Bibliografías temáticas / Flood forecasting

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Literatura académica sobre el tema "Flood forecasting"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 9 de junio de 2025

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Artículos de revistas sobre el tema "Flood forecasting"

Kechkhoshvili, Erekle, and Irina Khutsishvili. "For Flood Forecasting Issues." Works of Georgian Technical University, no. 2(532) (June 10, 2024): 265–72. http://dx.doi.org/10.36073/1512-0996-2024-2-265-272.

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. Global climate change has caused sharp increasing of natural calamities, including floods. In the course of recent period, over the entire world, every year there are occurring tens of cases of disastrous floods and waterflows characterized by damages worth of several millions and human losses. The issue of forecasting waterflows and floods, in general, is discussed in the article. There are given basic differentiating features-characteristics existing between spring floods and rain-caused waterflows. The methodology of forecasting related decision-making based on the Statistical Fuzzy Analysis is developed at Ivane Javakhishvili Tbilisi State University, which methodology can be used for flood forecasting. The methodology consists of two stages. At the first stage one and the same prognostic event is assessed using three methods, which allow to make independent forecast. At the second stage, according to the mentioned forecast, the final decision is made. The factors suggested for application of this methodology for flood forecasting are directly related to the climatic parameters of the territory and the state of a river bed and lower terrace. The basic goal of the methodology suggested for flood forecasting is timely reporting on an anticipated disaster.

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Xu, Wei, and Yong Peng. "Research on classified real-time flood forecasting framework based on K-means cluster and rough set." Water Science and Technology 71, no. 10 (March 20, 2015): 1507–15. http://dx.doi.org/10.2166/wst.2015.128.

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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.

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Ren, Juanhui, Bo Ren, Qiuwen Zhang, and Xiuqing Zheng. "A Novel Hybrid Extreme Learning Machine Approach Improved by K Nearest Neighbor Method and Fireworks Algorithm for Flood Forecasting in Medium and Small Watershed of Loess Region." Water 11, no. 9 (September 5, 2019): 1848. http://dx.doi.org/10.3390/w11091848.

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Sudden floods in the medium and small watershed by a sudden rainstorm and locally heavy rainfall often lead to flash floods. Therefore, it is of practical and theoretical significance to explore appropriate flood forecasting model for medium and small watersheds for flood control and disaster reduction in the loess region under the condition of underlying surface changes. This paper took the Gedong basin in the loess region of western Shanxi as the research area, analyzing the underlying surface and floods characteristics. The underlying surface change was divided into three periods (HSP1, HSP2, HSP3), and the floods were divided into three grades (great, moderate, small). The paper applied K Nearest Neighbor method and Fireworks Algorithm to improve the Extreme Learning Machine model (KNN-FWA-ELM) and proposed KNN-FWA-ELM hybrid flood forecasting model, which was further applied to flood forecasting of different underlying surface conditions and flood grades. Results demonstrated that KNN-FWA-ELM model had better simulation performance and higher simulation accuracy than the ELM model for flood forecasting, and the qualified rate was 17.39% higher than the ELM model. KNN-FWA-ELM model was superior to the ELM model in three periods and the simulation performance of three flood grades, and the simulation performance of KNN-FWA-ELM model was better in HSP1 stage floods and great floods.

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Mustamin, Muhammad Rifaldi, Farouk Maricar, Rita Tahir Lopa, and Riswal Karamma. "Integration of UH SUH, HEC-RAS, and GIS in Flood Mitigation with Flood Forecasting and Early Warning System for Gilireng Watershed, Indonesia." Earth 5, no. 3 (July 8, 2024): 274–93. http://dx.doi.org/10.3390/earth5030015.

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A flood forecasting and early warning system is critical for rivers that have a large flood potential, one of which is the Gilireng watershed, which floods every year and causes many losses in Wajo Regency, Indonesia. This research also introduces an integration model between UH SUH and HEC-RAS in flood impact analysis, as a reference for flood forecasting and early warning systems in anticipating the timing and occurrence of floods, as well as GIS in the spatial modeling of flood-prone areas. Broadly speaking, this research is divided into four stages, namely, a flood hydrological analysis using UH SUH, flood hydraulic tracing using a 2D HEC-RAS numerical model, the spatial modeling of flood-prone areas using GIS, and the preparation of flood forecasting and early warning systems. The results of the analysis of the flood forecasting and early warning systems obtained the flood travel time and critical time at the observation point, the total time required from the upstream observation point to level 3 at Gilireng Dam for 1 h 35 min, Mamminasae Bridge for 4 h 35 min, and Akkotengeng Bridge for 8 h 40 min. This is enough time for people living in flood-prone areas to evacuate to the 15 recommended evacuation centers.

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Langdon, M. "Forecasting flood." Engineering & Technology 4, no. 7 (April 25, 2009): 40–42. http://dx.doi.org/10.1049/et.2009.0706.

