from the epicenter and at four ocean bottom pressure sensors (DARTs) in the Indian Ocean. The governments of India, Indonesia, Sri Lanka, Thailand, and Maldives issued tsunami warnings for their coastlines. The United States' Pacific Tsunami Warning Center (PTWC) issued an Indian Ocean-wide Tsunami Watch Bulletin in its role as an Interim Service Provider for the region. Using an experimental real-time tsunami forecast model (RIFT), PTWC produced a series of tsunami forecasts during the event that were based on rapidly derived earthquake parameters, including initial location and Mwp magnitude estimates and the W-phase centroid moment tensor solutions (W-phase CMTs) obtained at PTWC and at the U. S. Geological Survey (USGS). We discuss the real-time forecast methodology and how successive, real-time tsunami forecasts using the latest W-phase CMT solutions improved the accuracy of the forecast.

  6. Real time tests for long lead-time forecasting of the magnetic field vectors within CMEs

    NASA Astrophysics Data System (ADS)

    Savani, Neel; Vourlidas, Angelos; Pulkkinen, Antti; Wold, Alexandra M.

    2016-07-01

    The direction of magnetic vectors within coronal mass ejections, CMEs, has significant importance for forecasting terrestrial behavior. We have developed a technique to estimate the time-varying magnetic field at Earth for periods within CMEs (Savani et al 2015, 2016). This technique reduces the complex dynamics in order to create a reliable prediction methodology to operate everyday under robust conditions. In this presentation, we focus on the results and skill scores of the forecasting technique calculated from 40 historical CME events from the pre-STEREO mission. Since these results provided substantial improvements in the long lead-time Kp index forecasts, we have now begun testing under real-time conditions. We will also show the preliminary results of our methodology under these real-time conditions within the CCMC hosted at NASA Goddard Space Flight Center.

  7. Real-Time Assessment of the 16 September 2015 Chile Tsunami and Implications for Near-Field Forecast

    NASA Astrophysics Data System (ADS)

    Tang, Liujuan; Titov, Vasily V.; Moore, Christopher; Wei, Yong

    2016-02-01

    The magnitude 8.3 earthquake in central Chile on 16 September 2015 and the resulting tsunami severely affected the region, with 15 deaths ( Onemi in Monitoreo por sismo de mayor intensidad. (In Spanish) [Available at: http://www.onemi.cl/alerta/se-declara-alerta-roja-por-sismo-de-mayor-intensidad-y-alarma-de-tsunami/], 2015), over one million evacuated, and flooding in nearby coastal cities. We present our real-time assessment of the 2015 Chile tsunami using the Short-term Inundation Forecasting for Tsunamis system, and post-event analyses with local community models in Chile. We evaluate three real-time tsunami sources, which were inverted at the time that the first quarter-, half-, and full-wave passed the first tsunameter (DART 32402, located approximately 580 km north-northwest of the epicenter), respectively. Measurement comparisons from 26 deep-ocean tsunameters and 38 coastal tide stations show that good model accuracies are achieved for all three sources, particularly for the local sites that recorded the most destructive waves. The study highlights the forecast speed, time and accuracy dependence, and their implications for the local forecast capability. Our analyses suggest that the tsunami's main origination area is about 100-200 km long and 100 km wide, to the north of the earthquake epicenter along the trench and the total estimated tsunami wave energy is 7.9 × 1013 J (with 13 % uncertainty). The study provides important guidelines for the earliest reliable estimate of tsunami energy and local forecasts. They can be obtained with the first quarter-wave of tsunameter recording. These results are also confirmed by a forecast analysis of the 2011 Japan tsunami. Furthermore, we find that the first half-wave tsunameter data are sufficient to accurately forecast the 2015 Chile tsunami, due to the specific orientation between the nearest tsunameter and the source. The study also suggests expanding the operational use of the local community models in real

  8. On selection of the optimal data time interval for real-time hydrological forecasting

    NASA Astrophysics Data System (ADS)

    Liu, J.; Han, D.

    2013-09-01

    With the advancement in modern telemetry and communication technologies, hydrological data can be collected with an increasingly higher sampling rate. An important issue deserving attention from the hydrological community is which suitable time interval of the model input data should be chosen in hydrological forecasting. Such a problem has long been recognised in the control engineering community but is a largely ignored topic in operational applications of hydrological forecasting. In this study, the intrinsic properties of rainfall-runoff data with different time intervals are first investigated from the perspectives of the sampling theorem and the information loss using the discrete wavelet transform tool. It is found that rainfall signals with very high sampling rates may not always improve the accuracy of rainfall-runoff modelling due to the catchment low-pass-filtering effect. To further investigate the impact of a data time interval in real-time forecasting, a real-time forecasting system is constructed by incorporating the probability distributed model (PDM) with a real-time updating scheme, the autoregressive moving-average (ARMA) model. Case studies are then carried out on four UK catchments with different concentration times for real-time flow forecasting using data with different time intervals of 15, 30, 45, 60, 90 and 120 min. A positive relation is found between the forecast lead time and the optimal choice of the data time interval, which is also highly dependent on the catchment concentration time. Finally, based on the conclusions from the case studies, a hypothetical pattern is proposed in three-dimensional coordinates to describe the general impact of the data time interval and to provide implications of the selection of the optimal time interval in real-time hydrological forecasting. Although nowadays most operational hydrological systems still have low data sampling rates (daily or hourly), the future is that higher sampling rates will become

  9. Use of Real-time Satellite Rainfall Information in a Global Flood Estimation System

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

    The TRMM Multi-satellite Precipitation Analysis (TMPA) is a merger of precipitation information from mainly passive microwave sensors on polar orbiting satellites. This information is cross-calibrated in terms of rainrate using data from the Tropical Rainfall Measuring Mission (TRMM) flying in an inclined orbit at 35°. A research quality analysis is produced a few months after observation time, but a real-time product is also generated within a few hours of observation. This real-time, or RT, product can be used to quickly diagnose heavy rain events over most of the globe. This rainfall information is also used as the key input into an experimental system, the Global Flood Monitoring System (GFMS), which produces real-time, quasi-global flood estimates. Images and output data are available for use by the community (http://oas.gsfc.nasa.gov/globalflood/). The method uses the 3-hr resolution composite rainfall analyses as input into a hydrological model that calculates water depth and streamflow at each grid (at 0.125 ° latitude-longitude) over the tropics and mid-latitudes. Flood detection and intensity estimates are based on water depth thresholds calculated from a 13-year retrospective run using the satellite rainfall and model. Examination of individual cases in real-time or retrospectively often indicates skill in detecting the occurrence of a flood event and a reasonable evolution of water depth (at the scale of the calculation) and downstream movement of high water levels. A recently published study evaluating calculated flood occurrence from the GFMS against a global flood event database is reviewed. The statistics indicate that flood detection results improve with longer duration (> 3 days) floods and that the statistics are impacted by the presence of large dams, which are not accounted for in the model calculations. Overall, for longer floods in basins without large dams, the Probability of Detection (POD) of floods is ~ 0.7, while the False Alarm Rate

  10. Real-time forecasting of sample failure in laboratory rock deformation experiments

    NASA Astrophysics Data System (ADS)

    Bell, Andrew; Main, Ian; Atkinson, Malcolm; Filgueira, Rosa; Meredith, Philip; Boon, Steve

    2013-04-01

    The ability to accurately forecast catastrophic failure in rocks is likely to be a key component in reliable eruption forecasting models. The processes controlling the approach to failure produce highly non-linear behaviour, with a large stochastic component due to material heterogeneity. In the laboratory, mechanical, hydraulic, and rock physical properties are known to change in systematic ways prior to catastrophic failure. The effectiveness of such signals in real-time forecasting has never been tested before in a controlled laboratory setting; previous work has often been qualitative in nature, and subject to retrospective selection bias. Here we describe a collaborative experiment in real-time data assimilation to explore the limits of predictability of rock failure in a best-case scenario. Data are streamed from a remote rock deformation laboratory to a user-friendly portal, where several proposed physical/stochastic models can be analyzed in parallel in real time, using a variety of statistical fitting techniques, including least squares regression, maximum likelihood fitting, Markov-chain Monte-Carlo and Bayesian analysis. The results are posted and regularly updated on the web site prior to catastrophic failure, to ensure a true and verifiable prospective test of forecasting power.

  11. Real-time deployment of artificial neural network forecasting models: Understanding the range of applicability

    NASA Astrophysics Data System (ADS)

    Bowden, Gavin J.; Maier, Holger R.; Dandy, Graeme C.

    2012-10-01

    When an operational artificial neural network (ANN) model is deployed, new input patterns are collected in order to make real-time forecasts. However, ANNs (like other empirical and statistical methods) are unable to reliably extrapolate beyond the calibration range. Consequently, when deployed in real-time operation there is a need to determine if new input patterns are representative of the data used in calibrating the model. To address this problem, a novel detection system for identifying uncharacteristic data patterns is presented. This approach combines a self-organizing map (SOM), to partition the data set, with nonparametric kernel density estimators to calculate local density estimates (LDE). The SOM-LDE method determines the degree to which a new input pattern can be considered to be contained within the domain of the calibration set. If a new pattern is found to be uncharacteristic, a warning can be issued with the forecast, and the ANN model retrained to include the new pattern. This approach of selectively retraining the model is compared to no retraining and the more computationally onerous case of retraining the model after each new sample. These three approaches are applied to forecast flow in the Kentucky River, USA, using multilayer perceptron (MLP) models. The results demonstrate that there is a significant advantage in retraining an ANN that has been deployed as a real-time, operational model, and that the SOM-LDE classifier is an effective approach for identifying the model's range of applicability and assessing the usefulness of the forecast.

  12. Evaluation of multiple hydraulic models in generating design/near-real time flood inundation extents under various geophysical settings

    NASA Astrophysics Data System (ADS)

    Liu, Z.; Rajib, M. A.; Jafarzadegan, K.; Merwade, V.

    2015-12-01

    Application of land surface/hydrologic models within an operational flood forecasting system can provide probable time of occurrence and magnitude of streamflow at specific locations along a stream. Creating time-varying spatial extent of flood inundation and depth requires the use of a hydraulic or hydrodynamic model. Models differ in representing river geometry and surface roughness which can lead to different output depending on the particular model being used. The result from a single hydraulic model provides just one possible realization of the flood extent without capturing the uncertainty associated with the input or the model parameters. The objective of this study is to compare multiple hydraulic models toward generating ensemble flood inundation extents. Specifically, relative performances of four hydraulic models, including AutoRoute, HEC-RAS, HEC-RAS 2D, and LISFLOOD are evaluated under different geophysical conditions in several locations across the United States. By using streamflow output from the same hydrologic model (SWAT in this case), hydraulic simulations are conducted for three configurations: (i) hindcasting mode by using past observed weather data at daily time scale in which models are being calibrated against USGS streamflow observations, (ii) validation mode using near real-time weather data at sub-daily time scale, and (iii) design mode with extreme streamflow data having specific return periods. Model generated inundation maps for observed flood events both from hindcasting and validation modes are compared with remotely sensed images, whereas the design mode outcomes are compared with corresponding FEMA generated flood hazard maps. The comparisons presented here will give insights on probable model-specific nature of biases and their relative advantages/disadvantages as components of an operational flood forecasting system.

  13. Real-time operative earthquake forecasting: the case of L'Aquila sequence

    NASA Astrophysics Data System (ADS)

    Marzocchi, W.; Lombardi, A.

    2009-12-01

    A reliable earthquake forecast is one of the fundamental components required for reducing seismic risk. Despite very recent efforts devoted to test the validity of available models, the present skill at forecasting the evolution of seismicity is still largely unknown. The recent Mw 6.3 earthquake - that struck near the city of L'Aquila, Italy on April 6, 2009, causing hundreds of deaths and vast damages - offered to scientists a unique opportunity to test for the first time the forecasting capability in a real-time application. Here, we describe the results of this first prospective experiment. Immediately following the large event, we began producing daily one-day earthquake forecasts for the region, and we provided these forecasts to Civil Protection - the agency responsible for managing the emergency. The forecasts are based on a stochastic model that combines the Gutenberg-Richter distribution of earthquake magnitudes and power-law decay in space and time of triggered earthquakes. The results from the first month following the L'Aquila earthquake exhibit a good fit between forecasts and observations, indicating that accurate earthquake forecasting is now a realistic goal. Our experience with this experiment demonstrates an urgent need for a connection between probabilistic forecasts and decision-making in order to establish - before crises - quantitative and transparent protocols for decision support.

  14. Real-Time CME Forecasting Using HMI Active-Region Magnetograms and Flare History

    NASA Technical Reports Server (NTRS)

    Falconer, David; Moore, Ron; Barghouty, Abdulnasser F.; Khazanov, Igor

    2011-01-01

    We have recently developed a method of predicting an active region s probability of producing a CME, an X-class Flare, an M-class Flare, or a Solar Energetic Particle Event from a free-energy proxy measured from SOHO/MDI line-of-sight magnetograms. This year we have added three major improvements to our forecast tool: 1) Transition from MDI magnetogram to SDO/HMI magnetogram allowing us near-real-time forecasts, 2) Automation of acquisition and measurement of HMI magnetograms giving us near-real-time forecasts (no older than 2 hours), and 3) Determination of how to improve forecast by using the active region s previous flare history in combination with its free-energy proxy. HMI was turned on in May 2010 and MDI was turned off in April 2011. Using the overlap period, we have calibrated HMI to yield what MDI would measure. This is important since the value of the free-energy proxy used for our forecast is resolution dependent, and the forecasts are made from results of a 1996-2004 database of MDI observations. With near-real-time magnetograms from HMI, near-real-time forecasts are now possible. We have augmented the code so that it continually acquires and measures new magnetograms as they become available online, and updates the whole-sun forecast from the coming day. The next planned improvement is to use an active region s previous flare history, in conjunction with its free-energy proxy, to forecast the active region s event rate. It has long been known that active regions that have produced flares in the past are likely to produce flares in the future, and that active regions that are nonpotential (have large free-energy) are more likely to produce flares in the future. This year we have determined that persistence of flaring is not just a reflection of an active region s free energy. In other words, after controlling for free energy, we have found that active regions that have flared recently are more likely to flare in the future.

  15. Global system for hydrological monitoring and forecasting in real time at high resolution

    NASA Astrophysics Data System (ADS)

    Ortiz, Enrique; De Michele, Carlo; Todini, Ezio; Cifres, Enrique

    2016-04-01

    This project presented at the EGU 2016 born of solidarity and the need to dignify the most disadvantaged people living in the poorest countries (Africa, South America and Asia, which are continually exposed to changes in the hydrologic cycle suffering events of large floods and/or long periods of droughts. It is also a special year this 2016, Year of Mercy, in which we must engage with the most disadvantaged of our Planet (Gaia) making available to them what we do professionally and scientifically. The project called "Global system for hydrological monitoring and forecasting in real time at high resolution" is Non-Profit and aims to provide at global high resolution (1km2) hydrological monitoring and forecasting in real time and continuously coupling Weather Forecast of Global Circulation Models, such us GFS-0.25° (Deterministic and Ensembles Run) forcing a physically based distributed hydrological model computationally efficient, such as the latest version extended of TOPKAPI model, named TOPKAPI-eXtended. Finally using the MCP approach for the proper use of ensembles for Predictive Uncertainty assessment essentially based on a multiple regression in the Normal space, can be easily extended to use ensembles to represent the local (in time) smaller or larger conditional predictive uncertainty, as a function of the ensemble spread. In this way, each prediction in time accounts for both the predictive uncertainty of the ensemble mean and that of the ensemble spread. To perform a continuous hydrological modeling with TOPKAPI-X model and have hot start of hydrological status of watersheds, the system assimilated products of rainfall and temperature derived from remote sensing, such as product 3B42RT of TRMM NASA and others.The system will be integrated into a Decision Support System (DSS) platform, based on geographical data. The DSS is a web application (For Pc, Tablet/Mobile phone): It does not need installation (all you need is a web browser and an internet

  16. Global system for hydrological monitoring and forecasting in real time at high resolution

    NASA Astrophysics Data System (ADS)

    Ortiz, Enrique; De Michele, Carlo; Todini, Ezio; Cifres, Enrique

    2016-04-01

    This project presented at the EGU 2016 born of solidarity and the need to dignify the most disadvantaged people living in the poorest countries (Africa, South America and Asia, which are continually exposed to changes in the hydrologic cycle suffering events of large floods and/or long periods of droughts. It is also a special year this 2016, Year of Mercy, in which we must engage with the most disadvantaged of our Planet (Gaia) making available to them what we do professionally and scientifically. The project called "Global system for hydrological monitoring and forecasting in real time at high resolution" is Non-Profit and aims to provide at global high resolution (1km2) hydrological monitoring and forecasting in real time and continuously coupling Weather Forecast of Global Circulation Models, such us GFS-0.25° (Deterministic and Ensembles Run) forcing a physically based distributed hydrological model computationally efficient, such as the latest version extended of TOPKAPI model, named TOPKAPI-eXtended. Finally using the MCP approach for the proper use of ensembles for Predictive Uncertainty assessment essentially based on a multiple regression in the Normal space, can be easily extended to use ensembles to represent the local (in time) smaller or larger conditional predictive uncertainty, as a function of the ensemble spread. In this way, each prediction in time accounts for both the predictive uncertainty of the ensemble mean and that of the ensemble spread. To perform a continuous hydrological modeling with TOPKAPI-X model and have hot start of hydrological status of watersheds, the system assimilated products of rainfall and temperature derived from remote sensing, such as product 3B42RT of TRMM NASA and others.The system will be integrated into a Decision Support System (DSS) platform, based on geographical data. The DSS is a web application (For Pc, Tablet/Mobile phone): It does not need installation (all you need is a web browser and an internet

  17. A practical MGA-ARIMA model for forecasting real-time dynamic rain-induced attenuation

    NASA Astrophysics Data System (ADS)

    Gong, Shuhong; Gao, Yifeng; Shi, Houbao; Zhao, Ge

    2013-05-01

    novel and practical modified genetic algorithm (MGA)-autoregressive integrated moving average (ARIMA) model for forecasting real-time dynamic rain-induced attenuation has been established by combining genetic algorithm ideas with the ARIMA model. It is proved that due to the introduction of MGA into the ARIMA(1,1,7) model, the MGA-ARIMA model has the potential to be conveniently applied in every country or area by creating a parameter database used by the ARIMA(1,1,7) model. The parameter database is given in this paper based on attenuation data measured in Xi'an, China. The methods to create the parameter databases in other countries or areas are offered, too. Based on the experimental results, the MGA-ARIMA model has been proved practical for forecasting dynamic rain-induced attenuation in real time. The novel model given in this paper is significant for developing adaptive fade mitigation technologies at millimeter wave bands.

  18. Optimization Based Data Mining Approah for Forecasting Real-Time Energy Demand

    SciTech Connect

    Omitaomu, Olufemi A; Li, Xueping; Zhou, Shengchao

    2015-01-01

    The worldwide concern over environmental degradation, increasing pressure on electric utility companies to meet peak energy demand, and the requirement to avoid purchasing power from the real-time energy market are motivating the utility companies to explore new approaches for forecasting energy demand. Until now, most approaches for forecasting energy demand rely on monthly electrical consumption data. The emergence of smart meters data is changing the data space for electric utility companies, and creating opportunities for utility companies to collect and analyze energy consumption data at a much finer temporal resolution of at least 15-minutes interval. While the data granularity provided by smart meters is important, there are still other challenges in forecasting energy demand; these challenges include lack of information about appliances usage and occupants behavior. Consequently, in this paper, we develop an optimization based data mining approach for forecasting real-time energy demand using smart meters data. The objective of our approach is to develop a robust estimation of energy demand without access to these other building and behavior data. Specifically, the forecasting problem is formulated as a quadratic programming problem and solved using the so-called support vector machine (SVM) technique in an online setting. The parameters of the SVM technique are optimized using simulated annealing approach. The proposed approach is applied to hourly smart meters data for several residential customers over several days.

  19. 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.

  20. Global Near Real-Time Satellite-based Flood Monitoring and Product Dissemination

    NASA Astrophysics Data System (ADS)

    Smith, M.; Slayback, D. A.; Policelli, F.; Brakenridge, G. R.; Tokay, M.

    2012-12-01

    Flooding is among the most destructive, frequent, and costly natural disasters faced by modern society, with several major events occurring each year. In the past few years, major floods have devastated parts of China, Thailand, Pakistan, Australia, and the Philippines, among others. The toll of these events, in financial costs, displacement of individuals, and deaths, is substantial and continues to rise as climate change generates more extreme weather events. When these events do occur, the disaster management community requires frequently updated and easily accessible information to better understand the extent of flooding and better coordinate response efforts. With funding from NASA's Applied Sciences program, we have developed, and are now operating, a near real-time global flood mapping system to help provide critical flood extent information within 24-48 hours after flooding events. The system applies a water detection algorithm to MODIS imagery received from the LANCE (Land Atmosphere Near real-time Capability for EOS) system at NASA Goddard. The LANCE system typically processes imagery in less than 3 hours after satellite overpass, and our flood mapping system can output flood products within ½ hour of acquiring the LANCE products. Using imagery from both the Terra (10:30 AM local time overpass) and Aqua (1:30 PM) platforms allows an initial assessment of flooding extent by late afternoon, every day, and more robust assessments after accumulating imagery over a longer period; the MODIS sensors are optical, so cloud cover remains an issue, which is partly overcome by using multiple looks over one or more days. Other issues include the relatively coarse scale of the MODIS imagery (250 meters), the difficulty of detecting flood waters in areas with continuous canopy cover, confusion of shadow (cloud or terrain) with water, and accurately identifying detected water as flood as opposed to normal water extents. We have made progress on some of these issues

  1. 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

  2. Application of Global Real-Time Landslide Forecasting System for International use

    NASA Astrophysics Data System (ADS)

    Kirschbaum, D. B.; Lerner-Lam, A.; Hong, Y.; Adler, R.

    2008-12-01

    The variability of natural hazard events by category significantly vary in their spatial and temporal extents and onsets, requiring a catered, and focused approach to appropriately address the risk and vulnerability of the specific hazard event. The advent of satellite data products has helped to monitor tropical cyclones, droughts, and flooding conditions and consequent impacts. Geophysical events such as earthquake are continually monitored on a global seismic network. However, a warning or monitoring system has not been established at larger scales for landslides, a hazard with the smallest spatial extent but highest frequency and arguably largest impacts globally. One of the major challenges in landslide hazard research is the field's focus on site specific investigations, drawing on high resolution surface data as well as detailed landslide inventories and rainfall information to provide an estimate of static landslide hazard susceptibility. Few studies have approached the issue of landslide risk and susceptibility from a dynamic standpoint to estimate the potential for landslide susceptibility conditions in a time frame that allows for a better understanding of the physical processes both scientifically and as it relates to societal response. To present a more dynamic representation of landslide hazard risk at larger spatial scales new research has developed an algorithm which couples a landslide hazard susceptibility map with real-time satellite derived rainfall to forecast areas with high landslide potential at the global scale. The algorithm draws on near-real time Tropical Rainfall Measuring Mission (TRMM) data as well as other satellite products to obtain a 3-hourly picture of locations across the world where the surface susceptibility conditions are high and the rainfall accumulation exceeds a defined threshold. The resulting forecasts are updated every 3 hours on a website, highlighting pixels satisfying these conditions on a 0.25º grid. The spatial

  3. 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.

  4. Integrating satellite and tower phenology: a case-study in real-time ecological forecasting

    NASA Astrophysics Data System (ADS)

    Dietze, M.

    2014-12-01

    Phenological transitions have large impacts on ecosystem processes, species interactions, and climate. However, phenology is a critical source of uncertainty in projections of climate change on terrestrial ecosystems and the current generation of ecosystem models are highly variable and biased in their phenology predictions. Most phenological modeling has focused on diagnosing phenological variability and predicting long term responses to climate scenarios. Phenological predictions for the current season, on the other hand, are being made based on long-term means or expert opinion rather than real data. To our knowledge previous research has not applied operational data assimilation approaches to produce operational, real-time forecasts of phenology. We present a phenology forecast data product that is automatically updated every day using current observations and weather forecasts. Specifically we fuse MODIS NDVI and PhenoCam based GCC with a threshold logistic process model at five sites across eastern forests, from North Carolina to New Hampshire. Prior to application, models were calibrated (2000-2012) using a Bayesian state space model. Forecasts for fall 2013, spring 2014, and fall 2014 were then generated on a daily basis using a particle filter. The system successfully tracked seasonal phenology but forecasts showed high uncertainty and sensitivity to alternative model structures. Furthermore, we found that current phenological models in the literature are not formulated in a way that allows for dynamic forecasts. Work remains to be done to extend this work to a fully spatial context. In particular there is a need to determine the spatial range of influence of the tower PhenoCam data and to account for both land cover and random effects. More broadly, this work demonstrates the possibilities for the development of real-time ecological forecasting in other areas.

  5. Real time air quality forecasting using integrated parametric and non-parametric regression techniques

    NASA Astrophysics Data System (ADS)

    Donnelly, Aoife; Misstear, Bruce; Broderick, Brian

    2015-02-01

    This paper presents a model for producing real time air quality forecasts with both high accuracy and high computational efficiency. Temporal variations in nitrogen dioxide (NO2) levels and historical correlations between meteorology and NO2 levels are used to estimate air quality 48 h in advance. Non-parametric kernel regression is used to produce linearized factors describing variations in concentrations with wind speed and direction and, furthermore, to produce seasonal and diurnal factors. The basis for the model is a multiple linear regression which uses these factors together with meteorological parameters and persistence as predictors. The model was calibrated at three urban sites and one rural site and the final fitted model achieved R values of between 0.62 and 0.79 for hourly forecasts and between 0.67 and 0.84 for daily maximum forecasts. Model validation using four model evaluation parameters, an index of agreement (IA), the correlation coefficient (R), the fraction of values within a factor of 2 (FAC2) and the fractional bias (FB), yielded good results. The IA for 24 hr forecasts of hourly NO2 was between 0.77 and 0.90 at urban sites and 0.74 at the rural site, while for daily maximum forecasts it was between 0.89 and 0.94 for urban sites and 0.78 for the rural site. R values of up to 0.79 and 0.81 and FAC2 values of 0.84 and 0.96 were observed for hourly and daily maximum predictions, respectively. The model requires only simple input data and very low computational resources. It found to be an accurate and efficient means of producing real time air quality forecasts.

  6. Global Near Real-Time MODIS and Landsat Flood Mapping and Product Delivery

    NASA Astrophysics Data System (ADS)

    Policelli, F. S.; Slayback, D. A.; Tokay, M. M.; Brakenridge, G. R.

    2014-12-01

    Flooding is the most destructive, frequent, and costly natural disaster faced by modern society, and is increasing in frequency and damage (deaths, displacements, and financial costs) as populations increase and climate change generates more extreme weather events. When major flooding events occur, the disaster management community needs frequently updated and easily accessible information to better understand the extent of flooding and coordinate response efforts. With funding from NASA's Applied Sciences program, we developed and are now operating a near real-time global flood mapping system to help provide flood extent information within 24-48 hours of events. The principal element of the system applies a water detection algorithm to MODIS imagery, which is processed by the LANCE (Land Atmosphere Near real-time Capability for EOS) system at NASA Goddard within a few hours of satellite overpass. Using imagery from both the Terra (10:30 AM local time overpass) and Aqua (1:30 PM) platforms allows the system to deliver an initial daily assessment of flood extent by late afternoon, and more robust assessments after accumulating cloud-free imagery over several days. Cloud cover is the primary limitation in detecting surface water from MODIS imagery. Other issues include the relatively coarse scale of the MODIS imagery (250 meters) for some events, the difficulty of detecting flood waters in areas with continuous canopy cover, confusion of shadow (cloud or terrain) with water, and accurately identifying detected water as flood as opposed to normal water extent. We are working on improvements to address these limitations. We have also begun delivery of near real time water maps at 30 m resolution from Landsat imagery. Although Landsat is not available daily globally, but only every 8 days if imagery from both operating platforms (Landsat 7 and 8) is accessed, it can provide useful higher resolution data on water extent when a clear acquisition coincides with an active

  7. Improving real-time inflow forecasting into hydropower reservoirs through a complementary modelling framework

    NASA Astrophysics Data System (ADS)

    Gragne, A. S.; Sharma, A.; Mehrotra, R.; Alfredsen, K.

    2015-08-01

    Accuracy of reservoir inflow forecasts is instrumental for maximizing the value of water resources and benefits gained through hydropower generation. Improving hourly reservoir inflow forecasts over a 24 h lead time is considered within the day-ahead (Elspot) market of the Nordic exchange market. A complementary modelling framework presents an approach for improving real-time forecasting without needing to modify the pre-existing forecasting model, but instead formulating an independent additive or complementary model that captures the structure the existing operational model may be missing. We present here the application of this principle for issuing improved hourly inflow forecasts into hydropower reservoirs over extended lead times, and the parameter estimation procedure reformulated to deal with bias, persistence and heteroscedasticity. The procedure presented comprises an error model added on top of an unalterable constant parameter conceptual model. This procedure is applied in the 207 km2 Krinsvatn catchment in central Norway. The structure of the error model is established based on attributes of the residual time series from the conceptual model. Besides improving forecast skills of operational models, the approach estimates the uncertainty in the complementary model structure and produces probabilistic inflow forecasts that entrain suitable information for reducing uncertainty in the decision-making processes in hydropower systems operation. Deterministic and probabilistic evaluations revealed an overall significant improvement in forecast accuracy for lead times up to 17 h. Evaluation of the percentage of observations bracketed in the forecasted 95 % confidence interval indicated that the degree of success in containing 95 % of the observations varies across seasons and hydrologic years.

  8. A centralized real-time controller for the reservoir's management on the Seine River using ensemble weather forecasting

    NASA Astrophysics Data System (ADS)

    Ficchi, Andrea; Raso, Luciano; Jay-Allemand, Maxime; Dorchies, David; Malaterre, Pierre-Olivier; Pianosi, Francesca; Van Overloop, Peter-Jules

    2013-04-01

    The reservoirs on the Seine River, upstream of Paris, are regulated with the objective of reducing floods and supporting low flows. The current management of these reservoirs is empirical, reactive, and decentralized, mainly based on filling curves, constructed from an analysis of historical floods and low flows. When inflows are significantly different from their seasonal average, this management strategy proves inefficient. Climate change is also a challenge, for the possible modification of future hydrologic conditions. To improve such management strategy, in this study we investigate the use of Tree-Based Model Predictive Control (TB-MPC), a proactive and centralized method that uses all the information available in real-time, including ensemble weather forecasting. In TB-MPC, a tree is generated from an ensemble of weather forecast. The tree structure summarizes the information contained in the ensemble, specifying the time, along the optimization horizon, when forecast trajectories diverge and thus uncertainty is expected to be resolved. This information is then used in the model predictive control framework. The TB-MPC controller is implemented in combination with the integrated model of the water system, including a semi-distributed hydrologic model of the watershed, a simplified hydraulic model of the river network, and the four reservoir models. Optimization takes into account the cost associated to floods and low-flows, and a penalty cost based on the final reservoir storages. The performances of the TB-MPC controller will be simulated and compared with those of deterministic MPC and with the actual management performances. This work is part of the Climaware European project (2010-2013) set up to develop and to assess measures for sustainable water resources management regarding adaptation to climate change.

  9. Cyberinfrastructure to support Real-time, End-to-End, High Resolution, Localized Forecasting

    NASA Astrophysics Data System (ADS)

    Ramamurthy, M. K.; Lindholm, D.; Baltzer, T.; Domenico, B.

