Thematische Bibliographien / Flood forecasting

Auswahl der wissenschaftlichen Literatur zum Thema „Flood forecasting“

Autor: Grafiati
Veröffentlicht am 4. Juni 2021
Zuletzt aktualisiert am 11. März 2023

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Inhaltsverzeichnis

  1. Zeitschriftenartikel
  2. Dissertationen
  3. Bücher
  4. Buchteile
  5. Konferenzberichte
  6. Berichte der Organisationen

Zeitschriftenartikel zum Thema "Flood forecasting":

1

Langdon, M. „Forecasting flood“. Engineering & Technology 4, Nr. 7 (25.04.2009): 40–42. http://dx.doi.org/10.1049/et.2009.0706.

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2

Xu, Wei, und Yong Peng. „Research on classified real-time flood forecasting framework based on K-means cluster and rough set“. Water Science and Technology 71, Nr. 10 (20.03.2015): 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.
3

Brilly, M., und M. Polic. „Public perception of flood risks, flood forecasting and mitigation“. Natural Hazards and Earth System Sciences 5, Nr. 3 (18.04.2005): 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.
4

Ren, Juanhui, Bo Ren, Qiuwen Zhang und 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, Nr. 9 (05.09.2019): 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.
5

Thiemig, V., B. Bisselink, F. Pappenberger und J. Thielen. „A pan-African Flood Forecasting System“. Hydrology and Earth System Sciences Discussions 11, Nr. 5 (27.05.2014): 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.
6

Arduino, G., P. Reggiani und E. Todini. „Recent advances in flood forecasting and flood risk assessment“. Hydrology and Earth System Sciences 9, Nr. 4 (07.10.2005): 280–84. http://dx.doi.org/10.5194/hess-9-280-2005.

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Abstract. Recent large floods in Europe have led to increased interest in research and development of flood forecasting systems. Some of these events have been provoked by some of the wettest rainfall periods on record which has led to speculation that such extremes are attributable in some measure to anthropogenic global warming and represent the beginning of a period of higher flood frequency. Whilst current trends in extreme event statistics will be difficult to discern, conclusively, there has been a substantial increase in the frequency of high floods in the 20th century for basins greater than 2x105 km2. There is also increasing that anthropogenic forcing of climate change may lead to an increased probability of extreme precipitation and, hence, of flooding. There is, therefore, major emphasis on the improvement of operational flood forecasting systems in Europe, with significant European Community spending on research and development on prototype forecasting systems and flood risk management projects. This Special Issue synthesises the most relevant scientific and technological results presented at the International Conference on Flood Forecasting in Europe held in Rotterdam from 3-5 March 2003. During that meeting 150 scientists, forecasters and stakeholders from four continents assembled to present their work and current operational best practice and to discuss future directions of scientific and technological efforts in flood prediction and prevention. The papers presented at the conference fall into seven themes, as follows.
7

Mistry, Shivangi, und Falguni Parekh. „Flood Forecasting Using Artificial Neural Network“. IOP Conference Series: Earth and Environmental Science 1086, Nr. 1 (01.09.2022): 012036. http://dx.doi.org/10.1088/1755-1315/1086/1/012036.

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Abstract The process of assessing the timing, amount, and period of flood events based on observed features of a river basin is known as flood forecasting. Floods cause lots of damage to properties and create a risk to human life. Flood forecasting is critical for developing appropriate flood risk management strategies, reducing flood hazards, evacuating people from flood-prone areas. The main objective of this study is to apply artificial neural networks for forecasting of river flow in the Deo River, located in Gujarat. Rainfall and discharge are the parameters considered for model development. The developed model is validated to test the accuracy of the model. Trained and validated models are evaluated using performance indices. Six alternative flood prediction models have been developed using ANN. These models are developed based on various training algorithms. A single layer feed forward back-propagation neural network with six different training algorithms (Scaled conjugate gradient, Levenberg Marquardt, Resilient back-propagation, Conjugate gradient, and Cascade forward back propagation, Bayesian regularization) was developed, with 70% of the data used for training and 30% for validation. The created models’ performance is assessed using statistical performance parameters. The best performance was obtained with an ANN model developed using the Cascade forward back-propagation training algorithm, which had a coefficient of correlation (r) of 0.83, a coefficient of determination (R2) of 0.70, and a root mean squared error (RMSE) of 5.58 for training and a coefficient of correlation (r) of 0.89, a coefficient of determination (R2) of 0.70, and a root mean squared error (RMSE) of 7.27 for validation. The forecast inflow is very close to the observed values. This study shows that ANN can be used to successfully predict floods, and the model developed can be used by flood control departments across the country for flood forecasting.
8

Puttinaovarat, Supattra, und Paramate Horkaew. „Application Programming Interface for Flood Forecasting from Geospatial Big Data and Crowdsourcing Data“. International Journal of Interactive Mobile Technologies (iJIM) 13, Nr. 11 (15.11.2019): 137. http://dx.doi.org/10.3991/ijim.v13i11.11237.