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Anafi, Nurin Fadhlina Mohd, Norzailawati Mohd Noor, and Hasti Widyasamratri. "A Systematic Review of Real-time Urban Flood Forecasting Model in Malaysia and Indonesia -Current Modelling and Challenge." Jurnal Planologi 20, no. 2 (October 31, 2023): 150. http://dx.doi.org/10.30659/jpsa.v20i2.30765.

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Several metropolitan areas in tropical Southeast Asia, mainly in Malaysia and Indonesia have lately been witnessing unprecedentedly severe flash floods owing to unexpected climate change. The fast water flooding has caused extraordinarily serious harm to urban populations and social facilities. In addition, urban Southeast Asia generally has insufficient capacity in drainage systems, complex land use patterns, and a largely susceptible population in confined urban regions. To lower the urban flood risk and strengthen the resilience of vulnerable urban populations, it has been of fundamental relevance to create real-time urban flood forecasting systems for flood disaster prevention agencies and the urban public. This review examined the state-of-the-art models of real-time forecasting systems for urban flash floods in Malaysia and Indonesia. The real-time system primarily comprises the following subsystems, i.e., rainfall forecasting, drainage system modeling, and inundation area mapping. This review described the current urban flood forecasting modeling for rainfall forecasting, physical-process-based hydraulic models for flood inundation prediction, and data-driven artificial intelligence (AI) models for the real-time forecasting system. The analysis found that urban flood forecasting modeling based on data-driven AI models is the most applied in many metropolitan locations in Malaysia and Indonesia. The analysis also evaluated the existing potential of data-driven AI models for real-time forecasting systems as well as the challenges towards it

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Thiemig, V., B. Bisselink, F. Pappenberger, and J. Thielen. "A pan-African Flood Forecasting System." Hydrology and Earth System Sciences Discussions 11, no. 5 (May 27, 2014): 5559–97. http://dx.doi.org/10.5194/hessd-11-5559-2014.

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Abstract. 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 paper the predictive capability is investigated in a hindcast mode, by reproducing hydrological predictions for the year 2003 where important floods were observed. Results were verified with ground measurements of 36 subcatchments as well as with 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 "Save flooding" 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.

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Brilly, M., and M. Polic. "Public perception of flood risks, flood forecasting and mitigation." Natural Hazards and Earth System Sciences 5, no. 3 (April 18, 2005): 345–55. http://dx.doi.org/10.5194/nhess-5-345-2005.

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Abstract. 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.

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V.E.S.Mahendra, Kumar, and Khasim Sayyad. "Development of a Flood Forecasting System using Artificial Neural Networks." International Journal for Modern Trends in Science and Technology 11, no. 05 (May 25, 2025): 1192–95. https://doi.org/10.5281/zenodo.15511223.

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Floods pose significant risks to communities, infrastructure, and ecosystems worldwide, making early warning systems essential for mitigating their impacts. Traditional flood forecasting models rely on complex mathematical equations and meteorological data, but they often fail to capture the nonlinear relationships between various factors influencing floods. This paper proposes the development of a flood forecasting system using Artificial Neural Networks (ANNs), which can model these nonlinear relationships more effectively. The proposed system uses real-time data from weather stations, river gauges, and historical flood records to predict flood occurrences. The accuracy of the ANN-based system is compared to conventional methods, and it is found to provide superior results in terms of prediction accuracy and reliability. This paper outlines the methodology, benefits, and implementation of the proposed flood forecasting system and discusses its potential for improving flood management and early warning strategies.

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Li, Jingyu, Yangbo Chen, Yanzheng Zhu, and Jun Liu. "Study of Flood Simulation in Small and Medium-Sized Basins Based on the Liuxihe Model." Sustainability 15, no. 14 (July 19, 2023): 11225. http://dx.doi.org/10.3390/su151411225.

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The uneven distribution of meteorological stations in small and medium-sized watersheds in China and the lack of measured hydrological data have led to difficulty in flood simulation and low accuracy in flood forecasting. Traditional hydrological models no longer achieve the forecasting accuracy needed for flood prevention. To improve the simulation accuracy of floods and maximize the use of hydrological information from small and medium-sized watersheds, high-precision hydrological models are needed as a support mechanism. This paper explores the applicability of the Liuxihe model for flood simulation in the Caojiang river basin and we compare flood simulation results of the Liuxihe model with a traditional hydrological model (Xinanjiang model). The results show that the Liuxihe model provides excellent simulation of field floods in Caojiang river basin. The average Nash–Sutcliffe coefficient is 0.73, the average correlation coefficient is 0.9, the average flood peak present error is 0.33, and the average peak simulation accuracy is 93.9%. Compared with the traditional flood hydrological model, the Liuxihe model simulates floods better with less measured hydrological information. In addition, we found that the particle swarm optimization (PSO) algorithm can improve the simulation of the model, and its practical application only needs one representative flood for parameter optimization, which is suitable for areas with little hydrological information. The study can support flood forecasting in the Caojiang river basin and provide a reference for the preparation of flood forecasting schemes in other small and medium-sized watersheds.

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Tesis sobre el tema "Flood forecasting"

Simoes, Nuno Eduardo da Cruz. "Urban pluvial flood forecasting." Thesis, Imperial College London, 2012. http://hdl.handle.net/10044/1/10545.