    2004-12-01

    From natural disasters such as flooding and forest fires to man-made disasters such as toxic gas releases, the impact of weather-influenced severe events on society can be profound. Understanding, predicting, and mitigating such local, mesoscale events calls for a cyberinfrastructure to integrate multidisciplinary data, tools, and services as well as the capability to generate and use high resolution data (such as wind and precipitation) from localized models. The need for such end to end systems -- including data collection, distribution, integration, assimilation, regionalized mesoscale modeling, analysis, and visualization -- has been realized to some extent in many academic and quasi-operational environments, especially for atmospheric sciences data. However, many challenges still remain in the integration and synthesis of data from multiple sources and the development of interoperable data systems and services across those disciplines. Over the years, the Unidata Program Center has developed several tools that have either directly or indirectly facilitated these local modeling activities. For example, the community is using Unidata technologies such as the Internet Data Distribution (IDD) system, Local Data Manger (LDM), decoders, netCDF libraries, Thematic Realtime Environmental Distributed Data Services (THREDDS), and the Integrated Data Viewer (IDV) in their real-time prediction efforts. In essence, these technologies for data reception and processing, local and remote access, cataloging, and analysis and visualization coupled with technologies from others in the community are becoming the foundation of a cyberinfrastructure to support an end-to-end regional forecasting system. To build on these capabilities, the Unidata Program Center is pleased to be a significant contributor to the Linked Environments for Atmospheric Discovery (LEAD) project, a NSF-funded multi-institutional large Information Technology Research effort. The goal of LEAD is to create an

  10. Application of Snpp/viirs Data in Near Real-Time Supra-Snow Flood Detection

    NASA Astrophysics Data System (ADS)

    Li, S.; Sun, D.; Goldberg, M.; Sjoberg, B.; Plumb, E. W.; Holloway, E.; Lindsey, S.; Kreller, M.

    2015-12-01

    Supra-snow/ice flood is very common in high latitude areas from winter to spring break-up seasons along rivers flowing to even higher latitude areas, but this flood type doesn't draw much attention due to poor ground conditions for river watch and ground observations. Satellite data from SNPP/VIIRS (Suomi-National Polar-orbit Partnership/Visible/Infrared Imager Radiometer Suite) instead have shown great advantages in supra-snow/ice flood detection due to its large swath coverage, multiple daily observations in high latitude areas and moderate spatial resolution. Thus, methods for supra-snow/ice water detection were developed to detect near real-time supra-snow/ice floods automatically using SNPP/VIIRS imagery. The methods were mainly based on spectral features of supra-snow/ice floodwater, assisting by geometry-based algorithm and object-based algorithm to remove cloud shadows and terrain shadows over snow/ice surface. The detected supra-snow/ice floodwater was further applied in water fraction retrieval for better representation of flood extent using a modified histogram method based on linear combination model. The developed methods were successfully applied in dynamic monitoring of 2015's supra-snow/ice flood along Sag River in Alaska, which was claimed as a state disaster by Alaska state government, and further tested with more than 1000 VIIRS granules year around. Analyses through visual inspection with VIIRS false-color composite images and quantitative comparison with Landsat-8 OLI images show promising and robust performance in detection of supra-snow/ice floodwater, indicating a high feasibility for the method to be applied in operations for near real-time supra-snow/ice flood detection.

  11. A Real-Time Measurement System for Long-Life Flood Monitoring and Warning Applications

    PubMed Central

    Marin-Perez, Rafael; García-Pintado, Javier; Gómez, Antonio Skarmeta

    2012-01-01

    A flood warning system incorporates telemetered rainfall and flow/water level data measured at various locations in the catchment area. Real-time accurate data collection is required for this use, and sensor networks improve the system capabilities. However, existing sensor nodes struggle to satisfy the hydrological requirements in terms of autonomy, sensor hardware compatibility, reliability and long-range communication. We describe the design and development of a real-time measurement system for flood monitoring, and its deployment in a flash-flood prone 650 km2 semiarid watershed in Southern Spain. A developed low-power and long-range communication device, so-called DatalogV1, provides automatic data gathering and reliable transmission. DatalogV1 incorporates self-monitoring for adapting measurement schedules for consumption management and to capture events of interest. Two tests are used to assess the success of the development. The results show an autonomous and robust monitoring system for long-term collection of water level data in many sparse locations during flood events. PMID:22666028

  12. A real-time, event-triggered storm surge forecasting system for the state of North Carolina

    NASA Astrophysics Data System (ADS)

    Mattocks, Craig; Forbes, Cristina

    A new real-time, event-triggered storm surge prediction system has been developed for the State of North Carolina to assist emergency managers, policy-makers and other government officials with evacuation planning, decision-making and resource deployment during tropical storm landfall and flood inundation events. The North Carolina Forecast System (NCFS) was designed and built to provide a rapid response assessment of hurricane threat, accomplished by driving a high-resolution, two-dimensional, depth-integrated version of the ADCIRC (Advanced Circulation) coastal ocean model with winds from a synthetic asymmetric gradient wind vortex. These parametric winds, calculated at exact finite-element mesh node locations and directly coupled to the ocean model at every time step, are generated from National Hurricane Center (NHC) forecast advisories the moment they are inserted into the real-time weather data stream, maximizing the number of hours of forecast utility. Tidal harmonic constituents are prescribed at the open water boundaries and applied as tidal potentials in the interior of the ocean model domain. A directional surface roughness parameterization that modulates the wind speed at a given location based on the types of land cover encountered upwind, a forest canopy sheltering effect, and a spatially varying distribution of Manning's-n friction coefficient used for computing the bottom/channel bed friction are also included in the storm surge model. Comparisons of the simulated wind speeds and phases against their real meteorological counterparts, of model elevations against actual sea surface elevations measured by NOAA tide gauges along the NC coast, and of simulated depth-averaged current velocities against Acoustic Doppler Current Profiler (ADCP) data, indicate that this new system produces remarkably realistic predictions of winds and storm surge.

  13. Data-Driven Geospatial Visual Analytics for Real-Time Urban Flooding Decision Support

    NASA Astrophysics Data System (ADS)

    Liu, Y.; Hill, D.; Rodriguez, A.; Marini, L.; Kooper, R.; Myers, J.; Wu, X.; Minsker, B. S.

    2009-12-01

    Urban flooding is responsible for the loss of life and property as well as the release of pathogens and other pollutants into the environment. Previous studies have shown that spatial distribution of intense rainfall significantly impacts the triggering and behavior of urban flooding. However, no general purpose tools yet exist for deriving rainfall data and rendering them in real-time at the resolution of hydrologic units used for analyzing urban flooding. This paper presents a new visual analytics system that derives and renders rainfall data from the NEXRAD weather radar system at the sewershed (i.e. urban hydrologic unit) scale in real-time for a Chicago stormwater management project. We introduce a lightweight Web 2.0 approach which takes advantages of scientific workflow management and publishing capabilities developed at NCSA (National Center for Supercomputing Applications), streaming data-aware semantic content management repository, web-based Google Earth/Map and time-aware KML (Keyhole Markup Language). A collection of polygon-based virtual sensors is created from the NEXRAD Level II data using spatial, temporal and thematic transformations at the sewershed level in order to produce persistent virtual rainfall data sources for the animation. Animated color-coded rainfall map in the sewershed can be played in real-time as a movie using time-aware KML inside the web browser-based Google Earth for visually analyzing the spatiotemporal patterns of the rainfall intensity in the sewershed. Such system provides valuable information for situational awareness and improved decision support during extreme storm events in an urban area. Our further work includes incorporating additional data (such as basement flooding events data) or physics-based predictive models that can be used for more integrated data-driven decision support.

  14. Global, Daily, Near Real-Time Satellite-based Flood Monitoring and Product Dissemination

    NASA Astrophysics Data System (ADS)

    Slayback, D. A.; Policelli, F. S.; Brakenridge, G. R.; Tokay, M. M.; Smith, M. M.; Kettner, A. J.

    2013-12-01

    Flooding is the most destructive, frequent, and costly natural disaster faced by modern society, and is expected to increase in frequency and damage with climate change and population growth. Some of 2013's major floods have impacted the New York City region, the Midwest, Alberta, Australia, various parts of China, Thailand, Pakistan, and central Europe. The toll of these events, in financial costs, displacement of individuals, and deaths, is substantial and continues to rise as climate change generates more extreme weather events. When these events do occur, the disaster management community requires frequently updated and easily accessible information to better understand the extent of flooding and better coordinate response efforts. With funding from NASA's Applied Sciences program, we developed and are now operating a near real-time global flood mapping system to help provide critical flood extent information within 24-48 hours of events. The system applies a water detection algorithm to MODIS imagery received from the LANCE (Land Atmosphere Near real-time Capability for EOS) system at NASA Goddard within a few hours of satellite overpass. Using imagery from both the Terra (10:30 AM local time overpass) and Aqua (1:30 PM) platforms allows an initial daily assessment of flooding extent by late afternoon, and more robust assessments after accumulating cloud-free imagery over several days. Cloud cover is the primary limitation in detecting surface water from MODIS imagery. Other issues include the relatively coarse scale of the MODIS imagery (250 meters), the difficulty of detecting flood waters in areas with continuous canopy cover, confusion of shadow (cloud or terrain) with water, and accurately identifying detected water as flood as opposed to normal water extents. We have made progress on many of these issues, and are working to develop higher resolution flood detection using alternate sensors, including Landsat and various radar sensors. Although these

  15. Web-Based Real Time Earthquake Forecasting and Personal Risk Management

    NASA Astrophysics Data System (ADS)

    Rundle, J. B.; Holliday, J. R.; Graves, W. R.; Turcotte, D. L.; Donnellan, A.

    2012-12-01

    Earthquake forecasts have been computed by a variety of countries and economies world-wide for over two decades. For the most part, forecasts have been computed for insurance, reinsurance and underwriters of catastrophe bonds. One example is the Working Group on California Earthquake Probabilities that has been responsible for the official California earthquake forecast since 1988. However, in a time of increasingly severe global financial constraints, we are now moving inexorably towards personal risk management, wherein mitigating risk is becoming the responsibility of individual members of the public. Under these circumstances, open access to a variety of web-based tools, utilities and information is a necessity. Here we describe a web-based system that has been operational since 2009 at www.openhazards.com and www.quakesim.org. Models for earthquake physics and forecasting require input data, along with model parameters. The models we consider are the Natural Time Weibull (NTW) model for regional earthquake forecasting, together with models for activation and quiescence. These models use small earthquakes ('seismicity-based models") to forecast the occurrence of large earthquakes, either through varying rates of small earthquake activity, or via an accumulation of this activity over time. These approaches use data-mining algorithms combined with the ANSS earthquake catalog. The basic idea is to compute large earthquake probabilities using the number of small earthquakes that have occurred in a region since the last large earthquake. Each of these approaches has computational challenges associated with computing forecast information in real time. Using 25 years of data from the ANSS California-Nevada catalog of earthquakes, we show that real-time forecasting is possible at a grid scale of 0.1o. We have analyzed the performance of these models using Reliability/Attributes and standard Receiver Operating Characteristic (ROC) tests. We show how the Reliability and

  16. Real-Time Flood Estimation by Using Radar Rainfall data and Distributed Rainfall-Runoff Model

    NASA Astrophysics Data System (ADS)

    Yu, P.; Chou, J.; Chiu, Y.; Yang, T.; Kuo, C.

    2011-12-01

    This study aims to establish a flood prediction model in Dajia River by using a grid-based distributed rainfall-runoff model (GDRRM) combined with the predicted QPESUMS radar rainfalls. Flood disasters caused damage to human and property. A proper flood prediction model can provide warning messages against disasters. Since the radar rainfall technology has been developed for years, it has the ability to represent the precipitation in each location. Coupling the real-time radar rainfall with the distributed rainfall-runoff model, it can be used to provide the probable flow in downstream. The study area, Dajia River basin, is located in central Taiwan. The river flow is mainly controlled by two major reservoirs, Shih-Kang Dam in downstream and Te-Chi Reservoir in upstream. Thus, three components are considered for establishing the flood prediction model. The first one is the application of radar rainfalls. The real-time and predicted (1-3hr ahead) radar rainfall data provided by the Central Weather Bureau, Taiwan were set as the input data. It can represent the actual distributed rainfalls of the basin. The second one is the estimation of reservoir inflow by using a GDRRM. The basin was divided into 1234 regular grids (1km by 1km) to exhibit the heterogeneity in the watershed. The parameters of GDRRM were generated by using DEM, Formosat-2 satellite image and soil map to represent the actual geography and physiography of each grid. With the input of real-time and predicted radar rainfall data, the inflow of Te-Chi Reservoir and Shih-Kang Dam can be calculated by using two GDRRMs respectively. The third one is the operation rules of reservoir used for simulating the outflow of the reservoirs during the flow simulation. Then, the downstream (Shih-Kang Dam) GDRRM coupled with the operation rules was used to calculate the outflow of the Te-Chi Reservoir. Thus, the flood can be predicted in advance during typhoon period. The results revealed that the flood prediction

  17. Flood and Landslide Applications of Near Real-time Satellite Rainfall Products

    NASA Technical Reports Server (NTRS)

    Hong, Yang; Adler, Robert F.; Negri, Andrew; Huffman, George J.

    2007-01-01

    Floods and associated landslides are one of the most widespread natural hazards on Earth, responsible for tens of thousands of deaths and billions of dollars in property damage every year. During 1993-2002, over 1000 of the more than 2,900 natural disasters reported were due to floods. These floods and associated landslides claimed over 90,000 lives, affected over 1.4 billion people and cost about $210 billion. The impact of these disasters is often felt most acutely in less developed regions. In many countries around the world, satellite-based precipitation estimation may be the best source of rainfall data due to lack of surface observing networks. Satellite observations can be of essential value in improving our understanding of the occurrence of hazardous events and possibly in lessening their impact on local economies and in reducing injuries, if they can be used to create reliable warning systems in cost-effective ways. This article addressed these opportunities and challenges by describing a combination of satellite-based real-time precipitation estimation with land surface characteristics as input, with empirical and numerical models to map potential of landslides and floods. In this article, a framework to detect floods and landslides related to heavy rain events in near-real-time is proposed. Key components of the framework are: a fine resolution precipitation acquisition system; a comprehensive land surface database; a hydrological modeling component; and landslide and debris flow model components. A key precipitation input dataset for the integrated applications is the NASA TRMM-based multi-satellite precipitation estimates. This dataset provides near real-time precipitation at a spatial-temporal resolution of 3 hours and 0.25deg x 0.25deg. By careful integration of remote sensing and in-situ observations, and assimilation of these observations into hydrological and landslide/debris flow models with surface topographic information, prediction of useful

  18. Generating Real-Time Tsunami Forecast Animations for Tsunami Warning Operations

    NASA Astrophysics Data System (ADS)

    Becker, N. C.; Wang, D.; Fryer, G. J.; Weinstein, S.

    2012-12-01

    The complex calculations inherent in tsunami forecast models once required supercomputers to solve and could only be deployed in an operational setting as a database of precomputed best-guess solutions for likely future tsunamis. More recently scientists at the Pacific Tsunami Warning Center (PTWC) developed a tsunami forecast model, RIFT, that takes an earthquake's centroid moment tensor solution—either from nearby historic events or rapidly determined by W-phase analysis—and solves the linear shallow water equations in real time with commercial off-the-shelf computer servers and open-source software tools (Wang et al., 2009). RIFT not only rapidly calculates tsunami forecasts in real time, but also generates and archives data grids easily ingested by other software packages to generate maps and animations in a variety of image, video, and geobrowser file formats (e.g., KML). These graphical products aid both operational and outreach efforts as they help PTWC scientists to rapidly ingest and comprehend large, complex data sets, to share these data with emergency managers, and to educate the general public about the behavior of tsunamis. Prior to developing animation capability PTWC used tsunami travel time contour maps to show expected arrival times of the first tsunami waves. Though useful to expert users, such maps can mislead a nonexpert as they do not show amplitude information and give the impression that tsunami waves have constant amplitudes throughout an ocean basin. A tsunami forecast "energy map" improves tsunami hazard communication by showing the variability in maximum wave heights, but does not show the timing of the maximum wave arrivals. A tsunami forecast animation, however, shows both how fast the tsunami will move and the distribution of its amplitudes over time, thus communicating key concepts about tsunami behavior such as reflection and refraction of waves, that the first arriving wave is not necessarily the largest wave, and that tsunami

  19. Experimental Real-time Seasonal Hydrologic Forecasting for the Columbia River Basin

    NASA Astrophysics Data System (ADS)

    Lettenmaier, D. P.; Wood, A. W.; Hamlet, A. F.; Kumar, A.

    2001-12-01

    We describe an "end-to-end" streamflow forecasting approach that uses National Centers for Atmospheric Prediction (NCEP) Global Spectral Model (GSM) climate forecasts to drive the Variable Infiltration Capacity (VIC) macroscale hydrologic model over the eastern U.S. and Columbia River basins. A key aspect of the method is a downscaling approach that compensates for bias in the atmospheric model ensembles, and downscales from the temporal (monthly aggregates) and spatial (two degrees latitude by longitude) of the atmospheric model to the finer temporal (daily) and spatial (one-eighth degree) resolution at which the hydrologic model is run. The method has been applied on an experimental basis during summers of 2000 in the eastern U.S., and the summer of 2001 in the Columbia River basin, both periods of severe drought in the respective regions. Each month during the test periods, 21-member global precipitation and temperature ensembles were provided by the NCEP Climate Modeling Branch, along with a rolling 240-member set of climatology ensembles. Initial hydrologic conditions for the forecasts were estimated using archived data streams produced by the Land Data Assimilation System (LDAS) retrospective data sets, updated for the most recent three months using archived real-time LDAS forcings. Forecasts were produced on an approximately monthly update cycle beginning in April 2000 for the eastern U.S., and April 2001 for the Columbia basin. In the Columbia, the basin-wide land surface initial conditions for forecasts made during the spring were quite sensitive to the real-time datastream used for hydrologic model initialization, and various methods were used to improve the reliability of the forcing data, especially in data-sparse mountainous regions. The forecast simulations predicted, among other effects, the migration of the center of the most severe area of soil moisture deficits in the eastern U.S. in 2000, and the severe deficit in snowpack, runoff and soil

  20. Real-time flash flood prediction system in the United States

    NASA Astrophysics Data System (ADS)

    Gourley, Jonathan; Flamig, Zachary; Vergara, Humberto; Clark, Robert; Kirstetter, Pierre; Terti, Galatia; Hong, Yang; Howard, Kenneth

    2015-04-01

    This study describes the Flooded Locations and Simulated Hydrographs (FLASH) project in the U.S. that has been designed to develop, evaluate, and transition the latest rainfall-driven flash flood prediction tools to the National Weather Service (NWS). The FLASH system is based on radar-based rainfall estimates from the Multi-Radar Multi-Sensor (MRMS) system, providing mosaicked rainfall rates at 1-km/2-min resolution across the conterminous U.S. Products include rainfall average recurrence intervals (ARIs), ratios of rainfall to flash flood guidance (FFG) values, and simulated streamflow ARIs from a distributed hydrologic modeling framework. NWS forecasters evaluated each of these experimental tools during the inaugural Hazardous Weather Testbed Hydrology experiment (HWT-Hydro) in the summer of 2014. In additional to a subjective evaluation from HWT-Hydro, we present findings from an objective evaluation of the distributed hydrologic modeling approaches that incorporate recent developments in a-priori parameter estimation, improvements in channel routing and model physics, model reanalysis leveraging datasets from the Multi-Year Reanalysis Of Remotely-Sensed Storms (MYRORSS) project, and error models developed for output products. FLASH skill is assessed using a very large dataset of observed flooding as measured by US Geologic Survey (USGS) stream gauges. In addition to the evaluation, this presentation will provide an overview of the latest advances in the FLASH project including forthcoming impact-specific products, probabilistic forecasts, and forcing from short-term precipitation forecasts.

  1. Adapting CALIPSO Climate Measurements for Near Real Time Analyses and Forecasting

    NASA Technical Reports Server (NTRS)

    Vaughan, Mark A.; Trepte, Charles R.; Winker, David M.; Avery, Melody A.; Campbell, James; Hoff, Ray; Young, Stuart; Getzewich, Brian J.; Tackett, Jason L.; Kar, Jayanta

    2011-01-01

    The Cloud-Aerosol Lidar and Infrared Pathfinder satellite Observations (CALIPSO) mission was originally conceived and designed as a climate measurements mission, with considerable latency between data acquisition and the release of the level 1 and level 2 data products. However, the unique nature of the CALIPSO lidar backscatter profiles quickly led to the qualitative use of CALIPSO?s near real time (i.e., ? expedited?) lidar data imagery in several different forecasting applications. To enable quantitative use of their near real time analyses, the CALIPSO project recently expanded their expedited data catalog to include all of the standard level 1 and level 2 lidar data products. Also included is a new cloud cleared level 1.5 profile product developed for use by operational forecast centers for verification of aerosol predictions. This paper describes the architecture and content of the CALIPSO expedited data products. The fidelity and accuracy of the expedited products are assessed via comparisons to the standard CALIPSO data products.

  2. A Real-time Irrigation Forecasting System in Jiefangzha Irrigation District, China

    NASA Astrophysics Data System (ADS)

    Cong, Z.

    2015-12-01

    In order to improve the irrigation efficiency, we need to know when and how much to irrigate in real time. If we know the soil moisture content at this time, we can forecast the soil moisture content in the next days based on the rainfall forecasting and the crop evapotranspiration forecasting. Then the irrigation should be considered when the forecasting soil moisture content reaches to a threshold. Jiefangzha Irrigation District, a part of Hetao Irrigation District, is located in Inner Mongolia, China. The irrigated area of this irrigation district is about 140,000 ha mainly planting wheat, maize and sunflower. The annual precipitation is below 200mm, so the irrigation is necessary and the irrigation water comes from the Yellow river. We set up 10 sites with 4 TDR sensors at each site (20cm, 40cm, 60cm and 80cm depth) to monitor the soil moisture content. The weather forecasting data are downloaded from the website of European Centre for Medium-Range Weather Forecasts (ECMWF). The reference evapotranspiration is estimated based on FAO-Blaney-Criddle equation with only the air temperature from ECMWF. Then the crop water requirement is forecasted by the crop coefficient multiplying the reference evapotranspiration. Finally, the soil moisture content is forecasted based on soil water balance with the initial condition is set as the monitoring soil moisture content. When the soil moisture content reaches to a threshold, the irrigation warning will be announced. The irrigation mount can be estimated through three ways: (1) making the soil moisture content be equal to the field capacity; (2) making the soil moisture saturated; or (3) according to the irrigation quota. The forecasting period is 10 days. The system is developed according to B2C model with Java language. All the databases and the data analysis are carried out in the server. The customers can log in the website with their own username and password then get the information about the irrigation forecasting

  3. A Real-time 3D Visualization of Global MHD Simulation for Space Weather Forecasting

    NASA Astrophysics Data System (ADS)

    Murata, K.; Matsuoka, D.; Kubo, T.; Shimazu, H.; Tanaka, T.; Fujita, S.; Watari, S.; Miyachi, H.; Yamamoto, K.; Kimura, E.; Ishikura, S.

    2006-12-01

    Recently, many satellites for communication networks and scientific observation are launched in the vicinity of the Earth (geo-space). The electromagnetic (EM) environments around the spacecraft are always influenced by the solar wind blowing from the Sun and induced electromagnetic fields. They occasionally cause various troubles or damages, such as electrification and interference, to the spacecraft. It is important to forecast the geo-space EM environment as well as the ground weather forecasting. Owing to the recent remarkable progresses of super-computer technologies, numerical simulations have become powerful research methods in the solar-terrestrial physics. For the necessity of space weather forecasting, NICT (National Institute of Information and Communications Technology) has developed a real-time global MHD simulation system of solar wind-magnetosphere-ionosphere couplings, which has been performed on a super-computer SX-6. The real-time solar wind parameters from the ACE spacecraft at every one minute are adopted as boundary conditions for the simulation. Simulation results (2-D plots) are updated every 1 minute on a NICT website. However, 3D visualization of simulation results is indispensable to forecast space weather more accurately. In the present study, we develop a real-time 3D webcite for the global MHD simulations. The 3-D visualization results of simulation results are updated every 20 minutes in the following three formats: (1)Streamlines of magnetic field lines, (2)Isosurface of temperature in the magnetosphere and (3)Isoline of conductivity and orthogonal plane of potential in the ionosphere. For the present study, we developed a 3-D viewer application working on Internet Explorer browser (ActiveX) is implemented, which was developed on the AVS/Express. Numerical data are saved in the HDF5 format data files every 1 minute. Users can easily search, retrieve and plot past simulation results (3D visualization data and numerical data) by using

  4. 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

  5. Towards Real Time Tsunami Forecasting without Source: A Data Assimilation Approach with Dense Tsunameter Network

    NASA Astrophysics Data System (ADS)

    Maeda, T.; Obara, K.; Shinohara, M.; Kanazawa, T.; Uehira, K.

    2013-12-01

    Real time tsunami observation networks with ocean bottom pressure sensors and GPS buoys are significantly improved in this decade, after several significant earthquakes such as the 2004 Sumatra-Andaman earthquake (Mw9.3) and the 2011 Off the Pacific Coast of Tohoku Earthquake (Mw9.0). In particular along the Pacific coast in northeastern Japan, a new cabled-type seismograph/tsunameter network are being under construction (e.g., Uehira et al., 2012; Saito, 2013). It will cover a vast area of roughly 400 km x 900 km including the source area of the Tohoku Earthquake, with average station separation of about 30 km. All stations are linked by ocean-bottom cables, and all data including seismic waves and tsunami heights will be collected in real time. To fully utilize power of such a dense observation network on tsunami hazard mitigation, we develop a new forecasting method that assimilates observed sea height with numerical simulation of tsunami propagation in real time. In this method, tsunami height at a certain time step is first predicted by numerical simulation and compared with the observed tsunami heights at stations. Residuals between observation and numerical forecast are used for correction of tsunami wave height and its flux. Then, the corrected wavefield are substituted for prediction of tsunami heights at the next time step by numerical simulation. We performed preliminary numerical test of the data assimilation technique with the layout of aforementioned tsunamater network in NE Japan as a feasibility study of real time tsunami forecasting. First we calculated tsunami wave propagation by the Tohoku earthquake based on the initial sea height estimated from the faulting in order to get the time series of sea height. The calculated time-series of tsunami height at the sensor locations are regarded as 'observed data' in the numerical test. Then, data assimilation is carried out for every one second to estimate the sea height based on the observed data. We

  6. Rainfall estimation for real time flood monitoring using geostationary meteorological satellite data

    NASA Astrophysics Data System (ADS)

    Veerakachen, Watcharee; Raksapatcharawong, Mongkol

    2015-09-01

    Rainfall estimation by geostationary meteorological satellite data provides good spatial and temporal resolutions. This is advantageous for real time flood monitoring and warning systems. However, a rainfall estimation algorithm developed in one region needs to be adjusted for another climatic region. This work proposes computationally-efficient rainfall estimation algorithms based on an Infrared Threshold Rainfall (ITR) method calibrated with regional ground truth. Hourly rain gauge data collected from 70 stations around the Chao-Phraya river basin were used for calibration and validation of the algorithms. The algorithm inputs were derived from FY-2E satellite observations consisting of infrared and water vapor imagery. The results were compared with the Global Satellite Mapping of Precipitation (GSMaP) near real time product (GSMaP_NRT) using the probability of detection (POD), root mean square error (RMSE) and linear correlation coefficient (CC) as performance indices. Comparison with the GSMaP_NRT product for real time monitoring purpose shows that hourly rain estimates from the proposed algorithm with the error adjustment technique (ITR_EA) offers higher POD and approximately the same RMSE and CC with less data latency.

  7. Heat wave over India during summer 2015: an assessment of real time extended range forecast

    NASA Astrophysics Data System (ADS)

    Pattanaik, D. R.; Mohapatra, M.; Srivastava, A. K.; Kumar, Arun

    2016-06-01

    Hot winds are the marked feature of summer season in India during late spring preceding the climatological onset of the monsoon season in June. Some years the conditions becomes very vulnerable with the maximum temperature (T max) exceeding 45 °C for many days over parts of north-western, eastern coastal states of India and Indo-Gangetic plain. During summer of 2015 (late May to early June) eastern coastal states, central and northwestern parts of India experienced severe heat wave conditions leading to loss of thousands of human life in extreme high temperature conditions. It is not only the loss of human life but also the animals and birds were very vulnerable to this extreme heat wave conditions. In this study, an attempt is made to assess the performance of real time extended range forecast (forecast up to 3 weeks) of this scorching T max based on the NCEP's Climate Forecast System (CFS) latest version coupled model (CFSv2). The heat wave condition was very severe during the week from 22 to 28 May with subsequent week from 29 May to 4 June also witnessed high T max over many parts of central India including eastern coastal states of India. The 8 ensemble members of operational CFSv2 model are used once in a week to prepare the weekly bias corrected deterministic (ensemble mean) T max forecast for 3 weeks valid from Friday to Thursday coinciding with the heat wave periods of 2015. Using the 8 ensemble members separately and the CFSv2 corresponding hindcast climatology the probability of above and below normal T max is also prepared for the same 3 weeks. The real time deterministic and probabilistic forecasts did indicate impending heat wave over many parts of India during late May and early June of 2015 associated with strong northwesterly wind over main land mass of India, delaying the sea breeze, leading to heat waves over eastern coastal regions of India. Thus, the capability of coupled model in providing early warning of such killer heat wave can be very

  8. Near-real-time Estimation and Forecast of Total Precipitable Water in Europe

    NASA Astrophysics Data System (ADS)

    Bartholy, J.; Kern, A.; Barcza, Z.; Pongracz, R.; Ihasz, I.; Kovacs, R.; Ferencz, C.

    2013-12-01

    Information about the amount and spatial distribution of atmospheric water vapor (or total precipitable water) is essential for understanding weather and the environment including the greenhouse effect, the climate system with its feedbacks and the hydrological cycle. Numerical weather prediction (NWP) models need accurate estimations of water vapor content to provide realistic forecasts including representation of clouds and precipitation. In the present study we introduce our research activity for the estimation and forecast of atmospheric water vapor in Central Europe using both observations and models. The Eötvös Loránd University (Hungary) operates a polar orbiting satellite receiving station in Budapest since 2002. This station receives Earth observation data from polar orbiting satellites including MODerate resolution Imaging Spectroradiometer (MODIS) Direct Broadcast (DB) data stream from satellites Terra and Aqua. The received DB MODIS data are automatically processed using freely distributed software packages. Using the IMAPP Level2 software total precipitable water is calculated operationally using two different methods. Quality of the TPW estimations is a crucial question for further application of the results, thus validation of the remotely sensed total precipitable water fields is presented using radiosonde data. In a current research project in Hungary we aim to compare different estimations of atmospheric water vapor content. Within the frame of the project we use a NWP model (DBCRAS; Direct Broadcast CIMSS Regional Assimilation System numerical weather prediction software developed by the University of Wisconsin, Madison) to forecast TPW. DBCRAS uses near real time Level2 products from the MODIS data processing chain. From the wide range of the derived Level2 products the MODIS TPW parameter found within the so-called mod07 results (Atmospheric Profiles Product) and the cloud top pressure and cloud effective emissivity parameters from the so

  9. Automating Flood Hazard Mapping Methods for Near Real-time Storm Surge Inundation and Vulnerability Assessment

    NASA Astrophysics Data System (ADS)

    Weigel, A. M.; Griffin, R.; Gallagher, D.

    2015-12-01

    Storm surge has enough destructive power to damage buildings and infrastructure, erode beaches, and threaten human life across large geographic areas, hence posing the greatest threat of all the hurricane hazards. The United States Gulf of Mexico has proven vulnerable to hurricanes as it has been hit by some of the most destructive hurricanes on record. With projected rises in sea level and increases in hurricane activity, there is a need to better understand the associated risks for disaster mitigation, preparedness, and response. GIS has become a critical tool in enhancing disaster planning, risk assessment, and emergency response by communicating spatial information through a multi-layer approach. However, there is a need for a near real-time method of identifying areas with a high risk of being impacted by storm surge. Research was conducted alongside Baron, a private industry weather enterprise, to facilitate automated modeling and visualization of storm surge inundation and vulnerability on a near real-time basis. This research successfully automated current flood hazard mapping techniques using a GIS framework written in a Python programming environment, and displayed resulting data through an Application Program Interface (API). Data used for this methodology included high resolution topography, NOAA Probabilistic Surge model outputs parsed from Rich Site Summary (RSS) feeds, and the NOAA Census tract level Social Vulnerability Index (SoVI). The development process required extensive data processing and management to provide high resolution visualizations of potential flooding and population vulnerability in a timely manner. The accuracy of the developed methodology was assessed using Hurricane Isaac as a case study, which through a USGS and NOAA partnership, contained ample data for statistical analysis. This research successfully created a fully automated, near real-time method for mapping high resolution storm surge inundation and vulnerability for the

  10. ASSESSMENT OF AN ENSEMBLE OF SEVEN REAL-TIME OZONE FORECASTS OVER EASTERN NORTH AMERICA DURING THE SUMMER OF 2004

    EPA Science Inventory

    The real-time forecasts of ozone (O3) from seven air quality forecast models (AQFMs) are statistically evaluated against observations collected during July and August of 2004 (53 days) through the Aerometric Information Retrieval Now (AIRNow) network at roughly 340 mon...