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Nowadays, natural disasters tend to increase and become more severe. They do affect life and belongings of great numbers of people. One kind of such disasters that hap-pen frequently almost every year is floods in all regions across the world. A prepara-tion measure to cope with upcoming floods is flood forecasting in each particular area in order to use acquired data for monitoring and warning to people and involved per-sons, resulting in the reduction of damage. With advanced computer technology and remote sensing technology, large amounts of applicable data from various sources are provided for flood forecasting. Current flood forecasting is done through computer processing by different techniques. The famous one is machine learning, of which the limitation is to acquire a large amount big data. The one currently used still requires manpower to download and record data, causing delays and failures in real-time flood forecasting. This research, therefore, proposed the development of an automatic big data downloading system from various sources through the development of applica-tion programming interface (API) for flood forecasting by machine learning. This research relied on 4 techniques, i.e., maximum likelihood classification (MLC), fuzzy logic, self-organization map (SOM), and artificial neural network with RBF Kernel. According to accuracy assessment of flood forecasting, the most accurate technique was MLC (99.2%), followed by fuzzy logic, SOM, and RBF (97.8%, 96.6%, and 83.3%), respectively.
9

Cluckie, I. D., und D. Han. „Fluvial Flood Forecasting“. Water and Environment Journal 14, Nr. 4 (August 2000): 270–76. http://dx.doi.org/10.1111/j.1747-6593.2000.tb00260.x.

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10

Wu, Heng Qing, Qiang Huang, Wei Xu und Shu Feng Xi. „Application of K-Means Cluster and Rough Set in Classified Real-Time Flood Forecasting“. Advanced Materials Research 1092-1093 (März 2015): 734–41. http://dx.doi.org/10.4028/www.scientific.net/amr.1092-1093.734.

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A new classified real-time flood forecasting framework was presented. Firstly, the historical floods were classified by K-means cluster, according to the hydrological factors. Then rough set was used to extract operation rules for flood forecasting. Following, the conceptual hydrological model was constructed and Genetic Algorithm (GA) was used to calibrate the hydrological model parameters. In simulation, River A is taken as study example. The categories of parameters are selected in operation according to flood information and rules. The result is compared with traditional flood forecasting. It demonstrates the performance of classified framework is improved in terms of accuracy and reliability.
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Zeitschriftenartikel Dissertationen Bücher

Dissertationen zum Thema "Flood forecasting":

1

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.
2

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.
3

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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4

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
6

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
8

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

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9

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.
10

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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Bücher zum Thema "Flood forecasting":

1

Romanowicz, Renata J., und Marzena Osuch, Hrsg. 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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4

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

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Reghezza-Zitt, Magali. Paris coule-t-il? [Paris]: Fayard, 2012.

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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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Buchteile zum Thema "Flood forecasting":

1

Sharma, Priyanka, Pravin Patil und 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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Cunge, J. A., M. Erlich, J. L. Negre und 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, und 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 und 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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Cloke, Hannah, Florian Pappenberger, Jutta Thielen und Vera Thiemig. „Operational European Flood Forecasting“. In Environmental Modelling, 415–34. Chichester, UK: John Wiley & Sons, Ltd, 2013. http://dx.doi.org/10.1002/9781118351475.ch25.

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Schultz, Gert A. „Flood Forecasting and Control“. In Remote Sensing in Hydrology and Water Management, 357–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-59583-7_16.

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Gutry-Korycka, M., A. Mirończuk und 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., und 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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Konferenzberichte zum Thema "Flood forecasting":

1

Yordanova, Valeriya, Silviya Stoyanova, Snezhanka Balabanova, Georgy Koshinchanov und 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.
2

Prohaska, Stevan, Aleksandra Ilić und 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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Rahman, Md Mafizur, Md Sabbir Mostafa Khan und 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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Abbassi, Kamel, Hamadi Lirathni, Mohamed Hechmi Jeridi und Tahar Ezzedine. „Flood Forecasting with Bayesian Approach“. In 2021 International Conference on Software, Telecommunications and Computer Networks (SoftCOM). IEEE, 2021. http://dx.doi.org/10.23919/softcom52868.2021.9559122.