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Two main approaches to enhance urban pluvial flood prediction were developed and tested in this research: (1) short-term rainfall forecast based on rain gauge networks, and (2) customisation of urban drainage models to improve hydraulic simulation speed. Rain gauges and level gauges were installed in the Coimbra (Portugal) and Redbridge (UK) catchment areas. The collected data was used to test and validate the approaches developed. When radar data is not available urban pluvial flooding forecasting can be based on networks of rain gauges. Improvements were made in the Support Vector Machine (SVM) technique to extrapolate rainfall time series. These improvements are: enhancing SVM prediction using Singular Spectrum Analysis (SSA) for pre-processing data; combining SSA and SVM with a statistical analysis that gives stochastic results. A method that integrates the SVM and Cascade-based downscaling techniques was also developed to carry out high-resolution (5-min) precipitation forecasting with longer lead time. Tests carried out with historical data showed that the new stochastic approach was useful for estimating the level of confidence of the rainfall forecast. The integration of the cascade method demonstrates the possibility of generating high-resolution rainfall forecasts with longer lead time. Tests carried out with the collected data showed that water level in sewers can be predicted: 30 minutes in advance (in Coimbra), and 45 minutes in advance (in Redbridge). A method for simplifying 1D1D networks is presented that increases computational speed while maintaining good accuracy. A new hybrid model concept was developed which combines 1D1D and 1D2D approaches in the same model to achieve a balance between runtime and accuracy. While the 1D2D model runs in about 45 minutes in Redbridge, the 1D1D and the hybrid models both run in less than 5 minutes, making this new model suitable for flood forecasting.

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Abdullah, Rozi. "Rainfall forecasting algorithms for real time flood forecasting." Thesis, University of Newcastle Upon Tyne, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296151.

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A fast catchment response usually leads to a shorter lag time, and under these conditions the forecast lead time obtained from a rainfall-runoff model or correlation between upstream and downstream flows may be infeasible for flood warning purposes. Additional lead time can be obtained from short-term quantitative rainfall forecasts that extend the flood warning time and increase the economic viability of a flood forecasting system. For this purpose algorithms which forecasts the quantitative rainfall amounts up to six hours ahead have been developed, based on lumped and distributed approaches. The lumped forecasting algorithm includes the essential features of storm dynamics such as rainband and raincell movements which are represented within the framework of a linear transfer function model. The dynamics of a storm are readily captured by radar data. A space-time rainfall model is used to generate synthetic radar data with known features, e.g. rainband and raincell velocities. This enables the algorithm to be assessed under ideal conditions, as errors are present in observed radar data. The transfer function algorithm can be summarised as follows. The dynamics of the rainbands and raincells are incorporated as inputs into the transfer function model. The algorithm employs simple spatial cross-correlation techniques to estimate the rainband and raincell velocities. The translated rainbands and raincells then form the auxiliary inputs to the transfer function. An optimal predictor based on minimum square error is then derived from the transfer function model, and its parameters are estimated from the auxiliary inputs and observed radar data in real-time using a recursive least squares algorithm. While the transfer-function algorithm forecasts areal rainfalls, a distributed approach which performs rainfall forecasting at a fine spatial resolution (referred to as the advection equation algorithm) is also evaluated in this thesis. The algorithm expresses the space-time rainfall on a Cartesian coordinate system via a partial differential advection equation. A simple explicit finite difference solution scheme is applied to the equation. A comparison of model parameter estimates is undertaken using a square root information filter data processing algorithm, and single-input single-output and multiple-input multiple-output least squares algorithms.

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Hopson, Thomas Moore. "Operational flood-forecasting for Bangladesh." Diss., Connect to online resource, 2005. http://wwwlib.umi.com/dissertations/fullcit/3165830.

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Baird, Laura. "Flood forecasting in ungauged catchments." Thesis, University of Bristol, 1989. http://hdl.handle.net/1983/b07e966f-e5c8-440e-b29c-f8f6324074b7.

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Fayegh, A. David. "Flood advisor : an expert system for flood estimation." Thesis, University of British Columbia, 1985. http://hdl.handle.net/2429/25069.

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Expert computer programs have recently emerged from research on artificial intelligence as a practical problem-solving tool. An expert system is a knowledge-based program that imitates the problem-solving behaviour of a human expert to solve complex real-world problems. While conventional programs organize knowledge on two levels: data and program, most expert programs organize knowledge on three levels: data, knowledge base, and control. Thus, what distinguishes such a system from conventional programs is that in most expert systems the problem solving model is treated as a separate entity rather than appearing only implicitly as part of the coding of the program. The purpose of this thesis is twofold. First, it is intended to demonstrate how domain-specific problem-solving knowledge may be represented in computer memory by using the frame representation technique. Secondly, it is intended to simulate a typical flood estimation situation, from the point-of-view of an expert engineer. A frame network was developed to represent, in data structures, the declarative, procedural, and heuristic knowledge necessary for solving a typical flow estimation problem. The control strategy of this computer-based consultant (FLOOD ADVISOR) relies on the concept that reasoning is dominated by a recognition process which is used to compare new instances of a given phenomena to the stereotyped conceptual framework used in understanding that phenomena. The primary purpose of the FLOOD ADVISOR is to provide interactive advice about the flow estimation technique most suitable to one of five generalized real-world situations. These generalizations are based primarily on the type and quantity of the data and resources available to the engineer. They are used to demonstrate how problem solving knowledge may be used to interactively assist the engineer in making difficult decisions. The expertise represented in this prototype system is far from complete and the recommended solution procedures for each generalized case are in their infancy. However, modifications may be easily implemented as the domain-specific expert knowledge becomes available. It is concluded that over the long term, this type of approach for building problem-solving models of the real world are computationally cheaper and easier to develop and maintain than conventional computer programs. Applied Science, Faculty of Civil Engineering, Department of Graduate