  11. 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.

  12. 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)

  13. Real-time forecast of aftershocks from a single seismic station signal

    NASA Astrophysics Data System (ADS)

    Lippiello, E.; Cirillo, A.; Godano, G.; Papadimitriou, E.; Karakostas, V.

    2016-06-01

    The evaluation of seismic hazard in the hours following large earthquakes is strongly affected by biases due to difficulties in determining earthquake location. This leads to the huge incompleteness of instrumental catalogs. Here we show that if, on the one hand, the overlap of aftershock coda waves hides many small events, on the other hand, it leads to a well-determined empirical law controlling the decay of the amplitude of the seismic signal at a given site. The fitting parameters of this law can be related to those controlling the temporal decay of the aftershock number, and it is then possible to obtain short-term postseismic occurrence probability from a single recorded seismic signal. We therefore present a novel procedure which, without requiring earthquake location, produces more accurate and almost real-time forecast, in a site of interest, directly from the signal of a seismic station installed at that site.

  14. A real time debris flow forecasting system for North Vancouver, British Columbia

    NASA Astrophysics Data System (ADS)

    Jakob, M.

    2009-04-01

    This paper details the scientific basis and results for the development of a real-time operational hydro-meteorological debris flow warning system for the North Shore Mountains of Vancouver. The main component of the warning system is a multivariate statistical model that demonstrates that the 4 week antecedent rainfall, the two day antecedent rainfall and the 48 hour storm rainfall intensity explain if a given storm will result in a debris flow or not. Discriminant functions were developed that allow real-time calculation of discriminant scores and thus the relative likelihood of a storm resulting in shallow landslides. A 48 hour forecast of spatially distributed rainfall on the North Shore Mountains is made through a high resolution climatic model generated by the Geophysical Disaster Computational Fluid Dynamic Centre at the University of British Columbia. This step is an integral part in predicting threshold exceedence well before debris flows would likely occur and thus allowing sufficient time to provide warning. The system would have two levels of warning. The first is based on a lower threshold exceedences and is called ‘Debris Flow Watch'. The second would be based on a higher threshold and is called ‘Debris Flow Warning". There will be at least several hours time to react to those exceedences before shallow landsliding is likely to occur.

  15. Forecasting Daily Patient Outflow From a Ward Having No Real-Time Clinical Data

    PubMed Central

    Tran, Truyen; Luo, Wei; Phung, Dinh; Venkatesh, Svetha

    2016-01-01

    Background: Modeling patient flow is crucial in understanding resource demand and prioritization. We study patient outflow from an open ward in an Australian hospital, where currently bed allocation is carried out by a manager relying on past experiences and looking at demand. Automatic methods that provide a reasonable estimate of total next-day discharges can aid in efficient bed management. The challenges in building such methods lie in dealing with large amounts of discharge noise introduced by the nonlinear nature of hospital procedures, and the nonavailability of real-time clinical information in wards. Objective Our study investigates different models to forecast the total number of next-day discharges from an open ward having no real-time clinical data. Methods We compared 5 popular regression algorithms to model total next-day discharges: (1) autoregressive integrated moving average (ARIMA), (2) the autoregressive moving average with exogenous variables (ARMAX), (3) k-nearest neighbor regression, (4) random forest regression, and (5) support vector regression. Although the autoregressive integrated moving average model relied on past 3-month discharges, nearest neighbor forecasting used median of similar discharges in the past in estimating next-day discharge. In addition, the ARMAX model used the day of the week and number of patients currently in ward as exogenous variables. For the random forest and support vector regression models, we designed a predictor set of 20 patient features and 88 ward-level features. Results Our data consisted of 12,141 patient visits over 1826 days. Forecasting quality was measured using mean forecast error, mean absolute error, symmetric mean absolute percentage error, and root mean square error. When compared with a moving average prediction model, all 5 models demonstrated superior performance with the random forests achieving 22.7% improvement in mean absolute error, for all days in the year 2014. Conclusions In the

  16. Evaluation of radar rainfall estimates and nowcasts to prevent flash flood in real time by using a road submersion warning tool

    NASA Astrophysics Data System (ADS)

    Versini, Pierre-Antoine; Sempere-Torres, Daniel

    2010-05-01

    Important damages occur in small headwater catchments when they are hit by severe storms with complex spatio-temporal structure, sometimes resulting in flash floods. As these catchments are mostly not covered by sensor networks, it is difficult to forecast these floods. This is particularly true for road submersions. These are major concerns for flood event managers. The use of Quantitative Precipitation Estimates and Forecasts (QPE/QPF) especially based on radar measurements could particularly be adequate to evaluate rainfall-induced risks. Although their characteristic time and space scales would make them suitable for flash flood modelling, the impact of their uncertainties remain uncertain and have to be evaluated. The Gard region (France) has been chosen as case study. This area is frequently affected by severe flash floods and different kinds of rainfall observations are available in real time: radar rainfall estimates and nowcasts from METEO FRANCE and the CALAMAR system from SPC (state authority in charge of flood forecasting). An application devoted to the road network, has also been recently developed for this region. It combines distributed hydro-meteorological very short range forecasts and vulnerability analysis to provide warnings of road submersions. The first results demonstrate that it is technically possible to provide distributed short-term forecasts for a large number of sites. The study also demonstrates that a reliable estimation of the spatial distribution of rainfall is essential. For this reason, the road submersion warning system can be used to evaluate the quality of rainfall estimates and nowcasts. The warning system has been tested on the specific storm of the 29-30 September 2007. During this event, more than 300mm dropped on the South part of the Gard and many roads were submerged. Each of the mentioned rainfall datasets (i.e. estimates and nowcasts) was available in real time. They have been used to forecast the exact location of

  17. Real time soil moisture forecasts for irrigation management: the Pre.G.I. project

    NASA Astrophysics Data System (ADS)

    Ceppi, A.; Ravazzani, G.; Mancini, M.; Salerno, R.

    2012-04-01

    In recent years frequent periods of water scarcity have enhanced the need to use water more carefully. Future climate change scenarios, combined with limited water resources require better irrigation management and planning for farmers' water cooperatives. This has occurred also in areas traditionally rich of water as Lombardy Region, in the North of Italy. In this study we show the development and implementation of a real-time drought forecasting system with a soil moisture hydrological alert, in particular we describe preliminary results of the Pre.G.I. Project, an Italian acronym that stands for "Hydro-Meteorological forecast for irrigation management", funded by Lombardy Region. The project develops a support decision system based on an ensemble weather prediction in the medium-long range (up to 30 days) with hydrological simulation of water balance to forecast the soil water content in every parcel over the Consorzio Muzza basin, in order to use the irrigation water in a wiser and thriftier way. The studied area covers 74,000 ha in the middle of the Po Valley, near Lodi city. The hydrological ensemble forecasts are based on 20 meteorological members of a modified version of the non-hydrostatic WRF model, with multiple nesting to scale to the region of interest. Different physical schemes are also used to take into account a larger variability; these data are provided by Epson Meteo Centre. The hydrological model used to generate the soil moisture and water table simulations is the rainfall-runoff distributed FEST-WB model, developed at Politecnico di Milano. The analysis shows the system reliability based on most significant case-studies occurred in the recent years.

  18. The JPL GRIP Portal - Serving Near Real-time Observation and Model Forecast for Hurricane Study

    NASA Astrophysics Data System (ADS)

    Li, P.; Hristova-Veleva, S. M.; Turk, F. J.; Vu, Q.; Knosp, B. W.; Lambrigtsen, B.; Poulsen, W. L.; Shen, T. J.; Licata, S. J.

    2010-12-01

    NASA conducted a field experiment, the Genesis and Rapid Intensification Processes (GRIP), in the summer of 2010 to better understand how tropical storms form and develop into major hurricanes. The DC-8 aircraft and the Global Hawk Unmanned Airborne System (UAS) were deployed loaded with instruments for measurements including lightning, temperature, 3D wind, precipitation, liquid and ice water contents, aerosol and cloud profiles. JPL created a web portal to collect, process and display both the satellite and the airborne observations in near real-time (NRT) and integrated then with the hurricane forecast models. The objective of the JPL GRIP portal is to provide environmental context and temporal continuity for the field campaign observations to help: (1) mission planning, (2) understanding of the physical processes, and (3) improving models through validation and data assimilation. Built on top of the JPL Tropical Cyclone Information System (TCIS) infrastructure, we developed a GRIP portal presenting a near-real time (NRT) basin-scale view of the atmospheric and surface conditions over the Atlantic, characterizing large-scale and storm-scale processes, as depicted by satellites and models. Using Google Earth embedded in the web browser and two independent calendars, we provide 3D visualization of a comprehensive collection of observations and model results as overlapping image overlays, wind vectors, curtain plots, or clickable tracks. We also provide Google Earth time animations of multiple data and model variables. In the portal, we offer more than two dozen NRT satellite products from a wide variety of instruments, model forecasts from four large-scale models (i.e., NOGAPS, GFS, ECMWF, and UKMET), and the best tracks and the forecast tracks from National Hurricane Center’s ATCF models. As they become available, we also display the airborne observations from HAMSR, APR2 and Dropsonde. It is a great challenge to set up a reliable infrastructure to collect data

  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. A Real-Time MODIS Vegetation Composite for Land Surface Models and Short-Term Forecasting

    NASA Technical Reports Server (NTRS)

    Case, Jonathan L.; LaFontaine, Frank J.; Kumar, Sujay V.; Jedlovec, Gary J.

    2011-01-01

    The NASA Short-term Prediction Research and Transition (SPoRT) Center is producing real-time, 1- km resolution Normalized Difference Vegetation Index (NDVI) gridded composites over a Continental U.S. domain. These composites are updated daily based on swath data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the polar orbiting NASA Aqua and Terra satellites, with a product time lag of about one day. A simple time-weighting algorithm is applied to the NDVI swath data that queries the previous 20 days of data to ensure a continuous grid of data populated at all pixels. The daily composites exhibited good continuity both spatially and temporally during June and July 2010. The composites also nicely depicted high greenness anomalies that resulted from significant rainfall over southwestern Texas, Mexico, and New Mexico during July due to early-season tropical cyclone activity. The SPoRT Center is in the process of computing greenness vegetation fraction (GVF) composites from the MODIS NDVI data at the same spatial and temporal resolution for use in the NASA Land Information System (LIS). The new daily GVF dataset would replace the monthly climatological GVF database (based on Advanced Very High Resolution Radiometer [AVHRR] observations from 1992-93) currently available to the Noah land surface model (LSM) in both LIS and the public version of the Weather Research and Forecasting (WRF) model. The much higher spatial resolution (1 km versus 0.15 degree) and daily updates based on real-time satellite observations have the capability to greatly improve the simulation of the surface energy budget in the Noah LSM within LIS and WRF. Once code is developed in LIS to incorporate the daily updated GVFs, the SPoRT Center will conduct simulation sensitivity experiments to quantify the impacts and improvements realized by the MODIS real-time GVF data. This presentation will describe the methodology used to develop the 1-km MODIS NDVI composites and

  1. Real-time tsunami inundation forecasting and damage mapping towards enhancing tsunami disaster resiliency

    NASA Astrophysics Data System (ADS)

    Koshimura, S.; Hino, R.; Ohta, Y.; Kobayashi, H.; Musa, A.; Murashima, Y.

    2014-12-01

    With use of modern computing power and advanced sensor networks, a project is underway to establish a new system of real-time tsunami inundation forecasting, damage estimation and mapping to enhance society's resilience in the aftermath of major tsunami disaster. The system consists of fusion of real-time crustal deformation monitoring/fault model estimation by Ohta et al. (2012), high-performance real-time tsunami propagation/inundation modeling with NEC's vector supercomputer SX-ACE, damage/loss estimation models (Koshimura et al., 2013), and geo-informatics. After a major (near field) earthquake is triggered, the first response of the system is to identify the tsunami source model by applying RAPiD Algorithm (Ohta et al., 2012) to observed RTK-GPS time series at GEONET sites in Japan. As performed in the data obtained during the 2011 Tohoku event, we assume less than 10 minutes as the acquisition time of the source model. Given the tsunami source, the system moves on to running tsunami propagation and inundation model which was optimized on the vector supercomputer SX-ACE to acquire the estimation of time series of tsunami at offshore/coastal tide gauges to determine tsunami travel and arrival time, extent of inundation zone, maximum flow depth distribution. The implemented tsunami numerical model is based on the non-linear shallow-water equations discretized by finite difference method. The merged bathymetry and topography grids are prepared with 10 m resolution to better estimate the tsunami inland penetration. Given the maximum flow depth distribution, the system performs GIS analysis to determine the numbers of exposed population and structures using census data, then estimates the numbers of potential death and damaged structures by applying tsunami fragility curve (Koshimura et al., 2013). Since the tsunami source model is determined, the model is supposed to complete the estimation within 10 minutes. The results are disseminated as mapping products to

  2. Global and Regional Real-time Systems for Flood and Drought Monitoring and Prediction

    NASA Astrophysics Data System (ADS)

    Hong, Y.; Gourley, J. J.; Xue, X.; Flamig, Z.

    2015-12-01

    A Hydrometeorological Extreme Mapping and Prediction System (HyXtreme-MaP), initially built upon the Coupled Routing and Excess STorage (CREST) distributed hydrological model, is driven by real-time quasi-global TRMM/GPM satellites and by the US Multi-Radar Multi-Sensor (MRMS) radar network with dual-polarimetric upgrade to simulate streamflow, actual ET, soil moisture and other hydrologic variables at 1/8th degree resolution quasi-globally (http://eos.ou.edu) and at 250-meter 2.5-mintue resolution over the Continental United States (CONUS: http://flash.ou.edu).­ Multifaceted and collaborative by-design, this end-to-end research framework aims to not only integrate data, models, and applications but also brings people together (i.e., NOAA, NASA, University researchers, and end-users). This presentation will review the progresses, challenges and opportunities of such HyXTREME-MaP System used to monitor global floods and droughts, and also to predict flash floods over the CONUS.

  3. 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.

  4. Cloud-Based Numerical Weather Prediction for Near Real-Time Forecasting and Disaster Response

    NASA Technical Reports Server (NTRS)

    Molthan, Andrew; Case, Jonathan; Venners, Jason; Schroeder, Richard; Checchi, Milton; Zavodsky, Bradley; Limaye, Ashutosh; O'Brien, Raymond

    2015-01-01

    The use of cloud computing resources continues to grow within the public and private sector components of the weather enterprise as users become more familiar with cloud-computing concepts, and competition among service providers continues to reduce costs and other barriers to entry. Cloud resources can also provide capabilities similar to high-performance computing environments, supporting multi-node systems required for near real-time, regional weather predictions. Referred to as "Infrastructure as a Service", or IaaS, the use of cloud-based computing hardware in an on-demand payment system allows for rapid deployment of a modeling system in environments lacking access to a large, supercomputing infrastructure. Use of IaaS capabilities to support regional weather prediction may be of particular interest to developing countries that have not yet established large supercomputing resources, but would otherwise benefit from a regional weather forecasting capability. Recently, collaborators from NASA Marshall Space Flight Center and Ames Research Center have developed a scripted, on-demand capability for launching the NOAA/NWS Science and Training Resource Center (STRC) Environmental Modeling System (EMS), which includes pre-compiled binaries of the latest version of the Weather Research and Forecasting (WRF) model. The WRF-EMS provides scripting for downloading appropriate initial and boundary conditions from global models, along with higher-resolution vegetation, land surface, and sea surface temperature data sets provided by the NASA Short-term Prediction Research and Transition (SPoRT) Center. This presentation will provide an overview of the modeling system capabilities and benchmarks performed on the Amazon Elastic Compute Cloud (EC2) environment. In addition, the presentation will discuss future opportunities to deploy the system in support of weather prediction in developing countries supported by NASA's SERVIR Project, which provides capacity building

  5. [Real-time irrigation forecast of cotton mulched with plastic film under drip irrigation based on meteorological date].

    PubMed

    Shen, Xiao-jun; Sun, Jing-sheng; Li, Ming-si; Zhang, Ji-yang; Wang, Jing-lei; Li, Dong-wei

    2015-02-01

    It is important to improve the real-time irrigation forecasting precision by predicting real-time water consumption of cotton mulched with plastic film under drip irrigation based on meteorological data and cotton growth status. The model parameters for calculating ET0 based on Hargreaves formula were determined using historical meteorological data from 1953 to 2008 in Shihezi reclamation area. According to the field experimental data of growing season in 2009-2010, the model of computing crop coefficient Kc was established based on accumulated temperature. On the basis of crop water requirement (ET0) and Kc, a real-time irrigation forecast model was finally constructed, and it was verified by the field experimental data in 2011. The results showed that the forecast model had high forecasting precision, and the average absolute values of relative error between the predicted value and measured value were about 3.7%, 2.4% and 1.6% during seedling, squaring and blossom-boll forming stages, respectively. The forecast model could be used to modify the predicted values in time according to the real-time meteorological data and to guide the water management in local film-mulched cotton field under drip irrigation. PMID:26094459

  6. Improvement in cloud predictions using satellite data assimilation for real-time forecasting

    NASA Astrophysics Data System (ADS)

    Vellore, R.; Koracin, D.; Wetzel, M.

    2006-12-01

    adiabatic lapse rate; (b) the second step is to compute the cloud top height using cloud base temperature, and the satellite- derived cloud top temperature following the wet adiabatic lapse rate in the cloud layer; (c) the third step is to obtain a representative lapse rate for the computing domain; (d) the fourth step is to compute the cloud top heights for the individual satellite pixels in the entire domain. The information on cloud top height and cloud top temperature obtained from the cloudy pixels is then dynamically assimilated into the model analysis using Cressman's objective analysis. Using the improved model analyses, a deterministic forecast will be carried out with an option of four-dimensional data assimilation of model winds and thermodynamic variables for a pre- forecast period of one complete diurnal cycle. Verification will be carried out using the hourly surface observations and cloud base measurements, and also using the satellite cloud imagery against the simulated cloud imagery and associated cloud products. The data assimilation of the derived cloud products is being tested in modeling systems such as the Mesoscale Model 5 (MM5) and the Weather Research Forecasting Model (WRF). The data assimilation of cloud products and verification is intended for the pre-processing module in a real-time forecasting system using various objective analysis procedures such as the Cressman-type, multi-quadric and 3DVAR. This study is to develop an efficient forecasting system to support naval aircraft and rotorcraft operations at the Fallon Naval Air Station, Fallon, Nevada.

  7. Detiding DART® Buoy Data for Real-Time Extraction of Source Coefficients for Operational Tsunami Forecasting

    NASA Astrophysics Data System (ADS)

    Percival, Donald B.; Denbo, Donald W.; Eblé, Marie C.; Gica, Edison; Huang, Paul Y.; Mofjeld, Harold O.; Spillane, Michael C.; Titov, Vasily V.; Tolkova, Elena I.

    2015-06-01

    US Tsunami Warning Centers use real-time bottom pressure (BP) data transmitted from a network of buoys deployed in the Pacific and Atlantic Oceans to tune source coefficients of tsunami forecast models. For accurate coefficients and therefore forecasts, tides and background noise at the buoys must be accounted for through detiding. In this study, five methods for coefficient estimation are compared, each of which handles detiding differently. The first three subtract off a tidal prediction based on (1) a localized harmonic analysis involving 29 days of data immediately preceding the tsunami event, (2) 68 preexisting harmonic constituents specific to each buoy, and (3) an empirical orthogonal function fit to the previous 25 h of data. Method (4) is a Kalman smoother that uses method (1) as its input. These four methods estimate source coefficients after detiding. Method (5) estimates the coefficients simultaneously with a two-component harmonic model that accounts for the tides. The five methods are evaluated using archived data from 11 DART® buoys, to which selected artificial tsunami signals are superimposed. These buoys represent a full range of observed tidal conditions and background BP noise in the Pacific and Atlantic, and the artificial signals have a variety of patterns and induce varying signal-to-noise ratios. The root-mean-square errors (RMSEs) of least squares estimates of source coefficients using varying amounts of data are used to compare the five detiding methods. The RMSE varies over two orders of magnitude among detiding methods, generally decreasing in the order listed, with method (5) yielding the most accurate estimate of the source coefficient. The RMSE is substantially reduced by waiting for the first full wave of the tsunami signal to arrive. As a case study, the five methods are compared using data recorded from the devastating 2011 Japan tsunami.

  8. 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

  9. Ensemble Data Assimilation with HSPF for Improved Real-Time Water Quality Forecasting

    NASA Astrophysics Data System (ADS)

    Kim, S.; Riazi, H.; rafieei nasab, A.; Shin, C.; Seo, D.

    2013-05-01

    An ensemble data assimilation (DA) procedure for the Hydrologic Simulation Program - Fortran (HSPF) model has been developed, tested and evaluated for implementation in real-time water quality forecasting. The procedure, referred to herein as MLEF-HSPF, uses maximum likelihood ensemble filter (MLEF) which combines strengths of variational assimilation (VAR) and ensemble Kalman filter (EnKF). To evaluate the procedure, MLEF-HSPF was run daily for a 2-yr period for the Kumho River Subbasin of the Nakdong River Basin in Korea. A set of performance measures was used to assess the marginal value of DA-aided predictions of stream flow and water quality variables such as water temperature, dissolved oxygen (DO), biochemical oxygen demand (BOD), ammonium (NH4), nitrate (NO3), phosphate (PO4) and chlorophyll a. Due to large dimensionality of the state vector and complexity of the biochemical processes involved, DA with HSPF poses additional challenges. In this presentation, we describe MLEF-HSPF, summarize the evaluation results and identify the challenges.

  10. Multiple indices method for real-time tsunami inundation forecast using a dense offshore observation network

    NASA Astrophysics Data System (ADS)

    Yamamoto, N.; Aoi, S.; Hirata, K.; Suzuki, W.; Kunugi, T.; Nakamura, H.

    2015-12-01

    We started to develop a new methodology for real-time tsunami inundation forecast system (Aoi et al., 2015, this meeting) using densely offshore tsunami observations of the Seafloor Observation Network for Earthquakes and Tsunamis (S-net), which is under construction along the Japan Trench (Kanazawa et al., 2012, JpGU; Uehira et al., 2015, IUGG). In our method, the most important concept is involving any type and/or form uncertainties in the tsunami forecast, which cannot be dealt with any of standard linear/nonlinear least square approaches. We first prepare a Tsunami Scenario Bank (TSB), which contains offshore tsunami waveforms at the S-net stations and tsunami inundation information calculated from any possible tsunami source. We then quickly select several acceptable tsunami scenarios that can explain offshore observations by using multiple indices and appropriate thresholds, after a tsunami occurrence. At that time, possible tsunami inundations coupled with selected scenarios are forecasted (Yamamoto et al., 2014, AGU). Currently, we define three indices: correlation coefficient and two variance reductions, whose L2-norm part is normalized either by observations or calculations (Suzuki et al., 2015, JpGU; Yamamoto et al., 2015, IUGG). In this study, we construct the TSB, which contains various tsunami source models prepared for the probabilistic tsunami hazard assessment in the Japan Trench region (Hirata et al., 2014, AGU). To evaluate the propriety of our method, we adopt the fault model based on the 2011 Tohoku earthquake as a pseudo "observation". We also calculate three indices using coastal maximum tsunami height distributions between observation and calculation. We then obtain the correlation between coastal and offshore indices. We notice that the index value of coastal maximum tsunami heights is closer to 1 than the index value of offshore waveforms, i.e., the coastal maximum tsunami height may be predictable within appropriate thresholds defined for

  11. 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.

  12. Evaluation of Real-time Hurricane Forecasts Using the Advanced Hurricane WRF Model for the 2007 Atlantic Hurricane Season.

    NASA Astrophysics Data System (ADS)

    Done, J. M.

    2007-12-01

    Real-time forecasts have been conducted with the Advanced Hurricane WRF Model (AHW) for named storms of the 2007 Atlantic hurricane season. Taking advantage of increased computational power over previous years, 5- day forecasts are conducted daily using three domains; two nests of 4km and 1.3km grid-spacing track the vortex within a fixed parent domain of 12km grid-spacing. In this presentation, forecast accuracy in terms of track and intensity will be presented. The quality of the forecast storm intensity can vary dramatically between storms, and sometimes between successive forecasts of a given storm. This variability in model performance is explored by analyzing the statistics of the observed and model storm intensities for the 2007 hurricane season. Conditions under which the model performs poorly are identified and a series of sensitivity simulations highlight aspects of the modeling system to which the forecast intensity is most sensitive.

  13. Real-time Specification and Forecasting for HF Links During Disturbed Conditions

    NASA Astrophysics Data System (ADS)

    Rice, D.; Hunsuker, R. D.; Eccles, J.; Sojka, J. J.

    2004-05-01

    The HF communications community has long been dependent on climatological ionosphere descriptions to support HF propagation programs. Additionally, these programs include solar zenith angle and frequency-squared variation of HF absorption but do not include space weather effects due to solar x-ray events and sporadic E layers. The usefulness of real-time specification and forecasting of HF links is desired in programs such as Operational Space Environment Network Display (OpSend). The creation of HF illumination maps requires proper specifications of D, E and F regions of the ionosphere. We present results and validation efforts of the Data-Driven D region (DDDR) model of HF absorption for mid-latitude HF paths. The DDDR programs assimilate real-time data such as the NOAA/GOES 12 x-ray measurements to produce space weather related absorption predictions. The data-driven model is being validated with observations from the HF Investigation of D-Region Ionospheric Variation Experiment (HIDIVE). Monitoring of standard time-frequency HF stations has been employed for the past three decades. The passive monitoring technique used in HIDIVE was mainly applied for studies of the high-latitude and equatorial ionosphere, thus long-term, quantitative data on the mid-latitude ionosphere are difficult to find in archival literature. HIDIVE is a careful examination of long-term observations HF absorption to study seasonal variation and space weather events. Simultaneous continuous measurements of NOAA/GOES 12 solar x-ray flux and calibrated HF signal strength were initiated in December 2002 to provide validation data for the DDDR model. Continuous recording of transmissions of standard time-frequency stations (WWV and WWVH) over the range of 2.5 to 20.0 MHz and 5-minute averages of 1.0 to 8.0 nm solar x-ray flux have been studied for 35 solar flares ranging from Class C to Class X from March through August 2003 during the descending phase of solar cycle 23. The monitoring

  14. Effects of Real-Time NASA Vegetation Data on Model Forecasts of Severe Weather

    NASA Technical Reports Server (NTRS)

    Case, Jonathan L.; Bell, Jordan R.; LaFontaine, Frank J.; Peters-Lidard, Christa D.

    2012-01-01

    The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed a Greenness Vegetation Fraction (GVF) dataset, which is updated daily using swaths of Normalized Difference Vegetation Index data from the Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA-EOS Aqua and Terra satellites. NASA SPoRT started generating daily real-time GVF composites at 1-km resolution over the Continental United States beginning 1 June 2010. A companion poster presentation (Bell et al.) primarily focuses on impact results in an offline configuration of the Noah land surface model (LSM) for the 2010 warm season, comparing the SPoRT/MODIS GVF dataset to the current operational monthly climatology GVF available within the National Centers for Environmental Prediction (NCEP) and Weather Research and Forecasting (WRF) models. This paper/presentation primarily focuses on individual case studies of severe weather events to determine the impacts and possible improvements by using the real-time, high-resolution SPoRT-MODIS GVFs in place of the coarser-resolution NCEP climatological GVFs in model simulations. The NASA-Unified WRF (NU-WRF) modeling system is employed to conduct the sensitivity simulations of individual events. The NU-WRF is an integrated modeling system based on the Advanced Research WRF dynamical core that is designed to represents aerosol, cloud, precipitation, and land processes at satellite-resolved scales in a coupled simulation environment. For this experiment, the coupling between the NASA Land Information System (LIS) and the WRF model is utilized to measure the impacts of the daily SPoRT/MODIS versus the monthly NCEP climatology GVFs. First, a spin-up run of the LIS is integrated for two years using the Noah LSM to ensure that the land surface fields reach an equilibrium state on the 4-km grid mesh used. Next, the spin-up LIS is run in two separate modes beginning on 1 June 2010, one continuing with the climatology GVFs while the

  15. 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.

  16. A Real-time, Two-way, Coupled, Refined, Forecasting System to Predict Coastal Storm Impacts

    NASA Astrophysics Data System (ADS)

    Armstrong, B. N.; Warner, J. C.; Signell, R. P.

    2012-12-01

    Storms are one of the primary environmental forces causing coastal change. These discrete events often produce large waves, storm surges, and flooding, resulting in coastal erosion. In addition, strong storm-generated currents may pose threats to life, property, and navigation. The ability to predict these events, their location, duration, and magnitude allows resource managers to better prepare for the storm impacts as well as guide post-storm survey assessments and recovery efforts. As a step towards increasing our event prediction capability we have developed an automated system to run a daily forecast of the Coupled Ocean - Atmosphere - Wave - Sediment Transport (COAWST) Modeling System, which includes the ocean model ROMS and the wave model SWAN. Management of the system is controlled on a high-performance computing cluster. Data required to drive the modeling system include wave, wind, atmospheric surface inputs, and climatology fields obtained from other modeling products. The Unidata Internet Data Distribution/Local Data Manager, nctoolbox and other NetCDF tools are used to access large data sets from the National Centers for Environmental Prediction (NCEP) and the Nomads http://nomads.ncep.noaa.gov site. The data are used to create forcings and boundary conditions for the ROMS and SWAN models that run on both a 5 km US east coast and a 1 km nested region in the Gulf of Maine. Improvements in the modeling system, data acquisition, and visualization methods required for the forecasting system are described. Results of the newly coupled and refined system show improvement for the prediction of the free surface due to the increased resolution from the grid refinement in the Bay of Fundy. The surface currents of the refined system are more consistent with climatology. The surface waves are permitted to interact with the surface currents and show tidal oscillations at certain locations. Additionally, wave heights during storm events are modified by wave

  17. Development of Real-time Tsunami Inundation Forecast Using Ocean Bottom Tsunami Networks along the Japan Trench

    NASA Astrophysics Data System (ADS)

    Aoi, S.; Yamamoto, N.; Suzuki, W.; Hirata, K.; Nakamura, H.; Kunugi, T.; Kubo, T.; Maeda, T.

    2015-12-01

    In the 2011 Tohoku earthquake, in which huge tsunami claimed a great deal of lives, the initial tsunami forecast based on hypocenter information estimated using seismic data on land were greatly underestimated. From this lesson, NIED is now constructing S-net (Seafloor Observation Network for Earthquakes and Tsunamis along the Japan Trench) which consists of 150 ocean bottom observatories with seismometers and pressure gauges (tsunamimeters) linked by fiber optic cables. To take full advantage of S-net, we develop a new methodology of real-time tsunami inundation forecast using ocean bottom observation data and construct a prototype system that implements the developed forecasting method for the Pacific coast of Chiba prefecture (Sotobo area). We employ a database-based approach because inundation is a strongly non-linear phenomenon and its calculation costs are rather heavy. We prepare tsunami scenario bank in advance, by constructing the possible tsunami sources, and calculating the tsunami waveforms at S-net stations, coastal tsunami heights and tsunami inundation on land. To calculate the inundation for target Sotobo area, we construct the 10-m-mesh precise elevation model with coastal structures. Based on the sensitivities analyses, we construct the tsunami scenario bank that efficiently covers possible tsunami scenarios affecting the Sotobo area. A real-time forecast is carried out by selecting several possible scenarios which can well explain real-time tsunami data observed at S-net from tsunami scenario bank. An advantage of our method is that tsunami inundations are estimated directly from the actual tsunami data without any source information, which may have large estimation errors. In addition to the forecast system, we develop Web services, APIs, and smartphone applications and brush them up through social experiments to provide the real-time tsunami observation and forecast information in easy way to understand toward urging people to evacuate.