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Mohssen, M. „Flood forecasting: the hard choice“. In FRIAR 2010. Southampton, UK: WIT Press, 2010. http://dx.doi.org/10.2495/friar100161.

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„Flood Forecasting Using Neural Networks“. In 1st International Workshop on Artificial Neural Networks: data preparation techniques and application development. SciTePress - Science and and Technology Publications, 2004. http://dx.doi.org/10.5220/0001148800090015.

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Pekárová, Pavla, Pavol Miklánek, Veronika Bačová Mitková, Marcel Garaj und Ján Pekár. „ASSESSMENT HARMONIZATION PROBLEMS OF THE LONG RETURN PERIOD FLOODS ON THE DANUBE RIVER“. 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.01.16.

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One of the basic problems of the flood hydrology was (and still is) the solution of the relationship between peak discharges of the flood waves and probability of their return period. The assessment of the design values along the Danube channel is more complicated due to application of different estimation methods of design values in particular countries downstream the Danube. Therefore, it is necessary to commence the harmonization of the flood design values assessment methods. All methods of estimating floods with a very long return period are associated with great uncertainties. Determining of the specific value of the 500- or 1000-year floods for engineering practice is extremely complex. Nowadays hydrologists are required to determine not only the specific design value of the flood, but it is also necessary to specify confidence intervals in which the flow of a given 100-, 500-, or 1000-year flood may occur with probability, for example, 90 %. The assessment of the design values Qmax can be done by several methods. In this study we have applied the statistical methods based on the assessment of the distribution function of measured time series of the maximum annual discharge. In order to apply regionalization methods for the estimation of the distribution function in this study we used only one distribution - the Pearson Type III distribution with logarithmic transformation of the data (log Pearson Type III distribution - LP3 distribution). To estimate regional skew coefficient for the Danube River we use 20 Qmax measured time series from water gauges along the Danube River from Germany to Ukraine. We firstly analyzed the occurrence of historic floods in several stations along the Danube River. Then we search relationship between the parameter of skewness of the log Pearson type III distribution function and runoff depth, altitude, or basin area in all 20 water gauge. Skewness coefficients of the LP3 distribution in the stations along the Danube River vary between –0.4 and 0.86.
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Ranit, Amitkumar Baburao, und P. V. Durge. „Flood Forecasting by Using Machine Learning“. In 2019 International Conference on Communication and Electronics Systems (ICCES). IEEE, 2019. http://dx.doi.org/10.1109/icces45898.2019.9002579.

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Janál, Petr, und Tomáš Kozel. „FUZZY LOGIC BASED FLASH FLOOD FORECAST“. 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.01.10.

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The flash flood forecasting remains one of the most difficult tasks in the operative hydrology worldwide. The torrential rainfalls bring high uncertainty included in both forecasted and measured part of the input rainfall data. The hydrological models must be capable to deal with such amount of uncertainty. The artificial intelligence methods work on the principles of adaptability and could represent a proper solution. The application of different methods, approaches, hydrological models and usage of various input data is necessary. The tool for real-time evaluation of the flash flood occurrence was assembled on the bases of the fuzzy logic. The model covers whole area of the Czech Republic and the nearest surroundings. The domain is divided into 3245 small catchments of the average size of 30 km2. Real flood episodes were used for the calibration and future flood events can be used for recalibration (principle of adaptability). The model consists of two fuzzy inference systems (FIS). The catchment predisposition for the flash flood occurrence is evaluated by the first FIS. The geomorphological characteristics and long-term meteorological statistics serve as the inputs. The second FIS evaluates real-time data. The inputs are: The predisposition for flash flood occurrence (gained from the first FIS), the rainfall intensity, the rainfall duration and the antecedent precipitation index. The meteorological radar measurement and the precipitation nowcasting serve as the precipitation data source. Various precipitation nowcasting methods are considered. The risk of the flash flood occurrence is evaluated for each small catchment every 5 or 10 minutes (the time step depends on the precipitation nowcasting method). The Fuzzy Flash Flood model is implemented in the Czech Hydrometeorological Institute (CHMI) – Brno Regional Office. The results are available for all forecasters at CHMI via web application for testing. The huge uncertainty inherent in the flash flood forecasting causes that fuzzy model outputs based on different nowcasting methods could vary significantly. The storms development is very dynamic and hydrological forecast could change a lot of every 5 minutes. That is why the fuzzy model estimates are intended to be used by experts only. The Fuzzy Flash Flood model is an alternative tool for the flash flood forecasting. It can provide the first hints of danger of flash flood occurrence within the whole territory of the Czech Republic. Its main advantage is very fast calculation and possibility of variant approach using various precipitation nowcasting inputs. However, the system produces large number of false alarms, therefore the long-term testing in operation is necessary and the warning releasing rules must be set.
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Balabanova, Snezhanka, Silviya Stoyanova, Vesela Stoyanova, Georgy Koshinchanov und Valeriya Yordanova. „HYDROLOGICAL FORECASTING AND ACTIVITIES IN BULGARIA IN THE FRAMEWORK OF THE DAREFFORT PROJECT“. In 22nd SGEM International Multidisciplinary Scientific GeoConference 2022. STEF92 Technology, 2022. http://dx.doi.org/10.5593/sgem2022/3.1/s12.13.