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Bagwell, Anne Marina. "A synoptically guided approach to determining suburbanization's impacts on the hydrology of the Red and White Clay Creeks, Pennsylvania and Delaware /." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file, 169 p, 2008. http://proquest.umi.com/pqdweb?did=1459905411&sid=7&Fmt=2&clientId=8331&RQT=309&VName=PQD.

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Zachary, A. Glen. "The estimated parameter flood forecasting model." Thesis, University of British Columbia, 1985. http://hdl.handle.net/2429/25148.

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Design flood estimates have traditionally been based on records of past events. However, there is a need for a method of estimating peak flows without these records. The Estimated Parameter Flood Forecasting Model (EPFFM) has been developed to provide such a method for small water resource projects based on a 200 year or less design flood. This "user friendly" computer model calculates the expected peak flow and its standard deviation from low, probable, and high estimates of thirteen user supplied parameters. These parameters describe physical characteristics of the drainage basin, infiltration rates, and rainstorm characteristics. The standard deviation provides a measure of reliability and is used to produce an 80% confidence interval on peak flows. The thesis briefly reviews existing flow estimation techniques and then describes the development of EPFFM. This includes descriptions of the Chicago method of rainfall hyetograph synthesis, Horton's infiltration equation, inflow by time-area method, Muskingum routing equation, and an approximate method of estimating the variance of multivariate equations since these are all used by EPFFM to model the physical and mathematical processes involved. Two examples are included to demonstrate EPFFM's ability to estimate a confidence interval, and compare these with recorded peak flows. Applied Science, Faculty of Civil Engineering, Department of Graduate

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Varoonchotikul, Pichaid. "Flood forecasting using artificial neural networks /." Lisse : Balkema, 2003. http://www.e-streams.com/es0704/es0704_3168.html.

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Baymani-Nezhad, Matin. "Real-time flood forecasting and updating." Thesis, University of Bristol, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.617587.

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Floods have potential destructive effects on socioeconomic facilities and cause serious risks for people. During the last decades lots of efforts have been carried out 10 overcome the difficulties caused by this natural phenomenon. In the past, most of the studies have been focused on developing mathematical models to forecast flood events in real -time to provide precautionary activities. The models are various from simple structures to models with high complexity and according to the climate conditions of the catchment under study, most appropriate model must be selected to predict flood events by using the existing recorded data from the catchment Rainfall-runoff model is the main component of a real-time flood forecasting model and transforms rainfall to runoff. The model commonly consists of a number of mathematical equations and parameters which are interconnected together for simulating runoff over a catchment. Since a model is a simplification of the real hydrological system, errors In simulation are unavoidable and influence on the simulation accuracy. Hence. the model should be selected properly and requires to be updated continuously to cope with probable hydrological changes which could create errors on model simulations. The current research focus on real-time flood forecasting by improving and developing rainfall-runoff models and indicating solutions to update the model to cope with frequent hydrological changes which can reduce the model performance. The research was started by evaluating optimisation schemes to derive the model parameters and an optimisation method was proposed based on Genetic algorithm concept. On the second stage, a new rainfall -runoff model called ERM, was introduced and suggested as a reliable model to use In rainfall -runoff modeling. Moreover, the adaptability of the ERM model parameters to cope with different errors occurred in terms of modeling was considered. Finally, in the last part of the thesis, the ERM model was coupled with a well-known numerical filter called the Kalman Filter and a real-time flood forecasting model was introduced.

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Cerda-Villafana, Gustavo. "Artificial intelligence techniques in flood forecasting." Thesis, University of Bristol, 2005. http://hdl.handle.net/1983/09d0faea-8622-4609-a33c-e4baefa304f5.