  18. 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

  19. Numerical modelling for real-time forecasting of marine oil pollution and hazard assessment

    NASA Astrophysics Data System (ADS)

    De Dominicis, Michela; Pinardi, Nadia; Bruciaferri, Diego; Liubartseva, Svitlana

    2015-04-01

    (MEDESS4MS) system, which is an integrated operational multi-model oil spill prediction service, that can be used by different users to run simulations of oil spills at sea, even in real time, through a web portal. The MEDESS4MS system gathers different oil spill modelling systems and data from meteorological and ocean forecasting systems, as well as operational information on response equipment, together with environmental and socio-economic sensitivity maps. MEDSLIK-II has been also used to provide an assessment of hazard stemming from operational oil ship discharges in the Southern Adriatic and Northern Ionian (SANI) Seas. Operational pollution resulting from ships consists of a movable hazard with a magnitude that changes dynamically as a result of a number of external parameters varying in space and time (temperature, wind, sea currents). Simulations of oil releases have been performed with realistic oceanographic currents and the results show that the oil pollution hazard distribution has an inherent spatial and temporal variability related to the specific flow field variability.

  20. Real-Time Ocean Forecasting System in support of U.S. Coast Guard's Search and Rescue Operations

    NASA Astrophysics Data System (ADS)

    Schoch, C.; Chao, Y.; Howlett, E.; Allen, A. A.

    2012-12-01

    This talk will describe a real-time ocean forecasting system off the U.S. west coast developed to enhance U.S. Coast Guard (USCG) decision support tools for search and rescue operations. The forecasting model is based on the Regional Ocean Modeling System (ROMS) with multi-domain nested configurations. A multi-scale 3-dimensional variational (3DVAR) data assimilation scheme is used to assimilate both in situ (e.g., gliders) and remotely sensed data from both satellite and land-based platforms (e.g., high-frequency (HF) radars). The performance of this real-time ocean forecasting system was evaluated during a two-week field experiment during July-August 2009 in Prince William Sound, Alaska. The 72-hour ocean forecast fields in Alaska's Prince William Sound and California coastal ocean are now produced in real-time and accessible by the USCG's decision support tool during search and rescue operations. Recent test results using the independent data collected by the USCG will be discussed.

  1. Operational Precipitation prediction in Support of Real-Time Flash Flood Prediction and Reservoir Management

    NASA Astrophysics Data System (ADS)

    Georgakakos, K. P.

    2006-05-01

    The presentation will outline the implementation and performance evaluation of a number of national and international projects pertaining to operational precipitation estimation and prediction in the context of hydrologic warning systems and reservoir management support. In all cases, uncertainty measures of the estimates and predictions are an integral part of the precipitation models. Outstanding research issues whose resolution is likely to lead to improvements in the operational environment are presented. The presentation draws from the experience of the Hydrologic Research Center (http://www.hrc-lab.org) prototype implementation projects at the Panama Canal, Central America, Northern California, and South-Central US. References: Carpenter, T.M, and K.P. Georgakakos, "Discretization Scale Dependencies of the Ensemble Flow Range versus Catchment Area Relationship in Distributed Hydrologic Modeling," Journal of Hydrology, 2006, in press. Carpenter, T.M., and K.P. Georgakakos, "Impacts of Parametric and Radar Rainfall Uncertainty on the Ensemble Streamflow Simulations of a Distributed Hydrologic Model," Journal of Hydrology, 298, 202-221, 2004. Georgakakos, K.P., Graham, N.E., Carpenter, T.M., Georgakakos, A.P., and H. Yao, "Integrating Climate- Hydrology Forecasts and Multi-Objective Reservoir Management in Northern California," EOS, 86(12), 122,127, 2005. Georgakakos, K.P., and J.A. Sperfslage, "Operational Rainfall and Flow Forecasting for the Panama Canal Watershed," in The Rio Chagres: A Multidisciplinary Profile of a Tropical Watershed, R.S. Harmon, ed., Kluwer Academic Publishers, The Netherlands, Chapter 16, 323-334, 2005. Georgakakos, K. P., "Analytical results for operational flash flood guidance," Journal of Hydrology, doi:10.1016/j.jhydrol.2005.05.009, 2005.

  2. Developing Real-Time Emissions Estimates for Enhanced Air Quality Forecasting

    EPA Science Inventory

    Exploring the relationship between ambient temperature, energy demand, and electric generating unit point source emissions and potential techniques for incorporating real-time information on the modulating effects of these variables using the Mid-Atlantic/Northeast Visibility Uni...

  3. Real-time global flood estimation using satellite-based precipitation and a coupled land surface and routing model

    SciTech Connect

    Wu, Huan; Adler, Robert F.; Tian, Yudong; Huffman, George J.; Li, Hongyi; Wang, JianJian

    2014-03-01

    A widely used land surface model, the Variable Infiltration Capacity (VIC) model, is coupled with a newly developed hierarchical dominant river tracing-based runoff-routing model to form the Dominant river tracing-Routing Integrated with VIC Environment (DRIVE) model, which serves as the new core of the real-time Global Flood Monitoring System (GFMS). The GFMS uses real-time satellite-based precipitation to derive flood monitoring parameters for the latitude band 50°N–50°S at relatively high spatial (~12 km) and temporal (3 hourly) resolution. Examples of model results for recent flood events are computed using the real-time GFMS (http://flood.umd.edu). To evaluate the accuracy of the new GFMS, the DRIVE model is run retrospectively for 15 years using both research-quality and real-time satellite precipitation products. Evaluation results are slightly better for the research-quality input and significantly better for longer duration events (3 day events versus 1 day events). Basins with fewer dams tend to provide lower false alarm ratios. For events longer than three days in areas with few dams, the probability of detection is ~0.9 and the false alarm ratio is ~0.6. In general, these statistical results are better than those of the previous system. Streamflow was evaluated at 1121 river gauges across the quasi-global domain. Validation using real-time precipitation across the tropics (30°S–30°N) gives positive daily Nash-Sutcliffe Coefficients for 107 out of 375 (28%) stations with a mean of 0.19 and 51% of the same gauges at monthly scale with a mean of 0.33. Finally, there were poorer results in higher latitudes, probably due to larger errors in the satellite precipitation input.

  4. Real-Time Global Flood Estimation Using Satellite-Based Precipitation and a Coupled Land Surface and Routing Model

    NASA Technical Reports Server (NTRS)

    Wu, Huan; Adler, Robert F.; Tian, Yudong; Huffman, George J.; Li, Hongyi; Wang, JianJian

    2014-01-01

    A widely used land surface model, the Variable Infiltration Capacity (VIC) model, is coupled with a newly developed hierarchical dominant river tracing-based runoff-routing model to form the Dominant river tracing-Routing Integrated with VIC Environment (DRIVE) model, which serves as the new core of the real-time Global Flood Monitoring System (GFMS). The GFMS uses real-time satellite-based precipitation to derive flood monitoring parameters for the latitude band 50 deg. N - 50 deg. S at relatively high spatial (approximately 12 km) and temporal (3 hourly) resolution. Examples of model results for recent flood events are computed using the real-time GFMS (http://flood.umd.edu). To evaluate the accuracy of the new GFMS, the DRIVE model is run retrospectively for 15 years using both research-quality and real-time satellite precipitation products. Evaluation results are slightly better for the research-quality input and significantly better for longer duration events (3 day events versus 1 day events). Basins with fewer dams tend to provide lower false alarm ratios. For events longer than three days in areas with few dams, the probability of detection is approximately 0.9 and the false alarm ratio is approximately 0.6. In general, these statistical results are better than those of the previous system. Streamflow was evaluated at 1121 river gauges across the quasi-global domain. Validation using real-time precipitation across the tropics (30 deg. S - 30 deg. N) gives positive daily Nash-Sutcliffe Coefficients for 107 out of 375 (28%) stations with a mean of 0.19 and 51% of the same gauges at monthly scale with a mean of 0.33. There were poorer results in higher latitudes, probably due to larger errors in the satellite precipitation input.

  5. 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

  6. 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.

  7. 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

  8. 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.

  9. A Satellite Driven Real-time Forecasting Platform in the Upper Zambezi Basin: A Multi-model Comparison

    NASA Astrophysics Data System (ADS)

    Valdes, J. B.; Wi, S.; Serrat-Capdevila, A.; Demaria, E. M.; Durcik, M.

    2015-12-01

    In large basins such as the Upper Zambezi where concentration times are of many days or even weeks, satellite precipitation products available in real-time become a key component enabling - with the use of hydrologic models - streamflow forecasts for downstream locations with enough lead time to inform decision-making. We present a real-time streamflow forecasting application based on this concept, using the TMPA and CMORPH rainfall products (which we bias-correct using the CHIRPS product) to force four distributed hydrologic models (VIC, HyMod, HBV, Sacramento) covering a variety of levels of model complexity. This study aims at establishing a multi-model satellite-based streamflow forecasting platform as a tool that can inform water management in real-time. This work is part of the efforts of the SERVIR Applied Sciences Team to bring NASA Earth Observation Applications into decision support tools for managing water resources in the Upper Zambezi, in collaboration with the Southern African Development Community Climate Services Center and the Zambezi Watercourse Commission.

  10. Recent development for near-field tsunami forecasting based on real time GNSS and offshore tsunami data

    NASA Astrophysics Data System (ADS)

    Ohta, Y.; Tsushima, H.; Kawamoto, S.; Miyagawa, K.; Yahagi, T.; Sato, Y.; Hino, R.; Demachi, T.; Iinuma, T.; Miura, S.

    2014-12-01

    The 2011 Tohoku earthquake and its associated tsunami clearly showed the need for an accurate tsunami early warning system. In a short time between the occurrence of earthquakes and associating tsunamis and the tsunami arrivals to near-field coastal inhabited regions, we can use many different kinds of observations for real-time tsunami forecasting. Since individual type of the observations has its advantages and disadvantages, it is strongly required to make use of multiple kinds of data for improving estimated size and arrival timing of imminent tsunamis by reinforcing one another. For example, the rapid analysis of short-period seismic wave data, such as earthquake early warning system in Japan will provide the first information on the size and location of an earthquake, helping issuing tsunami information immediately after earthquakes. Real-time GNSS data have an advantage over the short-time seismograms because robust estimations of location and dimension of coseismic faults can be derived from spatial patterns of permanent coseismic displacement measured by real-time GNSS data. It is one of the important lessons learnt from the 2011 Tohoku earthquake that estimation of reliable finite source fault models is indispensable in tsunami forecasting after massive earthquakes. Offshore measurements of coming tsunamis must be data most relevant to the arrival times and sizes of tsunamis along shorelines. However, it takes more time to obtain credible spatial distribution of tsunami wave height from the observations due to much slower propagation of tsunamis than seismic waves and deformations. In the presentation, we will introduce the current status of the real-time crustal deformation monitoring system based on the GNSS data developed by Geospatial Information Authority of Japan and Tohoku University. We also briefly introduce the real-time tsunami forecasting based on the offshore tsunami data, developed by the Meteorological Research Institute of Japan

  11. 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.

  12. 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.

  13. A Real-time, Coupled, Refined Forecasting System for Coastal Prediction

    NASA Astrophysics Data System (ADS)

    Armstrong, B. N.; Warner, J. C.; Signell, R. P.

    2010-12-01

    In the coastal zone, storms are one of the primary environmental forces causing coastal change. These discrete events often produce large waves, storm surges, and flooding, resulting in coastal erosion. In addition, strong storm-generated currents may pose threats to life, property, and navigation. The ability to predict these events, their location, duration, and magnitude allows resource managers to better prepare for the storm impacts as well as guide post-storm survey assessments and recovery efforts. As a step towards increasing our capability for prediction of these events and to help us study the physical processes that occur we have developed an automated system to run components of the Coupled Ocean - Atmosphere - Wave - Sediment Transport (COAWST) Modeling System as a daily forecast. The current daily system couples Regional Ocean Model System (ROMS) and Simulation Waves Nearshore (SWAN) models to predict currents, salinity, temperature, wave height and direction, and sediment transport for the US East Coast and Gulf of Mexico on a 5 km scale. As part of the system a refined grid for the area of Cape Hatteras, NC at a resolution of 1 km is included. Management of the system is controlled by the Windows Scheduler to start Matlab® and run scripts and functions. Data required by the modeling system include daily modeled wave, wind, atmospheric surface inputs, and climatology fields. The Unidata Internet Data Distribution/Local Data Manager (http://www.unidata.ucar.edu/software/ldm/) is used to download National Centers for Environmental Prediction (NCEP) GFS global 5 degree data and NCEP NAM Conus 12km data to a local server. The Matlab “structs” tool and NJ-Toolbox (http://njtbx.sourceforge.net/njdocs/njtbxhelp/njtbxhelp.html) are used to access these large data sets on the local server as well as Wave Watch 3 (WW3) and NCEP model data sets available remotely on the Nomads http://nomads.ncep.noaa.gov site and Hybrid Coordinate Ocean Model (HYCOM) data

  14. The Central European Flood in June 2013: Experiences from a Near-Real Time Disaster Analysis in Germany

    NASA Astrophysics Data System (ADS)

    Schröter, Kai; Khazai, Bijan; Mühr, Bernhard; Elmer, Florian; Bessel, Tina; Möhrle, Stella; Dittrich, André; Kreibich, Heidi; Fohringer, Joachim; Kunz-Plapp, Tina; Trieselmann, Werner; Kunz, Michael; Merz, Bruno

    2014-05-01

    The central European flood in June 2013 once again revealed that complete flood protection is not possible. Inundations caused severe damage to buildings, infrastructure and agricultural lands. Official estimates of total damage in Germany amount to approx. 8bn € which is lower than the damage caused by the August 2002 flood - the most expensive natural hazard experienced so far in Germany. Repeated and long lasting precipitation in combination with extremely adverse preconditions induced a large scale flood event. In Germany, particularly the catchment areas of the Danube and Elbe were affected. The June 2013 flood has been the most severe flood event in terms of spatial extent and magnitude of flood peaks in Germany during the last 60 years. Large scale inundation occurred as a consequence of levee breaches near Deggendorf (Danube), Groß Rosenau and Fischbeck (Elbe). The flood has had a great impact on people, transportation and the economy. In many areas more than 50,000 thousand people were evacuated. Electrical grid and local water supply utilities failed during the floods. Furthermore, traffic was disrupted in the interregional transportation network including federal highways and long distance railways. CEDIM analysed and assessed the flood event within its current research activity on near real time forensic disaster analysis (CEDIM FDA: www.cedim.de). This contribution gives an overview about the CEDIM FDA analyses' results. It describes the key hydro-meteorological factors that triggered this extraordinary event and draws comparisons to major flood events in August 2002 and July 1954. Further, it shows the outcomes of a rapid initial impact assessment on the district level using social, economic and institutional indicators which are supplemented with information on the number of people evacuated and transportation disruptions and combined with the magnitude of the event.

  15. 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.

  16. Real-time numerical forecast of global epidemic spreading: case study of 2009 A/H1N1pdm

    PubMed Central

    2012-01-01

    Background Mathematical and computational models for infectious diseases are increasingly used to support public-health decisions; however, their reliability is currently under debate. Real-time forecasts of epidemic spread using data-driven models have been hindered by the technical challenges posed by parameter estimation and validation. Data gathered for the 2009 H1N1 influenza crisis represent an unprecedented opportunity to validate real-time model predictions and define the main success criteria for different approaches. Methods We used the Global Epidemic and Mobility Model to generate stochastic simulations of epidemic spread worldwide, yielding (among other measures) the incidence and seeding events at a daily resolution for 3,362 subpopulations in 220 countries. Using a Monte Carlo Maximum Likelihood analysis, the model provided an estimate of the seasonal transmission potential during the early phase of the H1N1 pandemic and generated ensemble forecasts for the activity peaks in the northern hemisphere in the fall/winter wave. These results were validated against the real-life surveillance data collected in 48 countries, and their robustness assessed by focusing on 1) the peak timing of the pandemic; 2) the level of spatial resolution allowed by the model; and 3) the clinical attack rate and the effectiveness of the vaccine. In addition, we studied the effect of data incompleteness on the prediction reliability. Results Real-time predictions of the peak timing are found to be in good agreement with the empirical data, showing strong robustness to data that may not be accessible in real time (such as pre-exposure immunity and adherence to vaccination campaigns), but that affect the predictions for the attack rates. The timing and spatial unfolding of the pandemic are critically sensitive to the level of mobility data integrated into the model. Conclusions Our results show that large-scale models can be used to provide valuable real-time forecasts of

  17. Draft Forecasts from Real-Time Runs of Physics-Based Models - A Road to the Future

    NASA Technical Reports Server (NTRS)

    Hesse, Michael; Rastatter, Lutz; MacNeice, Peter; Kuznetsova, Masha

    2008-01-01

    The Community Coordinated Modeling Center (CCMC) is a US inter-agency activity aiming at research in support of the generation of advanced space weather models. As one of its main functions, the CCMC provides to researchers the use of space science models, even if they are not model owners themselves. The second focus of CCMC activities is on validation and verification of space weather models, and on the transition of appropriate models to space weather forecast centers. As part of the latter activity, the CCMC develops real-time simulation systems that stress models through routine execution. A by-product of these real-time calculations is the ability to derive model products, which may be useful for space weather operators. After consultations with NOAA/SEC and with AFWA, CCMC has developed a set of tools as a first step to make real-time model output useful to forecast centers. In this presentation, we will discuss the motivation for this activity, the actions taken so far, and options for future tools from model output.

  18. Ensemble forecasting of short-term system scale irrigation demands using real-time flow data and numerical weather predictions

    NASA Astrophysics Data System (ADS)

    Perera, Kushan C.; Western, Andrew W.; Robertson, David E.; George, Biju; Nawarathna, Bandara

    2016-06-01

    Irrigation demands fluctuate in response to weather variations and a range of irrigation management decisions, which creates challenges for water supply system operators. This paper develops a method for real-time ensemble forecasting of irrigation demand and applies it to irrigation command areas of various sizes for lead times of 1 to 5 days. The ensemble forecasts are based on a deterministic time series model coupled with ensemble representations of the various inputs to that model. Forecast inputs include past flow, precipitation, and potential evapotranspiration. These inputs are variously derived from flow observations from a modernized irrigation delivery system; short-term weather forecasts derived from numerical weather prediction models and observed weather data available from automatic weather stations. The predictive performance for the ensemble spread of irrigation demand was quantified using rank histograms, the mean continuous rank probability score (CRPS), the mean CRPS reliability and the temporal mean of the ensemble root mean squared error (MRMSE). The mean forecast was evaluated using root mean squared error (RMSE), Nash-Sutcliffe model efficiency (NSE) and bias. The NSE values for evaluation periods ranged between 0.96 (1 day lead time, whole study area) and 0.42 (5 days lead time, smallest command area). Rank histograms and comparison of MRMSE, mean CRPS, mean CRPS reliability and RMSE indicated that the ensemble spread is generally a reliable representation of the forecast uncertainty for short lead times but underestimates the uncertainty for long lead times.

  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. Real-time forecasting of near-field tsunamis based on source estimation from offshore tsunami data (Invited)

    NASA Astrophysics Data System (ADS)

    Tsushima, H.; Hayashi, Y.; Maeda, K.; Yokota, T.

    2013-12-01

    Near-field tsunamis in areas close to subduction zones can reach the coast in a few tens of minutes or less, and cause loss of life as well as severe damage to houses and infrastructures in coastal communities. Real-time tsunami forecasting is one of the effective ways to mitigate tsunami disasters. Transmission of a tsunami warning based on rapid and accurate tsunami forecasting to coastal communities helps the residents to make the decisions about their evacuation behaviors. Offshore tsunami data take an important role in tsunami forecasting. Tsunamis can be detected at offshore stations earlier than at coastal sites, and the data provide direct information about the impending tsunamis. In this paper, we present a method to forecast near-field tsunamis from offshore tsunami data using inversion and tsunami amplification factor techniques. We also introduce a prototype of tsunami forecasting system in which our forecasting method is installed. Our tsunami forecasting algorithm is based on a source estimation. For the algorithm, offshore tsunami waveform data are inverted for spatial distribution of an initial sea-surface displacement, and then tsunami waveforms are synthesized from the estimated source and pre-computed Green's functions by a linear superposition to forecast tsunamis at an offshore point near a coastal site. The predicted tsunami heights at the offshore points are amplified to obtain those at coastal sites using the amplification factors derived from actual tsunami observations empirically. No assumptions concerning the fault geometry and the size of an earthquake are required in the algorithm. An empirical amplification factor includes the effect of actual topography on tsunami heights that should be difficult to be modeled by the linear combination of the Green's functions. The predictions are repeated by progressively updating the offshore tsunami waveform data. Because individual predictions can be calculated within a few minutes, tsunami

  1. Tools and Products of Real-Time Modeling: Opportunities for Space Weather Forecasting

    NASA Technical Reports Server (NTRS)

    Hesse, Michael

    2009-01-01

    The Community Coordinated Modeling Center (CCMC) is a US inter-agency activity aiming at research in support of the generation of advanced space weather models. As one of its main functions, the CCMC provides to researchers the use of space science models, even if they are not model owners themselves. The second CCMC activity is to support Space Weather forecasting at national Space Weather Forecasting Centers. This second activity involves model evaluations, model transitions to operations, and the development of draft Space Weather forecasting tools. This presentation will focus on the last element. Specifically, we will discuss present capabilities, and the potential to derive further tools. These capabilities will be interpreted in the context of a broad-based, bootstrapping activity for modern Space Weather forecasting.

  2. Real-time Aerosol Forecasting over North America using RAP-Chem and the GSI.

    NASA Astrophysics Data System (ADS)

    Pagowski, M.

    2015-12-01

    RAP-Chem is an implementation of WRF-Chem meteorology-chemistry model that is run daily at NOAA/ESRL over continental domain for air-quality forecasting. The chemical forecasts are combined with observations of species using three-dimensional variational data assimilation procedure implemented in the Gridpoint Statistical Interpolation (GSI). In the presentation we detail the method of the assimilation and show verification statistics of the model performance.

  3. Real Time Air Quality Forecasting System for a Large Industrial Facility

    NASA Astrophysics Data System (ADS)

    Radonjic, Z.; Chambers, D.; Telenta, B.; Janjic, Z.

    2012-04-01

    Forecasts of air quality are provided using a weather forecasting model coupled with an air dispersion model. The advanced mesoscale WRF- NMM (Weather Research and Forecasting - Nonhydrostatic Mesoscale Model) is set up to provide meteorological forecasts initially over a larger domain with resolution 3 by 3 km which is subsequently nested down to a smaller domain of 1 by 1 km horizontal resolution around a copper smelter in Serbia. The refined meteorological forecast is used as input to drive the CALMET/CALPUFF modeling system to predict hour by hour concentrations of the facility's key pollutant (SO2). CALMET/CALPUFF is the U.S. EPA's regulatory model for long-range transport and on a case by case basis is applied in complex terrain and shore-line settings. The CALMET/CALPUFF modeling system is accepted as a regulatory model for short-range applications in several jurisdictions in Canada. The main goal of this paper is to demonstrate the good performance of the weather model in forecasting mode with fine resolution and in complex terrain, as well as the comparison of predicted SO2 air concentrations with measurements taken at four nearby air quality ambient monitoring stations. The forecasts of SO2 concentrations are used by the facility to adjust the production schedule to avoid high level concentrations in the city and maximize production during favourable meteorological conditions. Since the facility is located in a valley, during stagnant meteorological conditions there is a potential for the build up of high concentrations of SO2. With the use of this air quality forecasting system, the facility can avoid the worst meteorological situations and reduce concentrations in the populated areas.

  4. Predictive Skill of Meteorological Drought Based on Multi-Model Ensemble Forecasts: A Real-Time Assessment

    NASA Astrophysics Data System (ADS)

    Chen, L. C.; Mo, K. C.; Zhang, Q.; Huang, J.

    2014-12-01

    Drought prediction from monthly to seasonal time scales is of critical importance to disaster mitigation, agricultural planning, and multi-purpose reservoir management. Starting in December 2012, NOAA Climate Prediction Center (CPC) has been providing operational Standardized Precipitation Index (SPI) Outlooks using the North American Multi-Model Ensemble (NMME) forecasts, to support CPC's monthly drought outlooks and briefing activities. The current NMME system consists of six model forecasts from U.S. and Canada modeling centers, including the CFSv2, CM2.1, GEOS-5, CCSM3.0, CanCM3, and CanCM4 models. In this study, we conduct an assessment of the predictive skill of meteorological drought using real-time NMME forecasts for the period from May 2012 to May 2014. The ensemble SPI forecasts are the equally weighted mean of the six model forecasts. Two performance measures, the anomaly correlation coefficient and root-mean-square errors against the observations, are used to evaluate forecast skill.Similar to the assessment based on NMME retrospective forecasts, predictive skill of monthly-mean precipitation (P) forecasts is generally low after the second month and errors vary among models. Although P forecast skill is not large, SPI predictive skill is high and the differences among models are small. The skill mainly comes from the P observations appended to the model forecasts. This factor also contributes to the similarity of SPI prediction among the six models. Still, NMME SPI ensemble forecasts have higher skill than those based on individual models or persistence, and the 6-month SPI forecasts are skillful out to four months. The three major drought events occurred during the 2012-2014 period, the 2012 Central Great Plains drought, the 2013 Upper Midwest flash drought, and 2013-2014 California drought, are used as examples to illustrate the system's strength and limitations. For precipitation-driven drought events, such as the 2012 Central Great Plains drought

  5. Using Landslide Failure Forecast Models in Near Real Time: the Mt. de La Saxe case-study

    NASA Astrophysics Data System (ADS)

    Manconi, Andrea; Giordan, Daniele

    2014-05-01

    Forecasting the occurrence of landslide phenomena in space and time is a major scientific challenge. The approaches used to forecast landslides mainly depend on the spatial scale analyzed (regional vs. local), the temporal range of forecast (long- vs. short-term), as well as the triggering factor and the landslide typology considered. By focusing on short-term forecast methods for large, deep seated slope instabilities, the potential time of failure (ToF) can be estimated by studying the evolution of the landslide deformation over time (i.e., strain rate) provided that, under constant stress conditions, landslide materials follow creep mechanism before reaching rupture. In the last decades, different procedures have been proposed to estimate ToF by considering simplified empirical and/or graphical methods applied to time series of deformation data. Fukuzono, 1985 proposed a failure forecast method based on the experience performed during large scale laboratory experiments, which were aimed at observing the kinematic evolution of a landslide induced by rain. This approach, known also as the inverse-velocity method, considers the evolution over time of the inverse value of the surface velocity (v) as an indicator of the ToF, by assuming that failure approaches while 1/v tends to zero. Here we present an innovative method to aimed at achieving failure forecast of landslide phenomena by considering near-real-time monitoring data. Starting from the inverse velocity theory, we analyze landslide surface displacements on different temporal windows, and then apply straightforward statistical methods to obtain confidence intervals on the time of failure. Our results can be relevant to support the management of early warning systems during landslide emergency conditions, also when the predefined displacement and/or velocity thresholds are exceeded. In addition, our statistical approach for the definition of confidence interval and forecast reliability can be applied also to

  6. Predictive Methods for Real-Time Control of Flood Operation of a Multireservoir System: Methodology and Comparative Study

    NASA Astrophysics Data System (ADS)

    Niewiadomska-Szynkiewicz, Ewa; Malinowski, Krzysztof; Karbowski, Andrzej

    1996-04-01

    Predictive methods for real-time flood operation of water systems consisting of reservoirs located in parallel on tributaries to the main river are presented and discussed. The aspect of conflicting individual goals of the local decision units and other objectives important from an overall point of view is taken into account. The particular attention is focused on hierarchical control structure which provides framework for organization of an on-line reservoir management problem. The important factor involved in flood control the uncertainty with respect to future inflows is taken into consideration. A case study of the upper Vistula river basin system in the southern part of Poland is presented. Simulation results based on 11 historical floods are briefly described and discussed.

  7. 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

  8. Cooperative satellite-based flood detection, mapping, and river monitoring in near real time

    NASA Technical Reports Server (NTRS)

    Brakenridge, Robert G.; Nghiem, Son V.

    2004-01-01

    The North Atlantic Oscillation (NAO), the Pacific-North American (PNA) teleconnection pattern, and the El Nino-Southern Oscillation (ENSO) combine to influence the planetary wave structure over the northern hemisphere. Floods and droughts are associated around the world with ENSO through such teleconnections, and improved flood prediction relies on understanding them better. The scientific study of floods, and consistent measurements thereof, are needed in order to allow 'Greenhouse warming' predictions about flooding to be tested, and the hydrologic effects of other phenomena such as ENSO to be evaluated. The needed tasks are: 1) detection/warning of flooding, 2) flood magnitude assessment, 3) flood inundation mapping, and 4) preservation of the record of flooding. Accomplishing these same tasks provides direct local societal benefits as well: they can save lives and reduce economic loss. We emphasize that the basic science observations need not be divorced from the immediate practical applications: both can occur together, and just as is the case for meteorological remote sensing.

  9. Real-time hydrologic probability forecasting using ensemble dressing, with application to river Rhine

    NASA Astrophysics Data System (ADS)

    Verkade, Jan; Brown, James; Reggiani, Paolo; Weerts, Albrecht

    2013-04-01

    Hydrologic forecasts are affected by inherent uncertainties. These originate from multiple sources including atmospheric forcing, hydrologic model schematisation and its parameters, and observations used in the forecasting process. Hydrologic streamflow forecasts are often produced using ensemble forcing predictions without additional hydrologic post-processing. As a result, the streamflow ensembles can be underdispersive or overconfident because the estimated predictive distribution does not take into account hydrologic uncertainties. Under the assumption that the meteorological ensemble forecasts is unbiased, "ensemble dressing" constitutes a promising method for estimating combined forcing and hydrologic uncertainties. Hydrologic uncertainties are estimated from the joint distribution of streamflow simulations and observations, whereby simulations are produced using observed meteorological forcings. Each of the predicted streamflow ensemble members is then dressed using these estimates of hydrologic uncertainties. From the dressed ensembles, the combined predictive uncertainty distribution, i.e. the probability forecast is determined. The present paper describes a study for river Rhine where ensemble dressing is applied at multiple forecasting locations. Hydrologic uncertainties are characterised using the Hydrologic Uncertainty Processor. Streamflow ensembles are produced by routing the 5 member ECMWF reforecast ensembles of precipitation and temperature through a conceptual HBV rainfall-runoff model. The ensemble members are used to create conditional estimates of hydrologic uncertainty. The posterior predictive distribution is produced by averaging probability distributions of each of the dressed ensemble members. From a record of approx. 2,900 hindcasts, a number of verification metrics is determined. These include correlation coefficient, relative mean error, the Brier skill score, the continuous ranked probability skill score, and the relative operating

  10. Real-time forecasts of tomorrow's earthquakes in California: a new mapping tool

    USGS Publications Warehouse

    Gerstenberger, Matt; Wiemer, Stefan; Jones, Lucy

    2004-01-01

    We have derived a multi-model approach to calculate time-dependent earthquake hazard resulting from earthquake clustering. This file report explains the theoretical background behind the approach, the specific details that are used in applying the method to California, as well as the statistical testing to validate the technique. We have implemented our algorithm as a real-time tool that has been automatically generating short-term hazard maps for California since May of 2002, at http://step.wr.usgs.gov

  11. Construction of Real-time Forecast System on the Boreal Summer Intraseasonal Oscillation

    NASA Astrophysics Data System (ADS)

    Kim, H.; Wheeler, M. C.; Lee, J.; Gottschalck, J.