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Floods are the most frequent and widespread natural disasters worldwide (WMO, 2013). In 2006 for instance, exceptionally high river levels caused loss of lives and considerable economic losses in Serbia, Bulgaria and Romania. Thus risk prevention strategies were reconsidered and the need for common solutions for the Danubian countries was outlined. Non-structural measures to mitigate flood risk as is the improvement of forecasting capabilities on a basin-wide scale are known to be highly effective. The DAREFFORT project is a horizontal initiative to implement a flood risk mitigation measure in a joint and sustainable way on catchment level. The main output was the Danube Region Enhanced Flood Forecasting Cooperation that was a step towards creating the basis of ICPDR Danube Hydrologic Information System (HIS). This was only reached through a standardized data format utilized by the responsible national organizations and improved data exchange between the participating countries as reliable and comprehensive hydrologic data is the basis of sound forecasting in any country. In this paper the Bulgarian experience and contribution to the DAREFFORT project is presented. This study is aimed at overviewing the present status of the national forecasting capabilities and main topics are the process of the hydrological forecasting, data flow, data processing and data exchange.

Berichte der Organisationen zum Thema "Flood forecasting":

1

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

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2

Teillet, P. M., R. P. Gauthier, T. J. Pultz, A. Deschamps, G. Fedosejevs, M. Maloley, G. Ainsley und 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., und 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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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 und 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 und 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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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, Januar 2000. http://dx.doi.org/10.15760/etd.6535.

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Shrestha, M., und S. R. Chalise. Regional Cooperation for Flood Disaster Mitigation in the Hindu Kush-Himalayas; Report of Consultative Meeting on Developing a Framework for Flood Forecasting in the Hindu Kush-Himalayan Region: 15-18 May 2001, Kathmandu, Nepal. Kathmandu, Nepal: International Centre for Integrated Mountain Development (ICIMOD), 2001. http://dx.doi.org/10.53055/icimod.377.

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Piercy, Candice, Brandon Boyd, Emily Russ und Kyle Runion. Systematic beneficial use of dredged sediments : matching sediment needs with dredging requirements. Engineer Research and Development Center (U.S.), September 2022. http://dx.doi.org/10.21079/11681/45443.

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This technical note (TN) will outline a framework to identify beneficial and cost-effective coastal beneficial use of dredged sediment (BUDS) projects. Creation of a BUDS framework that can be applied at scale will promote sustainable BUDS practices, facilitating the delivery of flood risk management, social, and environmental benefits while still fulfilling the US Army Corps of Engineers (USACE) navigation mission. This proactive forecasting approach uses multi-criteria decision analysis (MCDA) and optimization tools to balance tradeoffs between navigation dredging and BUDS goals over project-scale timespans. The proposed framework utilizes available tools to quantify ecological system evolution and current and future dredging needs to develop a systems-level approach to BUDS. Required data include current and future information on (1) existing and planned natural and created aquatic ecological systems, which may include natural and nature-based features (NNBFs), (2) dredging requirements and costs, and (3) aquatic system physical and environmental data.
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Flood Situation Report August 2008. Vientiane, Lao PDR: Mekong River Commission Secretariat, September 2008. http://dx.doi.org/10.52107/mrc.ajhz90.

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The August 2008 event was the first regional flood episode for which the RFMMC provided forecasting services. The report discusses water levels, flood discharges, risk occurrence and prospect for rest of the 2008 flood season.

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