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The need for reliable, easy to set up and operate, hydrological forecasting systems is an appealing challenge to researchers working in the area of flood risk management. Currently, advancements in computing technology have provided water engineering with powerful tools in modelling hydrological processes, among them, Artificial Neural Networks (ANN) and genetic algorithms (GA). These have been applied in many case studies with different level of success. Despite the large amount of work published in this field so far, it is still a challenge to use ANN models reliably in a real-time operational situation. This thesis is set to explore new ways in improving the accuracy and reliability of ANN in hydrological modelling. The study is divided into four areas: signal preprocessing, integrated GA, schematic application of weather radar data, and multiple input in flow routing. In signal preprocessing, digital filters were adopted to process the raw rainfall data before they are fed into ANN models. This novel technique demonstrated that significant improvement in modelling could be achieved. A GA, besides finding the best parameters of the ANN architecture, defined the moving average values for previous rainfall and flow data used as one of the inputs to the model. A distributed scheme was implemented to construct the model exploiting radar rainfall data. The results from weather radar rainfall were not as good as the results from raingauge estimations which were used for comparison. Multiple input has been carried out modelling a river junction with excellent results and an extraction pump with results not so promising. Two conceptual models for flow routing modelling and a transfer function model for rainfall-runoff modelling have been used to compare the ANN model's performance, which was close to the estimations generated by the conceptual models and better than the transfer function model. The flood forecasting system implemented in East Anglia by the Environment Agency, and the NERC HYREX project have been the main data sources to test the model.

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Libros sobre el tema "Flood forecasting"

Romanowicz, Renata J., and Marzena Osuch, eds. Stochastic Flood Forecasting System. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18854-6.

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Muthusi, F. M. Somalia flood forecasting system. Nairobi, Kenya: Somalia Water and Land Information Management, 2009.

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Seneka, Michael. Paddle River Dam probable maximum flood. [Edmonton]: Alberta Environment, Environmental Assurance, Environmental Operations Division, Hydrology Branch, Surface Water Section, 2002.

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Ontoyin, Yakubu. Regional flood estimation in Ghanaian rivers. Accra, Ghana: Water Resources Research Institute (CSIR), 1985.

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Mastin, M. C. Real-time flood alert and simulation of river flood discharges in the Puyallup River Basin, Washington. Tacoma, Wash: U.S. Dept. of the Interior, U.S. Geological Survey, 1999.

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Dinicola, Karen. The "100-year flood". Tacoma, WA: USGS Washington Water Science Center, 1997.

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Dinicola, Karen. The " 100-year flood". Tacoma, WA: USGS Washington Water Science Center, 1997.

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Dinicola, Karen. The " 100-year flood". Tacoma, WA: USGS Washington Water Science Center, 1997.

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Dinicola, Karen. The " 100-year flood.". [Washington, D.C.?: U.S. Dept. of the Interior, U.S. Geological Survey, 1997.

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Dinicola, Karen. The " 100-year flood". Tacoma, WA: USGS Washington Water Science Center, 1997.

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Capítulos de libros sobre el tema "Flood forecasting"

Sharma, Priyanka, Pravin Patil, and Saeid Eslamian. "Flood Forecasting." In Flood Handbook, 131–50. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9780429463938-9.

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Sene, Kevin. "Flood Forecasting." In Flash Floods, 133–68. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5164-4_5.

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Sene, Kevin. "Flood Forecasting." In Hydrometeorology, 285–332. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-58269-1_8.

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Cunge, J. A., M. Erlich, J. L. Negre, and J. L. Rahuel. "Construction and Assessment of Flood Forecasting Scenarios in the Hydrological Forecasting System HFS/SPH." In Floods and Flood Management, 291–312. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-1630-5_20.

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Vuillaume, Jean-François, and Akinola Adesuji Komolafe. "Flood Modeling and Forecasting Uncertainty." In Flood Handbook, 63–96. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9780429463938-6.

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Mutreja, Kedar N., Yin Au-Yeung, and Ir Martono. "Flood Forecasting Model for Citanduy River Basin." In Flood Hydrology, 211–20. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3957-8_17.

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Todini, Ezio. "From Real-Time Flood Forecasting to Comprehensive Flood Risk Management Decision Support Systems." In Floods and Flood Management, 313–26. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-1630-5_21.

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Gutry-Korycka, M., A. Mirończuk, and A. Hościło. "Land Cover Change in the Middle River Vistula Catchment." In Stochastic Flood Forecasting System, 3–16. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18854-6_1.

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Romanowicz, Renata J., and Marzena Osuch. "Stochastic Transfer Function Based Emulator for the On-line Flood Forecasting." In Stochastic Flood Forecasting System, 159–70. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18854-6_10.

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Kiczko, Adam, Renata J. Romanowicz, Marzena Osuch, and Florian Pappenberger. "Adaptation of the Integrated Catchment System to On-line Assimilation of ECMWF Forecasts." In Stochastic Flood Forecasting System, 173–86. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18854-6_11.

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Actas de conferencias sobre el tema "Flood forecasting"

Boughale, Rahma, Amira Zrelli, and Tahar Ezzedine. "Enhancing Flood Forecasting with BiLSTM Networks." In 2024 IEEE/ACS 21st International Conference on Computer Systems and Applications (AICCSA), 1–6. IEEE, 2024. https://doi.org/10.1109/aiccsa63423.2024.10912620.

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Gunanandhini, S., Livyashree M, Maheshwaran R. R, Palanisamy K. S, and Niranjan Kumar V. "Forecasting and Real Time Flood Monitoring System using IoT." In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS), 1228–33. IEEE, 2024. http://dx.doi.org/10.1109/icaccs60874.2024.10716883.