    2013-12-01

    Hae-Jeong Kim1, Matthew C. Wheeler2, June-Yi Lee3 and Jon C. Gottschalck4 1APEC Climate Center, 12 Centum 7-ro, Haeundae-gu, Busan, 612-020, South Korea 2Centre for Australian Weather and Climate Research Bureau of Meteorology, Melbourne, Australia 3Global Monsoon Climate Laboratory, Pusan National University, Busan, Korea 4Climate Prediction Center, NOAA/National Weather Service, Washington D. C., USA *E-mail : shout@apcc21.org The boreal summer intraseasonal oscillation (BSISO) is one of the dominant mode of variability in the Asian summer monsoon and global monsoon (e.g. Webster et al., 1998; Lee et al., 2013). The BSISO influences summer monsoon onsets (e.g. Wang and Xie, 1997) and interacts with a wide range of atmospheric circulation and associated weather (e.g. Lee et al., 2011; Wang et al., 2012). In addition, the wet and dry spells of the BSISO strongly can influence extreme hydro-meteorological events, major driving forces of natural disasters (Lau and Waliser 2005). Thus, it is important to monitor and predict the BSISO. As the occurrence of and concern over extreme climate events rises, moreover, the provision of high-quality BSISO forecasts will become increasingly relevant. APCC has recently begun to provide the BSISO forecast information service at http://www.apcc21.org/eng/service/bsiso/fore/japcc030601.jsp. The forecast is contributed by the Australian Bureau of Meteorology, the US National Centers for Environmental Prediction, the European Center for Medium Range Weather Forecasts and UK Meteorology Office in cooperation with the CAS/WCRP Working Group on Numerical Experimentation (WGNE) Madden Julian Oscillation (MJO) Task Force. The APCC BSISO forecasts are displayed by newly developed indices proposed by Lee at al. (2013) that are able to overcome the limitation of the RMM index (Wheeler and Hendon, 2004) in terms of representing BSISO activity with northward propagation over off-equatorial monsoon domain. The BSISO forecast information can be

  12. 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

  13. Real-time forecasting and predictability of catastrophic failure events: from rock failure to volcanoes and earthquakes

    NASA Astrophysics Data System (ADS)

    Main, I. G.; Bell, A. F.; Naylor, M.; Atkinson, M.; Filguera, R.; Meredith, P. G.; Brantut, N.

    2012-12-01

    Accurate prediction of catastrophic brittle failure in rocks and in the Earth presents a significant challenge on theoretical and practical grounds. The governing equations are not known precisely, but are known to produce highly non-linear behavior similar to those of near-critical dynamical systems, with a large and irreducible stochastic component due to material heterogeneity. In a laboratory setting mechanical, hydraulic and rock physical properties are known to change in systematic ways prior to catastrophic failure, often with significant non-Gaussian fluctuations about the mean signal at a given time, for example in the rate of remotely-sensed acoustic emissions. The effectiveness of such signals in real-time forecasting has never been tested before in a controlled laboratory setting, and previous work has often been qualitative in nature, and subject to retrospective selection bias, though it has often been invoked as a basis in forecasting natural hazard events such as volcanoes and earthquakes. Here we describe a collaborative experiment in real-time data assimilation to explore the limits of predictability of rock failure in a best-case scenario. Data are streamed from a remote rock deformation laboratory to a user-friendly portal, where several proposed physical/stochastic models can be analysed in parallel in real time, using a variety of statistical fitting techniques, including least squares regression, maximum likelihood fitting, Markov-chain Monte-Carlo and Bayesian analysis. The results are posted and regularly updated on the web site prior to catastrophic failure, to ensure a true and and verifiable prospective test of forecasting power. Preliminary tests on synthetic data with known non-Gaussian statistics shows how forecasting power is likely to evolve in the live experiments. In general the predicted failure time does converge on the real failure time, illustrating the bias associated with the 'benefit of hindsight' in retrospective analyses

  14. JPSS application in a near real time regional numerical forecast system at CIMSS

    NASA Astrophysics Data System (ADS)

    Li, J.; Wang, P.; Han, H.; Zhu, F.; Schmit, T. J.; Goldberg, M.

    2015-12-01

    Observations from next generation of environmental sensors onboard the Suomi National Polar-Orbiting Parnership (S-NPP) and its successor, the Joint Polar Satellite System (JPSS), provide us the critical information for numerical weather forecast (NWP). How to better represent these satellite observations and how to get value added information into NWP system still need more studies. Recently scientists from Cooperative Institute of Meteorological Satellite Studies (CIMSS) at University of Wisconsin-Madison have developed a near realtime regional Satellite Data Assimilation system for Tropical storm forecasts (SDAT) (http://cimss.ssec.wisc.edu/sdat). The system is built with the community Gridpoint Statistical Interpolation (GSI) assimilation and advanced Weather Research Forecast (WRF) model. With GSI, SDAT can assimilate all operational available satellite data including GOES, AMSUA/AMSUB, HIRS, MHS, ATMS, AIRS and IASI radiances and some satellite derived products. In addition, some research products, such as hyperspectral IR retrieved temperature and moisture profiles, GOES imager atmospheric motion vector (AMV) and GOES sounder layer precipitable water (LPW), are also added into the system. Using SDAT as a research testbed, studies have been conducted to show how to improve high impact weather forecast by better handling cloud information in satellite data. Previously by collocating high spatial resolution MODIS data with hyperspectral resolution AIRS data, precise clear pixels of AIRS can be identified and some partially or thin cloud contamination from pixels can be removed by taking advantage of high spatial resolution and high accurate MODIS cloud information. The results have demonstrated that both of these strategies have greatly improved the hurricane track and intensity forecast. We recently have extended these methodologies into processing CrIS/VIIRS data. We also tested similar ideas in microwave sounders by the collocation of AMSU/MODIS and ATMS

  15. 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.

  16. A New Algorithm for Real-Time Tsunami Forecast Using a Dense Network of Cabled Ocean-Bottom Pressure Gauges

    NASA Astrophysics Data System (ADS)

    Yamamoto, N.; Aoi, S.; Hirata, K.; Kunugi, T.; Nakamura, H.; Suzuki, W.

    2014-12-01

    We started to develop a new algorithm for real-time tsunami forecast based on offshore tsunami observations with 150 cabled ocean-bottom pressure gauges of the Seafloor Observation Network for Earthquakes and Tsunamis (S-net), under construction along the Japan Trench (Kanazawa et al., 2012, JpGU; Uehira et al., 2012, AGU). The most important concept on the new algorithm is involving any type and/or form uncertainties in the tsunami forecast, which cannot be dealt with any of standard linear/nonlinear least square approaches. We first construct a tsunami scenario bank (TSB). It contains offshore tsunami waveforms at the 150 stations and maximum coastal tsunami heights, calculated using nonlinear long-wave theory with runup boundary condition from any possible tsunami sources (fault models) that affect target coastal regions. From TSB, then we quickly explore a range of several suitable tsunami scenarios, that can explain offshore observations. At the same time, maximum possible tsunami heights along the target coastlines, coupled with selected scenarios, are predicted. In the near future, it is possible to forecast real-time tsunami inundation by adding its component in TSB under the same strategy. In this study, we focus on near-field tsunami occurring off the Pacific coast of Tohoku and Hokkaido. Provisionally, we generate 1848 tsunami scenarios, prepared for a research project of nationwide Probabilistic Tsunami Hazard Assessment for Japan (Hirata et al., 2014, AOGS), to construct TSB. For a given pseudo "observed waveforms", the developing algorithm rapidly picks up an allowable range of tsunami scenarios from TSB. In this procedure, we use multiple indexes such as correlation coefficient, sum of squared residual, as well as geometric mean and geometric standard deviation in ratios of scenarios to observations. Use of multiple indexes rather than any single index as linear inversion does reinforce to obtain robust tsunami forecast.

  17. A Review of Real-Time Markov Model ENSO Forecast in 1996-2015: Why did it Forecast a Strong El Nino since March 2015?

    NASA Astrophysics Data System (ADS)

    Xue, Y.

    2015-12-01

    The Markov model for real time ENSO forecast at Climate Prediction Center of National Centers for Environmental Prediction (NCEP) is based on observed sea surface temperature, sea level from the NCEP ocean reanalysis, and pseudo wind stress from the Florida State University in 1980-1995. The Markov model is constructed in a reduced multivariate EOF (MEOF) space with 3 MEOFs. The cross-validated hindcast skill of NINO3.4 in 1980-1995 is competitive among dynamical and statistical models. The model was implemented into operation at CPC in early 2000s since it successfully forecasted the El Nino in winter 1997/98 starting from November 1996 initial conditions (I.C.). In this study, we assessed the real time forecast skill of ENSO by the Markov model in 1996-2015 and compared it with that of other operational forecast models. It is found that the Markov model has lower forecast skill of ENSO in the 2000s than that in the 1980s and 1990s, which is common among ENSO forecast models. The lower forecast skill of the Markov model in the 2000s can be attributed to weak precursor of positive heat content anomaly in the equatorial Pacific and a shorter lead time of the precursor relative to NINO3.4, both of which is related to the decadal change of ENSO. However, out of surprise, the Markov model successfully forecasted the El Nino in winter 2014/15 starting from February 2014 I.C.. In addition, the Markov model forecasted the continuation of the El Nino into the spring/summer/fall of 2015. Starting from March 2015 I.C., the Markov model forecasted a strong El Nino in winter 2015/16. This surprising long-lead forecast skill can be attributed to the positive second principal component (PC) of MEOF that leads NINO3.4 by 6-9 months, a precursor commonly seen in the 1980s and 1990s. This provided us confidence in the model forecast of a strong El Nino in winter 2015/16 that is highly consistent with the ensemble forecast of dynamical models.

  18. A Pro-active Real-time Forecasting and Decision Support System for Daily Management of Marine Works

    NASA Astrophysics Data System (ADS)

    Bollen, Mark; Leyssen, Gert; Smets, Steven; De Wachter, Tom

    2016-04-01

    Marine Works involving turbidity generating activities (eg. dredging, dredge spoil placement) can generate environmental stress in and around a project area in the form of sediment plumes causing light reduction and sedimentation. If these works are situated near sensitive habitats like sea-grass beds, coral reefs or sensitive human activities eg. aquaculture farms or water intakes, or if contaminants are present in the water soil environmental scrutiny is advised. Environmental Regulations can impose limitations to these activities in the form of turbidity thresholds, spill budgets, contaminant levels. Breaching environmental regulations can result in increased monitoring, adaptation of the works planning and production rates and ultimately in a (temporary) stop of activities all of which entail time and cost impacts for a contractor and/or client. Sediment plume behaviour is governed by the dredging process, soil properties and ambient conditions (currents, water depth) and can be modelled. Usually this is done during the preparatory EIA phase of a project, for estimation of environmental impact based on climatic scenarios. An operational forecasting tool is developed to adapt marine work schedules to the real-time circumstances and thus evade exceedance of critical threshold levels at sensitive areas. The forecasting system is based on a Python-based workflow manager with a MySQL database and a Django frontend web tool for user interaction and visualisation of the model results. The core consists of a numerical hydrodynamic model with sediment transport module (Mike21 from DHI). This model is driven by space and time varying wind fields and wave boundary conditions, and turbidity inputs (suspended sediment source terms) based on marine works production rates and soil properties. The resulting threshold analysis allows the operator to indicate potential impact at the sensitive areas and instigate an adaption of the marine work schedule if needed. In order to use

  19. Puget Sound Operational Forecast System - A Real-time Predictive Tool for Marine Resource Management and Emergency Responses

    SciTech Connect

    Yang, Zhaoqing; Khangaonkar, Tarang; Chase, Jared M.; Wang, Taiping

    2009-12-01

    To support marine ecological resource management and emergency response and to enhance scientific understanding of physical and biogeochemical processes in Puget Sound, a real-time Puget Sound Operational Forecast System (PS-OFS) was developed by the Coastal Ocean Dynamics & Ecosystem Modeling group (CODEM) of Pacific Northwest National Laboratory (PNNL). PS-OFS employs the state-of-the-art three-dimensional coastal ocean model and closely follows the standards and procedures established by National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS). PS-OFS consists of four key components supporting the Puget Sound Circulation and Transport Model (PS-CTM): data acquisition, model execution and product archive, model skill assessment, and model results dissemination. This paper provides an overview of PS-OFS and its ability to provide vital real-time oceanographic information to the Puget Sound community. PS-OFS supports pacific northwest region’s growing need for a predictive tool to assist water quality management, fish stock recovery efforts, maritime emergency response, nearshore land-use planning, and the challenge of climate change and sea level rise impacts. The structure of PS-OFS and examples of the system inputs and outputs, forecast results are presented in details.

  20. Factors controlling storm impacts on coastal barriers and beaches - A preliminary basis for near real-time forecasting

    USGS Publications Warehouse

    Morton, R.A.

    2002-01-01

    Analysis of ground conditions and meteorological and oceanographic parameters for some of the most severe Atlantic and Gulf Coast storms in the U.S. reveals the primary factors affecting morphological storm responses of beaches and barrier islands. The principal controlling factors are storm characteristics, geographic position relative to storm path, timing of storm events, duration of wave exposure, wind stress, degree of flow confinement, antecedent topography and geologic framework, sediment textures, vegetative cover, and type and density of coastal development. A classification of commonly observed storm responses demonstrates the sequential interrelations among (1) land elevations, (2) water elevations in the ocean and adjacent lagoon (if present), and (3) stages of rising water during the storm. The predictable coastal responses, in relative order from high frequency beach erosion to low frequency barrier inundation, include: beach erosion, berm migration, dune erosion, washover terrace construction, perched fan deposition, sheetwash, washover channel incision, washout formation, and forced and unforced ebb flow. Near real-time forecasting of expected storm impacts is possible if the following information is available for the coast: a detailed morphological and topographic characterization, accurate storm-surge and wave-runup models, the real-time reporting of storm parameters, accurate forecasts of the storm position relative to a particular coastal segment, and a conceptual model of geological processes that encompasses observed morphological changes caused by extreme storms.

  1. Real-time weather forecasting in the Western Mediterranean Basin: An application of the RAMS model

    NASA Astrophysics Data System (ADS)

    Gómez, I.; Caselles, V.; Estrela, M. J.

    2014-03-01

    A regional forecasting system based on the Regional Atmospheric Modeling System (RAMS) is being run at the CEAM Foundation. The model is started twice daily with a forecast range of 72 h. For the period June 2007 to August 2010 the verification of the model has been done using a series of automatic meteorological stations from the CEAM network and located within the Valencia Region (Western Mediterranean Basin). Air temperature, relative humidity and wind speed and direction of the output of the model have been compared with observations. For these variables, an operational verification has been performed by computing different statistical scores for 18 weather stations. This verification process has been carried out for each season of the year separately. As a result, it has been revealed that the model presents significant differences in the forecast of the meteorological variables analysed throughout the year. Moreover, due to the physical complexity of the area of study, the model presents different degree of accuracy between coastal and inland stations. Precipitation has also been verified by means of yes/no contingency tables as well as scatter plots. These tables have been built using 4 specific thresholds that have permitted to compute some categorical statistics. From the results found, it is shown that the precipitation forecast in the area of study is in general over-predicted, but with marked differences between the seasons of the year. Finally, dividing the available data by season of the year, has permitted us to analyze differences in the observed patterns for the magnitudes mentioned above. These results have been used to better understand the behavior of the RAMS model within the Valencia Region.

  2. Application of WRF/Chem-MADRID for real-time air quality forecasting over the Southeastern United States

    NASA Astrophysics Data System (ADS)

    Chuang, Ming-Tung; Zhang, Yang; Kang, Daiwen

    2011-11-01

    A Real-Time Air Quality Forecast (RT-AQF) system that is based on a three-dimensional air quality model provides a powerful tool to forecast air quality and advise the public with proper preventive actions. In this work, a new RT-AQF system is developed based on the online-coupled Weather Research and Forecasting model with Chemistry (WRF/Chem) with the Model of Aerosol Dynamics, Reaction, Ionization, and Dissolution (MADRID) (referred to as WRF/Chem-MADRID) and deployed in the southeastern U.S. during May-September, 2009. Max 1-h and 8-h average ozone (O 3) and 24-h average fine particulate matter (PM 2.5) are evaluated against surface observations from the AIRNow database in terms of spatial distribution, temporal variation, and domain-wide and region-specific discrete and categorical performance statistics. WRF/Chem-MADRID demonstrates good forecasting skill that is consistent with current RT-AQF models. The overpredictions of O 3 and underprediction of PM 2.5 are likely due to uncertainties in emissions such as those of biogenic volatile organic compounds (BVOCs) and ammonia, inaccuracies in simulated meteorological variables such as 2-m temperature, 10-m wind speed, and precipitation, and uncertainties in the boundary conditions. Sensitivity simulations show that the use of the online BVOC emissions can improve PM 2.5 forecast in areas with high BVOC emissions and adjusting lateral boundaries can improve domain-wide O 3 and PM 2.5 predictions. Several limitations and uncertainties are identified to further improve the model's forecasting skill.

  3. 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.

  4. Near real time Forensic Disaster Analysis of the central European flood in June 2013 in Germany: Impact and management

    NASA Astrophysics Data System (ADS)

    Khazai, Bijan; Bessel, Tina; Möhrle, Stella; Dittrich, André; Schröter, Kai; Mühr, Bernhard; Elmer, Florian; Kunz-Plapp, Tina; Trieselmann, Werner; Kunz, Michael

    2014-05-01

    Within its current research activity on near real time Forensic Disaster Analysis (FDA), researchers from the Center for Disaster Management and Risk Reduction Technology (CEDIM) aim to identify major risk drivers and to understand the root causes of disaster and infer the implications for disaster mitigation. A key component of this activity is the development of rapid assessment tools which allow for a science based estimate of disaster impacts. The central European flood in June 2013 caused in Germany severe damage to buildings, infrastructure and agricultural lands and has had a great impact on people, transportation and the economy. In many areas thousands of people were evacuated. Electrical grid and local water supply utilities failed during the floods. Furthermore, traffic was disrupted in the interregional transportation network including federal highways and long distance railways. CEDIM analysed the impact and management of the flood event within an FDA activity. An analysis on the amount and spatial distribution of flood-related Twitter messages in Germany revealed a high interest in the flood in the social media. Furthermore, an analysis of the resilience of selected affected areas in Germany has been carried out to assess the impact of the flood on the district level. The resilience indicator is based on social, economic and institutional indicators which are supplemented with information on the number of people evacuated and transportation disruptions. Combined with the magnitude of the event, an index is calculated that allows for a rapid initial but preliminary estimate of the flood impact. Results show high resilience of the administrative districts along the Danube while heavy impacts are seen along the Mulde and Elbe.

  5. Skill assessment of a real-time forecast system utilizing a coupled hydrologic and coastal hydrodynamic model during Hurricane Irene (2011)

    NASA Astrophysics Data System (ADS)

    Dresback, Kendra M.; Fleming, Jason G.; Blanton, Brian O.; Kaiser, Carola; Gourley, Jonathan J.; Tromble, Evan M.; Luettich, Richard A.; Kolar, Randall L.; Hong, Yang; Van Cooten, Suzanne; Vergara, Humberto J.; Flamig, Zac L.; Lander, Howard M.; Kelleher, Kevin E.; Nemunaitis-Monroe, Kodi L.

    2013-12-01

    Due to the devastating effects of recent hurricanes in the Gulf of Mexico (e.g., Katrina, Rita, Ike and Gustav), the development of a high-resolution, real-time, total water level prototype system has been accelerated. The fully coupled model system that includes hydrology is an extension of the ADCIRC Surge Guidance System (ASGS), and will henceforth be referred to as ASGS-STORM (Scalable, Terrestrial, Ocean, River, Meteorological) to emphasize the major processes that are represented by the system.The ASGS-STORM system incorporates tides, waves, winds, rivers and surge to produce a total water level, which provides a holistic representation of coastal flooding. ASGS-STORM was rigorously tested during Hurricane Irene, which made landfall in late August 2011 in North Carolina. All results from ASGS-STORM for the advisories were produced in real-time, forced by forecast wind and pressure fields computed using a parametric tropical cyclone model, and made available via the web. Herein, a skill assessment, analyzing wind speed and direction, significant wave heights, and total water levels, is used to evaluate ASGS-STORM's performance during Irene for three advisories and the best track from the National Hurricane Center (NHC). ASGS-STORM showed slight over-prediction for two advisories (Advisory 23 and 25) due to the over-estimation of the storm intensity. However, ASGS-STORM shows notable skill in capturing total water levels, wind speed and direction, and significant wave heights in North Carolina when utilizing Advisory 28, which had a slight shift in the track but provided a more accurate estimation of the storm intensity, along with the best track from the NHC. Results from ASGS-STORM have shown that as the forecast of the advisories improves, so does the accuracy of the models used in the study; therefore, accurate input from the weather forecast is a necessary, but not sufficient, condition to ensure the accuracy of the guidance provided by the system. While

  6. Multi-index method using offshore ocean-bottom pressure data for real-time tsunami forecast

    NASA Astrophysics Data System (ADS)

    Yamamoto, Naotaka; Aoi, Shin; Hirata, Kenji; Suzuki, Wataru; Kunugi, Takashi; Nakamura, Hiromitsu

    2016-07-01

    We developed a real-time tsunami forecast method using only pressure data collected from the bottom of the ocean via a dense offshore observation network. The key feature of the method is rapid matching between offshore tsunami observations and pre-calculated offshore tsunami spatial distributions. We first calculate the tsunami waveforms at offshore stations and the maximum coastal tsunami heights from any possible tsunami source model and register them in the proposed Tsunami Scenario Bank (TSB). When a tsunami occurs, we use multiple indices to quickly select dozens of appropriate tsunami scenarios that can explain the offshore observations. At the same time, the maximum coastal tsunami heights coupled with the selected tsunami scenarios are forecast. We apply three indices, which are the correlation coefficient and two kinds of variance reductions normalized by the L2-norm of either the observation or calculation, to match the observed spatial distributions with the pre-calculated spatial distributions in the TSB. We examine the ability of our method to select appropriate tsunami scenarios by conducting synthetic tests using a scenario based on "pseudo-observations." For these tests, we construct a tentative TSB, which contains tsunami waveforms at locations in the Seafloor Observation Network for Earthquakes and Tsunamis along the Japan Trench and maximum coastal tsunami heights, using about 2000 tsunami source models along the Japan Trench. Based on the test results, we confirm that the method can select appropriate tsunami scenarios within a certain precision by using the two kinds of variance reductions, which are sensitive to the tsunami size, and the correlation coefficient, which is sensitive to the tsunami source location. In this paper, we present the results and discuss the characteristics and behavior of the multi-index method. The addition of tsunami inundation components to the TSB is expected to enable the application of this method to real-time

  7. 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

  8. Real-time forecasting of Hong Kong beach water quality by 3D deterministic model.

    PubMed

    Chan, S N; Thoe, W; Lee, J H W

    2013-03-15

    Bacterial level (e.g. Escherichia coli) is generally adopted as the key indicator of beach water quality due to its high correlation with swimming associated illnesses. A 3D deterministic hydrodynamic model is developed to provide daily water quality forecasting for eight marine beaches in Tsuen Wan, which are only about 8 km from the Harbour Area Treatment Scheme (HATS) outfall discharging 1.4 million m(3)/d of partially-treated sewage. The fate and transport of the HATS effluent and its impact on the E. coli level at nearby beaches are studied. The model features the seamless coupling of near field jet mixing and the far field transport and dispersion of wastewater discharge from submarine outfalls, and a spatial-temporal dependent E. coli decay rate formulation specifically developed for sub-tropical Hong Kong waters. The model prediction of beach water quality has been extensively validated against field data both before and after disinfection of the HATS effluent. Compared with daily beach E. coli data during August-November 2011, the model achieves an overall accuracy of 81-91% in forecasting compliance/exceedance of beach water quality standard. The 3D deterministic model has been most valuable in the interpretation of the complex variation of beach water quality which depends on tidal level, solar radiation and other hydro-meteorological factors. The model can also be used in optimization of disinfection dosage and in emergency response situations. PMID:23337883

  9. New Measurements and Modeling Capability to Improve Real-time Forecast of Cascadia Tsunamis along U.S. West Coast

    NASA Astrophysics Data System (ADS)

    Wei, Y.; Titov, V. V.; Bernard, E. N.; Spillane, M. C.

    2014-12-01

    The tragedies of 2004 Sumatra and 2011 Tohoku tsunamis exposed the limits of our knowledge in preparing for devastating tsunamis, especially in the near field. The 1,100-km coastline of the Pacific coast of North America has tectonic and geological settings similar to Sumatra and Japan. The geological records unambiguously show that the Cascadia fault had caused devastating tsunamis in the past and this geological process will cause tsunamis in the future. Existing observational instruments along the Cascadia Subduction Zone are capable of providing tsunami data within minutes of tsunami generation. However, this strategy requires separation of the tsunami signals from the overwhelming high-frequency seismic waves produced during a strong earthquake- a real technical challenge for existing operational tsunami observational network. A new-generation of nano-resolution pressure sensors can provide high temporal resolution of the earthquake and tsunami signals without loosing precision. The nano-resolution pressure sensor offers a state-of the-science ability to separate earthquake vibrations and other oceanic noise from tsunami waveforms, paving the way for accurate, early warnings of local tsunamis. This breakthrough underwater technology has been tested and verified for a couple of micro-tsunami events (Paros et al., 2011). Real-time forecast of Cascadia tsunamis is becoming a possibility with the development of nano-tsunameter technology. The present study provides an investigation on optimizing the placement of these new sensors so that the forecast time can be shortened.. The presentation will cover the optimization of an observational array to quickly detect and forecast a tsunami generated by a strong Cascadia earthquake, including short and long rupture scenarios. Lessons learned from the 2011 Tohoku tsunami will be examined to demonstrate how we can improve the local forecast using the new technology We expect this study to provide useful guideline for

  10. 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.

  11. 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.

  12. LiDAR-Derived Flood-Inundation Maps for Real-Time Flood-Mapping Applications, Tar River Basin, North Carolina

    USGS Publications Warehouse

    Bales, Jerad D.; Wagner, Chad R.; Tighe, Kirsten C.; Terziotti, Silvia

    2007-01-01

    reaches at 0.305-meter increments for water levels ranging from bankfull to approximately the highest recorded water level at the downstream-most gage in each modeled reach. Inundated areas were identified by subtracting the water-surface elevation in each 1.5-meter by 1.5-meter grid cell from the land-surface elevation in the cell through an automated routine that was developed to identify all inundated cells hydraulically connected to the cell at the downstream-most gage in the model domain. Inundation maps showing transportation networks and orthoimagery were prepared for display on the Internet. These maps also are linked to the U.S. Geological Survey North Carolina Water Science Center real-time streamflow website. Hence, a user can determine the near real-time stage and water-surface elevation at a U.S. Geological Survey streamgage site in the Tar River basin and link directly to the flood-inundation maps for a depiction of the estimated inundated area at the current water level. Although the flood-inundation maps represent distinct boundaries of inundated areas, some uncertainties are associated with these maps. These are uncertainties in the topographic data for the hydraulic model computational grid and inundation maps, effective friction values (Manning's n), model-validation data, and forecast hydrographs, if used. The Tar River flood-inundation maps were developed by using a steady-flow hydraulic model. This assumption clearly has less of an effect on inundation maps produced for low flows than for high flows when it typically takes more time to inundate areas. A flood in which water levels peak and fall slowly most likely will result in more inundation than a similar flood in which water levels peak and fall quickly. Limitations associated with the steady-flow assumption for hydraulic modeling vary from site to site. The one-dimensional modeling approach used in this study resulted in good agreement between measurements and simulations. T

  13. A real time data acquisition system by satellite relay. [hydrology and flood measurement in New England

    NASA Technical Reports Server (NTRS)

    Cooper, S.

    1974-01-01

    The overall aim was to evaluate the future usefulness of satellites in the performance of coordination and management functions related to the operation of flood control and other multipurpose projects in New England. Results on the data collection portion of the work are presented. The principal task was to develop statistics that demonstrate the relationship between conventional means of acquiring hydrologic data and the contribution made by using the satellite and its data collection platforms. Main interest was in determining the availability, reliability and usability of the data. Significant results on DCS show that the DCP's are reliable and useful and satellite data collection appears feasible on a nationwide basis.

  14. Ocean Model Analysis and Prediction System (Ocean Maps): Operational Ocean Forecasting Base on Near Real-Time Satellite Altimetry

    NASA Astrophysics Data System (ADS)

    Brassington, G. B.

    2006-07-01

    BLU Elink> is a join t Australian governmen t initiative to develop Austr alia's f irst operational ocean forecasting system called O cean MAPS. The project has transitioned to th e implemen tation and trial phase using the infrastructure of the Bureau of Meteorology. OceanMAPS has a g lobal grid with 1/10° by 1/10° resolution in the Australian region (90E-180E, 70S- 16N) and uses the Modular Ocean Model version 4 optimised for the NEC SX6. The analysis uses an ensemb le based multi-variate optimal interpolation scheme wh ere model error cov ariances ar e der ived from a 72-member ensemble of in tra-seasonal anomalies based on a 12-year ocean only model integration. The scheme has been formulated to assimilate near real- time sea level heigh t anomalies processed from Jason-1, ENVISAT and Geosat Follow-On and profile observations including Argo, X BT and the TAO array. The operation al configuration including the data manag emen t of the near real- time observ ations is review ed.

  15. 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

  16. Evaluation of Wrf Real-Time Forecast during MC3E Period: Sensitivity of Model Configuration for Diurnal Precipitation Variation

    NASA Astrophysics Data System (ADS)

    Wu, D.; Matsui, T.; Tao, W.; Peters-Lidard, C. D.; Rienecker, M. M.; Hou, A. Y.

    2011-12-01

    The WRF-ARW model with high resolution was employed for the real-time forecast during the MC3E field campaign period (April 22 - June 6, 2011) over the SGP region. The model features new Goddard microphysics (Lang et al. 2011) and Goddard radiation schemes, and runs twice a day with 00Z and 12Z forecast cycle. Our primary goal is to examine the model's ability to simulate diurnal variation of precipitation and to identify physical processes that are essential for improving the forecast skills. The studies consisted with the comparisons among a composite of the WRF simulations during the campaign period with NLDAS (North-American Land Data Assimilation Systems) and NAM (North America Mesoscale Model) forecast. A set of the WRF simulations with different physics parameterization schemes and with different horizontal resolutions are also conducted to investigate effects of the model resolution and physics schemes on the propagating rainfall system over the SGP site. Results showed that the WRF simulation with fine (2km of grid spacing) and intermediate (6 ~ 10km of grid spacing) resolution with parameterized convective schemes could reproduce reasonable MCS propagation, thus diurnal rainfall cycles over the SGP site. However, even if using the same convective parameterization, with the coarse-resolution (18~30km of grid spacing) configuration, the WRF simulation do not capture the MCS propagation reasonably. This means that model effective resolution (10 times of grid spacing) needs to be less than 100km (i.e., 10km of grid spacing), which is close to the typical Rossby Radius of deformation in the Mid-latitude summertime disturbance (100~150km distance). In addition, hail option in the Goddard microphysics appears to be an effective option to reproduce a more realistic continental MCS structure in the WRF simulations.

  17. 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.

  18. 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.

  19. Development of a System to Generate Near Real Time Tropospheric Delay and Precipitable Water Vapor in situ at Geodetic GPS Stations, to Improve Forecasting of Severe Weather Events

    NASA Astrophysics Data System (ADS)

    Moore, A. W.; Bock, Y.; Geng, J.; Gutman, S. I.; Laber, J. L.; Morris, T.; Offield, D. G.; Small, I.; Squibb, M. B.