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K, Jaraline Kirubavathy, and Thulasi Bai V. "An Intelligent ResNet-152 CNN framework for Anticipatory Flood Forecasting." In 2024 International Conference on IoT, Communication and Automation Technology (ICICAT), 1104–8. IEEE, 2024. https://doi.org/10.1109/icicat62666.2024.10923325.

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Zeng, Peng, Wei Zhang, Yangjun Zhou, and Guohui Wei. "Bayesian Probability Intelligent Forecasting Model Based on Rainfall during Flood Season." In 2024 5th International Conference on Information Science, Parallel and Distributed Systems (ISPDS), 323–28. IEEE, 2024. http://dx.doi.org/10.1109/ispds62779.2024.10667504.

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Ramya, R., Mohamed Thanseem K, Vimalraj V, and Vinith Kumar S. "Flood Forecasting System based on Water Monitoring using IoT-Machine Learning." In 2025 5th International Conference on Trends in Material Science and Inventive Materials (ICTMIM), 1857–62. IEEE, 2025. https://doi.org/10.1109/ictmim65579.2025.10988088.

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Yordanova, Valeriya, Silviya Stoyanova, Snezhanka Balabanova, Georgy Koshinchanov, and Vesela Stoyanova. "FLASH FLOOD FORECASTING USING FLASH FLOOD GUIDANCE SYSTEM PRODUCTS." In 22nd SGEM International Multidisciplinary Scientific GeoConference 2022. STEF92 Technology, 2022. http://dx.doi.org/10.5593/sgem2022/3.1/s12.11.

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Flash floods are defined as rapidly developing extreme events caused by heavy or excessive amounts of rainfall. Flash floods usually occur over a relatively small area within six hours or less of the extreme event with quite a rapid streamflow rise and fall. Increased occurrence of flash flood events is expected due to climate change and increase in extreme precipitation events [1]. Flash flood forecasting is still a challenge for hydrologists and water professionals due to the complex nature of the event itself. Besides having sufficient background in hydrological and meteorological forecasting as well as information about local conditions yet an adequate approach for flash flood forecasting is needed. The Flash Flood Guidance System (FFGS) is widely recognized for enhancing the capacity to issue timely and accurate flash flood warnings by providing hydrological and meteorological forecasters with real-time information and products. FFGS is based on global data as well as national hydrometeorological data and analyses. In this paper the use of the Black Sea Middle East Flash Flood Guidance System (BSMEFFGS) products for flash flood forecasting by the hydrologists at the Hydrological Forecasting department at the National Institute of Meteorology and Hydrology, Bulgarian Academy of Sciences (NIMH) in Bulgaria is presented. An overview of the FFGS for Bulgaria with closer attention paid to the Flash Flood Guidance (FFG), Flash Flood Risk (FFR) and the Flash Flood Threat Products is introduced. Two case studies are also presented � a flash flood in the town of Shumen and another one in the area of the village of Popovitsa on September 28th 2015.

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Stoyanova, Vesela, Georgy Koshinchanov, and Silviya Stoyanova. "COMPARISON OF NATIONAL, EUROPEAN AND BLACK SEA REGION FLASH FLOOD FORECASTING PRODUCTS FOR THE TERRITORY OF BULGARIA." In 23rd SGEM International Multidisciplinary Scientific GeoConference 2023. STEF92 Technology, 2023. http://dx.doi.org/10.5593/sgem2023/3.1/s12.07.

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Flash floods occur in small areas and in a short time after an extreme precipitation event - generally less than six hours. The intensity of the precipitation, the location and distribution of the precipitation, the land use and topography, soil type, etc. determine just how quickly Flash Flooding can occur. These types of floods are extremely dangerous and it is very important to pay special attention to their forecasting. Due to the complex nature of flash floods, in addition to the qualification and experience of the hydrologist involved in forecasting this type of extreme event, it is important to analyze and compare the results of different forecasting products. This article presents the analyses and comparison of the results of three different products related to the forecasting of flash floods over the territory of Bulgaria. The forecast products considered are Flash Flood Guidance (FFG) of the Black Sea Middle East Flash Flood Guidance System (BSMEFFGS), ERIC - Numerical weather prediction based flash flood indicator of the European Flood Awareness System (EFAS) and the flash flood product of the National Institute of Meteorology and Hydrology (NIMH) - Bulgaria for forecasting flash floods in small watersheds based on the Rational Method (RM). Forecast information for several significant events in 2022 on the territory of the country were analyzed.

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Prohaska, Stevan, Aleksandra Ilić, and Pavla Pekarova. "ASSESSMENT OF STATISTICAL SIGNIFICANCE OF HISTORIC DANUBE FLOODS." In XXVII Conference of the Danubian Countries on Hydrological Forecasting and Hydrological Bases of Water Management. Nika-Tsentr, 2020. http://dx.doi.org/10.15407/uhmi.conference.01.05.