    2012-12-01

    We describe a system under development for generating ultra-low latency tropospheric delay and precipitable water vapor (PWV) estimates in situ at a prototype network of geodetic GPS sites in southern California, and demonstrating their utility in forecasting severe storms commonly associated with flooding and debris flow events along the west coast of North America through infusion of this meteorological data at NOAA National Weather Service (NWS) Forecast Offices and the NOAA Earth System Research Laboratory (ESRL). The first continuous geodetic GPS network was established in southern California in the early 1990s and much of it was converted to real-time (latency <1s) high-rate (1Hz) mode over the following decades. GPS stations are multi-purpose and can also provide estimates of tropospheric zenith delays, which can be converted into mm-accuracy PWV using collocated pressure and temperature measurements, the basis for GPS meteorology (Bevis et al. 1992, 1994; Duan et al. 1996) as implemented by NOAA with a nationwide distribution of about 300 GPS-Met stations providing PW estimates at subhourly resolution currently used in operational weather forecasting in the U.S. We improve upon the current paradigm of transmitting large quantities of raw data back to a central facility for processing into higher-order products. By operating semi-autonomously, each station will provide low-latency, high-fidelity and compact data products within the constraints of the narrow communications bandwidth that often occurs in the aftermath of natural disasters. The onsite ambiguity-resolved precise point positioning solutions are enabled by a power-efficient, low-cost, plug-in Geodetic Module for fusion of data from in situ sensors including GPS and a low-cost MEMS meteorological sensor package. The decreased latency (~5 minutes) PW estimates will provide the detailed knowledge of the distribution and magnitude of PW that NWS forecasters require to monitor and predict severe winter

  20. Surface Temperature Variation Prediction Model Using Real-Time Weather Forecasts

    NASA Astrophysics Data System (ADS)

    Karimi, M.; Vant-Hull, B.; Nazari, R.; Khanbilvardi, R.

    2015-12-01

    Combination of climate change and urbanization are heating up cities and putting the lives of millions of people in danger. More than half of the world's total population resides in cities and urban centers. Cities are experiencing urban Heat Island (UHI) effect. Hotter days are associated with serious health impacts, heart attaches and respiratory and cardiovascular diseases. Densely populated cities like Manhattan, New York can be affected by UHI impact much more than less populated cities. Even though many studies have been focused on the impact of UHI and temperature changes between urban and rural air temperature, not many look at the temperature variations within a city. These studies mostly use remote sensing data or typical measurements collected by local meteorological station networks. Local meteorological measurements only have local coverage and cannot be used to study the impact of UHI in a city and remote sensing data such as MODIS, LANDSAT and ASTER have with very low resolution which cannot be used for the purpose of this study. Therefore, predicting surface temperature in urban cities using weather data can be useful.Three months of Field campaign in Manhattan were used to measure spatial and temporal temperature variations within an urban setting by placing 10 fixed sensors deployed to measure temperature, relative humidity and sunlight. Fixed instrument shelters containing relative humidity, temperature and illumination sensors were mounted on lampposts in ten different locations in Manhattan (Vant-Hull et al, 2014). The shelters were fixed 3-4 meters above the ground for the period of three months from June 23 to September 20th of 2013 making measurements with the interval of 3 minutes. These high resolution temperature measurements and three months of weather data were used to predict temperature variability from weather forecasts. This study shows that the amplitude of spatial and temporal variation in temperature for each day can be predicted

  1. 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

  2. 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

  3. LAV@HAZARD: a Web-GIS Framework for Real-Time Forecasting of Lava Flow Hazards

    NASA Astrophysics Data System (ADS)

    Del Negro, C.; Bilotta, G.; Cappello, A.; Ganci, G.; Herault, A.

    2014-12-01

    Crucial to lava flow hazard assessment is the development of tools for real-time prediction of flow paths, flow advance rates, and final flow lengths. Accurate prediction of flow paths and advance rates requires not only rapid assessment of eruption conditions (especially effusion rate) but also improved models of lava flow emplacement. Here we present the LAV@HAZARD web-GIS framework, which combines spaceborne remote sensing techniques and numerical simulations for real-time forecasting of lava flow hazards. By using satellite-derived discharge rates to drive a lava flow emplacement model, LAV@HAZARD allows timely definition of parameters and maps essential for hazard assessment, including the propagation time of lava flows and the maximum run-out distance. We take advantage of the flexibility of the HOTSAT thermal monitoring system to process satellite images coming from sensors with different spatial, temporal and spectral resolutions. HOTSAT was designed to ingest infrared satellite data acquired by the MODIS and SEVIRI sensors to output hot spot location, lava thermal flux and discharge rate. We use LAV@HAZARD to merge this output with the MAGFLOW physics-based model to simulate lava flow paths and to update, in a timely manner, flow simulations. Thus, any significant changes in lava discharge rate are included in the predictions. A significant benefit in terms of computational speed was obtained thanks to the parallel implementation of MAGFLOW on graphic processing units (GPUs). All this useful information has been gathered into the LAV@HAZARD platform which, due to the high degree of interactivity, allows generation of easily readable maps and a fast way to explore alternative scenarios. We will describe and demonstrate the operation of this framework using a variety of case studies pertaining to Mt Etna, Sicily. Although this study was conducted on Mt Etna, the approach used is designed to be applicable to other volcanic areas around the world.

  4. A Real-Time Nowcast/Forecast System for Radar Electrojet Clutter Driven by Global Assimilative Models of the Ionosphere

    NASA Astrophysics Data System (ADS)

    Carrano, C. S.; Alcala, C. M.; Liang, P.; Groves, K. M.; Donatelli, D. E.; Daniell, R. E.

    2006-12-01

    -region. This deficiency is particularly pronounced during geomagnetic storm activity, when the ionospheric response deviates most from climatological behavior. The latest version of the SBR-IES tool can accept, as input, real-time specifications of the ionosphere provided by global assimilative models (e.g. PRISM or GAIM) that are currently or soon to be in operational use at AFWA. Forecasts of radar clutter can be generated using forecasts of the ionospheric state provided by the Ionospheric Forecast Model (IFM), for example. In the near future we plan to include the high resolution specification of the electric field provided by the real-time incoherent scatter radars of the Super Dual Auroral Radar Network (SuperDARN). It is expected that the use of data assimilative models to provide the background ionospheric densities, temperatures, and electric field will lead to substantially more accurate and high resolution predictions of radar electrojet clutter. Moreover, these improvements can be made without sacrificing real-time impact assessment requirements.

  5. 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

  6. Real-time bias-adjusted O 3 and PM 2.5 air quality index forecasts and their performance evaluations over the continental United States

    NASA Astrophysics Data System (ADS)

    Kang, Daiwen; Mathur, Rohit; Trivikrama Rao, S.

    2010-06-01

    The National Air Quality Forecast Capacity (NAQFC) system, which links NOAA's North American Mesoscale (NAM) meteorological model with EPA's Community Multiscale Air Quality (CMAQ) model, provided operational ozone (O 3) and experimental fine particular matter (PM 2.5) forecasts over the continental United States (CONUS) during 2008. This paper describes the implementation of a real-time Kalman Filter (KF) bias-adjustment technique to improve the accuracy of O 3 and PM 2.5 forecasts at discrete monitoring locations. The operational surface-level O 3 and PM 2.5 forecasts from the NAQFC system were post-processed by the KF bias-adjusted technique using near real-time hourly O 3 and PM 2.5 observations obtained from EPA's AIRNow measurement network. The KF bias-adjusted forecasts were created daily, providing 24-h hourly bias-adjusted forecasts for O 3 and PM 2.5 at all AIRNow monitoring sites within the CONUS domain. The bias-adjustment post-processing implemented in this study requires minimal computational cost; requiring less than 10 min of CPU on a single processor Linux machine to generate 24-h hourly bias-adjusted forecasts over the entire CONUS domain. The results show that the real-time KF bias-adjusted forecasts for both O 3 and PM 2.5 have performed as well as or even better than the previous studies when the same technique was applied to the historical O 3 and PM 2.5 time series from archived AQF in earlier years. Compared to the raw forecasts, the KF forecasts displayed significant improvement in the daily maximum 8-h O 3 and daily mean PM 2.5 forecasts in terms of both discrete (i.e., reduced errors, increased correlation coefficients, and index of agreement) and categorical (increased hit rate and decreased false alarm ratio) evaluation metrics at almost all locations during the study period in 2008.

  7. 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

  8. 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.

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

    NASA Astrophysics Data System (ADS)

    Cullmann, Johannes; Krauße, Thomas; Philipp, Andy

    -234; Cullmann, J., Schmitz, G.H., Görner, W., 2006. A new strategy for online flood forecasting in mountainous catchments. in: IAHS Red Book, vol. 303]. Merging of the singular parameter class models is done with the help of a sigmoidal weighting procedure. The new approach thus integrates all available information from the specially calibrated WaSiM-ETH class models, accounting for the different processes and dynamics governing the various event classes. For example it portrays the flood formation process with parameters accounting for the characteristics of the event class models. Implications arising from this study are demonstrated for a catchment in the Erzgebirge (Ore-mountains) in East Germany (1700 km). The computational efficiency, together with the convincing agreement between the predicted and observed flood peaks underlines the potential of the new parameterisation strategy in the context of operational real time forecasting.

  10. An integrated modeling framework for real-time irrigation scheduling: the benefit of spectroscopy and weather forecasts

    NASA Astrophysics Data System (ADS)

    Brook, Anna; Polinova, Maria; Housh, Mashor

    2016-04-01

    ). These studies have only incorporated short-term (weekly) forecasts, missing the potential benefit of the mid-term (seasonal) climate forecasts The latest progress in new data acquisition technologies (mainly in the field of Earth observation by remote sensing and imaging spectroscopy systems) as well as the state-of-the-art achievements in the fields of geographical information systems (GIS), computer science and climate and climate impact modelling enable to develop both integrated modelling and realistic spatial simulations. The present method is the use of field spectroscopy technology to keep constant monitoring of the field. The majority of previously developed decision support systems use satellite remote sensing data that provide very limited capabilities (conventional and basic parameters). The alternative is to use a more progressive technology of hyperspectral airborne or ground-based imagery data that provide an exhaustive description of the field. Nevertheless, this alternative is known to be very costly and complex. As such, we will present a low-cost imaging spectroscopy technology supported by detailed and fine-resolution field spectroscopy as a cost effective option for near field real-time monitoring tool. In order to solve the soil water balance and to predict the water irrigation volume a pedological survey is realized in the evaluation study areas.The remote sensing and field spectroscopy were applied to integrate continuous feedbacks from the field (e.g. soil moisture, organic/inorganic carbon, nitrogen, salinity, fertilizers, sulphur acid, texture; crop water-stress, plant stage, LAI , chlorophyll, biomass, yield prediction applying PROSPECT+SILT ; Fraction of Absorbed Photosynthetically Active Radiation FAPAR) estimated based on remote sensing information to minimize the errors associated with crop simulation process. A stochastic optimization model will be formulated that take into account both mid-term seasonal probabilistic climate prediction

  11. An evaluation of the real-time tropical cyclone forecast skill of the Navy Operational Global Atmospheric Prediction System in the western North Pacific

    SciTech Connect

    Fiorino, M.; Goerss, J.S.; Jensen, J.J.; Harrison, E.J. Jr. Naval Research Lab., Monterey, CA Fleet Numerical Oceanography Center, Monterey, CA ARC Professional Services Group, Inc., Landover, MD )

    1993-03-01

    The paper evaluates the meteorological quality and operational utility of the Navy Operational Global Atmospheric Prediction System (NOGAPS) in forecasting tropical cyclones. It is shown that the model can provide useful predictions of motion and formation on a real-time basis in the western North Pacific. The meterological characteristics of the NOGAPS tropical cyclone predictions are evaluated by examining the formation of low-level cyclone systems in the tropics and vortex structure in the NOGAPS analysis and verifying 72-h forecasts. The adjusted NOGAPS track forecasts showed equitable skill to the baseline aid and the dynamical model. NOGAPS successfully predicted unusual equatorward turns for several straight-running cyclones. 35 refs.

  12. An evaluation of the real-time tropical cyclone forecast skill of the Navy Operational Global Atmospheric Prediction System in the western North Pacific

    NASA Technical Reports Server (NTRS)

    Fiorino, Michael; Goerss, James S.; Jensen, Jack J.; Harrison, Edward J., Jr.

    1993-01-01

    The paper evaluates the meteorological quality and operational utility of the Navy Operational Global Atmospheric Prediction System (NOGAPS) in forecasting tropical cyclones. It is shown that the model can provide useful predictions of motion and formation on a real-time basis in the western North Pacific. The meterological characteristics of the NOGAPS tropical cyclone predictions are evaluated by examining the formation of low-level cyclone systems in the tropics and vortex structure in the NOGAPS analysis and verifying 72-h forecasts. The adjusted NOGAPS track forecasts showed equitable skill to the baseline aid and the dynamical model. NOGAPS successfully predicted unusual equatorward turns for several straight-running cyclones.

  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 electromagnetic monitoring system used for short-term earthquakes forecast related to the seismic-active Vrancea zone

    NASA Astrophysics Data System (ADS)

    Stanica, Dumitru; Armand Stanica, Dragos

    2016-04-01

    The existence of the pre-seismic electromagnetic signals related to the earthquakes is still under scientific debate and requires new reliable information about their possible inter-relationship. In this paper, to obtain new insights into the seismic active Vrancea zone (Romania), a 3-D magnetotelluric imaging has been used to strengthen the connection between the geodynamic model and a possible generation mechanism of the intermediate depth earthquakes. Consequently, it is considered that before an earthquake initiation, due to the torsion effect, a high stress reached inside the seismogenic volume that may generates dehydration and rupture processes of the rocks, associated with the fluid migration through the lithospheric faults system, what leads to the resistivity changes. These changes have been investigated by using ULF electromagnetic data recorded in real time at the Geodynamic Observatory Provita de Sus (GOPS), placed on the Carpathian Electrical Conductivity Anomaly (CECA) at about 100km far from the seismic active Vrancea zone. The daily mean distribution of the normalized function Bzn(f) = Bz(f)/Bperp(f) (where: Bz is vertical component of the geomagnetic field; Bperp is geomagnetic component perpendicular to strike; f is frequency in Hz) and its standard deviation are performed by using a FFT band-pass filter analysis in the ULF range 0.001Hz to 0.0083Hz, for which a 2-D geoelectrical structure under GOPS has been identified. To provide reliable information in anticipating the likelihood occurrence of an earthquake of Mw higher than 4, a statistical analysis based on standardized random variable equation has been used to identify the anomalous intervals on the new time series (Bzn*) carried out in a span of three years (2013-2015). The final conclusion is that the Bzn* shows a significant anomalous effect some days (weeks) before an impending earthquake and it should be used for short-term earthquakes forecast.

  15. An integrated modeling framework for real-time irrigation scheduling: the benefit of spectroscopy and weather forecasts

    NASA Astrophysics Data System (ADS)

    Brook, Anna; Polinova, Maria; Housh, Mashor

    2016-04-01

    ). These studies have only incorporated short-term (weekly) forecasts, missing the potential benefit of the mid-term (seasonal) climate forecasts The latest progress in new data acquisition technologies (mainly in the field of Earth observation by remote sensing and imaging spectroscopy systems) as well as the state-of-the-art achievements in the fields of geographical information systems (GIS), computer science and climate and climate impact modelling enable to develop both integrated modelling and realistic spatial simulations. The present method is the use of field spectroscopy technology to keep constant monitoring of the field. The majority of previously developed decision support systems use satellite remote sensing data that provide very limited capabilities (conventional and basic parameters). The alternative is to use a more progressive technology of hyperspectral airborne or ground-based imagery data that provide an exhaustive description of the field. Nevertheless, this alternative is known to be very costly and complex. As such, we will present a low-cost imaging spectroscopy technology supported by detailed and fine-resolution field spectroscopy as a cost effective option for near field real-time monitoring tool. In order to solve the soil water balance and to predict the water irrigation volume a pedological survey is realized in the evaluation study areas.The remote sensing and field spectroscopy were applied to integrate continuous feedbacks from the field (e.g. soil moisture, organic/inorganic carbon, nitrogen, salinity, fertilizers, sulphur acid, texture; crop water-stress, plant stage, LAI , chlorophyll, biomass, yield prediction applying PROSPECT+SILT ; Fraction of Absorbed Photosynthetically Active Radiation FAPAR) estimated based on remote sensing information to minimize the errors associated with crop simulation process. A stochastic optimization model will be formulated that take into account both mid-term seasonal probabilistic climate prediction

  16. Characterizing 13 Years of Surface Water Variability from MODIS-based Near Real-Time Flood Mapping Products in the Indus River, Tonle Sap Lake, and Lake Chad.

    NASA Astrophysics Data System (ADS)

    Slayback, D. A.; Brakenridge, G. R.; Policelli, F. S.

    2015-12-01

    Driven by an increase in extreme weather events in a warming world, flooding appears to be increasing in many regions. Since 2012, we have been using the twice-daily near-global observations of the two MODIS instruments to operate a near real-time flood mapping capability. Primarily intended to support disaster response efforts, our system generates daily near-global maps of flood water extent, at 250 m resolution. Although cloud cover is a challenge, the twice-daily coverage from the Terra and Aqua satellites helps to capture most major events. We use the MOD44W product (the "MODIS 250-m land-water mask") to differentiate "normal" water from flood water. Products from the system are freely available, and used by disaster response agencies and academic and industry researchers. An open question, however, is: how "normal" are recently observed floods? Destructive and — as reported by the press — record floods seem to be occurring more and more frequently. With the MODIS archive going back to 1999 (Terra satellite) and 2002 (Aqua satellite), we now have more than a decade of twice-daily near-global observations to begin answering this question. Although the 13 years of available twice-daily data (2002-2015) are not sufficient to fully characterize surface water normals (e.g., 100-year floods), we can start examining recent trends in surface water extent and flood frequency. To do so, we have back-processed our surface water product through mid-2002 (Aqua launch) for a few regions, and have used this to evaluate the variability in surface water extent and flood frequency. These results will eventually feed back into an improved characterization of flood water in our near real-time flood product. Here we will present results on trends in surface water extent and flood frequency for a few regions, including the Indus in Pakistan, the Tonle Sap lake in Cambodia, and lake Chad in Africa.

  17. 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.

  18. 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

  19. The FAST-T approach for operational, real time, short term hydrological forecasting: Results from the Betania Hydropower Reservoir case study

    NASA Astrophysics Data System (ADS)

    Domínguez, Efraín; Angarita, Hector; Rosmann, Thomas; Mendez, Zulma; Angulo, Gustavo

    2013-04-01

    A viable quantitative hydrological forecasting service is a combination of technological elements, personnel and knowledge, working together to establish a stable operational cycle of forecasts emission, dissemination and assimilation; hence, the process for establishing such system usually requires significant resources and time to reach an adequate development and integration in order to produce forecasts with acceptable levels of performance. Here are presented the results of this process for the recently implemented Operational Forecast Service for the Betania's Hydropower Reservoir - or SPHEB, located at the Upper-Magdalena River Basin (Colombia). The current scope of the SPHEB includes forecasting of water levels and discharge for the three main streams affluent to the reservoir, for lead times between +1 to +57 hours, and +1 to +10 days. The core of the SPHEB is the Flexible, Adaptive, Simple and Transient Time forecasting approach, namely FAST-T. This comprises of a set of data structures, mathematical kernel, distributed computing and network infrastructure designed to provide seamless real-time operational forecast and automatic model adjustment in case of failures in data transmission or assimilation. Among FAST-T main features are: an autonomous evaluation and detection of the most relevant information for the later configuration of forecasting models; an adaptively linearized mathematical kernel, the optimal adaptive linear combination or OALC, which provides a computationally simple and efficient algorithm for real-time applications; and finally, a meta-model catalog, containing prioritized forecast models at given stream conditions. The SPHEB is at present feed by the fraction of hydrological monitoring network installed at the basin that has telemetric capabilities via NOAA-GOES satellites (8 stages, approximately 47%) with data availability of about a 90% at one hour intervals. However, there is a dense network of 'conventional' hydro

  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. 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.

  2. Application of WRF - SWAT OpenMI 2.0 based models integration for real time hydrological modelling and forecasting

    NASA Astrophysics Data System (ADS)

    Bugaets, Andrey; Gonchukov, Leonid

    2014-05-01

    Intake of deterministic distributed hydrological models into operational water management requires intensive collection and inputting of spatial distributed climatic information in a timely manner that is both time consuming and laborious. The lead time of the data pre-processing stage could be essentially reduced by coupling of hydrological and numerical weather prediction models. This is especially important for the regions such as the South of the Russian Far East where its geographical position combined with a monsoon climate affected by typhoons and extreme heavy rains caused rapid rising of the mountain rivers water level and led to the flash flooding and enormous damage. The objective of this study is development of end-to-end workflow that executes, in a loosely coupled mode, an integrated modeling system comprised of Weather Research and Forecast (WRF) atmospheric model and Soil and Water Assessment Tool (SWAT 2012) hydrological model using OpenMI 2.0 and web-service technologies. Migration SWAT into OpenMI compliant involves reorganization of the model into a separate initialization, performing timestep and finalization functions that can be accessed from outside. To save SWAT normal behavior, the source code was separated from OpenMI-specific implementation into the static library. Modified code was assembled into dynamic library and wrapped into C# class implemented the OpenMI ILinkableComponent interface. Development of WRF OpenMI-compliant component based on the idea of the wrapping web-service clients into a linkable component and seamlessly access to output netCDF files without actual models connection. The weather state variables (precipitation, wind, solar radiation, air temperature and relative humidity) are processed by automatic input selection algorithm to single out the most relevant values used by SWAT model to yield climatic data at the subbasin scale. Spatial interpolation between the WRF regular grid and SWAT subbasins centroid (which are

  3. 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.

  4. Real-time forecasting of ICME shock arrivals at L1 during the "April Fool’s Day" epoch: 28 March  21 April 2001

    NASA Astrophysics Data System (ADS)

    Sun, W.; Dryer, M.; Fry, C. D.; Deehr, C. S.; Smith, Z.; Akasofu, S.-I.; Kartalev, M. D.; Grigorov, K. G.

    2002-07-01

    The Sun was extremely active during the "April Fool’s Day" epoch of 2001. We chose this period between a solar flare on 28 March 2001 to a final shock arrival at Earth on 21 April 2001. The activity consisted of two presumed helmet-streamer blowouts, seven M-class flares, and nine X-class flares, the last of which was behind the west limb. We have been experimenting since February 1997 with real-time, end-to-end forecasting of interplanetary coronal mass ejection (ICME) shock arrival times. Since August 1998, these forecasts have been distributed in real-time by e-mail to a list of interested scientists and operational USAF and NOAA forecasters. They are made using three different solar wind models. We describe here the solar events observed during the April Fool’s 2001 epoch, along with the predicted and actual shock arrival times, and the ex post facto correction to the real-time coronal shock speed observations. It appears that the initial estimates of coronal shock speeds from Type II radio burst observations and coronal mass ejections were too high by as much as 30%. We conclude that a 3-dimensional coronal density model should be developed for application to observations of solar flares and their Type II radio burst observations.

  5. 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.

  6. Prediction and uncertainty of Hurricane Sandy (2012) explored through a real-time cloud-permitting ensemble analysis and forecast system assimilating airborne Doppler radar observations

    NASA Astrophysics Data System (ADS)

    Munsell, Erin B.; Zhang, Fuqing

    2014-03-01

    the Pennsylvania State University (PSU) real-time convection-permitting hurricane analysis and forecasting system (WRF-EnKF) that assimilates airborne Doppler radar observations, the sensitivity and uncertainty of forecasts initialized several days prior to landfall of Hurricane Sandy (2012) are assessed. The performance of the track and intensity forecasts of both the deterministic and ensemble forecasts by the PSU WRF-EnKF system show significant skill and are comparable to or better than forecasts produced by operational dynamical models, even at lead times of 4-5 days prior to landfall. Many of the ensemble members correctly capture the interaction of Sandy with an approaching midlatitude trough, which precedes Sandy's forecasted landfall in the Mid-Atlantic region of the United States. However, the ensemble reveals considerable forecast uncertainties in the prediction of Sandy. For example, in the ensemble forecast initialized at 0000 UTC 26 October 2012, 10 of the 60 members do not predict a United States landfall. Using ensemble composite and sensitivity analyses, the essential dynamics and initial condition uncertainties that lead to forecast divergence among the members in tracks and precipitation are examined. It is observed that uncertainties in the environmental steering flow are the most impactful factor on the divergence of Sandy's track forecasts, and its subsequent interaction with the approaching midlatitude trough. Though the midlatitude system does not strongly influence the final position of Sandy, differences in the timing and location of its interactions with Sandy lead to considerable differences in rainfall forecasts, especially with respect to heavy precipitation over land.

  7. The RHYTMME system: an operational real-time warning and mapping system for flash floods, debris flows, landslide and rock falls in Southeastern France.

    NASA Astrophysics Data System (ADS)

    Fouchier, Catherine; Mériaux, Patrice; Atger, Frédéric; Ecrepont, Stéphane; Liébault, Frédéric; Bertrand, Mélanie; Bel, Coraline; Batista, Dominique; Azemard, Pierre; Saint-Martin, Clotilde; Javelle, Pierre

    2016-04-01

    Almost all municipalities of Southeastern France are concerned by natural hazards triggered by heavy rainfalls such as floods, debris flows, landslides and rock falls. Although some tools exist to forecast and monitor heavy rains and floods in France, their spatial resolution sometimes does not meet the needs of local risk managers who have to monitor events at a small spatial scale. In order to improve the risk management in the mountainous and Mediterranean areas of Southeastern France, Irstea and Météo-France have led the RHYTMME project. The goal of this project is to improve the ability to forecast and localize high-risk rainfall-induced hazards in the Provence-Alpes-Côte d'Azur administrative area. This goal is currently under achievement thanks to the implementation of a real-time warning and mapping system for rainfall induced natural hazards, fed by radar data and whose outputs are made available via the Internet to operators in charge of risk management (local and regional authorities, emergency and rescue services, road and rail networks managers, ...). This system provides maps which display in real-time: - the radar estimations of rainfall for different rain durations and at the spatial resolution of 1 km² (Westrelin et al., 2013), - the estimation of the scarcity of these rainfall estimations, also at the spatial resolution of 1 km², thanks to a comparison with threshold values provided by a regionalized stochastic hourly point rainfall generator (Arnaud et al., 2007), - an anticipation of the rivers discharges, computed at the outlet of 1700 watersheds of Southeastern France thanks to the AIGA warning system which combines a rainfall runoff model and an estimation of the scarcity of the discharges thanks to a comparison with threshold values (Javelle et al., 2014). Maps of susceptibility to debris flow, landslide and rock falls can also be displayed in the RHYTMME warning system along with the real time maps of rainfall hazard (Batista, 2013a

  8. 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.

  9. 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

  10. Real-time prediction of atmospheric Lagrangian coherent structures based on forecast data: An application and error analysis

    NASA Astrophysics Data System (ADS)

    BozorgMagham, Amir E.; Ross, Shane D.; Schmale, David G.

    2013-09-01

    The language of Lagrangian coherent structures (LCSs) provides a new means for studying transport and mixing of passive particles advected by an atmospheric flow field. Recent observations suggest that LCSs govern the large-scale atmospheric motion of airborne microorganisms, paving the way for more efficient models and management strategies for the spread of infectious diseases affecting plants, domestic animals, and humans. In addition, having reliable predictions of the timing of hyperbolic LCSs may contribute to improved aerobiological sampling of microorganisms with unmanned aerial vehicles and LCS-based early warning systems. Chaotic atmospheric dynamics lead to unavoidable forecasting errors in the wind velocity field, which compounds errors in LCS forecasting. In this study, we reveal the cumulative effects of errors of (short-term) wind field forecasts on the finite-time Lyapunov exponent (FTLE) fields and the associated LCSs when realistic forecast plans impose certain limits on the forecasting parameters. Objectives of this paper are to (a) quantify the accuracy of prediction of FTLE-LCS features and (b) determine the sensitivity of such predictions to forecasting parameters. Results indicate that forecasts of attracting LCSs exhibit less divergence from the archive-based LCSs than the repelling features. This result is important since attracting LCSs are the backbone of long-lived features in moving fluids. We also show under what circumstances one can trust the forecast results if one merely wants to know if an LCS passed over a region and does not need to precisely know the passage time.

  11. 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.

  12. 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

    the system. The added values of the satellite data is largest when these observations are assimilated in combination with distributed discharge observations. These results show the potential of remotely sensed soil moisture observations to improve near-real time flood forecasting in large catchments.

  13. 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

  14. 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

  15. Tsunami forecast by joint inversion of real-time tsunami waveforms and seismic of GPS data: application to the Tohoku 2011 tsunami

    USGS Publications Warehouse

    Yong, Wei; Newman, Andrew V.; Hayes, Gavin P.; Titov, Vasily V.; Tang, Liujuan

    2014-01-01

    Correctly characterizing tsunami source generation is the most critical component of modern tsunami forecasting. Although difficult to quantify directly, a tsunami source can be modeled via different methods using a variety of measurements from deep-ocean tsunameters, seismometers, GPS, and other advanced instruments, some of which in or near real time. Here we assess the performance of different source models for the destructive 11 March 2011 Japan tsunami using model–data comparison for the generation, propagation, and inundation in the near field of Japan. This comparative study of tsunami source models addresses the advantages and limitations of different real-time measurements with potential use in early tsunami warning in the near and far field. The study highlights the critical role of deep-ocean tsunami measurements and rapid validation of the approximate tsunami source for high-quality forecasting. We show that these tsunami measurements are compatible with other real-time geodetic data, and may provide more insightful understanding of tsunami generation from earthquakes, as well as from nonseismic processes such as submarine landslide failures.

  16. Tsunami Forecast by Joint Inversion of Real-Time Tsunami Waveforms and Seismic or GPS Data: Application to the Tohoku 2011 Tsunami

    NASA Astrophysics Data System (ADS)

    Wei, Yong; Newman, Andrew V.; Hayes, Gavin P.; Titov, Vasily V.; Tang, Liujuan

    2014-12-01

    Correctly characterizing tsunami source generation is the most critical component of modern tsunami forecasting. Although difficult to quantify directly, a tsunami source can be modeled via different methods using a variety of measurements from deep-ocean tsunameters, seismometers, GPS, and other advanced instruments, some of which in or near real time. Here we assess the performance of different source models for the destructive 11 March 2011 Japan tsunami using model-data comparison for the generation, propagation, and inundation in the near field of Japan. This comparative study of tsunami source models addresses the advantages and limitations of different real-time measurements with potential use in early tsunami warning in the near and far field. The study highlights the critical role of deep-ocean tsunami measurements and rapid validation of the approximate tsunami source for high-quality forecasting. We show that these tsunami measurements are compatible with other real-time geodetic data, and may provide more insightful understanding of tsunami generation from earthquakes, as well as from nonseismic processes such as submarine landslide failures.

  17. 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.

  18. 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.

  19. 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

  20. 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.

  1. The near real time Forensic Disaster Analysis of the central European flood in June 2013 - A graphical representation of the main results

    NASA Astrophysics Data System (ADS)

    Schröter, Kai; Elmer, Florian; Trieselmann, Werner; Kreibich, Heidi; Kunz, Michael; Khazai, Bijan; Dransch, Doris; Wenzel, Friedemann; Zschau, Jochen; Merz, Bruno; Mühr, Bernhard; Kunz-Plapp, Tina; Möhrle, Stella; Bessel, Tina; Fohringer, Joachim

    2014-05-01

    The Central European flood of June 2013 is one of the most severe flood events that have occurred in Central Europe in the past decades. All major German river basins were affected (Rhine, Danube, and Elbe as well as the smaller Weser catchment).In terms of spatial extent and event magnitude, it was the most severe event at least since 1950. Within the current research focus on near real time forensic disaster analysis, the Center for Disaster Management and Risk Reduction Technology (CEDIM) assessed and analysed the multiple facets of the flood event from the beginning. The aim is to describe the on-going event, analyse the event sources, link the physical characteristics to the impact and consequences of the event and to understand the root causes that turn the physical event into a disaster (or prevent it from becoming disastrous). For the near real time component of this research, tools for rapid assessment and concise presentation of analysis results are essential. This contribution provides a graphical summary of the results of the CEDIM-FDA analyses on the June 2013 flood. It demonstrates the potential of visual representations for improving the communication and hence usability of findings in a rapid, intelligible and expressive way as a valuable supplement to usual event reporting. It is based on analyses of the hydrometeorological sources, the flood pathways (from satellite imagery, data extraction from social media), the resilience of the affected regions, and causal loss analysis. The prototypical representation of the FDA-results for the June 2013 flood provides an important step in the development of graphical event templates for the visualisation of forensic disaster analyses. These are intended to become a standard component of future CEDIM-FDA event activities.

  2. 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

  3. An Evaluation of Real-Time Streamflow Forecasts From a Distributed, Physically Based, Hydrologic Model Applied in the Upper Rio Grande Basin.

    NASA Astrophysics Data System (ADS)

    Gorham, T. A.; Boyle, D. P.; McConnell, J. R.; Hobson, A. N.

    2002-12-01

    Different uses compete for the water resources of the Upper Rio Grande Basin including agriculture, municipalities, industry, recreation, ecology and water quality. For water operations management in the Upper Rio Grande, resource managers rely on accurate forecasts (both short and long term) of streamflow at several locations, or nodes on the river. In this study, the USGS Precipitation Runoff Modeling System (PRMS) is used to predict quantity of runoff in the headwater basin above the USGS streamflow gage near Del Norte, Colorado. Because fine-tuning of the PRMS can result in improved forecasts, predictions were made using three adaptations of the model: 1) low-spatial resolution, 2) high-spatial resolution, 3) using an alternate method of distributing climate variables throughout the basin. A post-forecast evaluation of the real-time streamflow forecasts is made via comparisons with forecasts made by the National Resources Conservation Service (NRCS). This study is highly collaborative between researchers at the Desert Research Institute (DRI) and the USGS as part of the NSF funded Center for Sustainability of semi-Arid Hydrology and Riparian Areas (SAHRA) efforts to improve models of snow distribution and snowmelt processes.

  4. Flood delineation from synthetic aperture radar data with the help of a priori knowledge from historical acquisitions and digital elevation models in support of near-real-time flood mapping

    NASA Astrophysics Data System (ADS)

    Schlaffer, Stefan; Hollaus, Markus; Wagner, Wolfgang; Matgen, Patrick

    2012-10-01

    The monitoring of flood events with synthetic aperture radar (SAR) sensors has attracted a considerable amount of attention during the last decade, owing to the growing interest in using spaceborne data in near-real time flood management. Most existing methods for classifying flood extent from SAR data rely on pure image processing techniques. In this paper, we propose a method involving a priori knowledge about an area taken from a multitemporal time series and a digital elevation model. A time series consisting of ENVISAT ASAR acquisitions was geocoded and coregistered. Then, a harmonic model was fitted to each pixel time series. The standardised residuals of the model were classified as flooded when exceeding a certain threshold value. Additionally, the classified flood extent was limited to flood-prone areas which were derived from a freely available DEM using the height above nearest drainage (HAND) index. Comparison with two different reference datasets for two different flood events showed that the approach yielded realistic results but underestimated the inundation extent. Among the possible reasons for this are the rather coarse resolution of 150 m and the sparse data coverage for a substantial part of the time series. Nevertheless, the study shows the potential for production of rapid overviews in near-real time in support of early response to flood crises.

  5. 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.

  6. 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.

  7. 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

  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

    prediction (NWP) model can provide realistic looking rainfall forecasts, significant uncertainties remain in timing, location and whether a particular feature develops or not. Generally the smaller the scale of the rainfall feature, the shorter the lead-time at which these uncertainties become important. Therefore ensembles are needed to provide uncertainty context for longer lead-time G2G flow forecasts, particularly for small-scale RRCs. A systematic assessment framework has been developed for exploring and understanding the utility of G2G flood forecasts for RRCs. Firstly perfect knowledge of rainfall observations is assumed for past and future times, so as not to confound the hydrological model analysis with errors from rainfall forecasts. Secondly an assessment is made of using deterministic rainfall forecasts (from NWP UKV) in a full emulation of real-time G2G forecasts, and using foreknowledge of rainfall observations as a reference baseline. Finally use of rainfall forecast ensembles with G2G to produce probabilistic flood forecasts is considered, empploying a combination of case-study and longer-term analyses. Blended Ensemble rainfall forecasts (combining radar ensemble nowcast and NWP rainfalls) are assessed in two forms: forecasts out to 24 hours updated 4 times a day, and nowcasts out to 7 hours updated every 15 minutes. Results from the assessment will be presented along with candidates for new operational products and tools that can support flood warning for RRCs, taking account of the inherent uncertainty in the forecasts.

  9. 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.

  10. 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

  11. Evaluation of Maximum Likelihood Ensemble Filter for Real-Time Assimilation of Streamflow Data in Operational Streamflow Forecasting

    NASA Astrophysics Data System (ADS)

    Rafieei Nasab, A.; Seo, D.; LEE, H.; Kim, S.

    2012-12-01

    Various data assimilation (DA) methods have been used and are being explored for use in operational streamflow forecasting. For ensemble forecasting, Ensemble Kalman filter (EnKF) is an appealing candidate for familiarity and relative simplicity. EnKF, however, is optimal only if the observation equation is linear. As such, without an iterative approach, EnKF may not be appropriate for assimilating streamflow data into soil moisture accounting models. Maximum likelihood ensemble filter (MLEF), on the other hand, is not subject to the above limitation. Also, as an ensemble extension of variational assimilation (VAR), MLEF offers a strong connection with the traditional single-valued forecast process through the control, or the maximum likelihood, solution. In this work, we apply MLEF to the Sacramento (SAC) soil moisture accounting model and unit hydrograph (UH) for assimilation of streamflow, precipitation and potential evaporation (PE) data. A comparison between VAR and the control run of MLEF is made to verify the performance of MLEF, including that of the gradient approximation which does not require adjoint code. Sensitivity analysis is then performed to assess the performance of MLEF with respect to the ensemble size, the number of streamflow observations assimilated in each cycle, the statistical parameters for observation errors in streamflow, precipitation and PE, and for model error associated with the runoff from SAC. We also identify the science issues and challenges toward operationalization.

  12. 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.

  13. 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

  14. 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.

  15. 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

  16. Three-Month Real-Time Dengue Forecast Models: An Early Warning System for Outbreak Alerts and Policy Decision Support in Singapore

    PubMed Central

    Shi, Yuan; Liu, Xu; Kok, Suet-Yheng; Rajarethinam, Jayanthi; Liang, Shaohong; Yap, Grace; Chong, Chee-Seng; Lee, Kim-Sung; Tan, Sharon S.Y.; Chin, Christopher Kuan Yew; Lo, Andrew; Kong, Waiming; Ng, Lee Ching; Cook, Alex R.

    2015-01-01

    Background: With its tropical rainforest climate, rapid urbanization, and changing demography and ecology, Singapore experiences endemic dengue; the last large outbreak in 2013 culminated in 22,170 cases. In the absence of a vaccine on the market, vector control is the key approach for prevention. Objectives: We sought to forecast the evolution of dengue epidemics in Singapore to provide early warning of outbreaks and to facilitate the public health response to moderate an impending outbreak. Methods: We developed a set of statistical models using least absolute shrinkage and selection operator (LASSO) methods to forecast the weekly incidence of dengue notifications over a 3-month time horizon. This forecasting tool used a variety of data streams and was updated weekly, including recent case data, meteorological data, vector surveillance data, and population-based national statistics. The forecasting methodology was compared with alternative approaches that have been proposed to model dengue case data (seasonal autoregressive integrated moving average and step-down linear regression) by fielding them on the 2013 dengue epidemic, the largest on record in Singapore. Results: Operationally useful forecasts were obtained at a 3-month lag using the LASSO-derived models. Based on the mean average percentage error, the LASSO approach provided more accurate forecasts than the other methods we assessed. We demonstrate its utility in Singapore’s dengue control program by providing a forecast of the 2013 outbreak for advance preparation of outbreak response. Conclusions: Statistical models built using machine learning methods such as LASSO have the potential to markedly improve forecasting techniques for recurrent infectious disease outbreaks such as dengue. Citation: Shi Y, Liu X, Kok SY, Rajarethinam J, Liang S, Yap G, Chong CS, Lee KS, Tan SS, Chin CK, Lo A, Kong W, Ng LC, Cook AR. 2016. Three-month real-time dengue forecast models: an early warning system for outbreak

  17. 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

  18. 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.

  19. DEVELOPMENT OF A REAL-TIME FORECASTING SYSTEM FOR ROAD FACILITIES IN YAMAGUCHI PREFECTURE AND ITS PRACTICAL APPLICATIONS

    NASA Astrophysics Data System (ADS)

    Yamane, Satoru; Yoshimura, Takashi; Miyamoto, Ayaho

    Maintenance strategy for road facilities on the road network which is an essential lifeline supporting our life is becoming a major social concern in safety and securer civil societies for not only Yamaguchi prefecture but also other prefectures in recently. This paper describes a road facilities maintenance man-agement support system combined with the latest information and communication technologies, such as the information function at the position of the GPS cellular phone with web GIS, etc. By using the system, because information can be shared by using location information function, photograph function, e-mail functionality, and web GIS of the GPS cellular phone to its maximum, an efficient, effective maintenance management can be done. From the comparison of the results of applying the system to an actual road network in Yamaguchi prefecture area, the road administrator can in real time confirm the position and the situation of the facilities damage of the road from the Internet, and a quick mending can be done.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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

  5. 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

    reduced predictability at intermediate-size basins. Overall, the fusion of high-resolution radar nowcasts and the convenient parallel capabilities of the distributed hydrologic model provide an efficient framework for generating real-time flood forecasts suitable for operational environments.

  6. Real-time forecasting at weekly timescales of the SST and SLA of the Ligurian Sea with a satellite-based ocean forecasting (SOFT) system

    NASA Astrophysics Data System (ADS)

    ÁLvarez, A.; Orfila, A.; Tintoré, J.

    2004-03-01

    Satellites are the only systems able to provide continuous information on the spatiotemporal variability of vast areas of the ocean. Relatively long-term time series of satellite data are nowadays available. These spatiotemporal time series of satellite observations can be employed to build empirical models, called satellite-based ocean forecasting (SOFT) systems, to forecast certain aspects of future ocean states. SOFT systems can predict satellite-observed fields at different timescales. The forecast skill of SOFT systems forecasting the sea surface temperature (SST) at monthly timescales has been extensively explored in previous works. In this work we study the performance of two SOFT systems forecasting, respectively, the SST and sea level anomaly (SLA) at weekly timescales, that is, providing forecasts of the weekly averaged SST and SLA fields with 1 week in advance. The SOFT systems were implemented in the Ligurian Sea (Western Mediterranean Sea). Predictions from the SOFT systems are compared with observations and with the predictions obtained from persistence models. Results indicate that the SOFT system forecasting the SST field is always superior in terms of predictability to persistence. Minimum prediction errors in the SST are obtained during winter and spring seasons. On the other hand, the biggest differences between the performance of SOFT and persistence models are found during summer and autumn. These changes in the predictability are explained on the basis of the particular variability of the SST field in the Ligurian Sea. Concerning the SLA field, no improvements with respect to persistence have been found for the SOFT system forecasting the SLA field.

  7. 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.

  8. Net-zero Building Cluster Simulations and On-line Energy Forecasting for Adaptive and Real-Time Control and Decisions

    NASA Astrophysics Data System (ADS)

    Li, Xiwang

    Buildings consume about 41.1% of primary energy and 74% of the electricity in the U.S. Moreover, it is estimated by the National Energy Technology Laboratory that more than 1/4 of the 713 GW of U.S. electricity demand in 2010 could be dispatchable if only buildings could respond to that dispatch through advanced building energy control and operation strategies and smart grid infrastructure. In this study, it is envisioned that neighboring buildings will have the tendency to form a cluster, an open cyber-physical system to exploit the economic opportunities provided by a smart grid, distributed power generation, and storage devices. Through optimized demand management, these building clusters will then reduce overall primary energy consumption and peak time electricity consumption, and be more resilient to power disruptions. Therefore, this project seeks to develop a Net-zero building cluster simulation testbed and high fidelity energy forecasting models for adaptive and real-time control and decision making strategy development that can be used in a Net-zero building cluster. The following research activities are summarized in this thesis: 1) Development of a building cluster emulator for building cluster control and operation strategy assessment. 2) Development of a novel building energy forecasting methodology using active system identification and data fusion techniques. In this methodology, a systematic approach for building energy system characteristic evaluation, system excitation and model adaptation is included. The developed methodology is compared with other literature-reported building energy forecasting methods; 3) Development of the high fidelity on-line building cluster energy forecasting models, which includes energy forecasting models for buildings, PV panels, batteries and ice tank thermal storage systems 4) Small scale real building validation study to verify the performance of the developed building energy forecasting methodology. The outcomes of

  9. Basic atmospheric measurements via Arduino Uno microcontroller with commercially available sensors towards simple real-time weather forecasting for increased classroom engagement

    NASA Astrophysics Data System (ADS)

    Eckel, Ryan; Tanner, Meghan; Senevirathne, Indrajith

    Makers, engineers and the applied physics community have adapted Arduino microcontrollers due to their versatility, robustness and cost effectiveness. Arduino microcontroller environment coupled with commercially available sensors have been used to systematically measure, record and analyze temperature, humidity and barometric pressure for building a simplified weather station for subsequent educational purposes. This data will become available in classroom settings for real-time analysis towards simple weather forecasting. Setup was assembled via breadboard, wire and simple soldering with an Arduino Uno ATmega328P microcontroller connected to a PC. The microcontroller was programmed with Arduino Software while the bootloader was used to upload the code. Commercial DHT22 humidity and temperature sensor, and BMP180 barometric pressure sensor were used to obtain relative humidity, temperature and the barometric pressure. A weather resistant enclosure protected the system while stable real-time data measurements were obtained, and uploaded onto the PC. The data was used to predict atmospheric conditions and lifting condensation level (LCL). Discussion will focus on capabilities and limitations of these systems and corresponding teaching aspects. Lock Haven University Nanotechnology Program.

  10. Real-Time Epidemic Monitoring and Forecasting of H1N1-2009 Using Influenza-Like Illness from General Practice and Family Doctor Clinics in Singapore

    PubMed Central

    Ong, Jimmy Boon Som; Chen, Mark I-Cheng; Cook, Alex R.; Lee, Huey Chyi; Lee, Vernon J.; Lin, Raymond Tzer Pin; Tambyah, Paul Ananth; Goh, Lee Gan

    2010-01-01

    Background Reporting of influenza-like illness (ILI) from general practice/family doctor (GPFD) clinics is an accurate indicator of real-time epidemic activity and requires little effort to set up, making it suitable for developing countries currently experiencing the influenza A (H1N1 -2009) pandemic or preparing for subsequent epidemic waves. Methodology/Principal Findings We established a network of GPFDs in Singapore. Participating GPFDs submitted returns via facsimile or e-mail on their work days using a simple, standard data collection format, capturing: gender; year of birth; “ethnicity”; residential status; body temperature (°C); and treatment (antiviral or not); for all cases with a clinical diagnosis of an acute respiratory illness (ARI). The operational definition of ILI in this study was an ARI with fever of 37.8°C or more. The data were processed daily by the study co-ordinator and fed into a stochastic model of disease dynamics, which was refitted daily using particle filtering, with data and forecasts uploaded to a website which could be publicly accessed. Twenty-three GPFD clinics agreed to participate. Data collection started on 2009-06-26 and lasted for the duration of the epidemic. The epidemic appeared to have peaked around 2009-08-03 and the ILI rates had returned to baseline levels by the time of writing. Conclusions/Significance This real-time surveillance system is able to show the progress of an epidemic and indicates when the peak is reached. The resulting information can be used to form forecasts, including how soon the epidemic wave will end and when a second wave will appear if at all. PMID:20418945

  11. 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

    through strong security and workflow capabilities. A physical network diagram and a work flow scheme of all the models, codes and databases used to achieve the NEWS algorithm are presented. They constitute a first step in the development of a platform for providing real time flood forecasting services on the web to mitigate 21st century weather phenomena.

  12. 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

    interoperability through strong security and workflow capabilities. A physical network diagram and a work flow scheme of all the models, codes and databases used to achieve the NEWS algorithm are presented. They constitute a first step in the development of a platform for providing real time flood forecasting services on the web to mitigate 21st century weather phenomena.

  13. 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.

  14. 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.

  15. 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

  16. Forecast skill of a high-resolution real-time mesoscale model designed for weather support of operations at Kennedy Space Center and Cape Canaveral Air Station

    NASA Technical Reports Server (NTRS)

    Taylor, Gregory E.; Zack, John W.; Manobianco, John

    1994-01-01

    NASA funded Mesoscale Environmental Simulations and Operations (MESO), Inc. to develop a version of the Mesoscale Atmospheric Simulation System (MASS). The model has been modified specifically for short-range forecasting in the vicinity of KSC/CCAS. To accomplish this, the model domain has been limited to increase the number of horizontal grid points (and therefore grid resolution) and the model' s treatment of precipitation, radiation, and surface hydrology physics has been enhanced to predict convection forced by local variations in surface heat, moisture fluxes, and cloud shading. The objective of this paper is to (1) provide an overview of MASS including the real-time initialization and configuration for running the data pre-processor and model, and (2) to summarize the preliminary evaluation of the model's forecasts of temperature, moisture, and wind at selected rawinsonde station locations during February 1994 and July 1994. MASS is a hydrostatic, three-dimensional modeling system which includes schemes to represent planetary boundary layer processes, surface energy and moisture budgets, free atmospheric long and short wave radiation, cloud microphysics, and sub-grid scale moist convection.

  17. 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

  18. Long-range forecast of all India summer monsoon rainfall using adaptive neuro-fuzzy inference system: skill comparison with CFSv2 model simulation and real-time forecast for the year 2015

    NASA Astrophysics Data System (ADS)

    Chaudhuri, S.; Das, D.; Goswami, S.; Das, S. K.

    2016-02-01

    All India summer monsoon rainfall (AISMR) characteristics play a vital role for the policy planning and national economy of the country. In view of the significant impact of monsoon system on regional as well as global climate systems, accurate prediction of summer monsoon rainfall has become a challenge. The objective of this study is to develop an adaptive neuro-fuzzy inference system (ANFIS) for long range forecast of AISMR. The NCEP/NCAR reanalysis data of temperature, zonal and meridional wind at different pressure levels have been taken to construct the input matrix of ANFIS. The membership of the input parameters for AISMR as high, medium or low is estimated with trapezoidal membership function. The fuzzified standardized input parameters and the de-fuzzified target output are trained with artificial neural network models. The forecast of AISMR with ANFIS is compared with non-hybrid multi-layer perceptron model (MLP), radial basis functions network (RBFN) and multiple linear regression (MLR) models. The forecast error analyses of the models reveal that ANFIS provides the best forecast of AISMR with minimum prediction error of 0.076, whereas the errors with MLP, RBFN and MLR models are 0.22, 0.18 and 0.73 respectively. During validation with observations, ANFIS shows its potency over the said comparative models. Performance of the ANFIS model is verified through different statistical skill scores, which also confirms the aptitude of ANFIS in forecasting AISMR. The forecast skill of ANFIS is also observed to be better than Climate Forecast System version 2. The real-time forecast with ANFIS shows possibility of deficit (65-75 cm) AISMR in the year 2015.

  19. 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

  20. 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

    makes the tighter collaboration work among these hydrological models. In addition, in order to make communication between system users and decision makers efficient and effective, a real-time and multi-user communication platform, designated as Co-life, is incorporated in the present study. Through its application sharing function, the flood forecasting results can be displayed for all attendees situated at different locations to help the processes of decision making for hazard mitigation. Fig. 2 shows the cyber-conference of WRA officials with the Co-life system for hazard mitigation during the typhoon event.

  1. 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

  2. Towards real-time eruption forecasting in the Auckland Volcanic Field: application of BET_EF during the New Zealand National Disaster Exercise `Ruaumoko'

    NASA Astrophysics Data System (ADS)

    Lindsay, Jan; Marzocchi, Warner; Jolly, Gill; Constantinescu, Robert; Selva, Jacopo; Sandri, Laura

    2010-03-01

    The Auckland Volcanic Field (AVF) is a young basaltic field that lies beneath the urban area of Auckland, New Zealand’s largest city. Over the past 250,000 years the AVF has produced at least 49 basaltic centers; the last eruption was only 600 years ago. In recognition of the high risk associated with a possible future eruption in Auckland, the New Zealand government ran Exercise Ruaumoko in March 2008, a test of New Zealand’s nation-wide preparedness for responding to a major disaster resulting from a volcanic eruption in Auckland City. The exercise scenario was developed in secret, and covered the period of precursory activity up until the eruption. During Exercise Ruaumoko we adapted a recently developed statistical code for eruption forecasting, namely BET_EF (Bayesian Event Tree for Eruption Forecasting), to independently track the unrest evolution and to forecast the most likely onset time, location and style of the initial phase of the simulated eruption. The code was set up before the start of the exercise by entering reliable information on the past history of the AVF as well as the monitoring signals expected in the event of magmatic unrest and an impending eruption. The average probabilities calculated by BET_EF during Exercise Ruaumoko corresponded well to the probabilities subjectively (and independently) estimated by the advising scientists (differences of few percentage units), and provided a sound forecast of the timing (before the event, the eruption probability reached 90%) and location of the eruption. This application of BET_EF to a volcanic field that has experienced no historical activity and for which otherwise limited prior information is available shows its versatility and potential usefulness as a tool to aid decision-making for a wide range of volcano types. Our near real-time application of BET_EF during Exercise Ruaumoko highlighted its potential to clarify and possibly optimize decision-making procedures in a future AVF eruption

  3. 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.

  4. Real-Time Forecasting Of Streamflow And Water Loss/Gain In A River System By Using A Robust Multivariate Bayesian Regression Model

    NASA Astrophysics Data System (ADS)

    Ticlavilca, A. M.; McKee, M.; Walker, W.

    2009-12-01

    This research presents a model that simultaneously forecasts streamflow one and two days ahead, and water loss/gain in a river reach between two reservoirs one day ahead and for the next two days. The reservoir operator can take into account these real-time predictions and decide whether to increase/decrease the releases from the upstream reservoir in order to compensate the water loss/gain and manage the streamflow entering the downstream reservoir efficiently. The model inputs are the past daily data of climate (maximum and minimum temperature), streamflow, reservoir releases, water loss/gain in the river, and irrigation canal diversions. The model is developed in the form of a multivariate relevance vector machine (MVRVM) that is based on a multivariate Bayesian regression approach. Based on this Bayesian approach, a predictive confidence interval is obtained from the model that captures the uncertainty of both the model and the data. The model is applied to the river system located in the Lower Sevier River Basin near Delta, Utah. The results show that the model learns the input-output patterns with good accuracy. A bootstrap analysis is used to guarantee robustness of the estimated model parameters. Test results demonstrate good performance of predictions and statistics that indicate robust model generalization abilities.

  5. Real-time Reservoir Operation Based on a Combination of Long-term and Short-term Optimization and Hydrological Ensemble Forecasts

    NASA Astrophysics Data System (ADS)

    Meier, P.; Tilmant, A.; Boucher, M.; Anctil, F.

    2012-12-01

    features two large and three smaller reservoirs. Electricity is produced at four of the five dams. Besides the production of hydropower, the reservoirs are used to mitigate floods and for recreational purposes. The hydrological ensemble forecasts are generated with the HYDROTEL model, using meteorological ensemble forecasts issued by Environment Canada as forcing data. The framework described above is applied on a rolling horizon optimization mode on a period of almost two years from March 2002 to the end of 2003. The autumn of 2003 is characterized by a period of strong rainfall events which eventually led to flooding in parts of the river basin. Results show, that this coupled optimization is able to maximize the power production without neglecting the multiple purposes of the reservoir. While the optimization process itself is relatively straight-forward, taking a decision form the set of optimal policies is not. For each ensemble member of the hydrological forecast an optimal operation policy is obtained. Therefore, a strategy of decision taking has to be developed, which allows the incorporation of the information provided by the ensemble forecast as a whole. Different decision making strategies are presented and assessed.

  6. Improvements in NOAA SURFRAD and ISIS sites for near real-time solar irradiance for verification of NWP solar forecasts for the DOE NOAA Solar Forecast Improvement Project (SFIP)

    NASA Astrophysics Data System (ADS)

    Lantz, K. O.; McComiskey, A. C.; Long, C. N.; Marquis, M.; Olson, J. B.; James, E.; Benjamin, S.; Clack, C.

    2015-12-01

    The DOE-NOAA Solar Forecasting Improvement Project's (SFIP) main goal is to improve solar forecasting and thereby increase penetration of solar renewable energy on the electric grid. NOAA's ISIS and SURFRAD network is part of this initiative by providing high quality solar irradiance measurements for verification of improvements in solar forecasting for the short-term, day ahead, and ramp events. There are 14 ISIS and SURFRAD stations across the continental United States. We will give an overview of recent improvements in the networks for this project. The NOAA SURFRAD team has three main components: 1) In addition to the existing stations, two mobile SURFRAD stations have been built and deployed for 1 year each at two separate solar utility plants. 2) NOAA SURFRAD/ISIS will update the communications at their sites to provide near real-time data for verification activities at the 14 sites. 3) Global horizontal irradiance (GHI), direct normal solar irradiance (DNI), and aerosol optical depth at various spatial and temporal averaging will be compared to forecasts from the 3-km High-Resolution Rapid Refresh (HRRR) and an advanced version of the 13-km Rapid Refresh (RAP) models. We will explore statistical correlations between in-coming and out-going shortwave radiation and longwave radiation at the surface for specific meteorological regimes and how well these are captured by NWP models.

  7. Comprehensive evaluation of multi-year real-time air quality forecasting using an online-coupled meteorology-chemistry model over southeastern United States

    NASA Astrophysics Data System (ADS)

    Zhang, Yang; Hong, Chaopeng; Yahya, Khairunnisa; Li, Qi; Zhang, Qiang; He, Kebin

    2016-08-01

    An online-coupled meteorology-chemistry model, WRF/Chem-MADRID, has been deployed for real time air quality forecast (RT-AQF) in southeastern U.S. since 2009. A comprehensive evaluation of multi-year RT-AQF shows overall good performance for temperature and relative humidity at 2-m (T2, RH2), downward surface shortwave radiation (SWDOWN) and longwave radiation (LWDOWN), and cloud fraction (CF), ozone (O3) and fine particles (PM2.5) at surface, tropospheric ozone residuals (TOR) in O3 seasons (May-September), and column NO2 in winters (December-February). Moderate-to-large biases exist in wind speed at 10-m (WS10), precipitation (Precip), cloud optical depth (COT), ammonium (NH4+), sulfate (SO42-), and nitrate (NO3-) from the IMPROVE and SEARCH networks, organic carbon (OC) at IMPROVE, and elemental carbon (EC) and OC at SEARCH, aerosol optical depth (AOD) and column carbon monoxide (CO), sulfur dioxide (SO2), and formaldehyde (HCHO) in both O3 and winter seasons, column nitrogen dioxide (NO2) in O3 seasons, and TOR in winters. These biases indicate uncertainties in the boundary layer and cloud process treatments (e.g., surface roughness, microphysics cumulus parameterization), emissions (e.g., O3 and PM precursors, biogenic, mobile, and wildfire emissions), upper boundary conditions for all major gases and PM2.5 species, and chemistry and aerosol treatments (e.g., winter photochemistry, aerosol thermodynamics). The model shows overall good skills in reproducing the observed multi-year trends and inter-seasonal variability in meteorological and radiative variables such as T2, WS10, Precip, SWDOWN, and LWDOWN, and relatively well in reproducing the observed trends in surface O3 and PM2.5, but relatively poor in reproducing the observed column abundances of CO, NO2, SO2, HCHO, TOR, and AOD. The sensitivity simulations using satellite-constrained boundary conditions for O3 and CO show substantial improvement for both spatial distribution and domain-mean performance

  8. 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

  9. 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

  10. 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

  11. Shyska environment: taking decisions in real-time

    NASA Astrophysics Data System (ADS)

    García, S. G.

    2003-04-01

    The flash floods constitute one of the main problems of the Mediterranean semiarid zones. The convective storms of high intensity, typical of these zones, produce hydrological events that can turn out to be catastrophic. Due to the scanty existing time between the runoff hydrograph peak and the associated rainfall, the warning of flooding must be based on meteorological and hydrological forecasting. The coordination among the responsible authorities in the operational flood management, needs real-time operative decision support system. The use of Geographical Information Systems (GIS) in the development of these support tools, it constitutes a foreseeable direction. The development and use of new methodologies and tools that allows the operative integration of spatiotemporal information from different sources as input to distributed spatially rainfall-runoff models, are proposed. In this work, a real-time operative decision support computer system named Shyska is applied. This environment integrates the potentiality of GIS to the processing of information to different space-time scales, and spatially distributed hydrologic models oriented to runoff simulation and prediction, using topographical attributes extracted from DEM. Its final aim is to assist Hydrological Information Automatic Systems (SAIH systems in Spain), facilitating information management and use in real-time when alert and flash flood situations occur. Shyska has been applied to semiarid basins in the southeast of Spain, using pluviometric information from telemetric networks and products of remote sensing (rainfall fields), presenting satisfactory results. Finally, it is to be underlined that this work incorporates the use of new technologies (GIS, remote sensing, Digital Elevation Models) and recent developments in aspects of estimation and treatment of the spatial distribution of soil moisture and the flow in the unsaturated zone, in semiarid zones. The main objective is to improve the forecasts

  12. 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.

  13. Development of regional earthquake early warning and structural health monitoring system and real-time ground motion forecasting using front-site waveform data (Invited)

    NASA Astrophysics Data System (ADS)

    Motosaka, M.

    2009-12-01

    This paper presents firstly, the development of an integrated regional earthquake early warning (EEW) system having on-line structural health monitoring (SHM) function, in Miyagi prefecture, Japan. The system makes it possible to provide more accurate, reliable and immediate earthquake information for society by combining the national (JMA/NIED) EEW system, based on advanced real-time communication technology. The author has planned to install the EEW/SHM system to the public buildings around Sendai, a million city of north-eastern Japan. The system has been so far implemented in two buildings; one is in Sendai, and the other in Oshika, a front site on the Pacific Ocean coast for the approaching Miyagi-ken Oki earthquake. The data from the front-site and the on-site are processed by the analysis system which was installed at the analysis center of Disaster Control Research Center, Tohoku University. The real-time earthquake information from JMA is also received at the analysis center. The utilization of the integrated EEW/SHM system is addressed together with future perspectives. Examples of the obtained data are also described including the amplitude depending dynamic characteristics of the building in Sendai before, during, and after the 2008/6/14 Iwate-Miyagi Nairiku Earthquake, together with the historical change of dynamic characteristics for 40 years. Secondary, this paper presents an advanced methodology based on Artificial Neural Networks (ANN) for forward forecasting of ground motion parameters, not only PGA, PGV, but also Spectral information before S-wave arrival using initial part of P-waveform at a front site. The estimated ground motion information can be used as warning alarm for earthquake damage reduction. The Fourier Amplitude Spectra (FAS) estimated before strong shaking with high accuracy can be used for advanced engineering applications, e.g. feed-forward structural control of a building of interest. The validity and applicability of the method

  14. Evaluation of the United States National Air Quality Forecast Capability experimental real-time predictions in 2010 using Air Quality System ozone and NO2 measurements

    NASA Astrophysics Data System (ADS)

    Chai, T.; Kim, H.-C.; Lee, P.; Tong, D.; Pan, L.; Tang, Y.; Huang, J.; McQueen, J.; Tsidulko, M.; Stajner, I.