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Data on historic floods along the Danube River exist since the year 1012. In the Middle Ages, floods were estimated based on historical documents, including original handwritten notes, newspaper articles, chronicles, formal letters, books, maps and photographs. From 1500 until the beginning of organized water regime observations, floods were hydraulically reconstructed based on water marks on old buildings in cities along the Danube (Passau, Melk, Emmersdorf an der Donau, Spilz, Schonbuhen and Bratislava). The paper presents a procedure for assessing the statistical significance of registered historic floods using a comprehensive method for defining theoretical flood hydrographs at hydrological stations. The approach is based on correlation analysis of two basic flood hydrograph parameters – maximum hydrograph ordinate (peak) and flood wave volume. The PROIL model is used to define the probability of simultaneous occurrence of these parameters. It defines the exceedance probability of two random variables, in the specific case two hydrograph parameters of the form: P{Qmax more equal to qmax,p)∩(Wmax more equal to wmax,p)} = P (1) where: Qmax – maximum hydrograph ordinate (peak); qmax,p – maximum discharge of the probability of occurrence p; Wmax – maximum hydrograph volume; wmax,p – maximum flood wave volume of the probability of occurrence p; P – exceedance probability. Spatial positions of the lines of exceedance of two flood hydrograph parameters and the empirical points of the corresponding parameters of the considered historic flood in the correlation field Qmax - Wmax, allow direct assessment of the exceedance probability of a historic flood, or its statistical significance. The proposed procedure was applied in practice to assess the statistical significance of the biggest floods registered along the Danube in the sector from its mouth to the Djerdap 1 Dam. The linear trend in the time-series of maximum annual flows at a representative hydrological station and the frequency of historic floods in the considered sector of the Danube are discussed at the end of the paper.

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Сергеев, Евгений Борисович. "METHOD OF FORECASTING FLOOD DANGER." In Экономика и социум в современных исследованиях: сборник статей международной научной конференции (Великий Новгород, Май 2024), 17–20. Crossref, 2024. http://dx.doi.org/10.58351/240518.2024.48.83.002.

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В статье затронут вопрос оценки угроз паводковой опасности. Представлен метод оценки вероятности развития ЧС, вызываемых весенними и дождевыми паводками. Для краткосрочного прогноза описана бальная система оценки угроз ЧС, базирующаяся на текущих данных о состоянии природной среды. Показан конкретный пример применения указанной бальной системы. The article touches upon the issue of assessing the threats of flood danger. A method for estimating the probability of emergencies caused by spring and rain floods is presented. For a short-term forecast, a point-based emergency threat assessment system based on current data on the state of the natural environment is described. An example of the application of the specified point system is shown.

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Rahman, Md Mafizur, Md Sabbir Mostafa Khan, and Md Fayzul Kabir Pasha. "Simple Approach for Flood Forecasting." In Joint Conference on Water Resource Engineering and Water Resources Planning and Management 2000. Reston, VA: American Society of Civil Engineers, 2000. http://dx.doi.org/10.1061/40517(2000)413.

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Informes sobre el tema "Flood forecasting"

Peters, John C. Application of Rainfall-Runoff Simulation for Flood Forecasting. Fort Belvoir, VA: Defense Technical Information Center, June 1993. http://dx.doi.org/10.21236/ada273140.

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Shi, Jimeng, Vitalii Stebliankin, Zhaonan Wang, Shaowen Wang, and Giri Narasimhan. Graph Transformer Network for Flood Forecasting with Heterogeneous Covariates. Purdue University, October 2023. http://dx.doi.org/10.5703/1288284317672.

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Teillet, P. M., R. P. Gauthier, T. J. Pultz, A. Deschamps, G. Fedosejevs, M. Maloley, G. Ainsley, and A. Chichagov. A Soil Moisture Sensorweb for Use in Flood Forecasting Applications. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2003. http://dx.doi.org/10.4095/220059.

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Melaney, M., and S. Frey. Enhancing flood and drought forecasting tools in the South Nation River Watershed. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2018. http://dx.doi.org/10.4095/306552.

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Hugeback, Kyle. A Comparison of HREF and HRRRE Predictions for Ensemble Flash Flood Forecasting. Ames (Iowa): Iowa State University, January 2018. http://dx.doi.org/10.31274/cc-20240624-24.

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Charley, William J. The Estimation of Rainfall for Flood Forecasting Using Radar and Rain Gage Data. Fort Belvoir, VA: Defense Technical Information Center, September 1988. http://dx.doi.org/10.21236/ada200802.

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Deschamps, A., T. J. Pultz, A. Pietroniro, and K. Best. Temporal soil moisture estimates from Radarsat-1 and Envisat ASAR for flood forecasting. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2004. http://dx.doi.org/10.4095/220092.

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Perera, Duminda, Ousmane Seidou, Jetal Agnihotri, Mohamed Rasmy, Vladimir Smakhtin, Paulin Coulibaly, and Hamid Mehmood. Flood Early Warning Systems: A Review Of Benefits, Challenges And Prospects. United Nations University Institute for Water, Environment and Health, August 2019. http://dx.doi.org/10.53328/mjfq3791.