    2013-10-01

    The National Air Quality Forecast Capability (NAQFC) project provides the US with operational and experimental real-time ozone predictions using two different versions of the three-dimensional Community Multi-scale Air Quality (CMAQ) modeling system. Routine evaluation using near-real-time AIRNow ozone measurements through 2011 showed better performance of the operational ozone predictions. In this work, quality-controlled and -assured Air Quality System (AQS) ozone and nitrogen dioxide (NO2) observations are used to evaluate the experimental predictions in 2010. It is found that both ozone and NO2 are overestimated over the contiguous US (CONUS), with annual biases of +5.6 and +5.1 ppbv, respectively. The annual root mean square errors (RMSEs) are 15.4 ppbv for ozone and 13.4 ppbv for NO2. For both species the overpredictions are most pronounced in the summer. The locations of the AQS monitoring sites are also utilized to stratify comparisons by the degree of urbanization. Comparisons for six predefined US regions show the highest annual biases for ozone predictions in Southeast (+10.5 ppbv) and for NO2 in the Lower Middle (+8.1 ppbv) and Pacific Coast (+7.1 ppbv) regions. The spatial distributions of the NO2 biases in August show distinctively high values in the Los Angeles, Houston, and New Orleans areas. In addition to the standard statistics metrics, daily maximum eight-hour ozone categorical statistics are calculated using the current US ambient air quality standard (75 ppbv) and another lower threshold (70 ppbv). Using the 75 ppbv standard, the hit rate and proportion of correct over CONUS for the entire year are 0.64 and 0.96, respectively. Summertime biases show distinctive weekly patterns for ozone and NO2. Diurnal comparisons show that ozone overestimation is most severe in the morning, from 07:00 to 10:00 local time. For NO2, the morning predictions agree with the AQS observations reasonably well, but nighttime concentrations are overpredicted by around

  15. Evaluation of the United States National Air Quality Forecast Capability experimental real-time predictions in 2010 using Air Quality System ozone and NO2 measurements

    NASA Astrophysics Data System (ADS)

    Chai, T.; Kim, H.-C.; Lee, P.; Tong, D.; Pan, L.; Tang, Y.; Huang, J.; McQueen, J.; Tsidulko, M.; Stajner, I.

    2013-05-01

    The National Air Quality Forecast Capability (NAQFC) project provides the US with operational and experimental real-time ozone predictions using two different versions of the three-dimensional Community Multi-scale Air Quality (CMAQ) Modeling System. Routine evaluation using near-real-time AIRNow ozone measurements through 2011 showed better performance of the operational ozone predictions. In this work, quality-controlled and -assured Air Quality System (AQS) ozone and nitrogen dioxide (NO2) observations are used to evaluate the experimental predictions in 2010, with a view towards their improvement. It is found that both ozone and NO2 are overestimated over the contiguous US (CONUS), with annual biases of +5.6 ppbv and +5.1 ppbv, respectively. The annual root mean square errors (RMSEs) are 15.4 ppbv for ozone and 13.4 ppbv for NO2. For both species the over-predictions are most pronounced in the summer. The locations of the AQS monitoring sites are also utilized to stratify comparisons by the degree of urbanization. Comparisons for six predefined US regions show the highest annual biases for ozone predictions in Southeast (+10.5 ppbv) and for NO2 in the Lower Middle (+8.1 ppbv) and Pacific Coast (+7.1 ppbv) regions. The spatial distributions of the NO2 biases in July and August show distinctively high values in Los Angeles, Houston, and New Orleans areas. In addition to the standard statistics metrics, daily maximum eight-hour ozone categorical statistics are calculated using the current US ambient air quality standard (75 ppbv) and another lower threshold (70 ppbv). Using the 75 ppbv standard, the hit rate and proportion of correct over CONUS for the entire year are 0.64 and 0.96, respectively. Summertime biases show distinctive weekly patterns for ozone and NO2. Diurnal comparisons show that ozone overestimation is most severe in the morning, from 07:00 to 10:00 local time. For NO2, the morning predictions agree with the AQS observations reasonably well, but

  16. 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.

  17. 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

  18. Evaluation of the MACC operational forecast system - potential and challenges of global near-real-time modelling with respect to reactive gases in the troposphere

    NASA Astrophysics Data System (ADS)

    Wagner, A.; Blechschmidt, A.-M.; Bouarar, I.; Brunke, E.-G.; Clerbaux, C.; Cupeiro, M.; Cristofanelli, P.; Eskes, H.; Flemming, J.; Flentje, H.; George, M.; Gilge, S.; Hilboll, A.; Inness, A.; Kapsomenakis, J.; Richter, A.; Ries, L.; Spangl, W.; Stein, O.; Weller, R.; Zerefos, C.

    2015-03-01

    Monitoring Atmospheric Composition and Climate (MACC/MACCII) currently represents the European Union's Copernicus Atmosphere Monitoring Service (CAMS) (http://www.copernicus.eu), which will become fully operational in the course of 2015. The global near-real-time MACC model production run for aerosol and reactive gases provides daily analyses and 5 day forecasts of atmospheric composition fields. It is the only assimilation system world-wide that is operational to produce global analyses and forecasts of reactive gases and aerosol fields. We have investigated the ability of the MACC analysis system to simulate tropospheric concentrations of reactive gases (CO, O3, and NO2) covering the period between 2009 and 2012. A validation was performed based on CO and O3 surface observations from the Global Atmosphere Watch (GAW) network, O3 surface observations from the European Monitoring and Evaluation Programme (EMEP) and furthermore, NO2 tropospheric columns derived from the satellite sensors SCIAMACHY and GOME-2, and CO total columns derived from the satellite sensor MOPITT. The MACC system proved capable of reproducing reactive gas concentrations in consistent quality, however, with a seasonally dependent bias compared to surface and satellite observations: for northern hemispheric surface O3 mixing ratios, positive biases appear during the warm seasons and negative biases during the cold parts of the years, with monthly Modified Normalised Mean Biases (MNMBs) ranging between -30 and 30% at the surface. Model biases are likely to result from difficulties in the simulation of vertical mixing at night and deficiencies in the model's dry deposition parameterization. Observed tropospheric columns of NO2 and CO could be reproduced correctly during the warm seasons, but are mostly underestimated by the model during the cold seasons, when anthropogenic emissions are at a highest, especially over the US, Europe and Asia

  19. Evaluation of the MACC operational forecast system - potential and challenges of global near-real-time modelling with respect to reactive gases in the troposphere

    NASA Astrophysics Data System (ADS)

    Wagner, A.; Blechschmidt, A.-M.; Bouarar, I.; Brunke, E.-G.; Clerbaux, C.; Cupeiro, M.; Cristofanelli, P.; Eskes, H.; Flemming, J.; Flentje, H.; George, M.; Gilge, S.; Hilboll, A.; Inness, A.; Kapsomenakis, J.; Richter, A.; Ries, L.; Spangl, W.; Stein, O.; Weller, R.; Zerefos, C.

    2015-12-01

    The Monitoring Atmospheric Composition and Climate (MACC) project represents the European Union's Copernicus Atmosphere Monitoring Service (CAMS) (http://www.copernicus.eu/), which became fully operational during 2015. The global near-real-time MACC model production run for aerosol and reactive gases provides daily analyses and 5-day forecasts of atmospheric composition fields. It is the only assimilation system worldwide that is operational to produce global analyses and forecasts of reactive gases and aerosol fields. We have investigated the ability of the MACC analysis system to simulate tropospheric concentrations of reactive gases covering the period between 2009 and 2012. A validation was performed based on carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) surface observations from the Global Atmosphere Watch (GAW) network, the O3 surface observations from the European Monitoring and Evaluation Programme (EMEP) and, furthermore, NO2 tropospheric columns, as well as CO total columns, derived from satellite sensors. The MACC system proved capable of reproducing reactive gas concentrations with consistent quality; however, with a seasonally dependent bias compared to surface and satellite observations - for northern hemispheric surface O3 mixing ratios, positive biases appear during the warm seasons and negative biases during the cold parts of the year, with monthly modified normalised mean biases (MNMBs) ranging between -30 and 30 % at the surface. Model biases are likely to result from difficulties in the simulation of vertical mixing at night and deficiencies in the model's dry deposition parameterisation. Observed tropospheric columns of NO2 and CO could be reproduced correctly during the warm seasons, but are mostly underestimated by the model during the cold seasons, when anthropogenic emissions are at their highest level, especially over the US, Europe and Asia. Monthly MNMBs of the satellite data

  20. Meteo-marine parameters from the TS-X satellite on a near-real time basis and wave model forecast validation

    NASA Astrophysics Data System (ADS)

    Gebhardt, Claus; Susanne, Lehner; Pleskachevsky, Andrey; Jacobsen, Sven; Rosenthal, Wolfgang; Hoffmann, Peter; Kieser, Jens; Bruns, Thomas

    2015-04-01

    The TerraSAR-X satellite (TS-X), which was launched in 2007, hosts a state-of-the-art X-band Synthetic Aperture Radar (SAR). As a result of its relatively low orbit (ca. 500 km), it has improved properties against SAR-specific image distortion of the ocean surface. Being a SAR sensor, its observations are independent off sun light and cloud conditions. Meteo-marine parameters are provided on a near real time basis. Both sea state and wind conditions are derived from the radar images. This is accomplished by empirical retrieval algorithms. For the wind retrieval, the backscatter coefficients of radar scatterometers for different wind speeds and directions are utilised (XMOD2). For the wave retrieval, the empirical algorithm XWAVE has been developed as transformation of the radar images into two dimensional wave spectra. Spectral properties like the peak wavelength, peak period, and significant wave height are derived from the wave spectra. In our presentation, we give an overview on the TS-X wave and wind retrieval algorithms (XWAVE and XMOD2). The quality of the sea state results from TS-X is demonstrated by comparisons to collocated buoy measurements. In the German Bight, the forecast quality of the high-resolution coastal wave model CWAM of the German Meteorological Service (DWD) is validated using TS-X results. The comparison of the numerical model hindcasts with TS-X satellite data complements and extends the conventional validation by in-situ data. This is because of the large areal coverage and in-situ sensors cannot be easily deployed in certain regions, e.g. in locations with strong ship traffic.

  1. 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.

  2. 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.

  3. 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

  4. 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.

  5. Automatic removal of outliers in hydrologic time series and quality control of rainfall data: processing a real-time database of the Local System for Flood Monitoring in Klodzko County, Poland

    NASA Astrophysics Data System (ADS)

    Mizinski, Bartlomiej; Niedzielski, Tomasz; Kryza, Maciej; Szymanowski, Mariusz

    2013-04-01

    Real-time hydrological forecasting requires the highest quality of both hydrologic and meteorological data collected in a given river basin. Large outliers may lead to inaccurate predictions, with substantial departures between observations and prognoses considered even in short term. Although we need the correctness of both riverflow and rainfall data, they cannot be processed in the same way to produce a filtered output. Indeed, hydrologic time series at a given gauge can be interpolated in time domain after having detected suspicious values, however if no outlier has been detected at the upstream sites. In the case of rainfall data, interpolation is not suitable as we cannot verify the potential outliers at a given site against data from other sites especially in the complex terrain. This is due to the fact that very local convective events may occur, leading to large rainfall peaks at a limited space. Hence, instead of interpolating data, we rather perform a flagging procedure that only ranks outliers according to the likelihood of occurrence. Following the aforementioned assumptions, we have developed a few modules that serve a purpose of a fully automated correction of a database that is updated in real-time every 15 minutes, and the main objective of the work was to produce a high-quality database for a purpose of hydrologic rainfall-runoff modeling and ensemble prediction. The database in question is available courtesy of the County Office in Kłodzko (SW Poland), the institution which owns and maintains the Local System for Flood Monitoring in Kłodzko County. The dedicated prediction system, known as HydroProg, is now being built at the University of Wrocław (Poland). As the entire prediction system, the correction modules work automatically in real time and are developed in R language. They are plugged in to a larger IT infrastructure. Hydrologic time series, which are water levels recorded every 15 minutes at 22 gauges located in Kłodzko County, are

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The performance of conventional filtering methods can be degraded by ignoring the time lag between soil moisture and discharge response when discharge observations are assimilated into streamflow modelling. This has led to the ongoing development of more optimal ways to implement sequential data ass...

  7. 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.

  8. 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.

  9. 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

  10. 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

  11. 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

  12. 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.

  13. 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

  14. The use of real-time off-site observations as a methodology for increasing forecast skill in prediction of large wind power ramps one or more hours ahead of their impact on a wind plant.

    SciTech Connect

    Martin Wilde, Principal Investigator

    2012-12-31

    ABSTRACT Application of Real-Time Offsite Measurements in Improved Short-Term Wind Ramp Prediction Skill Improved forecasting performance immediately preceding wind ramp events is of preeminent concern to most wind energy companies, system operators, and balancing authorities. The value of near real-time hub height-level wind data and more general meteorological measurements to short-term wind power forecasting is well understood. For some sites, access to onsite measured wind data - even historical - can reduce forecast error in the short-range to medium-range horizons by as much as 50%. Unfortunately, valuable free-stream wind measurements at tall tower are not typically available at most wind plants, thereby forcing wind forecasters to rely upon wind measurements below hub height and/or turbine nacelle anemometry. Free-stream measurements can be appropriately scaled to hub-height levels, using existing empirically-derived relationships that account for surface roughness and turbulence. But there is large uncertainty in these relationships for a given time of day and state of the boundary layer. Alternatively, forecasts can rely entirely on turbine anemometry measurements, though such measurements are themselves subject to wake effects that are not stationary. The void in free-stream hub-height level measurements of wind can be filled by remote sensing (e.g., sodar, lidar, and radar). However, the expense of such equipment may not be sustainable. There is a growing market for traditional anemometry on tall tower networks, maintained by third parties to the forecasting process (i.e., independent of forecasters and the forecast users). This study examines the value of offsite tall-tower data from the WINDataNOW Technology network for short-horizon wind power predictions at a wind farm in northern Montana. The presentation shall describe successful physical and statistical techniques for its application and the practicality of its application in an operational

  15. 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

  16. 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.

  17. 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.

  18. 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.

  19. 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

  20. 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

  1. An Evaluation of Real-time Air Quality Forecasts and their Urban Emissions over Eastern Texas During the Summer of 2006 Second Texas Air Quality Study Field Study

    EPA Science Inventory

    Forecasts of ozone (O3) and particulate matter (diameter less than 2.5 µm, PM2.5) from seven air quality forecast models (AQFMs) are statistically evaluated against observations collected during August and September of 2006 (49 days) through the AIRNow netwo...

  2. 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

  3. 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.

  4. 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.

  5. Short range forecasting of sea breeze generated thunderstorms at the Kennedy Space Center: A real-time experiment using a primitive equation mesoscale numerical model

    NASA Technical Reports Server (NTRS)

    Lyons, Walter A.; Schuh, Jerome A.; Moon, Dennis; Pielke, Roger A.; Cotton, William; Arritt, Raymond

    1987-01-01

    The operational efficiency of using guidance from a mesoscale numerical model to improve sea breeze thunderstorm forecasts at and around the Shuttle landing strip was assessed. The Prognostic Three-Dimensional Mesoscale (P3DM) model, developed as a sea breeze model, reveals a strong correlation between regions of mesoscale convergence and the triggering of sea breeze convection thunderstorms. The P3DM was modified to generate stability parameters familiar to the operational forecaster. In addition to the mesoscale fields of wind, vertical motion, moisture, temperature, a stability indicator, a combination of model-predicted K and Lifted Indices and the maximum grid cell vertical motion, were proposed and tested. Results of blind tests indicate that a forecaster, provided with guidance derived from model output, could improve local thunderstorm forecasts.

  6. Near-field tsunami forecast system based on near real-time seismic moment tensor estimation in the regions of Indonesia, the Philippines, and Chile

    NASA Astrophysics Data System (ADS)

    Inazu, Daisuke; Pulido, Nelson; Fukuyama, Eiichi; Saito, Tatsuhiko; Senda, Jouji; Kumagai, Hiroyuki

    2016-05-01

    We have developed a near-field tsunami forecast system based on an automatic centroid moment tensor (CMT) estimation using regional broadband seismic observation networks in the regions of Indonesia, the Philippines, and Chile. The automatic procedure of the CMT estimation has been implemented to estimate tsunamigenic earthquakes. A tsunami propagation simulation model is used for the forecast and hindcast. A rectangular fault model based on the estimated CMT is employed to represent the initial condition of tsunami height. The forecast system considers uncertainties due to two possible fault planes and two possible scaling laws and thus shows four possible scenarios with these associated uncertainties for each estimated CMT. The system requires approximately 15 min to estimate the CMT after the occurrence of an earthquake and approximately another 15 min to make the tsunami forecast results including the maximum tsunami height and its arrival time at the epicentral region and near-field coasts available. The retrospectively forecasted tsunamis were evaluated by the deep-sea pressure and tide gauge observations, for the past eight tsunamis ( M w 7.5-8.6) that occurred throughout the regional seismic networks. The forecasts ranged from half to double the amplitudes of the deep-sea pressure observations and ranged mostly within the same order of magnitude as the maximum heights of the tide gauge observations. It was found that the forecast uncertainties increased for greater earthquakes (e.g., M w > 8) because the tsunami source was no longer approximated as a point source for such earthquakes. The forecast results for the coasts nearest to the epicenter should be carefully used because the coasts often experience the highest tsunamis with the shortest arrival time (e.g., <30 min).

  7. 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.

  8. 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

  9. 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.

  10. 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.

  11. 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

  12. 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.

  13. Real-Time Bias-Adjusted O3 and PM2.5 Air Quality Index Forecasts and their Performance Evaluations over the Continental United States

    EPA Science Inventory

    The National Air Quality Forecast Capacity (NAQFC) system, which links NOAA's North American Mesoscale (NAM) meteorological model with EPA's Community Multiscale Air Quality (CMAQ) model, provided operational ozone (O3) and experimental fine particular matter (PM2...

  14. ADAPTATION AND APPLICATION OF THE COMMUNITY MULTISCALE AIR QUALITY (CMAQ) MODELING SYSTEM FOR REAL-TIME AIR QUALITY FORECASTING DURING THE SUMMER OF 2004

    EPA Science Inventory

    The ability to forecast local and regional air pollution events is challenging since the processes governing the production and sustenance of atmospheric pollutants are complex and often non-linear. Comprehensive atmospheric models, by representing in as much detail as possible t...

  15. Development of a real time streamflow monitoring system for the Indian sub-continental basins

    NASA Astrophysics Data System (ADS)

    Shah, H. L.; Mishra, V.

    2015-12-01

    Real-time streamflow monitoring is essential in the Indian sub-continental river basins as a large population is affected by floods. Moreover, streamflow monitoring may help in managing the water resources in the agriculture dominated region. In the Indian sub-continental basins, it is challenging to obtain the real time information of streamflow, which is valuable for reservoir operations, water management, and flood forecasts. We setup the Variable Infiltration Capacity (VIC) hydrological model at daily temporal resolution and 0.25◦ spatial resolution using the bias corrected satellite precipitation product from the Tropical rainfall Measurement Mission Real Time (TRMM-3B42RTV7) and bias corrected temperature product from the Global Ensemble Forecast System (GEFS), version 2. Near-real-time precipitation and temperatures are bias corrected using the historic precipitation and temperature data from the India Meteorological Department (IMD). Moreover, we evaluated data assimilation approaches to improve the real-time monitoring of streamflow in the sub-continental basins.

  16. Real-time air quality forecasting over the southeastern United States using WRF/Chem-MADRID: Multiple-year assessment and sensitivity studies

    NASA Astrophysics Data System (ADS)

    Yahya, Khairunnisa; Zhang, Yang; Vukovich, Jeffrey M.

    2014-08-01

    An air quality forecasting system is a tool for protecting public health by providing an early warning system against harmful air pollutants. In this work, the online-coupled Weather Research and Forecasting Model with Chemistry with the Model of Aerosol Dynamics, Reaction, Ionization and Dissolution (WRF/Chem-MADRID) is used to forecast ozone (O3) and fine particles (PM2.5) concentrations over the southeastern U.S. for three O3 seasons from May to September in 2009, 2010, and 2011 and three winters from December to February during 2009-2010, 2010-2011, and 2011-2012. The forecasted chemical concentrations and meteorological variables are evaluated with observations from networks data in terms of spatial distribution, temporal variation, and discrete and categorical performance statistics. The model performs well for O3 and satisfactorily for PM2.5 in terms of both discrete and categorical evaluations but larger biases exist in PM species. The model biases are due to uncertainties in meteorological predictions, emissions, boundary conditions, chemical reactions, as well as uncertainties/differences in the measurement data used for evaluation. Sensitivity simulations show that using MEGAN online biogenic emissions and satellite-derived wildfire emissions result in improved performance for PM2.5 despite a degraded performance for O3. A combination of both can reduce normalize mean bias of PM2.5 from -18.3% to -11.9%. This work identifies a need to improve the accuracy of emissions by using dynamic biogenic and fire emissions that are dependent on meteorological conditions, in addition to the needs for more accurate anthropogenic emissions for urban areas and more accurate meteorological forecasts.

  17. 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.

  18. 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.

  19. 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.

  20. Evaluation of real-time hydrometeorological ensemble prediction on hydrologic scales in Northern California

    NASA Astrophysics Data System (ADS)

    Georgakakos, Konstantine P.; Graham, Nicholas E.; Modrick, Theresa M.; Murphy, Michael J.; Shamir, Eylon; Spencer, Cristopher R.; Sperfslage, Jason A.

    2014-11-01

    The paper presents an evaluation of real time ensemble forecasts produced during 2010-2012 by the demonstration project INFORM (Integrated Forecast and Reservoir Management) in Northern California. In addition, the innovative elements of the forecast component of the INFORM project are highlighted. The forecast component is designed to dynamically downscale operational multi-lead ensemble forecasts from the Global Ensemble Forecast System (GEFS) and the Climate Forecast system (CFS) of the National Centers of Environmental Prediction (NCEP), and to use adaptations of the operational hydrologic models of the US National Weather Service California Nevada River Forecast Center to provide ensemble reservoir inflow forecasts in real time. A full-physics 10-km resolution (10 km on the side) mesoscale model was implemented for the ensemble prediction of surface precipitation and temperature over the domain of Northern California with lead times out to 16 days with 6-hourly temporal resolution. An intermediate complexity regional model with a 10 km resolution was implemented to downscale the NCEP CFS ensemble forecasts for lead times out to 41.5 days. Methodologies for precipitation and temperature model forecast adjustment to comply with the corresponding observations were formulated and tested as regards their effectiveness for improving the ensemble predictions of these two variables and also for improving reservoir inflow forecasts. The evaluation is done using the real time databases of INFORM and concerns the snow accumulation and melt seasons. Performance is measured by metrics that range from those that use forecast means to those that use the entire forecast ensemble. The results show very good skill in forecasting precipitation and temperature over the subcatchments of the INFORM domain out to a week in advance for all basins, models and seasons. For temperature, in some cases, non-negligible skill has been obtained out to four weeks for the melt season

  1. 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.

  2. Real-time radiography

    SciTech Connect

    Bossi, R.H.; Oien, C.T.

    1981-02-26

    Real-time radiography is used for imaging both dynamic events and static objects. Fluorescent screens play an important role in converting radiation to light, which is then observed directly or intensified and detected. The radiographic parameters for real-time radiography are similar to conventional film radiography with special emphasis on statistics and magnification. Direct-viewing fluoroscopy uses the human eye as a detector of fluorescent screen light or the light from an intensifier. Remote-viewing systems replace the human observer with a television camera. The remote-viewing systems have many advantages over the direct-viewing conditions such as safety, image enhancement, and the capability to produce permanent records. This report reviews real-time imaging system parameters and components.

  3. 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).

  4. 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).

  5. 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

  6. Evaluating the co-production of a near real time Earthquake Aftershock forecasting tool for humanitarian risk assessment and emergency planning

    NASA Astrophysics Data System (ADS)

    Quinn, Keira; Hope, Max; McCloskey, John; NicBhloscaidh, Mairead; Jimenez, Abigail; Dunlop, Paul

    2015-04-01

    Concern Worldwide and the University of Ulster Geophysics Research Group are engaged in a project to co-produce a suite of software and mapping tools to assess aftershock hazard in near real-time during the emergency response phase of earthquake disaster, and inform humanitarian emergency planning and response activities. This paper uses a social learning approach to evaluate this co-production process. Following Wenger (1999) we differentiate between the earthquake science and humanitarian communities of practice (CoP) along three dimensions: enterprise (the purpose of CoPs and the problems participants are working to address), repertoire (knowledge, skills, language), and identity (values and boundaries). We examine the effectiveness of learning between CoP, focusing on boundary work and objects, and various organisational structures and aspects of the wider political economy of learning that enable and hinder the co-production process. We conclude by identifying a number of ways to more effectively integrate earthquake science into humanitarian decision-making, policy development and programme design.

  7. 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

  8. 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.

  9. 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.

  10. 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

  11. 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

  12. A New Method for Near Real Time Precipitation Estimates Using a Derived Statistical Relationship between Precipitable Water Vapor and Precipitation

    NASA Astrophysics Data System (ADS)

    Roman, J.

    2015-12-01

    The IPCC 5th Assessment found that the predicted warming of 1oC would increase the risk of extreme events such as heat waves, droughts, and floods. Weather extremes, like floods, have shown the vulnerability and susceptibility society has to these extreme weather events, through impacts such as disruption of food production, water supply, health, and damage of infrastructure. This paper examines a new way of near-real time forecasting of precipitation. A 10-year statistical climatological relationship was derived between precipitable water vapor (PWV) and precipitation by using the NASA Atmospheric Infrared Sounder daily gridded PWV product and the NASA Tropical Rainfall Measuring Mission daily gridded precipitation total. Forecasting precipitation estimates in real time is dire for flood monitoring and disaster management. Near real time PWV observations from AIRS on Aqua are available through the Goddard Earth Sciences Data and Information Service Center. In addition, PWV observations are available through direct broadcast from the NASA Suomi-NPP ATMS/CrIS instrument, the operational follow on to AIRS. The derived climatological relationship can be applied to create precipitation estimates in near real time by utilizing the direct broadcasting capabilities currently available in the CONUS region. The application of this relationship will be characterized through case-studies by using near real-time NASA AIRS Science Team v6 PWV products and ground-based SuomiNet GPS to estimate the current precipitation potential; the max amount of precipitation that can occur based on the moisture availability. Furthermore, the potential contribution of using the direct broadcasting of the NUCAPS ATMS/CrIS PWV products will be demonstrated. The analysis will highlight the advantages of applying this relationship in near-real time for flash flood monitoring and risk management. Relevance to the NWS River Forecast Centers will be discussed.

  13. 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.

  14. 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.

  15. Real-Time Simulation

    NASA Technical Reports Server (NTRS)

    1997-01-01

    Coryphaeus Software, founded in 1989 by former NASA electronic engineer Steve Lakowske, creates real-time 3D software. Designer's Workbench, the company flagship product, is a modeling and simulation tool for the development of both static and dynamic 3D databases. Other products soon followed. Activation, specifically designed for game developers, allows developers to play and test the 3D games before they commit to a target platform. Game publishers can shorten development time and prove the "playability" of the title, maximizing their chances of introducing a smash hit. Another product, EasyT, lets users create massive, realistic representation of Earth terrains that can be viewed and traversed in real time. Finally, EasyScene software control the actions among interactive objects within a virtual world. Coryphaeus products are used on Silican Graphics workstation and supercomputers to simulate real-world performance in synthetic environments. Customers include aerospace, aviation, architectural and engineering firms, game developers, and the entertainment industry.

  16. 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

    The African Flood Forecasting System (AFFS) is a probabilistic flood forecast system for medium- to large-scale African river basins, with lead times of up to 15 days. The key components are the hydrological model LISFLOOD, the African GIS database, the meteorological ensemble predictions of the ECMWF and critical hydrological thresholds. In this study the predictive capability is investigated, to estimate AFFS' potential as an operational flood forecasting system for the whole of Africa. This is done in a hindcast mode, by reproducing pan-African hydrological predictions for the whole year of 2003 where important flood events were observed. Results were analysed in two ways, each with its individual objective. The first part of the analysis is of paramount importance for the assessment of AFFS as a flood forecasting system, as it focuses on the detection and prediction of flood events. Here, results were verified with reports of various flood archives such as Dartmouth Flood Observatory, the Emergency Event Database, the NASA Earth Observatory and Reliefweb. The number of hits, false alerts and missed alerts as well as the Probability of Detection, False Alarm Rate and Critical Success Index were determined for various conditions (different regions, flood durations, average amount of annual precipitations, size of affected areas and mean annual discharge). The second part of the analysis complements the first by giving a basic insight into the prediction skill of the general streamflow. For this, hydrological predictions were compared against observations at 36 key locations across Africa and the Continuous Rank Probability Skill Score (CRPSS), the limit of predictability and reliability were calculated. 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

  17. 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

  18. 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.

  19. 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.

  20. Real-Time PCR

    NASA Astrophysics Data System (ADS)

    Evrard, A.; Boulle, N.; Lutfalla, G. S.

    Over the past few years there has been a considerable development of DNA amplification by polymerase chain reaction (PCR), and real-time PCR has now superseded conventional PCR techniques in many areas, e.g., the quantification of nucleic acids and genotyping. This new approach is based on the detection and quantification of a fluorescent signal proportional to the amount of amplicons generated by PCR. Real-time detection is achieved by coupling a thermocycler with a fluorimeter. This chapter discusses the general principles of quantitative real-time PCR, the different steps involved in implementing the technique, and some examples of applications in medicine. The polymerase chain reaction (PCR) provides a way of obtaining a large number of copies of a double-stranded DNA fragment of known sequence. This DNA amplification technique, developed in 1985 by K. Mullis (Cetus Corporation), saw a spectacular development over the space of a few years, revolutionising the methods used up to then in molecular biology. Indeed, PCR has many applications, such as the detection of small amounts of DNA, cloning, and quantitative analysis (assaying), each of which will be discussed further below.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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

  6. 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.

  7. Real time Faraday spectrometer

    DOEpatents

    Smith, Jr., Tommy E.; Struve, Kenneth W.; Colella, Nicholas J.

    1991-01-01

    This invention uses a dipole magnet to bend the path of a charged particle beam. As the deflected particles exit the magnet, they are spatially dispersed in the bend-plane of the magnet according to their respective momenta and pass to a plurality of chambers having Faraday probes positioned therein. Both the current and energy distribution of the particles is then determined by the non-intersecting Faraday probes located along the chambers. The Faraday probes are magnetically isolated from each other by thin metal walls of the chambers, effectively providing real time current-versus-energy particle measurements.

  8. 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

  9. 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

  10. 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.

  11. 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.

  12. 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

  13. 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.

  14. 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.

  15. 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

  16. 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.

  17. 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.

  18. 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

  19. 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).

  20. 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

  1. Real time automated inspection

    DOEpatents

    Fant, Karl M.; Fundakowski, Richard A.; Levitt, Tod S.; Overland, John E.; Suresh, Bindinganavle R.; Ulrich, Franz W.

    1985-01-01

    A method and apparatus relating to the real time automatic detection and classification of characteristic type surface imperfections occurring on the surfaces of material of interest such as moving hot metal slabs produced by a continuous steel caster. A data camera transversely scans continuous lines of such a surface to sense light intensities of scanned pixels and generates corresponding voltage values. The voltage values are converted to corresponding digital values to form a digital image of the surface which is subsequently processed to form an edge-enhanced image having scan lines characterized by intervals corresponding to the edges of the image. The edge-enhanced image is thresholded to segment out the edges and objects formed by the edges are segmented out by interval matching and bin tracking. Features of the objects are derived and such features are utilized to classify the objects into characteristic type surface imperfections.

  2. Real time automated inspection

    DOEpatents

    Fant, K.M.; Fundakowski, R.A.; Levitt, T.S.; Overland, J.E.; Suresh, B.R.; Ulrich, F.W.

    1985-05-21

    A method and apparatus are described relating to the real time automatic detection and classification of characteristic type surface imperfections occurring on the surfaces of material of interest such as moving hot metal slabs produced by a continuous steel caster. A data camera transversely scans continuous lines of such a surface to sense light intensities of scanned pixels and generates corresponding voltage values. The voltage values are converted to corresponding digital values to form a digital image of the surface which is subsequently processed to form an edge-enhanced image having scan lines characterized by intervals corresponding to the edges of the image. The edge-enhanced image is thresholded to segment out the edges and objects formed by the edges by interval matching and bin tracking. Features of the objects are derived and such features are utilized to classify the objects into characteristic type surface imperfections. 43 figs.

  3. 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 i