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Floods are major water-related disasters that affect millions of people resulting in thousands of mortalities and billiondollar losses globally every year. Flood Early Warning Systems (FEWS) - one of the floods risk management measures - are currently operational in many countries. The UN Office for Disaster Risk Reduction recognises their importance and strongly advocates for an increase in their availability under the targets of the Sendai Framework for Disaster Risk Reduction, and Sustainable Development Goals (SDGs). However, despite widespread recognition of the importance of FEWS for disaster risk reduction (DRR), there’s a lack of information on their availability and status around the world, their benefits and costs, challenges and trends associated with their development. This report contributes to bridging these gaps by analyzing the responses to a comprehensive online survey with over 80 questions on various components of FEWS (risk knowledge, monitoring and forecasting, warning dissemination and communication, and response capabilities), investments into FEWS, their operational effectiveness, benefits, and challenges. FEWS were classified as technologically “basic”, “intermediate” and “advanced” depending on the existence and sophistication of FEWS` components such as hydrological data = collection systems, data transfer systems, flood forecasting methods, and early warning communication methods. The survey questionnaire was distributed to flood forecasting and warning centers around the globe; the primary focus was developing and least-developed countries (LDCs). The questionnaire is available here: https://inweh.unu.edu/questionnaireevaluation-of-flood-early-warning-systems/ and can be useful in its own right for similar studies at national or regional scales, in its current form or with case-specific modifications. Survey responses were received from 47 developing (including LDCs) and six developed countries. Additional information for some countries was extracted from available literature. Analysis of these data suggests the existence of an equal number of “intermediate” and “advanced” FEWS in surveyed river basins. While developing countries overall appear to progress well in FEWS implementation, LDCs are still lagging behind since most of them have “basic” FEWS. The difference between types of operational systems in developing and developed countries appear to be insignificant; presence of basic, intermediate or advanced FEWS depends on available investments for system developments and continuous financing for their operations, and there is evidence of more financial support — on the order of USD 100 million — to FEWS in developing countries thanks to international aid. However, training the staff and maintaining the FEWS for long-term operations are challenging. About 75% of responses indicate that river basins have inadequate hydrological network coverage and back-up equipment. Almost half of the responders indicated that their models are not advanced and accurate enough to produce reliable forecasts. Lack of technical expertise and limited skilled manpower to perform forecasts was cited by 50% of respondents. The primary reason for establishing FEWS, based on the survey, is to avoid property damage; minimizing causalities and agricultural losses appear to be secondary reasons. The range of the community benefited by FEWS varies, but 55% of FEWS operate in the range between 100,000 to 1 million of population. The number of flood disasters and their causalities has declined since the year 2000, while 50% of currently operating FEWS were established over the same period. This decline may be attributed to the combined DRR efforts, of which FEWS are an integral part. In lower-middle-income and low-income countries, economic losses due to flood disasters may be smaller in absolute terms, but they represent a higher percentage of such countries’ GDP. In high-income countries, higher flood-related losses accounted for a small percentage of their GDP. To improve global knowledge on FEWS status and implementation in the context of Sendai Framework and SDGs, the report’s recommendations include: i) coordinate global investments in FEWS development and standardise investment reporting; ii) establish an international hub to monitor the status of FEWS in collaboration with the national responsible agencies. This will support the sharing of FEWS-related information for accelerated global progress in DRR; iii) develop a comprehensive, index-based ranking system for FEWS according to their effectiveness in flood disaster mitigation. This will provide clear standards and a roadmap for improving FEWS’ effectiveness, and iv) improve coordination between institutions responsible for flood forecasting and those responsible for communicating warnings and community preparedness and awareness.

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Messina, Francesca, Ioannis Georgiou, Melissa Baustian, Travis Dahl, Jodi Ryder, Michael Miner, and Ronald Heath. Real-time forecasting model development work plan. Engineer Research and Development Center (U.S.), September 2023. http://dx.doi.org/10.21079/11681/47599.

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The objective of the Lowermost Mississippi River Management Program is to move the nation toward more holistic management of the lower reaches of the Mississippi River through the development and use of a science-based decision-making framework. There has been substantial investment in the last decade to develop multidimensional numerical models to evaluate the Lowermost Mississippi River (LMMR) hydrodynamics, sediment transport, and salinity dynamics. The focus of this work plan is to leverage the existing scientific knowledge and models to improve holistic management of the LMMR. Specifically, this work plan proposes the development of a real-time forecasting (RTF) system for water, sediment, and selected nutrients in the LMMR. The RTF system will help inform and guide the decision-making process for operating flood-control and sediment-diversion structures. This work plan describes the primary components of the RTF system and their interactions. The work plan includes descriptions of the existing tools and numerical models that could be leveraged to develop this system together with a brief inventory of existing real-time data that could be used to validate the RTF system. A description of the tasks that would be required to develop and set up the RTF system is included together with an associated timeline.

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Zarekarizi, Mahkameh. Ensemble Data Assimilation for Flood Forecasting in Operational Settings: From Noah-MP to WRF-Hydro and the National Water Model. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6535.

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